Robert E. Chapman

Benefits and Costs of Research:

A Case Study of Construction Systems 

Integration and Automation Technologies in 

Commercial Buildings

NISTIR 6763

National Institute of Standards and Technology

Technology Administration, U.S. Department of Commerce

Office of Applied Economics

Building and Fire Research Laboratory

Gaithersburg, Maryland 20899

Benefits and Costs of Research: A Case Study

of Construction Systems Integration and

Automation Technologies in Commercial

Buildings

 Robert E. Chapman

 Sponsored by:

 National Institute of Standards and Technology

 Building and Fire Research Laboratory

December 2001

U.S. DEPARTMENT OF COMMERCE

Donald L. Evans, Secretary

TECHNOLOGY ADMINISTRATION

Philip J. Bond, Under Secretary for Technology

NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY

Arden L. Bement, Jr., Director

U.S. Department of Commerce

Technology Administration

National Institute of Standards and Technology

Office of Applied Economics

Building and Fire Research Laboratory

Gaithersburg, MD 20899

NISTIR 6763

iii

Abstract

The National Institute of Standards and Technology (NIST) is improving its resource

allocation process by doing “microstudies” of its research impacts on society. This report

is one of a series of microstudies prepared by NIST’s Building and Fire Research

Laboratory (BFRL).

This report focuses on a critical analysis of the economic impacts of past, ongoing, and

planned research on BFRL’s construction systems integration and automation

technologies (CONSIAT) program. The CONSIAT program is an interdisciplinary

research effort within BFRL—in collaboration with the Construction Industry Institute,

the private sector, other federal agencies, and other laboratories within NIST—to develop

key enabling technologies, standard communication protocols, and advanced

measurement technologies needed to deliver fully-integrated and automated project

process (FIAPP) products and services to the construction industry.

This case study of BFRL’s CONSIAT-related research, development, and deployment

effort illustrates how to apply in practice a series of standardized methods to evaluate and

compare the economic impacts of alternative research investments. It is presented in

sufficient detail to understand the basis for the economic impact assessment and to

reproduce the results. It is based on past, ongoing, and planned research efforts. Thus, it

includes CONSIAT-related investment costs that have already occurred along with

estimates of future investment costs and cost savings due to the use of FIAPP products

and services.

The results of this study demonstrate that the use of FIAPP products and services will

generate substantial cost savings to the owners and managers of commercial buildings

and to contractors engaged in the construction of those buildings. The present value of

savings nationwide expected from the use of FIAPP products and services is nearly $1.4

billion (measured in 1997 dollars). Furthermore, because of BFRL’s involvement,

FIAPP products and services are expected to be commercially available in 2005. If

BFRL had not participated in the development of FIAPP products and services, the

commercial introduction of FIAPP products and services is expected to be delayed until

2009. Consequently, potential cost savings accruing to commercial building owners and

managers and to contractors over the period 2005 through 2008 would have been

foregone. The present value of these cost savings is approximately $120 million. These

cost savings measure the value of BFRL’s contribution for its CONSIAT-related

investment costs of approximately $29.1 million. Stated in present value terms, every

public dollar invested in BFRL’s CONSIAT-related research, development, and

deployment effort is expected to generate $4.13 in cost savings to the public.

Keywords

Building economics; commercial buildings; construction; delivery time; economic

analysis; impact evaluation; integration and automation; life-cycle costing; safety
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Preface

This study was conducted by the Office of Applied Economics in the Building and Fire

Research Laboratory (BFRL) at the National Institute of Standards and Technology

(NIST). The study is designed to estimate the economic impacts resulting from BFRL

research and to estimate the return on BFRL’s research investment dollars. The intended

audience is the National Institute of Standards and Technology as well as other

government and private research groups that are concerned with evaluating how

efficiently they allocated their past, present, and future research budgets.

The measurement of economic impacts of research is a major interest of BFRL and of

NIST. Managers need to know the impact of their research programs in order to achieve

the maximum social benefits from their limited budgets. The standardized methods for

measuring economic impacts employed in this study are essential to support BFRL’s

effort to evaluate the cost effectiveness of completed and ongoing research projects. As

additional experience is gained with the application of these standardized methods, their

use will enable BFRL to select the “best” among competing research programs for future

funding, to evaluate how cost effective are existing research programs, and to defend or

terminate programs on the basis of their economic impact. This need for measurement

methods exists across programs in BFRL, in NIST, and in other research laboratories.
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Acknowledgements

The author wishes to thank all those who contributed so many excellent ideas and

suggestions for this report. They include Dr. Shyam Sunder of the Structures Division at

NIST’s Building and Fire Research Laboratory (BFRL), manager of BFRL’s construction

systems integration and automation technologies (CONSIAT) product development

effort, for his technical guidance, suggestions, and support. The author wishes to thank

the Construction Industry Institute (CII) for providing an aggregated, project-level data

set covering the industrial and commercial sectors. Special thanks are extended to Dr.

Stephen R. Thomas, CII Associate Director for Benchmarking, for his stimulating

discussions on the uses of these data. Special appreciation is extended to Drs. Harold E.

Marshall and Sieglinde K. Fuller of BFRL’s Office of Applied Economics for the

thoroughness of their reviews and for their many insights and to Mrs. J’amie L. Maynard

for her assistance in preparing the manuscript for review and publication. Special

appreciation is extended to Ms. Christine A. Izzo who helped immensely with the data

collection and analysis effort and who provided numerous technical and editorial

comments on earlier drafts of this report. Thanks are due to the entire BFRL CONSIAT

product development team for their assistance in formulating this research effort. And

special thanks are due each of the CONSIAT project leaders—Dr. Kent A. Reed, Mr.

Mark E. Palmer, Mr. Robert R. Lipman, Dr. William C. Stone, Ms. Geraldine S. Cheok,

and Ms. Karen M. Furlani—for their assistance in identifying CONSIAT-related benefits

and costs and for their helpful comments on an earlier draft of this report. The report has

also benefited from the review and technical comments provided by Dr. Christoph

Witzgall of NIST's Information Technology Laboratory.
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Table of Contents

Abstract..............................................................................................................................iii

Preface................................................................................................................................. v

Acknowledgements........................................................................................................... vii

List of Tables ..................................................................................................................... xi

List of Figures..................................................................................................................xiii

List of Exhibits.................................................................................................................. xv

List of Acronyms ............................................................................................................. xvi

Executive Summary......................................................................................................... xix

1 Introduction............................................................................................................. 1

1.1 Background............................................................................................................. 1

1.2 Purpose.................................................................................................................... 2

1.3 Scope and Approach ............................................................................................... 3

2 A Methodology for Analyzing Economic Impacts................................................. 5

2.1 Types of Analysis ................................................................................................... 5

2.1.1 Baseline Analysis............................................................................................ 5

2.1.2 Sensitivity Analysis ........................................................................................ 5

2.2 Overview of Evaluation Methods ........................................................................... 6

2.2.1 Present Value of Net Benefits and Present Value of Net Savings.................. 7

2.2.2 Benefit-to-Cost Ratio and Savings-to-Investment Ratio ................................ 8

2.2.3 Adjusted Internal Rate of Return .................................................................... 9

2.2.4 Summary of Methods.................................................................................... 10

2.3 Presentation and Analysis of the Results of an Economic Impact Assessment.... 12

2.3.1 Significance of Research Effort.................................................................... 12

2.3.2 Analysis Strategy .......................................................................................... 14

2.3.3 Calculation of Benefits, Costs, and Additional Measures ............................ 15

3 Building and Fire Research Laboratory’s CONSIAT Program............................ 17

3.1 Fully-Integrated and Automated Project Processes: What They Are and What

They Will Do ........................................................................................................ 17

3.2 The FIATECH Consortium: A Vehicle for Delivering Fully-Integrated and

Automated Project Processes to the Construction Industry.................................. 20

3.3 Key Components of BFRL’s CONSIAT Program ............................................... 21

3.3.1 Project Information Management System Technologies.............................. 25
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3.3.2 Advanced Graphical User Interfaces for Construction Project Delivery

Systems ......................................................................................................... 26

3.3.3 Product Data Standards for Industrial Facilities ........................................... 28

3.3.4 Site Measurement System Communication Standards and Practices........... 29

3.3.5 Procedures and Metrics for Construction Range Imaging and Registration. 31

3.3.6 Measurement Processes and Metrics for Construction Component

Tracking ........................................................................................................ 32

3.3.7 Enhanced Construction Object Recognition Through Sensor Fusion .......... 34

3.3.8 Economic Analysis of FIAPP Products and Services................................... 35

4 Market for FIAPP Products and Services ............................................................. 37

4.1 Value of Construction Put in Place....................................................................... 38

4.2 Overview of the Commercial Sector..................................................................... 44

4.3 Characteristics of Commercial Buildings ............................................................. 48

5 Strategy for Identifying, Collecting, and Measuring FIAPP-Related Benefits

and Costs............................................................................................................... 55

5.1 Identification of Key Stakeholders ....................................................................... 55

5.2 Classification of FIAPP-Related Benefits and Cost Savings................................ 56

5.3 Classification of FIAPP-Related Cost Increases and Benefit Reductions............ 64

5.4 How FIAPP-Related Benefits and Costs Accrue to Stakeholders........................ 68

6 Data and Assumptions for the CONSIAT Economic Impact Assessment ........... 73

6.1 Data Sources ......................................................................................................... 73

6.1.1 Baseline Measures of Construction Industry Practices................................. 73

6.1.2 The CII Benchmarking and Metrics Database.............................................. 74

6.1.3 Other Data Sources ....................................................................................... 80

6.2 Defining the Base Case and the FIAPP Alternative ............................................. 83

6.3 Estimating Significant FIAPP-Related Benefits and Costs .................................. 84

6.3.1 Benefits and Cost Savings ............................................................................ 84

6.3.1.1 Reduced First Costs.......................................................................... 85

6.3.1.2 Reduced Maintenance and Repair Costs .......................................... 86

6.3.1.3 Reductions in Construction-Related Accidents................................ 87

6.3.1.4 Reductions in Delivery Time ........................................................... 90

6.3.1.5 Higher Net Income for Contractors.................................................. 90

6.3.2 Cost Increases and Benefit Reductions......................................................... 91

6.3.2.1 New-Technology Introduction Costs ............................................... 92

6.3.2.2 Increased Research and Development Costs.................................... 92

6.4 Key Assumptions and Analysis Issues ................................................................. 94

6.4.1 Base Year for Computing Benefits and Costs .............................................. 94

6.4.2 Length of the Study Period ........................................................................... 95

6.4.3 Discount Rate................................................................................................ 95

6.4.4 Diffusion Process.......................................................................................... 96

6.4.5 Dealing with Uncertainty............................................................................ 103

6.4.6 Measuring BFRL’s Contribution ................................................................ 104
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7 Baseline Analysis of Economic Impacts............................................................. 107

7.1 BFRL Summary Impact Statement..................................................................... 110

7.2 Cost Savings Nationwide.................................................................................... 110

7.3 Measuring the Value of BFRL’s Contribution and the Return on BFRL’s

CONSIAT-Related Investments ......................................................................... 119

8 Sensitivity Analysis of Economic Impacts ......................................................... 125

8.1 Methodology....................................................................................................... 125

8.2 Key Variables...................................................................................................... 127

8.3 Sensitivity Results............................................................................................... 129

8.3.1 Changing One Input.................................................................................... 129

8.3.2 Changing All Eleven Inputs in Combination.............................................. 139

9 Summary and Suggestions for Further Research................................................ 155

9.1 Summary............................................................................................................. 155

9.2 Suggestions for Further Research ....................................................................... 158

9.2.1 The Development of a Standard Classification of Research Benefits and

Costs............................................................................................................ 158

9.2.2 Factors Affecting the Diffusion of New Technologies............................... 159

9.2.3 Conducting Ex Ante Evaluations with Scheduled Follow-ups ................... 159

9.2.4 Evaluations Based on Multiattribute Decision Analysis ............................ 160

References....................................................................................................................... 163

Disclaimer:

Certain trade names and company products are mentioned in the text in order to

adequately specify the technical procedures and equipment used. In no case does such

identification imply recommendation or endorsement by the National Institute of

Standards and Technology, nor does it imply that the products are necessarily the best

available for the purpose.

List of Tables

Table 2-1. Summary of Appropriateness of Each Standardized Evaluation Method

 for Each Decision Type ..................................................................................11

Table 4-1. Value of Construction Put in Place in Millions of Constant 1997 Dollars .....40

Table 4-2. Value of Construction Put in Place in Millions of Current Dollars ................41

Table 4-3. Value of Construction Put in Place: Sector Totals and Sum Total in

 Millions of Constant 1997 Dollars..................................................................42

Table 4-4. Value of Construction Put in Place: Sector Totals and Sum Total in

 Millions of Current Dollars.............................................................................43
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Table 5-1. Hierarchy of FIAPP Stakeholders by Groups and Classes of Individual

 FIAPP Stakeholders........................................................................................57

Table 5-2. Assignment of Classes of Individual FIAPP Stakeholders to FIAPP

 Stakeholder Groups.........................................................................................58

Table 5-3. FIAPP-Related Benefits (or Cost Savings) for All Stakeholders....................60

Table 5-4. FIAPP-Related Cost Increases (or Benefit Reductions) for All

Stakeholders.....................................................................................................66

Table 5-5. Types of FIAPP-Related Benefits (or Cost Savings) Classified by

 Stakeholder Group ..........................................................................................70

Table 5-6. Types of FIAPP-Related Cost Increases (or Benefit Reductions) Classified

 by Stakeholder Group .....................................................................................71

Table 6-1. Summary of Selected Results from the Statistical Analyses of the CII

 Project Data.....................................................................................................80

Table 6-2. Average Costs of Construction Site Injuries: 1997 .........................................89

Table 6-3. BFRL Investment Costs by Fiscal Year ...........................................................93

Table 6-4. Baseline Case of Pη(t) (α = 4.0, β = 0.5, η = 0.4).........................................101

Table 6-5. Estimated Investment Costs as a Function of the Year of First Commercial

 Use ................................................................................................................106

Table 7-1. Baseline Values for the Diffusion Models and of FIAPP-Related

 Investments and Installations by Year: 1993 – 2017....................................113

Table 7-2. Baseline Cost Savings by Category and in Total in Millions of 1997

Dollars by Year: 1993-2017...........................................................................116

Table 7-3. Baseline Computation of Present Value Cost Savings Nationwide in

 Millions of 1997 Dollars: 1993-2017 ...........................................................118

Table 7-4. Summary of BFRL Research Investments: 1993-2017.................................120

Table 7-5. Estimated Cost Savings in Millions of 1997 Dollars Attributable

 to BFRL ........................................................................................................123

Table 8-1. Baseline and Extreme Values of the Eleven Input Variables Used in the

 Sensitivity Analysis ......................................................................................128
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Table 8-2. Summary Statistics Due to Changes in the Input Variable Alpha.................130

Table 8-3. Summary Statistics Due to Changes in the Input Variable Beta...................131

Table 8-4. Summary Statistics Due to Changes in the Input Variable Eta.....................132

Table 8-5. Summary Statistics Due to Changes in the Input Variable Discount Rate....133

Table 8-6. Summary Statistics Due to Changes in the Input Variable First Cost

 Savings..........................................................................................................133

Table 8-7. Summary Statistics Due to Changes in the Input Variable Maintenance

 and Repair Cost Savings ...............................................................................134

Table 8-8. Summary Statistics Due to Changes in the Input Variable Reductions in

 Delivery Time ...............................................................................................134

Table 8-9. Summary Statistics Due to Changes in the Input Variable Higher

 Contractor Net Income..................................................................................135

Table 8-10. Summary Statistics Due to Changes in the Input Variable New-

 Technology Introduction Costs ...................................................................135

Table 8-11. Summary Statistics Due to Changes in the Input Variable Time of

First Use......................................................................................................136

Table 8-12. Summary Statistics Due to Changes in the Input Variable Length

of Delay.......................................................................................................137

Table 8-13. Results of the Deterministic Sensitivity Analysis for the Combined

Effects Due to Changes of the Input Variables “Targeted” Time of

First Use and the Length of the Delay ........................................................138

Table 8-14. Summary Statistics Due to Changes in All of the Input Variables .............139

Table 8-15. Percentiles for Statistical Measures Due to Changes in All of the Input

Variables.......................................................................................................142

List of Figures

Figure 3-1. Information Flows Model for Commercial Buildings and Industrial

Facilities...........................................................................................................19
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Figure 3-2. Schematic Diagram of BFRL’s CONSIAT Program.....................................23

Figure 4-1. 1997 Breakdown of $657 Billion Construction Market..................................44

Figure 4-2. 1997 Breakdown of $206 Billion Commercial Sector....................................46

Figure 4-3. Total Number of Commercial Buildings by Size Category: 1995..................50

Figure 4-4. Total Commercial Floorspace by Building Size Category: 1995 ...................50

Figure 4-5. Total Commercial Floorspace by Building Size Category and Number of

 Floors: 1995 ....................................................................................................51

Figure 4-6. Total Number of Commercial Buildings by Year of Construction: 1995.......52

Figure 4-7. Total Commercial Floorspace by Year of Construction: 1995 .......................52

Figure 4-8. Total Commercial Floorspace by Year of Construction and Number of

 Floors: 1995 ....................................................................................................53

Figure 6-1. CII Database by Respondent Type.................................................................77

Figure 6-2. CII Database by Industry Type ......................................................................77

Figure 6-3. CII Database by Cost Category......................................................................79

Figure 6-4. CII Database by Project Nature......................................................................79

Figure 6-5. Recordable Incidence Rate and Lost Workday Case Incidence Rate for

 Years 1989-1997.............................................................................................88

Figure 6-6. Baseline Case of Pη(t) by t(year)..................................................................102

Figure 8-1. Present Value of Cost Savings Nationwide in Millions of 1997 Dollars.....145

Figure 8-2. Present Value of Cost Savings Attributable to BFRL in Millions of 1997

Dollars ..........................................................................................................146

Figure 8-3. Present Value of BFRL’s Investment Costs in Millions of 1997 Dollars....147

Figure 8-4. Present Value of Net Savings Attributable to BFRL in Millions of 1997

Dollars ..........................................................................................................151

Figure 8-5. Savings to Investment Ratio on BFRL’s Research and Development

Investment ....................................................................................................152
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Figure 8-6. Adjusted Internal Rate of Return on BFRL’s Research and Development

 Investment ...................................................................................................153

List of Exhibits

Exhibit 2-1. Format for Summarizing the Economic Impacts of BFRL Research

Efforts ...........................................................................................................13

Exhibit 7-1. Summary of Economic Impacts of BFRL Research on Construction

Systems Integration and Automation Technologies in Commercial

Buildings.....................................................................................................108
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List of Acronyms

Acronym Definition

AHP Analytical Hierarchy Process

AIRR Adjusted Internal Rate of Return

AISC American Institute for Steel Construction

AP Application Protocol

ASHRAE American Society of Heating, Refrigerating, and Air-Conditioning Engineers

ASTM American Society for Testing and Materials

BCR Benefit-to-Cost Ratio

BFRL Building and Fire Research Laboratory

BLS Bureau of Labor Statistics

CAD Computer-Aided Design

CADD Computer-Aided Design and Drafting

CAM Computer-Aided Manufacturing

CBECS Commercial Building Energy Consumption Survey

CDF Cumulative Distribution Function

CII Construction Industry Institute

CONSIAT Construction Systems Integration and Automation Technologies

CPI Consumer Price Index

DOE Department of Energy

ECS Emissions Control System

EDI Electronic Data Interchange

EMCS Energy Management and Control System

EPC Engineer-Procure-Construct

ERP Enterprise Resource Planning

FIAPP Fully-Integrated and Automated Project Process

FIATECH Fully-Integrated and Automated Project Processes Technologies

FY Fiscal Year

GPS Global Positioning System

GUI Graphical User Interface

HVAC Heating, Ventilation, and Air-Conditioning

IAI International Alliance for Interoperability

IEC International Electrotechnical Commission

IEEE Institute of Electronics and Electrical Engineers

IFC Industry Foundation Classes

IFMA International Facilities Management Association

IRS Internal Revenue Service

ISO International Organization for Standardization
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IT Information Technology

LCC Life-Cycle Cost

LADAR LAser Detection and Ranging

LWCIR Lost Workday Case Incidence Rate

MARR Minimum Attractive Rate of Return

NIST National Institute of Standards and Technology

NLS Non-Line-of-Sight

NSTC National Science and Technology Council

OAE Office of Applied Economics

OMB Office of Management and Budget

PLIB Parts LIBrary

PVB Present Value of Benefits

PVC Present Value of Non-Investment Costs

PVC Present Value of Combined Costs

PVI Present Value of Investment Costs

PVNB Present Value of Net Benefits

PVNS Present Value of Net Savings

PVS Present Value of Savings

R&D Research and Development

RFID Radio Frequency Identification

RIR Recordable Incidence Rate

SIR Savings-to-Investment Ratio

STEP Standard for the Exchange of Product

TV Terminal Value

VRML Virtual Reality Modeling Language
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Executive Summary

This report focuses on measuring and documenting the economic impacts of BFRL’s

Construction Systems Integration and Automation Technologies (CONSIAT) program in

commercial buildings. The CONSIAT program is an interdisciplinary research effort

within BFRL—in collaboration with the Construction Industry Institute, the private

sector, other federal agencies, and other laboratories within NIST—to develop key

enabling technologies, standard communication protocols, and advanced measurement

technologies needed to deliver fully-integrated and automated project process (FIAPP)

products and services to the construction industry.

This case study of BFRL’s CONSIAT-related research, development, and deployment

effort illustrates how to apply in practice a series of standardized methods to evaluate and

compare the economic impacts of alternative research investments. It is presented in

sufficient detail for the reader to understand the basis for the economic impact assessment

and to reproduce the results. It is based on past, ongoing, and planned research efforts.

Thus, it includes CONSIAT-related investment costs that have already occurred along

with estimates of future investment costs and cost savings due to the use of FIAPP

products and services in commercial buildings.

Chapter 2 presents the five economic evaluation methods (i.e., economic measures) that

are most appropriate for measuring the benefit and cost impacts of research programs: (1)

present value of net benefits (PVNB); (2) present value of net savings (PVNS); (3)

benefit-to-cost ratio (BCR); (4) savings-to-investment ratio (SIR); and (5) adjusted

internal rate of return (AIRR). The PVNB (PVNS) measures the overall magnitude of

the benefits (cost savings) net of the costs of undertaking the research. The BCR (SIR)

measures the benefits (cost savings) per unit cost of the research. The AIRR is the annual

percentage yield from a project over the study period, taking into account the

reinvestment of interim receipts. All five methods apply to accept/reject decisions. Both

PVNB and PVNS are appropriate for design/size decisions (selecting one among

mutually exclusive alternatives). BCR, SIR, and AIRR are appropriate for ranking

alternatives under a budget constraint. A format for summarizing economic impacts of

research investments is presented in Exhibit 2.1.

Chapter 3 describes BFRL’s CONSIAT-related research, development, and deployment

effort and each of its eight key areas of research. The CONSIAT effort within BFRL is

aimed at producing a suite of products and services that integrate a wide variety of

planning, design, and construction activities. How these products and services perform in

a “synergistic” and reliable manner is at the heart of BFRL’s CONSIAT program. The

goal of BFRL’s CONSIAT program is to produce FIAPP products and services that will

result in significant reductions in both the delivery time of constructed facilities and the

life-cycle costs of those facilities. These products and services are being developed for

use by building owners and operators, construction contractors, architects, engineers, and

other providers of professional services.
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Chapter 4 provides a snapshot of the US construction industry. As such, it provides the

context within which the scope and size of the market for FIAPP products and services is

defined. Information is first presented on the value of construction put in place to show

the size of the construction industry and each of its four sectors. The four sectors are

residential, commercial, industrial, and public works. Information on the commercial

sector is then presented to focus on its importance within the overall construction

industry and to define its key components. Special emphasis is then placed on

identifying and detailing the key characteristics of the commercial sector. Detailing the

key characteristics of the commercial sector is crucial, because investments in FIAPP

products and services affect not only new construction activities but additions and

alterations as well as maintenance and repair activities.

A strategy for identifying, collecting, and measuring FIAPP-related benefits and costs is

presented in Chapter 5. The strategy identifies key stakeholders (e.g., commercial

building owners and managers), presents comprehensive lists of FIAPP-related benefits

and costs, and documents the relationships between benefits, costs, and stakeholders.

The strategy was developed through an iterative process. First, information was solicited

from all of the members of the BFRL CONSIAT team. Second, the lists were refined and

organized into a suite of “classification” hierarchies. Third, the classification hierarchies

were distributed to the BFRL CONSIAT project leaders and, upon their review of the

classification hierarchies, critiqued in a series of meetings. The meetings with the BFRL

CBS project leaders also sought to identify subject matter experts for follow-on

discussions. Finally, subject matter experts from industry and government were

interviewed. These interviews were used to finalize the analysis strategy and the

classification hierarchies as well as to collect information on current industry practices

and to identify additional data sources.

Chapter 6 describes the data and assumptions used to evaluate the economic impacts of

installing FIAPP products and services in commercial buildings. The goal of Chapter 6 is

fourfold. First, it establishes the sources and validity of the data used in the CONSIAT

economic impact assessment. Second, it defines the base case and the FIAPP alternative.

Third, it produces estimated values for key sets of benefits and costs. Fourth, it

documents the process by which key assumptions were established, including how the

values of key parameters were set. For example, the study period over which costs and

savings are measured consists of the 25 years from 1993 through 2017. The base year is

1997, and all dollar amounts are calculated in present value 1997 dollars. The discount

rate is 7 % (real), which is the OMB discount rate in effect for all benefit-cost analyses of

public investments that provide benefits or incur costs to the general public.

The CONSIAT economic impact assessment was carried out in two stages. In the first

stage, a baseline analysis was performed. In the baseline analysis, all input variables

used to calculate the economic measures are set at their likely values. It is important to

recognize that the term baseline analysis is used to denote a complete analysis in all

respects but one; it does not address the effects of uncertainty. In the second stage,

eleven input variables were varied both singly and in combination according to an
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experimental design. Monte Carlo simulations are employed to evaluate how changing

the value of these variables affects the calculated values of the economic measures.

In Chapter 7 (see Exhibit 7-1), the results of the baseline analysis demonstrate that the

use of FIAPP products and services will generate substantial cost savings to commercial

building owners and managers and to contractors engaged in the construction of those

buildings. The present value of savings nationwide expected from the use of FIAPP

products and services is almost $1.4 billion (measured in 1997 dollars). Furthermore,

because of BFRL’s involvement, FIAPP products and services are expected to be

commercially available in 2005. If BFRL had not participated in the development of

FIAPP products and services, the commercial introduction of FIAPP products and

services is expected to be delayed until 2009. Consequently, potential cost savings

accruing to commercial building owners and managers and to contractors over the period

2005 through 2008 would have been foregone. The present value of these cost savings is

approximately $120 million. These cost savings measure the value of BFRL’s

contribution for its CONSIAT-related investment costs of approximately $29.1 million.

Stated in present value terms, every public dollar invested in BFRL’s CONSIAT-related

research, development, and deployment efforts is expected to generate $4.13 in cost

savings to the public (i.e., an SIR of 4.13). The annual percentage yield (AIRR) from

BFRL’s CONSIAT-related investments over the study period is 13.3 %.

Chapter 8 covers the sensitivity analysis. The objective of the sensitivity analysis was to

evaluate how uncertainty in the values of each of the eleven input variables, both singly

and in combination, translates into changes in each of the six economic measures. The

six economic measures evaluated in the sensitivity analysis are: (1) the present value of

savings nationwide, PVSALL; (2) the present value of savings due to BFRL, PVSBFRL; (3)

the present value of BFRL’s CONSIAT-related investment costs, PVCBFRL; (4) the

present value of net savings due to BFRL, PVNSBFRL; (5) the savings-to-investment ratio

on BFRL’s CONSIAT-related investments, SIRBFRL; and (6) the adjusted internal rate of

return on BFRL’s CONSIAT-related investments, AIRRBFRL. The major advantage of

the sensitivity analysis is that it produces results that can be tied to probabilistic levels of

significance for each economic measure (e.g., the probability that PVNSBFRL is greater

than or equal to zero, SIRBFRL is greater than or equal to 1.0, or AIRRBFRL is greater than

or equal to the discount rate, each of which would indicate that BFRL’s CONSIATrelated investments were cost effective).

The results of the sensitivity analysis serve to validate the results of the baseline analysis.

For example, each Monte Carlo simulation in which a single input variable was varied

produced 1,000 observations for each of the six economic measures. Ten of the 11 such

simulations produced values for the median and mean that were nearly identical to the

corresponding value calculated in the baseline analysis for that measure. The final Monte

Carlo simulation, in which all eleven of the input variables were varied in combination,

also produced 1,000 observations for each of the six economic measures. In this case, the

median value for each economic measure was less than the corresponding value

calculated in the baseline analysis for that measure. In addition, the results from this

Monte Carlo simulation reveal that the present value of net savings due to BFRL,
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PVNSBFRL, can be negative. This implies that there is some non-zero probability that

BFRL’s CONSIAT-related investments are not cost effective. On the opposite extreme,

however, PVNSBFRL may exceed $800 million in 1997 dollars.

The fact that the range of values for an economic measure is so wide prompted an indepth examination of the results of the final Monte Carlo simulation for three of the six

economic measures. These measures are particularly helpful in understanding BFRL’s

contribution, since each measure provides a different perspective. The first, the present

value of net savings due to BFRL, is a magnitude measure; it shows a dollar value to the

public net of BFRL’s CONSIAT-related investments. The second, the savings-toinvestment ratio on BFRL’s CONSIAT-related investments, is a multiplier; it shows, in

present value terms, how many dollars the public receives for each public dollar spent.

The third, the adjusted internal rate of return on BFRL’s CONSIAT-related investments,

is a rate of return; it shows the return on the public monies going into the development of

FIAPP products and services throughout the 25-year study period.

For each of the three economic measures, less than 250 observations out of 1,000 were

responsible for the observed “uneconomical” outcome. Stated another way, there is at

least a 75 % probability that BFRL’s CONSIAT-related investments are cost effective.

This underscores the importance of using multiple measures in order to ensure

consistency in decision making.

Chapter 9 discusses additional areas of research that might be of value to government

agencies and other institutions that are concerned with an efficient allocation of their

research budgets. These areas of research are concerned with: (1) the development of a

standard classification of research benefits and costs; (2) factors affecting the diffusion of

new technologies; (3) conducting prospective evaluations with scheduled follow-ups; and

(4) evaluations based on multiattribute decision analysis.
1

1 Introduction

1.1 Background

The pressures of competing in the global marketplace are affecting nearly every U.S.

business. Now more than ever, U.S. businesses are finding that they must continually

improve their products and services if they are to survive and prosper. Research, with its

potential for incremental and breakthrough improvement, is of central importance to most

businesses’ continuous improvement efforts. A key component of the competitiveness

problem is the “inability of American companies (or, more accurately, the U.S.-based

portions of what are fast becoming global technology firms) to transform discoveries

quickly into high-quality products and into processes for designing, manufacturing,

marketing, and distributing such products.” 1

Increasingly, the winners in the competitiveness race are those businesses that most

rapidly make use of the fruits of research (e.g., new data, insights, inventions, and

prototypes). Efforts underway at the National Institute of Standards and Technology

(NIST) and elsewhere in the U.S. focus on speeding up the commercial application of

basic and applied research results. The purpose of this report is to respond to the

following question: “how do we measure the results of our investments in technology

development and application?” 2 A case study approach is used to illustrate how

standardized evaluation methods may be used to measure the economic impacts of such

investments.

NIST’s research laboratories assist all sectors of U.S. industry through focused research

programs and standards development activities. Each laboratory has strong working

relationships with industrial, trade, and professional organizations in its areas of

technology concentration. The program of NIST’s Building and Fire Research

Laboratory (BFRL) is guided by a prioritized research agenda developed by experts from

the building and fire communities. Its performance prediction and measurement

technologies enhance the competitiveness of U.S. industry and public safety.

Specifically, BFRL is dedicated to improving the life-cycle quality and economy of

constructed facilities. BFRL studies structural, mechanical, and environmental

engineering; fire science and fire safety engineering; and building materials.

To further strengthen its ties to industry, BFRL is participating in the Subcommittee on

Construction and Building of the National Science and Technology Council (NSTC).

The NSTC, a cabinet-level group charged with setting federal technology policy,

coordinates research strategies across a broad cross-section of public and private

interests. The Subcommittee on Construction and Building coordinates and defines

 

1

Reich, Robert W. 1989. “The Quiet Path to Technological Preeminence.” Scientific American (October):

pp. 41-47.

2

Good, Mary, and Arati Prabhakar. 1994. “Foreword.” In Mark Bello and Michael Baum, Setting

Priorities and Measuring Results at the National Institute of Standards and Technology. Gaithersburg,

MD: National Institute of Standards and Technology.
2

priorities for federal research, development, and deployment related to the industries that

produce, operate, and maintain constructed facilities, including, buildings and

infrastructure. Seven goals to enhance the competitiveness of the U.S. construction

industry are explicit in the mission of the Subcommittee.3

BFRL has long recognized the value of measuring the impacts of its research program.

Previous studies have shown that even modest research efforts within BFRL are capable

of producing significant impacts.4

 One reason for such outcomes is the unique mix of

research facilities and skills possessed by BFRL and its staff. Through many years of

active collaboration with its various user communities, BFRL’s research findings are

highly regarded when new construction, building, and disaster mitigation technologies

are considered for introduction into the U.S. market.

Information and automation technologies are core components of the strategic plans of

the U.S. construction industry. Advances in information and automation technologies

have been identified as key components for achieving the National Construction Goals.

The U.S. chemical industry identifies information systems as a key technical discipline in

its Technology Vision 2020 5

 and predicts achieving the smooth flow of information—

from concept through design to construction and into plant maintenance and operation—

will promote the use of automation and improve economic competitiveness. The 1999

Strategic Plan of the Construction Industry Institute (CII) identifies six major industry

trends that will shape the construction industry in the next century.6

 CII identified fullyintegrated and automated project processes (FIAPPs) as the most significant trend and

predicts it will revolutionize the construction industry.7 Characteristics of FIAPP

products and services include one-time data entry; interoperability with design,

construction, and operation processes (e.g., virtual construction and construction

automation); and user friendly input/output techniques. Significant economic impacts are

anticipated from the adoption and use of FIAPP products and services within the

construction industry.

1.2 Purpose

This report is the fifth in a series of impact studies prepared by BFRL.8 It focuses on

BFRL’s Construction Systems Integration and Automation Technologies (CONSIAT)

 

3

For a detailed description of these goals and how the Subcommittee on Construction and Building is

approaching them, see Wright, Richard N., Arthur H. Rosenfeld, and Andrew J. Fowell. 1995.

Construction and Building: Federal Research and Development in Support of the U.S. Construction

Industry. Washington, DC: National Science and Technology Council. 4

Marshall, Harold E., and Rosalie T. Ruegg. 1979. Efficient Allocation of Research Funds: Economic

Evaluation Methods with Case Studies in Building Technology. NBS Special Publication 558.

Gaithersburg, MD: National Bureau of Standards.

5

 American Chemical Society. 1996. Technology Vision 2020. Washington, DC: American Chemical

Society.

6

 Construction Industry Institute. 1999. 1999 Strategic Plan. Austin, TX: Construction Industry Institute. 7 Ibid. p. 15. 8 The first report in the series focuses on two building technology applications: (1) ASHRAE Standard 90-

75 for residential energy conservation; and (2) 235 shingles, an improved asphalt shingle for sloped roofing

(see Chapman, Robert E., and Sieglinde K. Fuller. 1996. Benefits and Costs of Research: Two Case
3

program. The CONSIAT program is an interdisciplinary research effort within BFRL—

in collaboration with CII, the private sector, other federal agencies, and other laboratories

within NIST—to develop key enabling technologies, standard communication protocols,

and advanced measurement technologies needed to deliver FIAPP products and services

to the construction industry.

BFRL’s CONSIAT program is aimed at producing a suite of products and services that

integrate a wide variety of planning, design, and construction activities. How these

products and services perform in a “synergistic” and reliable manner is at the heart of

BFRL’s CONSIAT program.

The goal of BFRL’s CONSIAT program is to produce products and services that will

result in significant reductions in both the delivery time of constructed facilities and the

life-cycle costs of those facilities. These products and services are being developed for

use by building owners and operators, construction contractors, architects, engineers, and

other providers of professional services.

1.3 Scope and Approach

FIAPP products and services help all four construction industry sectors—industrial,

commercial, public works, and residential. This report employs standardized methods to

evaluate the expected economic impacts of the adoption and use of FIAPP products and

services in commercial buildings (e.g., office buildings, educational facilities, and

hospitals).

The “case study” approach employed here illustrates how to evaluate and compare the

economic impacts of research investments. Standardized methods are used in this report

and others in the series to ensure consistency in the measurement of economic impacts.

The measurement methods employed here are applicable to other programs in BFRL, in

NIST, and in other research laboratories.

The report has eight chapters in addition to the Introduction. The methodology and the

standardized methods employed in the study to measure the CONSIAT program’s

economic impacts are described in Chapter 2. Standardized methods are used to define

 Studies in Building Technology. NISTIR 5840. Gaithersburg, MD: National Institute of Standards and

Technology). The second report focuses on a fire technology application: the Fire Safety Evaluation

System for health care facilities (see Chapman, Robert E., and Stephen F. Weber. 1996. Benefits and

Costs of Research: A Case Study of the Fire Safety Evaluation System. NISTIR 5863. Gaithersburg, MD:

National Institute of Standards and Technology). The third report focuses on the research, development,

deployment, and use of cybernetic building systems in office buildings (see Chapman, Robert E. 1999.

Benefits and Costs of Research: A Case Study of Cybernetic Building Systems. NISTIR 6303.

Gaithersburg, MD: National Institute of Standards and Technology). The fourth report focuses on the

research, development, deployment, and use of construction systems integration and automation

technologies in industrial facilities (see Chapman, Robert E. 2000. Benefits and Costs of Research: A

Case Study of Construction Systems Integration and Automation Technologies in Industrial Facilities.

NISTIR 6501. Gaithersburg, MD: National Institute of Standards and Technology).
4

the key measures of the economic impacts of research investments. A format for

summarizing the economic impacts of research investments is also presented.

The body of this report, Chapters 3 through 8, consists of a case study of fully-integrated

and automated project process systems and technologies in commercial buildings. The

approach is to present all CONSIAT-related information in sufficient detail for the reader

both to understand the basis for the economic impact assessment and make it possible to

reproduce the results of the economic impact assessment. The CONSIAT case study is

ex ante (i.e., prospective) in that it estimates impacts from on-going and planned research

as well as past research.

The CONSIAT case study estimates the economic impacts to the commercial buildings

sector of the construction industry from BFRL's research effort aimed at the development

and introduction of a suite of FIAPP products and services. Chapter 3 describes BFRL’s

CONSIAT program. Both the overall CONSIAT research and development effort and

the eight key areas of research, which are its constituent parts, are described. Chapter 4

provides an overview of the construction industry. The overview provides the context

within which the market for FIAPP products and services is defined. A strategy for

measuring FIAPP-related benefits and costs is presented in Chapter 5. The strategy

identifies key stakeholders (e.g., building owners and managers), presents comprehensive

lists of FIAPP-related benefits and costs, and documents the relationships between

benefits, costs, and stakeholders. Assumptions about those years over which costs and

savings are tabulated, the appropriate discount rate, and the rate and level of adoption of

FIAPP products and services in commercial buildings are necessary to measure the

economic impacts of fully-integrated and automated project process systems and

technologies. These assumptions, and the supporting data upon which these assumptions

are based, are described in Chapter 6. In addition, Chapter 6 develops estimates of the

key benefits and costs that are the focus of the ex ante impact assessment. These

“significant few” benefits and costs are well-defined subsets of the comprehensive lists

presented in Chapter 5. Estimates of the cost savings from using FIAPP products and

services in commercial buildings are the focus of Chapter 7. In addition, that part of

dollar savings that appears attributable specifically to BFRL’s research and development

effort is estimated. A two-page summary of the CONSIAT case study is given in Section

7.1. Chapter 8 includes a sensitivity analysis to provide the reader with additional

background and perspective on the economic impacts of BFRL’s CONSIAT program in

commercial buildings. The purpose of the sensitivity analysis is to evaluate the impact of

changing the values of a number of key variables whose values are uncertain. Monte

Carlo techniques are employed to evaluate how changing the values of these key

variables in combination affects the calculated values of the key measures of the

economic impacts of fully-integrated and automated project process systems and

technologies in commercial buildings. Chapter 9 concludes the report with a summary

and suggestions for further research.
5

2 A Methodology for Analyzing Economic Impacts

This chapter focuses on laying out a methodology for conducting and summarizing an

economic impact assessment. The methodology is based on two types of analysis, five

measures of economic performance, and a format for summarizing the results of an

economic impact assessment. The two types of analysis are baseline analysis and

sensitivity analysis. They are described in Section 2.1. The five measures of economic

performance are present value of net benefits, present value of net savings, benefit-to-cost

ratio, savings-to-investment ratio, and adjusted internal rate of return. They are described

in Section 2.2. The format for summarizing the results of the economic impact

assessment is described in Section 2.3.

2.1 Types of Analysis

2.1.1 Baseline Analysis

A baseline analysis is the starting point for conducting an economic impact assessment.

In the baseline analysis, all data (i.e., all input variables and any functional relationships

among these variables) entering into the benefit, cost, and savings calculations are set at

their likely values. For selected types of data, the input values are fixed (e.g., a physical

constant or a value that is mandated by legislation). The input values associated with

these data types are considered to be known with certainty. For other types of data, the

likely values reflect the fact that some information associated with these data is uncertain.

In that case, the values of any data subject to uncertainty are set based on some measure

of central tendency.9

 Throughout this report, likely value and baseline value are used

interchangeably. Baseline data represent a fixed state of analysis based on likely values.

For this reason, the results and the analysis of these results are referred to as the baseline

analysis. Throughout this report, the term baseline analysis is used to denote a complete

analysis in all respects but one; it does not address the effects of uncertainty.

2.1.2 Sensitivity Analysis

Sensitivity analysis measures the impact on project outcomes of changing the values of

one or more key input variables about which there is uncertainty. Sensitivity analysis can

be performed for any measure of economic performance (e.g., present value of net

benefits, present value of net savings, benefit-to-cost ratio, savings-to-investment ratio,

adjusted internal rate of return). Since sensitivity analysis is easy to use and understand,

it is widely used in the economic evaluation of government and private-sector

applications. Office of Management and Budget Circular A-94 recommends sensitivity

 

9

 Two common measures of central tendency are the mean (e.g., the sum of the individual values of the

items divided by the number of items in the sample) and the median (e.g., the middle value in a rank

ordering of the individual values of the items in the sample). In most cases in this report, the mean is used

as the measure of central tendency. Any case where the median is used as the measure of central tendency

is clearly indicated in the text. Consequently, if no explicit reference is made to the measure of central

tendency, the measure used is the mean.
6

analysis to federal agencies as one technique for treating uncertainty in input variables.10

Therefore, a sensitivity analysis complements the baseline analysis by evaluating the

changes in output measures when selected key sets of data vary about their baseline

values. Readers interested in a comprehensive survey on methods for dealing with

uncertainty for use in government and private-sector applications are referred to the study

by Marshall11 and the subsequent video12 and workbook.13

2.2 Overview of Evaluation Methods

Several methods of economic evaluation are available to measure the economic

performance of a research program, a new technology, a building, a building system, or

like investment, over a specified time period. These methods include, but are not limited

to, present value of net benefits, present value of net savings, benefit-to-cost ratio,

savings-to-investment ratio, and the adjusted internal rate of return. These methods differ

in the way in which they are calculated and, to some extent, in their applicability to

particular types of investment decisions. The five methods described in this section are

based on American Society for Testing and Materials (ASTM) standard practices.14

Detailed descriptions of each of the standardized methods are given in Chapman and

Fuller.15 Readers interested in an excellent, in-depth survey covering these as well as

other methods are referred to Ruegg and Marshall.16

In order to describe each of the five standardized methods, it is necessary to first

introduce and define a series of terms. These terms are used to define each of the

standardized methods. Throughout this section the following terms are used as the basis

for defining the standardized methods:

a* = the alternative under analysis;

t = a unit of time, where –ta is the earliest point (i.e., beginning of the

study period) before the base year (i.e., t=0) and T is the last point

after the base year (i.e., end of the study period);

L = the length of the study period (e.g., t

a + T);

 

10 Executive Office of the President. 1992. OMB Circular A-94. Washington, DC: Office of Management

and Budget.

11Marshall, Harold E. 1988. Techniques for Treating Uncertainty and Risk in the Economic Evaluation of

Building Investments. NIST Special Publication 757. Gaithersburg, MD: National Institute of Standards

and Technology.

12Marshall, Harold E. 1992. Uncertainty and Risk—Part II in the Audiovisual Series on Least-Cost Energy

Decisions for Buildings. Gaithersburg, MD: National Institute of Standards and Technology. 13Marshall, Harold E. 1993. Least-Cost Energy Decisions for Buildings—Part II: Uncertainty and Risk

Video Training Workbook. NISTIR 5178. Gaithersburg, MD: National Institute of Standards and

Technology.

14American Society for Testing and Materials (ASTM). Fourth Edition, 1999. ASTM Standards on

Building Economics. West Conshohocken, PA: American Society for Testing and Materials. 15Chapman and Fuller, Two Case Studies in Building Technology, pp. 27-37. 16Ruegg, Rosalie T. and Harold E. Marshall. 1990. Building Economics: Theory and Practice. New York:

Chapman and Hall.
7

Bt

a* = the benefits for alternative a* in year t;

It

a* = the investment costs for alternative a* in year t;

Ct

a* = the non-investment costs for alternative a* in year t;

Ct

a* = the combined cost for alternative a* in year t (i.e.,

Ct

a*

 = It

a* + Cta* );

St

a* = the savings for alternative a* in year t;

d = the discount rate expressed as a decimal.

Throughout this section the prefix, PV, is used to designate dollar denominated quantities

in present value terms. The present value is derived by discounting (i.e., using the

discount rate) to adjust all benefits, costs, and savings—past, present, and future—to the

base year (i.e., t=0). The dollar denominated quantities defined above and their

associated present value terms are: the present value of benefits (PVB), the present value

of investment costs (PVI), the present value of non-investment costs (PVC), the present

value of combined costs (PVC), and the present value of savings (PVS).

2.2.1 Present Value of Net Benefits and Present Value of Net Savings

The present value of net benefits (PVNB) method is reliable, straightforward, and widely

applicable for finding the economically efficient choice among alternatives (e.g., building

systems). It measures the amount of net benefits from investing in a given alternative

instead of investing in the foregone opportunity (e.g., some other alternative or

maintenance of the status quo).

PVNB is computed by subtracting the time-adjusted costs of an investment from its timeadjusted benefits. If PVNB is positive, the investment is economic; if it is zero, the

investment is as good as the next best investment opportunity; if it is negative, the

investment is uneconomical. Emphasis is on economic efficiency because the method is

appropriate for evaluating alternatives that compete on benefits, such as revenue or other

advantages that are measured in dollars, in addition to costs.

The present value of net savings (PVNS) method is the PVNB method recast to fit the

situation where there are no significant benefits in terms of revenue or the like, but there

are reductions in future costs (e.g., reductions in the cost of ownership to consumers).17

By treating savings like revenue benefits, the PVNB method may be reformulated as the

PVNS method.

 

17If there are any benefits, say in the form of revenues or other positive cash flows; add them to the cost

savings associated with the alternative under analysis.
8

The PVNB for a given alternative, a*, may be expressed as:

( )/(1 ) * *

* * *

a t

t

T

t t

a

t

a a a

B d

PVNB PVB PV

a

= − +

= −

∑=−

C

C

2.1

If there are no important benefits in terms of revenue or the like, but there are reductions

in future costs, then, the PVNS for a given alternative, a*, may be expressed as:

( ) t T

t t

a

t

a

t

a a a

S I d

PVNS PVS PVI

a

/(1 ) * *

* * *

= − +

= −

∑=−

2.2

If the decision maker anticipates revenues from the investment, then use the PVNB

measure. If the decision maker expects costs to be reduced, then use the PVNS measure.

The PVNS measure is one of the methods used in the construction systems integration

and automation technologies (CONSIAT) case study (see Chapters 7 and 8).

2.2.2 Benefit-to-Cost Ratio and Savings-to-Investment Ratio

The benefit-to-cost ratio (BCR) and the savings-to-investment ratio (SIR) are numerical

ratios whose sizes indicate the economic performance of an investment. The BCR is

computed as benefits, net of future non-investment costs, divided by investment costs.

The SIR is savings divided by investment costs. The SIR is the BCR method recast to fit

the situation where the investment’s primary advantage is lower costs. SIR is to BCR as

PVNS is to PVNB.

A ratio less than 1.0 indicates an uneconomic investment; a ratio of 1.0 indicates an

investment whose benefits or savings just equal its costs; and a ratio greater than 1.0

indicates an economic project. A ratio of, say, 4.75 means that the investor (e.g., the

general public for a public-sector research program) can expect to receive $4.75 for every

$1.00 invested (e.g., public funds expended), over and above the required rate of return

imposed by the discount rate.

The BCR for a given alternative, a*, may be expressed as:
9

( )

( )

∑

∑

=−

=−

+

− +

=

= −

T

t t

a t

t

T

t t

a t

t

a

t

a a a a

a

a

I d

B C d

BCR PVB PVC PVI

/(1 )

/(1 )

/

*

* *

* * * *

2.3

The SIR for alternative a* may be expressed as:

∑

∑

=−

=−

+

+

=

=

T

t t

a t

t

T

t t

a t

t

a a a

a

a

I d

S d

SIR PVS PVI

/(1 )

/(1 )

/

*

*

* * *

2.4

As was the case for the PVNB and PVNS measures, use the BCR if the decision maker

anticipates revenues from the investment, and use the SIR if the decision maker

anticipates costs to be reduced. The SIR measure is the second method used in the

CONSIAT case study (see Chapters 7 and 8).

2.2.3 Adjusted Internal Rate of Return

The adjusted internal rate of return (AIRR) is the annual yield from a project over the

study period, taking into account reinvestment of interim receipts. Because the AIRR

calculation explicitly includes the reinvestment of all net cash flows, it is instructive to

introduce a new term, terminal value (TV). The terminal value of an investment, a*, is

the future value (i.e., the value at the end of the study period) of reinvested net cash flows

excluding all investment costs. The terminal value for an investment a*, is denoted as

TVa*.

The reinvestment rate in the AIRR calculation is equal to the minimum attractive rate of

return (MARR), which is assumed to equal the discount rate, d, a constant. When the

reinvestment rate is made explicit, all investment costs are easily expressible as a time

equivalent initial outlay (i.e., a value at the beginning of the study period) and all noninvestment cash flows (e.g., benefits, non-investment costs, savings) as a time equivalent

terminal amount. This allows a straightforward comparison of the amount of money that

comes out of the investment (i.e., the terminal value) with the amount of money put into

the investment (i.e., the time equivalent initial outlay).

The AIRR is defined as the interest rate, r*, applied to the terminal value, TVa*, which

equates (i.e., discounts) it to the time equivalent value of the initial outlay of investment
10

costs. It is important to note that all investment costs are discounted to a time equivalent

initial outlay (i.e., to the beginning of the study period) using the discount rate, d.

Several procedures exist for calculating the AIRR. These procedures are derived and

described in detail in the report by Chapman and Fuller.18 The most convenient

procedure for calculating the AIRR is based on its relationship to the BCR (SIR). This

procedure results in a closed-form solution for r*. The AIRR—expressed as a decimal—

is that value of r* for which:

(1 )( ) 1

(1 )( ) 1

1

1

*

*

*

= + −

= + −

a L

a L

d SIR

r d BCR

2.5

The AIRR measure is the third method used in the CONSIAT case study (see Chapters 7

and 8).

2.2.4 Summary of Methods19

The methods presented in the previous sections provide the basis for evaluating the

economic performance of research investments. The equations underlying the methods

presented earlier are all based on ASTM standard practices. All of the methods are

appropriate for evaluating accept or reject type decisions. Among these methods one

should recognize several distinctions that relate to the type of investment decision the

decision maker is facing.

There are four basic types of investment decisions for which an economic analysis is

appropriate:

(1) whether to accept or reject a given project;

(2) the most efficient project size/level, system, or design;

(3) the optimal combination of interdependent projects (i.e., the right mix of sizes/levels,

systems, and designs for a group of interdependent projects); and

(4) how to prioritize or rank independent projects when the allowable budget can not

fund them all.

 

18Chapman and Fuller, Two Case Studies in Building Technology, pp. 35-37. 19For a comprehensive treatment of how to choose among economic evaluation methods, see the

NIST/BFRL video (Marshall, Harold E. 1995. Choosing Economic Evaluation Methods—Part III in the

Audiovisual Series on Least-Cost Energy Decisions for Buildings. Gaithersburg, MD: National Institute of

Standards and Technology) and workbook (Marshall, Harold E. 1995. Least-Cost Energy Decisions for

Buildings—Part III: Choosing Economic Evaluation Methods Video Training Workbook. NISTIR 5604.

Gaithersburg, MD: National Institute of Standards and Technology).
11

Each type of investment decision is important in a research environment. First and

foremost, decision makers need to know whether or not a particular project or program

should be undertaken in the first place. Second, how should a particular research

project/program be configured? The third type of decision builds on the second and

introduces an important concept, interdependence. Many research projects/programs are

multidisciplinary and are analogous to a portfolio. In addition, there may be both

economies of scale (e.g., spreading out the use of specialized equipment) and of scope

(e.g., packaging of staff talents). Consequently, for a given set of skills, laboratory

facilities, candidate projects, and implied interdependencies, the problem becomes how to

choose that combination of projects which maximizes PVNB (PVNS). The fourth type of

decision introduces a budget constraint. The key here is how to get the most impact for

the given budget amount.

Table 2-1 provides a summary of when it is appropriate to use each of the evaluation

methods described earlier. Note that the PVNB (PVNS) method is appropriate in three of

the four cases. Only in the presence of a budget constraint is the use of PVNB (PVNS)

inappropriate and even in that case it plays an important role in computing the aggregate

measure of performance.

Table 2-1. Summary of Appropriateness of Each Standardized Evaluation Method

for Each Decision Type

 Decision Type

PVNB

PVNS

BCR

SIR AIRR

Accept/Reject Yes Yes Yes

Design/Size Yes No No

Combination

(Interdependent)

Yes No No

Priority/Ranking

(Independent)

No Yes Yes

In summary, there are several reasons why multiple measures of economic performance

are necessary. First and foremost, managers want to know if a particular research project

is economic. Reference to Table 2-1 shows that all of the evaluation methods address

this type of decision. Furthermore, these evaluation methods may be used ex ante for

emerging technologies as well as ex post for past research projects. Second, as issues of

design, sizing, and packaging combinations of projects become the focus of attention—as

often occurs in conjunction with budget reviews—the PVNB (PVNS) method emerges as

the principle means for evaluating a project’s or program’s merits.20 Finally, the

tightening budget picture involves setting priorities. Consequently, decision makers need

both measures of magnitude, provided by PVNB (PVNS), and of return, provided by

either the BCR (SIR) or the AIRR, to assess economic performance. Multiple measures,

when used appropriately, ensure consistency in both setting priorities and selecting

 

20If incremental values of the BCR (SIR) or AIRR are computed, they can be used to make design/size and

packaging decisions. See Ruegg and Marshall, Building Economics, pp. 54-58 and 85-87.
12

projects for funding. The results from the CONSIAT case study presented in Chapters 7

and 8 illustrate the importance of multiple measures of economic performance.

2.3 Presentation and Analysis of the Results of an Economic Impact Assessment

The presentation and analysis of the results of an economic impact assessment are central

to understanding and accepting its findings. If the presentation is clear and concise, and

if the analysis strategy is logical, complete, and carefully spelled out, then the results will

stand up under close scrutiny. The purpose of this section is to outline a generic

framework for economic impact studies that meets the two previously cited conditions.

The generic framework is built upon the following three factors: (1) the significance of

the research effort; (2) the analysis strategy; and (3) the calculation of key benefit and

cost measures. A specific framework, tailored to BFRL, is given in Exhibit 2-1; it is also

used as the basis for summarizing the CONSIAT case study (see Section 7.1).

The discussion that follows relates the three factors for the generic framework referenced

above to the specific framework given in Exhibit 2-1. Exposition of the generic

framework serves two purposes. First, it provides a means for organizing the way to

present material associated with an in-depth economic impact assessment. Second, it

provides a vehicle for clearly and concisely presenting the salient results of the analysis.

Such a short summary is appropriate for use by senior research managers (e.g., laboratory

directors) as the basis for statements on the benefits of the research project or program to

the public. A two-page summary of the CONSIAT case study is provided at the

beginning of Chapter 7.

2.3.1 Significance of Research Effort

This section of an economic impact assessment sets the stage for the results that follow.

The goal at this point is to clearly describe:

(1) why the research is important and how the organization conducting the

research became involved; and

(2) why some or all of the changes brought about were due to the research

organization’s contribution.

Emphasis is placed on providing dollar estimates to define the magnitude of the problem.

If any non-financial characteristics are of key importance to senior management, list and

describe them briefly. A clear tie into the research organization’s mission or vision is

included to demonstrate why the organization conducting the research is well qualified

and well positioned to participate in the research effort. The section concludes with a

statement of the research organization’s contribution.
13

Exhibit 2-1. Format for Summarizing the Economic Impacts of BFRL Research

Efforts

1.a Significance of Research Effort:

Describe why the research is important and how BFRL

became involved.

Describe the changes brought about by the BFRL

research effort.

1.b Key Points:

Highlight two or three key points which

convey why this research effort is

important.

2. Analysis Strategy:

Describe how the present value of total benefits (savings) to the nation stemming from all

contributions to the research effort was determined.

Describe how the present value of total costs to the nation stemming from all contributors to the

research effort was determined.

Describe how the present value net benefits (savings) to the nation was determined.

Describe how the present value of total benefits (savings) attributable to BFRL’s research effort was

determined.

Describe how the present value of total costs attributable to BFRL’s research effort was determined.

Describe how the present value of net benefits (savings) attributable to BFRL’s research effort was

determined.

Describe how any additional measures were calculated and how BFRL’s contribution was

determined.

Summarize key data and assumptions: (a) Base year; (b) Length of study period; (c) Discount rate or

minimum acceptable rate of return; (d) Data; and (e) other.

3.b Key Measures:

Report the calculated value of the

Present Value of Net Benefits (PVNB)

or the Present Value of Net Savings

(PVNS) attributable to BFRL and at

least one of the following:

 Benefit-to-Cost Ratio (BCR) or

Savings-to-Investment Ratio (SIR)

 Adjusted Internal Rate of Return

(AIRR)

3.a Calculation of Benefits, Costs, and Additional

Measures:

Total Benefits (Savings):

Report the present value of the total benefits (savings)

attributable to BFRL’s research effort.

Total Costs:

Report the present value of the total costs attributable to

BFRL’s research effort.

Net Benefits (Savings):

Report the present value of net benefits (savings)

attributable to BFRL’s research effort.

Additional Measures:

Report the values of any additional measures calculated.

3.c Traceability

Cite references to specific ASTM

standard practices, ASTM adjuncts, or

any other standards, codes, or regulations

used.
14

2.3.2 Analysis Strategy

This section of an economic impact assessment focuses on documenting the steps taken

to ensure that the analysis strategy is logical and complete. Particular emphasis is placed

on summarizing the key assumptions, including any constraints that limited the scope of

the study. Responses are provided for key assumptions concerning: (a) the base year for

the study; (b) the length of the study period; and (c) the discount rate or minimum

acceptable rate of return used.

Special emphasis is placed on documenting the sources and validity of any data used to

make estimates or projections of key benefit and cost measures. This section establishes

an audit trail from the raw data, through data manipulations (e.g., represented by

equations and formulae), to the results which describe how to determine:

(1) the present value of total benefits (savings) to the nation stemming from

all contributors to the research effort under study, any benefits (savings) to

users of products (materials, equipment, software, or procedures)

stemming from the research effort under study, and any third parties

affected positively by either the research effort or the use of products

stemming from the research effort;

(2) the present value of total costs for all contributors to the research effort

under study, any costs to users of products stemming from the research

effort under study, and any third parties affected negatively by either the

research effort or the use of products stemming from the research effort;

(3) the present value of net benefits (savings) to the nation stemming from all

contributors to the research effort under study, any users of products

stemming from the research effort under study, and any third parties

affected by either the research effort or the use of products stemming from

the research effort;

(4) the present value of total benefits (savings) attributable to the research

organization’s contribution;

(5) the present value of total costs attributable to the research organization’s

contribution;

(6) the present value of net benefits (savings) attributable to the research

organization’s contribution; and

(7) the way in which any additional measures were calculated and how the

research organization’s contribution was determined.
15

2.3.3 Calculation of Benefits, Costs, and Additional Measures

This section of an economic impact assessment focuses on reporting the calculated values

of the key benefit and cost measures, as well as any additional measures that are deemed

appropriate, and establishing traceabilty to standardized practices or, where appropriate,

to statutory documents or procedures. It consists of three subsections, designated as 3.a,

3.b, and 3.c. Subsection 3.a includes descriptive information as well as calculated values.

Subsection 3.b reports calculated values for key summary impact measures. Subsection

3.c is included to ensure traceability to appropriate national standards, codes, or

regulations.

In subsection 3.a, report summaries (e.g., using text, mathematical expressions, tables,

graphs, comparative statistics) of the following information:

(1) the present value of the total benefits attributable to the research

organization’s contribution;

(2) the present value of the total costs attributable to the research

organization’s contribution;

(3) the present value of net benefits attributable to the research organization’s

contribution; and

(4) the values of any additional measures calculated.

In subsection 3.b, report the calculated value of the present value of net benefits or the

present value of net savings attributable to the research organization’s contribution and at

least one of the following:

(a) the benefit-to-cost ratio or the savings-to-investment ratio; or

(b) the adjusted internal rate of return.

In subsection 3.c, cite references to specific ASTM standard practices, ASTM adjuncts,

or any other standards, codes, or regulations used.
16
17

3 Building and Fire Research Laboratory’s CONSIAT Program

3.1 Fully-Integrated and Automated Project Processes: What They Are and What

They Will Do

Information technologies have transformed many aspects of our daily lives and

revolutionized industries in both the manufacturing and service sectors. Within the

construction industry, the changes have so far been less radical. However, the use of

information technologies offers a clear potential for revolutionary change in the

effectiveness with which construction-related processes are executed and the value they

add to construction industry stakeholders. Recent exponential growth in computer,

network, and wireless capabilities, coupled with improved 3D CAD (computer-aided

design) and object-oriented software tools, have made it possible to apply information

technologies in all aspects of the facility life cycle—design, construction, commissioning,

operation, and decommissioning.

Computer, automobile, and aircraft manufacturers have taken the lead in improving the

integration of design and manufacturing, harnessing automation technology, and in using

electronic standards to replace paper for many types of documents. Unfortunately, the

construction industry has not yet used information technologies as effectively to improve

and automate its design, construction, and operational processes. There is still

widespread use of paper as a medium to capture and exchange information among project

participants, and relatively little use of design and automation tools that depend on

computer-readable product descriptions.

The Construction Industry Institute (CII) predicts that fully-integrated and automated

project processes (FIAPPs) will be a vehicle for transforming the way construction

projects are designed, built, and operated. 21 FIAPPs will result in significant reductions

in both the delivery time and life-cycle costs of constructed facilities. FIAPPs are also

expected to result in reductions in construction-related accidents. Characteristics of

FIAPP products and services include one-time data entry; interoperability with design,

construction, and operation processes (e.g., virtual construction and construction

automation); and user friendly input/output techniques.

The context within which FIAPP products and services are defined is shown

schematically in Figure 3-1. Figure 3-1 is a stylized information flows model for a

typical commercial building or industrial facility; it spans the entire building/facility life

cycle—from concept through disposition.

The information flows model is configured as a four-tiered set of activities. Each activity

is represented by a rectangle. Interactions between activities (i.e., information flows) are

represented by arrows. It is important to note that each activity shown in Figure 3-1 can

be subdivided into sub-activities and used to highlight information flows between sub-

 

21 Construction Industry Institute. 1999. 1999 Strategic Plan. Austin, TX: Construction Industry Institute.
18

activities. These information flows, although not shown, are treated as implicit in Figure

3-1. In several cases, interactions with feedback are made explicit. This is done to

highlight the most significant feedback loops between activities. In principle, there is

substantial feedback between many of the activities shown in Figure 3-1.

Business need provides the impetus for planning, designing, constructing, operating, and

disposing of a building/facility. Consequently, business need is placed at the left-hand

side of the uppermost tier (i.e., the first tier). Project management activities are placed on

the first tier because they span all phases in the life cycle of the building/facility.

Regulatory bodies are also placed on the first tier.

The activities on the second and third tiers are laid out in sequential order. Interactions

between these two tiers are closely coupled. A dashed line is placed between the second

and third tier; it is used to separate functions performed by the two major groups of

participants: (1) owners and operators—the first and second tiers; and (2) architects and

engineers, constructors, and suppliers and fabricators—the third and fourth tiers. The key

participants are designated in bold face font. The fourth tier is limited to suppliers and

fabricators; it spans most of the phases in the building/facility life cycle.

A closer examination of Figure 3-1 reveals an interesting outcome. The entire

construction phase is shown as a single activity in Figure 3-1: Prepare Site, Construct

Facility, Pre-Commission. As will be seen in Sections 3.2 and 3.3, much of the interest

in FIAPP products and services is concerned with the construction phase. However, as is

shown in Figure 3-1, there are many information flows going into or coming out of the

construction phase. Thus, to better understand the value of FIAPP products and services,

it is useful to review what these information flows represent.

The information flows model is a mapping of project processes during a building’s/

facility’s life cycle. Integrating and automating these project processes involves

managing and manipulating information flows within and between the activities shown in

Figure 3-1. Thus, a FIAPP consists of a data warehouse with a real-time capability

enabling information to be passed, operated upon, and retained for future reference. This

information could be used to control the position of construction equipment, specify a set

of work tasks, or electronically store the “as built” status of a building element. The data

warehouse component of a FIAPP includes design data, supplier data, project data, site

data, resource data, and codes and standards data. The real-time capability is supported

through two key enabling technologies—the Construction Site Measurement System and

the Project Information Management System. Linkages between the enabling

technologies, the data warehouse, and other FIAPP components rely on standard

interoperability and communication protocols and advanced measurement technologies

(see Section 3.3).
19

Figure 3-1. Information Flows Model for Commercial Buildings and Industrial FacilitiesOwners andOperators---------------------------------------------------------------------------------------------------------------------------------------Architects and Engineers,Constructors, and Suppliersand Fabricators

Business Need

 Project Management Activities

Produce and MaintainFinancial Viability

Plan and ControlProject

Obtain AgreementWith Regulatory Bodies

Produce ConceptualFacility Design

Review and ApproveDetailed FacilityDesign

CommissionFacility

Operate andMaintain Facility

Produce ConceptualEngineering Design

Produce DetailedFacility Design

Prepare Site,Construct Facility,Pre-commission

Service Facilityand Equipment

Suppliers andFabricators Procure and Control Equipment, Materials, and Services
20

3.2 The FIATECH Consortium: A Vehicle for Delivering Fully-Integrated and

Automated Project Processes to the Construction Industry

The FIATECH Consortium is envisioned as a CII affiliated, collaborative, not-for-profit,

research consortium established to conduct leveraged research and development to

accelerate the deployment of FIAPP products and services in the commercial

marketplace. Specifically, the FIATECH Consortium is needed to make FIAPP real for

CII members and the rest of the construction industry.

The construction industry faces special challenges in reaping the full benefits of the

information technology revolution that has brought and continues to bring rich rewards to

many industries. These challenges include low R&D investment, fragmentation, and its

unique project-oriented character. Recognizing these challenges, CII has made FIAPP a

top priority.

The FIATECH Consortium will seek to achieve breakthrough, technology-intensive

process changes. First, it will enable the seamless integration and management of project

information within the context of an entire building/facility life cycle and enterprise-wide

resource planning system. And second, it will bring live wireless data from the

construction site into the project management information loop.

The FIATECH Consortium will conduct leveraged R&D in partnership with suppliers,

with firms in the software/information technology industries, and within the public

sector. In addition to NIST, likely public sector partners include the Department of

Transportation, the U.S. Army Corps of Engineers, and the General Services

Administration.

NIST’s participation in the FIATECH Consortium focuses on providing the

measurements, standard interoperability and communication protocols, and information

technology tools to enable FIAPP along with economic assessments to quantify its

impact. Consortium-developed products and services will be tested and demonstrated in

a distributed testbed environment.

The Consortium-developed products and services, when fully deployed, will enable

significant cycle time and life-cycle cost reductions in the delivery of capital projects by

digitally linking all facets of the design, fabrication, and construction process. Drawing

on documented economic success in industries such as manufacturing, the FIATECH

Consortium will develop and adopt open interoperability and communication standards to

achieve significant cycle time and life-cycle cost reductions.

Specifically, the goals of the FIATECH Consortium are to:

• Reduce design changes and rework through concurrent engineering;

• Enable better control of project schedule and cost;

• Improve supply chain management, including tracking of materials, components, and

labor;
21

• Rapidly detect and rectify differences between intended design and actual

construction; and

• Capture the “as-built” status of a project for later use in building/facility operation,

maintenance, and renovation.

To achieve these goals, the FIATECH Consortium has established a framework of

guiding principles and primary purposes. The guiding principles of the Consortium are

twofold. First, the Consortium will be an industry-driven, not-for-profit, collaborative

research consortium with dues-paying member companies. Second, it will maintain a

“safe harbor” environment with third party independence where companies can come

together to achieve mutually beneficial results by pooling talent, substantially leverage

their scarce R&D dollars, spread risks, and achieve robust solutions. The Consortium

will collaborate broadly with diverse partners—public, private, and academe—and create

alliances.

The primary purposes of the Consortium are to:

• Conduct, sponsor, fund, direct, and otherwise promote research, development, and

demonstration of technologies and practices for significant cycle time and life-cycle

cost reductions in the delivery, operation, and maintenance of capital projects;

• Assist in the implementation of such technologies and practices;

• Provide a forum for the examination and discussion of technical issues having a

significant impact on cycle time and life-cycle costs of capital projects;

• Serve as a national clearinghouse, library, and data source for information in these

areas; and

• Publish or sponsor articles, press releases, newsletters, and other publications on these

topics.

3.3 Key Components of BFRL’s CONSIAT Program

BFRL is working towards a prototype suite of FIAPP systems and technologies being

tested and deployed by 2004. To achieve this goal, BFRL is working with building/

facility owners, contractors, equipment and systems manufacturers and service providers,

software developers, building/facility operators, trade associations, professional societies,

standards organizations, university researchers, and other government agencies. Strategic

partnerships for the overall CONSIAT research, development, and deployment effort will

employ the FIATECH Consortium (see Section 3.2).

BFRL’s CONSIAT-related research, development, and deployment effort is multi-year

and multi-tiered. Throughout this effort, BFRL will employ field demonstration projects.

These projects are by design collaborative activities, since they seek to gain early

involvement of key construction industry stakeholders to ensure that the products and

services developed are focused on stakeholder needs and address explicitly any potential

barriers to adoption. BFRL’s major milestones for these collaborative activities include

field demonstrations of: (1) key FIAPP system components and associated information

technology (IT) protocols/tools; (2) FIAPP subsystems and associated IT protocols/tools
22

for earthwork, foundation, and the structural steel delivery process and the pipe spools

and piping systems delivery process; (3) first-generation prototype FIAPP system and

associated IT protocols/tools based largely on the innovative use of proven technologies;

(4) advanced site measurement systems and innovative uses of information technologies;

and (5) second-generation prototype FIAPP system and associated IT protocols/tools that

build on advanced site measurement systems and innovative uses of information

technologies.

The first generation prototype FIAPP system, targeted for completion in 2002, will

demonstrate feasibility, focusing on innovative process changes based largely on the

integration of robust/proven technologies. The second generation prototype system,

targeted for completion in 2004, will integrate advanced functional capabilities, building

on new measurement systems and innovative uses of information technology.

The overall CONSIAT research, development, and deployment effort is built around

eight key projects (see Sections 3.3.1 through 3.3.8). In addition, the overall effort

includes a Virtual FIAPP Testbed and a series of full-scale field demonstration projects.

A schematic for how the eight key projects fit together and how BFRL will work with

industry to develop FIAPP products and services is shown in Figure 3-2. Each of the

eight key projects is represented by a rectangle in the figure. These activities are

undertaken and funded primarily by NIST. Those activities undertaken by private sector

entities are represented by ovals in the figure. The Virtual FIAPP Testbed and the field

demonstration projects are a hybrid activity, involving a broad cross-section of

participants; they are represented in the figure by the rectangles with rounded edges.

Unidirectional arrows or bi-directional arrows (i.e., including a feedback mechanism)

represent information flows between activities. Coordination of and feedback between

the BFRL projects and the private sector entities is facilitated by BFRL’s CONSIAT

program manager.

Figure 3-2 includes a user/vendor tier. Owners and contractors are classified as users of

FIAPP products and services. Vendors include equipment and systems manufacturers

and service providers and software developers. Because many different users will adopt

and install FIAPP products and services and many different vendors will develop and

offer commercial products and/or services, the figure uses an ellipsis (...) to reflect the

indeterminacy of the number of users and the number of vendors in the user/vendor tier.

Figure 3-2 shows the importance of the field demonstration projects to BFRL’s efforts.

Once the field demonstration projects are completed, the private sector moves into a fullscale market adoption process. This process will evolve over a number of years as

FIAPP products and services diffuse throughout the marketplace.
23

Figure 3-2. Schematic Diagram of BFRL’s CONSIAT Program

Virtual FIAPP Testbed

Site Measurement

System

Communication

Standards & Practices

Construction Range

Imaging and

Registration

C

Pr

Product Data

Standards for

Industrial Facilities

Advanced

Graphical User

Interfaces

Commercial

Products and

Services

Market

Adoption

Field

Demonstration

Projects

Economic Analysis

for FIAPP Products

& Services

...

Measurement Processes

& Metrics for

Construction

Component Tracking

Project Information

Management

System

Technologies

Users:

Owners/

Contractors

Users:

Owners/ ...

Contractors
24

BFRL will produce a series of intermediate outputs prior to the deployment of a

prototype suite of FIAPP products and services. These outputs are described briefly in

the series of bullets that follow:

• Develop enabling technologies aimed at producing a Construction Site Measurement

System and a Project Information Management System.

• Develop standard interoperability and communication protocols for the open

exchange of information among building/facility owners, designers, construction

products and equipment manufacturers and suppliers, contractors, and building/

facility managers. Protocols being developed include:

 Data exchange protocol for piping design, fabrication, installation, and

inspection;

 Identification and fiducial marking protocols for discrete components; and

 LiveView construction site management system interoperability protocols.

• Develop advanced measurement technologies, including:

 Ability to compare “as-is” and “as-designed” geometry, location, and

orientation of construction objects incorporating advanced VRML (Virtual

Reality Modeling Language) techniques;

 Ability to characterize LADAR (LAser Detection And Ranging) measurement

accuracy as a function of range, surface texture, and reflectivity for a range of

conditions anticipated at construction sites;

 Mobile scanning system with wireless transport of range data to a remote site;

 Robust post-processing technique for registration and volumetric calculations

of LADAR-acquired terrain data; and

 Integration of project information management system with construction

metrology systems using LiveView to enable all data communications.

• Construct a Virtual FIAPP Testbed—an open, distributed system environment with

modular (plug-and-play) architecture—in the laboratory to facilitate the development

and evaluation of new products and systems by manufacturers and external service

providers.

• Develop a Consortium of building/facility owners, contractors, construction products

and equipment manufacturers, and service providers interested in producing, testing,

demonstrating, and buying and selling FIAPP products and services.

• Conduct a prospective economic impact assessment of BFRL’s CONSIAT-related

research, monitor outcomes, and conduct a follow-up economic impact assessment.

• Demonstrate the integration of FIAPP products, services, and concepts in real

construction projects.
25

3.3.1 Project Information Management System Technologies

An effective project delivery process depends on the availability of current and correct

information for all the participants, wherever they are and whenever they need it.

Achieving the seamless integration of project information has become a primary goal for

many organizations associated with the building and construction industries, ranging

from CII to the Construction and Building Subcommittee of the President’s National

Science and Technology Council. Ongoing benchmarking studies by organizations like

the Business Roundtable and CII confirm the business stake in this goal.

BFRL’s CONSIAT program envisions a number of technology developments that will

make more and richer information available to project participants. These developments,

and the information resources associated with them, include: (1) more complete

descriptions of the project design itself and of the products and services procured for the

project; (2) large amounts of previously unavailable spatio-temporal information

supporting the realization of the project design on the construction site; and (3)

comprehensive commissioning procedures tied to the “as-built” condition of the resulting

facility.

Integrating and managing these information resources presents significant technical

challenges. Two very different approaches have emerged during this decade. The first is

a data-driven approach based on the evolution of computer-aided design systems. Open

system standards such as the International Organization for Standardization’s STandard

for the Exchange of Product model data (ISO/STEP) and Parts LIBrary (ISO/PLIB) and

the International Alliance for Interoperability’s Industry Foundation Classes (IAI/IFC)

are being developed to capture the complex, deep-structured technical data created using

computerized tools. These standards allow the exchange and sharing of information in

such a way that it remains as functional in the receiving system as it was in the

originating system, with integration taking place at the level of individual information

elements.

However, neither the standards nor the systems based on them yet cover all the

functionality needed in a typical project, and especially not throughout the entire delivery

process. This has led to a second, document-driven approach based on the evolution of

the Internet and the World Wide Web. Frequently called project extranets, systems based

on this approach collect and distribute project information, ranging from long-lived

design drawings to transitory email requests, as configuration-controlled documents, with

integration taking place at the level of hyperlinks among the documents. Compared to

the first approach, the information exchanged in this approach is shallow-structured and

superficially integrated. Because the basic unit of information is a document, however,

these systems automatically cover all the functionality needed in a traditional documentcentric project. Consequently, their use is beginning to make a difference in the U.S.

construction industry.

As the technologies envisioned in the CONSIAT program emerge, the need to merge
26

these two approaches to the integration and management of project information will

become acute. The objective of this project is to develop the standards-based opensystem technologies needed in next-generation project information management systems

and demonstrate them in prototype systems.

To enable the capture of technical project information, BFRL will implement selected

portions of emerging product data standards in experimental databases. The ISO/STEP

and PLIB standards suites and the IAI/IFC are relevant starting points. Because several

CONSIAT projects share an interest in the fabrication, delivery, and erection of structural

steelwork, BFRL will collaborate in this project with the American Institute for Steel

Construction (AISC) as its members begin implementing the CIMSteel Integration

Standard CIS(2), which is based on STEP technology and is the precursor of the STEP

Application Protocol 230 on structural steelwork. In following years, this project will

widen its focus to include other systems, such as piping and HVAC, and their associated

controls.

To deal with the spatial-temporal information envisioned to be streaming to the project

information system from on-site construction metrology systems, this project will

document candidate use cases in collaboration with the companion project (see Section

3.3.4); assess the state of the art in temporal database research; and implement a suitable

data model in experimental databases. Many such models are available, and none is

considered to be universally applicable, so this task is expected to be an iterative

implement-and-test cycle that continues over several years.

Initially, this project dealt only with the information needed by other CONSIAT projects

to track certain work processes relating to the fabrication and erection of steelwork, and

the creation of experimental supporting databases. In following years, BFRL will install

a commercial project extranet system and couple it with the experimental databases. In

addition, coupling and integration mechanisms will be developed to account for

additional work processes and other systems.

NIST construction projects, most notably the addition of a new emissions control system

(ECS) to Building 205 at the NIST Gaithersburg site, will be used as the source of

information for populating and testing the experimental databases coupled to a project

extranet system. The resulting system will be used to provide information management

support for other CONSIAT projects as they test emerging construction metrology

systems.

3.3.2 Advanced Graphical User Interfaces for Construction Project Delivery

Systems

The technical challenges faced in integrating and managing project information were

addressed in the previous subsection. Once available, however, accessing and viewing

this information presents additional technical challenges. Traditional presentation

mechanisms are based on static views of tabular data extracted from databases and stored
27

two-dimensional drawings and documents. These mechanisms have been carried directly

into the graphical user interface (GUI), based on Web browser technology, used in

document-based project extranet systems. While the industry has a decade of experience

in developing graphical user interfaces for 3-dimensional project models, these were

typically based on proprietary CAD-system data structures and presented static design

models with only limited access to non-geometric information.

As the technologies envisioned in the CONSIAT program emerge, the user will be

challenged to deal with increasing volumes of increasingly complex information, and the

need for facile and intuitive graphical user interfaces will increase dramatically. The

objective of this project is to develop standards-based, open-system technologies for

accessing and viewing construction project information and demonstrate them in

prototype systems.

BFRL will build on the work begun in fiscal year (FY) 1999 to model typical

construction products using the Virtual Reality Modeling Language (VRML) 97

specification developed by the Web3D Consortium and promulgated as ISO/IEC 14772.

The use of VRML continues to offer an advantage because any user can employ widely

available Web browser technology to access and display the results. The construction

object definitions that were developed using VRML programming capabilities will be

extended and enhanced to include more functionality. Techniques for improving

performance will be explored, for example, by reimplementing critical JavaScript code as

Java applets. Additional Java applets tied to the VRML External Authoring Interface will

be used to import project information to the VRML model from external information

sources based on standard data representations. Techniques for creating intuitive links

from the VRML model to non-geometric information will be explored, as well as

innovative techniques for accessing and displaying non-geometric information using

VRML directly. Techniques for dealing with spatio-temporal information (e.g., time

sequences of equipment movements on the construction site) will be explored.

Techniques for comparing different geometry models representing, for example, the asdesigned and as-built conditions of some item of interest, will be explored. The emerging

third-generation VRML specification, called X3D, will be reviewed and critiqued for its

applicability to construction applications.

Because several CONSIAT projects share an interest in the fabrication, delivery, and

erection of structural steelwork, BFRL will collaborate in this project with the AISC as

its members begin implementing the CIMSteel Integration Standard CIS(2), which is

based on ISO/STEP technology and is the precursor of the STEP Application Protocol

230 on structural steelwork. A mapping between VRML data structures and CIS(2)

geometry representations has been developed. A transfer between VRML and CIS(2)

provides for the seemless conversion of CIS(2) files to VRML.

Exemplary models will be built using NIST construction projects, most notably the

Building 205 ECS at the NIST Gaithersburg site, as the source of information. The

results will be evaluated in terms of their ability to support typical user work processes.
28

3.3.3 Product Data Standards for Industrial Facilities

The U.S. capital facilities industry is seeking to improve the design and delivery of

constructed facilities through advanced uses of information technologies—CADD, CAM,

ERP (computer-aided design and drafting, computer-aided manufacturing, enterprise

resource planning)—and the integration of information systems (e.g., automation of the

exchange and sharing of information among systems). Although many of the leading

engineering organizations have adopted 3D modeling and information integration

technologies, the capabilities and benefits of these technologies are not being exploited

fully in the fabrication, inspection, and construction phases of projects.

The many computerized systems in use for the design and construction of facilities can be

integrated only at great cost because of their incompatible proprietary representations of

information. Standard, neutral information representations and exchange methods are

needed that allow system vendors to be innovative and yet allow system users to

exchange and share information about industrial facilities automatically. The evolving

international standard ISO 10303, Product Data Representation and Exchange, known as

STEP, is providing a base technology for developing information exchange protocols.

Leading organizations in the U.S. process plant industries recognize the potential benefits

of STEP application protocols (APs) and are working with NIST to develop protocols

that meet the needs of the process plant industries. The shipbuilding industry has adopted

CAM technologies for improvements in the fabrication and assembly of piping. These

CAM successes and lessons from improvements in ship piping fabrication could be

useful to advancing the capabilities of the U.S. process plant industries. Additionally, the

piping and HVAC delivery process could be improved with the use of advanced

component tagging technologies. U.S. industry needs data exchange protocols, test cases,

and guidance for fully leveraging CAD, CAM, and eBusiness technologies and

progressing toward achievement of FIAPP capabilities. This project works with the

chemical, pharmaceutical, power, semiconductor, leverage, engineering and construction,

CADD/CAM, shipbuilding, and pipe fabrication industries to resolve these challenges.

Many of the current applications of CADD/CAM tools focus on automating the

documentation of engineering decisions. These applications have improved the

productivity of the engineering phase of industrial projects, but have not been used to

improve the fabrication, inspection, installation, and commissioning phases of industrial

projects.

Beyond documenting procedures for reducing potential rework costs, this project will

investigate improvements in the fabrication, delivery, and installation of piping systems.

The primary use of CADD for piping fabrication is still focused on replicating the

drafting of isometric drawings and spool drawings to illustrate the configuration, shapes,

and connections of piping components. Large savings in labor, time, and errors are

possible by reducing the need for the piping engineer to develop intermediate isometric

drawings for the piping fabricator. Rather, this project will develop a piping fabrication

protocol for conveying the piping 3D design information directly to the piping fabricator
29

for automated pipe spool definition and materials management. This protocol will also

be useful for conveying spool and installation information to the project information

systems and back to the piping engineer, when necessary. PlantSTEP, PIEBASE, and the

CII Research Team 152 (3D CADD in the Fully-Integrated and Automated Project

Process) identified piping fabrication and installation as construction tasks which could

be improved with the reliable exchange of 3D piping model information.

This project will provide to the CONSIAT program insights on the needs and uses of

piping design and delivery information as part of the project delivery process and insights

on eBusiness for capital facilities. This project will use prior CONSIAT results in

component tracking and placement for steel construction in investigating the utility of

similar improvements in piping material tracking and installation.

3.3.4 Site Measurement System Communication Standards and Practices

There is presently little in the way of live information from a construction job site that

flows back into the design, planning, fabrication, and transportation stages of a project.

Because these facets of project management are not automatically linked, it is extremely

difficult to take advantage of recent advances in information technology to speed

construction delivery time through concurrent engineering. Lack of information closure

with the job site specifically precludes: (1) comparison of “as-is” with “should-be” to

avoid or rapidly rectify differences between the intended design and the actual

construction; (2) construction site control and resource management to avoid delays by

tracking material, equipment, and labor to meet schedule while ensuring quality; and (3)

capturing “as-built” status of a completed project for use in operation and maintenance

over the building/facility life cycle. In order to bring construction into the information

loop, a myriad of real-time sensors and human feedback must find a means to be

wirelessly brought from the job site to a digital storehouse—a temporal database—that

can be mined and used to facilitate work process modeling and construction management.

One of the significant problems facing developers of new construction metrology systems

is the difficulty of interfacing to a wide variety of subsystems and integrating them into a

useful whole. Furthermore, the subsystems are often numerous, and the “best” for any

task can change rapidly. At present, there is no generally accepted approach for software

subsystems in a construction site measurement system to use for exchanging data with

one another. As a result, significant effort in implementing such systems goes into

creating different software interfaces for each sensor, actuator, GUI, and database. Since

there is often little prospect of interface-software reuse (because there is so little

standardization), system developers are often hesitant to change, upgrade, or experiment

with new subsystems. To avoid this productivity loss, a standard, broadly accepted

means to communicate information between construction site measurement subsystems is

needed. Ideally, this interface should work whether the subsystems reside on the same

computer, or on different ones. Also, ideally, this protocol should be designed to provide

maximum flexibility of physical networks capable of realizing the protocol.
30

Research at NIST is predicated on the availability of live spatial data—the position and

orientation of all objects on site. This includes the geometry of amorphous22 objects,

which are not neatly classified as “components,” as well as task and component-specific

knowledge. We are using many state-of-the-art and emerging sensing technologies to

achieve this, including phase differential GPS (global positioning system), fanning lasers,

NLS (non-line-of-sight) tracking systems, and LADAR. We must consider how one is to

mine this wealth of chronological data and present it to the various consumers: project/

construction managers, crew foremen, laborers, engineers, fabricators, building owners,

and automated machinery.

The intent at NIST is to develop OSI Application Level protocols applicable to a variety

of lower-level layers, for example wireless and wired technology as the site conditions

permit. There are envisioned to be two protocols, one to facilitate the exchange of raw

sensory data (SensorView) and one to facilitate the exchange of object state information

(LiveView). These protocols are envisioned to be promulgated through standards setting

bodies such as IEEE or ISO.

Work on SensorView revolves around solving issues in: (1) packet standards (including

speed, and reliability) needed in meeting OSI communications assumptions necessary for

use and development of LiveView; (2) data representation standards (e.g. STEP); (3)

interoperability assessment and evaluation testing and standards; (4) representation of

uncertainty of sensed quantities of field sensors; and (5) support for data fusion

applications. Current plans call for a sample implementation of SensorView to be

developed to support structural steel tracking and excavation activities. Trial testing of

SensorView will demonstrate the transfer of data relevant to structural steel tracking and

excavation. This extends the capability of the previous LiveView demonstration with

regard to de-coupling of raw sensor data from the model changes implied by the data.

BFRL has already begun looking at the issues surrounding object state communication

from the construction site with the LiveView protocol. Yet significant issues remain to be

addressed in LiveView such as interfacing with SensorView and integration into a larger

Site Measurement System. This larger system is more than simply having two protocols

and applications that utilize them. Such a larger system includes expected behaviors of

applications, typical networking configurations and services, practices for quality

assurance of the entire system, and practices to enable easy integration of additional

applications.

A set of standard practices will be developed and documented that will enable

applications to be generated that turn raw sensor data from SensorView into object states

that can be reported to construction management applications over LiveView. To state this

explicitly, SensorView is a protocol for transmission of field sensor data to an

intermediate-processing agent. LiveView is a protocol that transports processed registered

object states (e.g. global positions and orientations of manufactured parts; registered

 

22 Amorphous objects are items which do not fall under the category of engineered components. They

include such things as excavation topography; status of a concrete pour; piles of raw materials; and the

status of a paving operation.
31

terrain models, etc.) to a project database. Since the suite of potential types of

measurements is large, no single solution can be provided, however, a set of standard

practices and standard system architecture can reduce the cost of development of

metrology systems for construction metrology companies. Such practices are envisioned

to be promulgated via interoperability protocols, as a separate standard, or as an annex to

the protocol standards.

3.3.5 Procedures and Metrics for Construction Range Imaging and Registration

This project focuses on the use of new fast laser ranging technologies and threedimensional analysis to non-intrusively scan a construction site and to extract useful

information from these data for project planning purposes. This versatile capability will

directly address both the BFRL CONSIAT program and the National Construction Goal

of reducing delivery time by 50 %.

BFRL’s Construction Metrology and Automation Group in collaboration with NIST’s

Information Technology Laboratory and BFRL’s Computer Integrated Construction

Group has made strides in the use of LADAR for rapidly assessing the status and

quantitative change of amorphous objects on a construction site.

Specifically, BFRL has succeeded in live acquisition of 3D earthmoving topography,

transmission of this data to a remote host, combining the live data with extant data from

other, previous LADAR sweeps, and developed the subsequent post-processing

technology to produce both a 3D representation of the changed state of the terrain

following excavation and quantitative information concerning such measures as the

amount of material removed and the amount remaining.

Current efforts focus on the evaluation of registration and surface generation

methods/algorithms. This evaluation involves a three-part process. In the first part, the

characteristics (accuracy, noise, and related uncertainties) of the sensor would be

determined. This “calibration” would be performed in an indoor facility, which allows

for a controlled environment. In the second part, mathematical procedures are used to

determine the statistical uncertainties of particular calculations (e.g., volume) based on

the results of the instrument calibration. In the third part, the characteristics of the

methods/algorithms used to generate the 3D model would be determined (how well does

it handle missing points, outliers, discontinuities, vertical surfaces, etc.).

In the first and third parts of the process, a set of metrics has to be established to measure

the accuracy. In the first part (sensor evaluation), the metrics depend largely on the

sensor characteristics and are relatively straightforward for range calibration. However,

determination of the angular accuracy of the scanner is more complicated for scanning

sensors due to divergence of the laser beam and because some scanners use lasers outside

of the visible range. In the third part (evaluation of surface generation methods/

algorithms), these metrics are harder to establish. One way to evaluate the method/

algorithm is to compare it with a reference surface (ground truth). Initially, simple
32

objects of known shape and volume will be used to evaluate the method/algorithm in an

indoor controlled environment. This effort will be followed by using objects with more

complex shapes. The accuracy of the method/algorithm will be evaluated based on how

well known volumes are reproduced.

The same data gathered for the evaluation of the accuracy of surfaces may be used to

develop methods for automatic registration. In addition, the protocols for target-based

registration need to be developed. These protocols will include target size, shape, and

material. Comparison between target-based and automatic registration will be conducted.

Performance measures for registration will be needed for that purpose. To some extent,

the protocol for the evaluation of surface generation method/algorithms will already

provide some indication of the ability to register separate scans. However, additional

testing protocols are needed to assess the uncertainties caused by the registration process.

Future work will focus on a demonstration of a complete prototype information

management system integrated with automated earthwork tracking systems and a

subsequent field demonstration of a second-generation prototype project information

management system incorporating advanced earthwork tracking techniques, including

real-time extraction of in-scene machinery.

3.3.6 Measurement Processes and Metrics for Construction Component Tracking

Significant amounts of time are spent every day on construction sites across the United

States in efforts to locate and identify components, sub-assemblies, and tools. Finding a

needed item is only part of the problem. Once a component or assembly has been

incorporated into the building or plant, other questions need to be answered: (1) was the

component the right one for that location? (2) where was it finally positioned and what

was its orientation? (3) were there any problems associated with the component or with

its placement into the structure? and (4) do the appropriate managers, engineers, and

planners know this information? Knowledge of this information, in a quick and accurate

fashion, would dramatically improve productivity and lower construction costs. The

goals of this project are to develop standards for part ID and tracking that the

construction industry will adopt; to develop means for real-time tracking of these items

and wirelessly transmitting that information to a construction project database; and,

finally, to demonstrate the utility of these techniques on full-scale construction sites.

This project will address the problem of identifying, registering, and tracking discrete

construction components and sub-assemblies on a construction site, specifically steel

frame components. BFRL’s approach uses a customized, interactive web-based graphical

user interface operating on a field-portable computer as the field agent interface.

Automated field data collection via peripherals, such as a laser-based real-time spatial

positioning system, bar code and RFID scanning systems, and wireless data links, are

integrated seamlessly through the browser. The field inspector uses either the bar code

scanner or RFID reader to acquire the part identification. Then an interactive session

begins in which the field computer queries, by means of the wireless link, a remote job
33

site database that then returns information concerning the part. The returned information

also includes a three-dimensional model of the component indicating available fiducial

point locations to help guide the user through the spatial (x, y, z) data collection process.

In the current NIST laboratory work, the new component location data is sent to a virtual

site simulator that allows contractors, engineers, and owners to independently view the

job site in 3D and to observe the status of construction, including the current position and

orientation of components. In this fashion an automated “as-built” database is

constructed in the course of routine quality control tracking. The system is powerful

enough to permit real-time tracking of construction machinery as well as other mobile

capital assets.

Prior year work on this project has produced a prototype system of integrated hardware

and software to measure the position and orientation of steel frame components and subassemblies at the job site and to allow for web-based interactive information entry and

query while at a component’s location. The prototype system was field-tested at the

Building 205 ECS project on the NIST, Gaithersburg campus. A subset of the ECS

structural steel framework was tracked, addressing integration and implementation issues,

including: locating, mounting and registering fanning laser transmitters on the site,

tagging steel for identification, developing a simple methodology for identifying fiducial

point locations, defining and transforming local-to-world coordinate systems, and testing

the LiveView wireless data transmission protocol. The data collected at the ECS site was

post-processed for integration with the project information database and 3D virtual site

simulator.

Current research focuses on the development of standard methods of practice and

performance metrics that are needed to assure interoperability and accuracy in this class

of field metrology. Three specific areas of research are being addressed.

1. Metrics for Registration of Multiple Fanning Laser Transmitters: This work is

essential to providing the standards needed to enable uniform and accurate coordinate

registration of large-scale laser-based coordinate measurement systems used to track

components on construction sites.

2. Standard Methods for Automatic Fiducial Point Identification: At present, fiducial

points are manually identified for each part on a project in a time consuming process.

Standard methods are needed to automatically mine this information from existing

(CIS(2)) data representations. Visual advisories need to be provided to the inspector

so that available fiducial points are quickly identifiable and easily located.

3. Measures of Performance for Part Locator Algorithms: This work seeks to develop

standard NIST-traceable measures of performance for pose determination (i.e. its

position and orientation) based on 3D large-scale coordinate measurement system

input.

This project will work with industry to develop realistic business cases for deployment of

the technologies based on the standards. Collaborations will be sought with construction
34

industry partners to ensure that the standards being developed are responsive to industry

needs and are compatible with other industry standardization efforts.

3.3.7 Enhanced Construction Object Recognition Through Sensor Fusion

Imaging sensors such as LADARs are used to rapidly acquire data of a scene to generate

3D models. Increased interest in this technology is due to the substantial growth in

applications for real-time scene updates created by advances in imaging sensor software

and hardware. Current applications include site surveillance, map/terrain

update/bathymetry/recognizance, indoor/outdoor visual inspection, collision avoidance,

and generating “as-builts.”

Imaging sensors are used to obtain two- or three-dimensional arrays of values such as

range, intensity, or other characteristics of a scene. Currently available LADARs can

gather two pieces of information – namely range to an object and strength of the returned

signal (intensity). Various methods are used to convert the data, which are collected in

the form of point clouds, into meaningful 3D models of the actual environment for

visualization and scene interpretation. The need for accurate representations varies with

the purpose of the application. In the construction industry, an accurate representation

aids in determining payment for completed work, determining if errors are being made,

and in tracking work progress on a project.

The points within a point cloud are indistinguishable from each other with regards to their

origin (i.e., there is no way to tell if a point is reflected from a tree or from a building).

As a result, the methods used to generate the models treat all points identically and the

results are indistinguishable “humps/bumps” in the scene. Current surface generation

methods using LADAR data require intensive manual intervention to recognize, replace,

and/or remove objects within a scene. As a result, aids to object identification have been

recognized by the end users as a highly desirable feature and a high priority area of

research.

To that end, a hybrid LADAR is needed - a LADAR that can simultaneously acquire

“intelligent” information about an object in addition to existing generic point cloud data

when conducting a scan of a scene such as a construction site. The combined information

from this type of sensor would be a major step towards automatic object identification.

In addition, there is a growing need for metrics that can determine the accuracy of the

various object identification methods that currently exist and/or are under development.

This project focuses on investigations of: (1) the ability of using a LADAR to acquire

information (such as color, type of material, distinct patterns, and fiducial point

identifiers) about an object in addition to range data, and (2) the fusion of LADAR and

other sensors. These “other sensors” could be (a) RFID tags that contain encrypted data

about the scanned object (e.g., beam, column, part number), (b) micro-reflective sensors

arranged in a standardized pattern or at pre-determined fiducial locations on an object to
35

allow for identification, and/or (c) a camera to provide a concurrent image of a scanned

scene.

The additional information from the hybrid LADAR does not identify objects per se, but

is used to aid in object identification. Additional intelligence will have to be supplied/

added to (a) pick out and extract the data (points) from a point cloud for further

processing, and (b) correctly identify the object. In the first instance, user intervention is

the most likely source of this intelligence. In the latter instance, the intelligence could be

in the form of user intervention, image processing algorithms (if a camera is used), and/or

a database containing objects that would most likely be found in a particular scene.

Image processing algorithms are currently research tools that have been successfully used

to identify a limited number of objects in a scene. A standard protocol (point density;

object size, shape, and placement; partial obstruction; etc.) and test environment will

have to be developed to measure the accuracy of these algorithms. If a database is

utilized to provide the intelligence, probability analyses will have to be performed to

determine confidence limits.

The project will be divided into four phases. The first phase will involve investigating

various technologies that can be combined with LADAR to yield additional information

about a scene to aid in object identification. The second phase will involve developing a

sensor that combines the capabilities of a LADAR with the most promising technology(s)

identified in the first phase. The third phase involves developing a standard protocol and

measures of performance for the various object recognition algorithms. The fourth phase

will be testing the hybrid LADAR at a construction site.

3.3.8 Economic Analysis of FIAPP Products and Services

FIAPP products and services are one means to improve the performance of the project

delivery process by reducing cycle time, construction costs, and construction-related

accidents. But investments in and the use of FIAPP products and services will be

forthcoming only if industry perceives that the economic benefits outweigh the costs of

using such products and services. Being able to demonstrate net economic savings from

using FIAPP products and services will encourage their acceptance and use. Economic

support for the overall CONSIAT effort addresses the need for information on the

economic consequences of investing in FIAPP products and services in two distinct

ways.

First, the Office of Applied Economics (OAE) is conducting ex ante (i.e., prospective)

economic impact assessments of BFRL’s CONSIAT-related research, will monitor

outcomes, and will conduct a follow-up economic impact assessment. The subject of this

report is the ex ante economic impact assessment of one segment of the market for

FIAPP products and services—commercial buildings.
36

Second, OAE will develop user-friendly, decision-support software to facilitate the

economic evaluation of FIAPP products and services and the identification of costeffective levels of investment in these products and services. To make cost-effective

choices for investments in FIAPP products and services, decision makers must have data

on benefits and costs associated with these products and services, information on who

bears the costs and reaps the benefits, and tools (methods and software) for measuring

those benefits and costs. Having a package of economic tools that helps users and

stakeholders identify and measure the benefits and costs of choosing between FIAPP

products and services and traditional products and services will accelerate the

introduction and acceptance of FIAPP products and services in the U.S. and abroad.

Thus, OAE will produce an integrated software package providing life-cycle cost (LCC)

measurement capabilities for evaluating FIAPP products and services. To assure industry

acceptance of the software package, it will be made consistent with ASTM’s LCC

standard practice, E 917.23

 

23 American Society for Testing and Materials. 1999. Standard Practice for Measuring Life-Cycle Costs of

Buildings and Building Systems. E 917. West Conshohocken, PA: American Society for Testing and

Materials.
37

4 Market for FIAPP Products and Services

The construction industry is a key component of the US economy and is vital to its

continued growth. Investment in plant and facilities, in the form of construction activity,

provides the basis for the production of products and the delivery of services. Investment

in infrastructure promotes the smooth flow of goods and services and the movement of

individuals. Investment in housing accommodates new households and allows existing

households to expand or improve their housing. Clearly, construction activities affect

nearly every aspect of the US economy.24

This chapter provides a snapshot of the US construction industry. As such, it provides

the context within which the scope and size of the market for FIAPP products and

services is defined. The chapter contains three sections.

Section 4.1 presents information on the value of construction put in place to show the size

of the construction industry and each of its four sectors. The four sectors, which taken

together define the construction industry, are residential, commercial, industrial, and

public works. Data from the seven-year period 1994 through 2000 are used to highlight

the magnitude of construction-related investments in each sector. Data from 1997 are

then used to establish the relative shares of construction-related investments for each

sector.

Section 4.2 uses information on the commercial sector both to focus on its importance

within the overall construction industry and to define its key components. Information on

investment activity, the number of commercial buildings, and the amount of commercial

floorspace is used to identify both those characteristics that are changing and those that

are remaining constant. Information showing the relative share of construction-related

investments in the key components of the commercial sector is also presented.

Section 4.3 places special emphasis on identifying and detailing the key characteristics of

the commercial sector. Information detailing key characteristics (e.g., building

floorspace and year of construction) is needed to define the scope and size of the market

for FIAPP products and services in the commercial sector. Detailing the key

characteristics of the commercial sector is crucial, because investments in FIAPP

products and services affect not only new construction activities but additions and

alterations as well as maintenance and repair activities. Ways in which these key

characteristics affect the calculation of FIAPP-related benefits and costs are discussed in

Chapter 6.

 

24 Readers interested in learning more about construction statistics, their sources and interpretation, are

referred to the document by Rogers (Rogers, R. Mark. 1994. Handbook of Key Economic Indicators. Burr

Ridge, IL: Irwin Professional Publishing).
38

4.1 Value of Construction Put in Place

This section provides information on a key indicator of construction activity; the value of

construction put in place. Data published by the US Bureau of the Census are used to

establish the composition of construction expenditures by type of construction/function

(e.g., non-residential/office building). These expenditures are then assigned to the four

key construction industry sectors. The reference document used throughout this section

is the Current Construction Reports series C30 publication Value of Construction Put in

Place.25 A brief description of the “C30 report” follows. Special attention is given to the

organization of the data in the C30 report and how these data map into the four key

construction industry sectors. The section concludes with tabular and graphical

summaries of the value of construction put in place.

Construction expenditures data are published monthly in the Current Construction

Reports series C30 publication Value of Construction Put in Place. Construction

expenditures refer to actual construction rather than planned or just initiated activity. It is

noteworthy that the C30 report covers both private residential and non-residential

construction activities and public sector construction activities.

The value of construction put in place is a measure of the value of construction installed

or erected at a site during a given period. For an individual project, this includes: (1) cost

of materials installed or erected; (2) cost of labor and a proportionate share of

construction equipment rental; (3) contractor’s profit; (4) cost of architectural and

engineering work; (5) miscellaneous overhead and office costs chargeable to the project

on the owner’s books; and (6) interest and taxes paid during construction. Expenses do

not include the cost of land nor do they include maintenance and repairs to existing

structures or service facilities.

The C30 data are compiled via survey and through indirect estimation. In the context of

the C30 survey, construction includes the following: (1) new buildings and structures; (2)

additions, alterations, conversions, expansions, reconstruction, renovations,

rehabilitations, and major replacements (e.g., the complete replacement of a roof or a

heating system); (3) mechanical and electrical installations (e.g., plumbing, heating,

electrical work, and other similar building services); (4) site preparation and outside

construction of fixed structures or facilities (e.g., sidewalks, highways and streets, water

supply lines, sewers, and similar facilities which are built into or fixed to the land); (5)

installation of boilers, overhead hoists and cranes, and blast furnaces; (6) fixed, largely

site-fabricated equipment not housed in a building (e.g., petroleum refineries and

chemical plants); and (7) cost and installation of construction materials placed inside a

building and used to support production machinery (e.g., concrete platforms, overhead

steel girders, and pipes).

 

25 US Department of Commerce. 2001. Current Construction Reports: Value of Construction Put in

Place. C30. Washington, DC: US Bureau of the Census.
39

The data presented in the C30 report are summarized in Tables 4-1 and 4-2. To facilitate

comparisons between this report and the C30 report, Tables 4-1 and 4-2 use the same row

and column headings as are used in the C30 report.

Tables 4-1 and 4-2 record annual values for the years 1994 through 2000. Table 4-1

records annual values in millions of constant 1997 dollars. Table 4-2 records annual

values in millions of current dollars.26 Reference to Table 4-1 reveals that total

construction expenditures in real terms have increased modestly over the seven-year

period (i.e., from $571.3 billion to $727.5 billion). When the effects of inflation are

included, the rate of increase appears more pronounced. Table 4-2 shows total

construction expenditures in current dollars.

Tables 4-1 and 4-2 are organized to allow for in-depth analyses of the components/

subcomponents of total construction expenditures. To facilitate such analyses, the data

presented in Tables 4-1 and 4-2 are initially divided into two parts: (1) private

construction; and (2) public construction.

Private construction contains two major components—residential buildings and nonresidential buildings—plus a number of subcomponents. Both the two major components

and the subcomponents are shown as headings in the first column of Tables 4-1 and 4-2.

The residential buildings component includes new private housing and improvements.

New private housing includes new houses, apartments, condominiums, and town houses.

New private housing units are classified as “1 unit” or “2 or more units.” The value of

improvements put in place are a direct measure of the value of residential additions and

alterations activities.

The non-residential buildings component includes industrial, office buildings, hotels and

motels, and “other commercial” (e.g., shopping centers, banks, service stations,

warehouses, and other categories). Also falling under the non-residential buildings

component are religious, educational, hospital and institutional, and “miscellaneous” nonresidential buildings.

 

26 Inflation reduces the purchasing power of the dollar over time; deflation increases it. When amounts are

stated in actual prices as of the year in which they occur, they are said to be in current dollars. Current

dollars are dollars of any one year’s purchasing power, inclusive of inflation/deflation. That is, they reflect

changes in purchasing power of the dollar from year to year. In contrast, constant dollars are dollars of

uniform purchasing power, exclusive of inflation/deflation. Constant dollars indicate what the same good

or service would cost at different times if there were no change in the general price level to change the

purchasing power of the dollar. For additional information on conducting economic analyses using either

constant dollars or current dollars, see Fuller, Sieglinde K., and Stephen R. Petersen. 1996. Life-Cycle

Costing Manual for the Federal Energy Management Program. NIST Handbook 135. Gaithersburg, MD:

National Institute of Standards and Technology.
40

Table 4-1. Value of Construction Put in Place in Millions of Constant 1997 Dollars

e Estimated. n/a Not available.

1994 1995 1996 1997 1998 1999 2000e

571,271 586,538 633,670 656,630 692,876 715,203 727,462

439,110 449,367 489,838 501,749 537,585 553,609 562,131

262,659 261,129 290,341 289,014 306,260 326,447 322,704

184,639 180,951 197,385 198,063 218,041 233,527 233,647

169,156 162,066 176,395 175,179 194,119 208,020 208,632

15,483 18,885 20,991 22,883 23,922 25,507 25,015

78,020 80,177 92,956 90,951 88,220 92,920 89,058

132,262 144,146 158,963 172,990 185,651 183,216 196,098

31,829 35,919 37,409 36,739 39,410 32,655 36,618

24,386 27,040 28,801 34,305 41,106 43,582 48,530

5,111 7,508 11,270 12,898 14,423 14,916 14,674

41,290 45,030 49,769 51,809 52,176 53,477 55,056

4,254 4,567 4,683 5,777 6,419 7,016 7,170

5,302 5,799 6,963 8,693 9,441 9,156 10,686

13,490 11,875 12,167 13,546 13,427 12,750 13,950

6,600 6,409 7,901 9,223 9,249 9,664 9,415

3,547 3,182 3,778 3,815 4,170 4,165 4,237

37,464 37,856 34,244 33,638 38,966 37,066 37,702

11,129 11,711 12,198 12,416 12,974 14,246 14,491

26,335 26,145 22,047 21,222 25,992 22,820 23,211

3,673 3,704 4,542 4,922 5,584 4,602 n/a

16,403 14,832 11,579 11,325 12,053 13,155 n/a

5,161 6,629 4,877 4,006 7,124 3,669 n/a

1,097 981 1,048 969 1,231 1,393 n/a

3,178 3,054 2,511 2,292 2,537 2,715 2,884

132,161 137,170 143,833 154,882 155,291 161,595 165,330

54,370 60,971 65,554 71,867 71,333 72,706 78,155

4,217 4,960 5,214 5,230 4,988 5,258 4,865

1,611 1,592 1,435 999 983 866 972

22,388 27,219 29,528 34,385 35,273 37,176 41,868

4,344 4,472 4,769 5,152 3,802 3,713 3,763

21,809 22,727 24,608 26,100 26,288 25,693 26,687

41,145 39,711 40,759 44,105 47,228 50,098 46,566

2,549 3,179 2,676 2,556 2,462 1,976 2,028

6,997 6,659 6,205 5,739 5,302 5,618 5,379

9,566 8,889 10,120 10,392 9,898 10,464 10,155

5,110 4,971 5,802 6,419 6,649 7,114 6,907

12,426 12,791 12,717 13,803 12,417 13,621 16,141

Type of Construction Millions of Constant (1997) Dollars

Total Construction

Private Construction

Residential Buildings

 New Housing Units

 1 Unit

 2 Units or more

 Improvements

 Nonresidential buildings

 Industrial

 Office

 Hotel, motels

 Other commercial

 Religious

 Educational

 Hospital and institutional

 Miscellaneous

 Farm nonresidential

 Public Utilities

 Telecommunications

 Other public utilities

 Railroads

 Electric light and power

 Gas

 Petroleum pipelines

 All other private

Public Construction

 Buildings

 Housing and redevelopment

 Industrial

 Educational

 Hospital

 Other

 Water supply facilities

 Miscellaneous public

 Highways and streets

 Military facilities

 Conservation and development

 Sewer systems
41

Table 4-2. Value of Construction Put in Place in Millions of Current Dollars

e Estimated. n/a Not available.

1994 1995 1996 1997 1998 1999 2000e

519,539 555,591 613,535 656,630 711,759 764,233 807,850

399,346 425,658 474,273 501,749 552,236 591,561 624,249

238,874 247,351 281,115 289,014 314,607 348,826 358,365

167,919 171,404 191,113 198,063 223,983 249,536 259,466

153,838 153,515 170,790 175,179 199,409 222,280 231,687

14,081 17,889 20,324 22,883 24,574 27,256 27,779

70,955 75,947 90,002 90,951 90,624 99,290 98,899

120,285 136,541 153,912 172,990 190,711 195,776 217,768

28,947 34,024 36,220 36,739 40,484 34,894 40,665

22,178 25,613 27,886 34,305 42,226 46,570 53,893

4,648 7,112 10,912 12,898 14,816 15,939 16,295

37,551 42,654 48,188 51,809 53,598 57,143 61,140

3,869 4,326 4,534 5,777 6,594 7,497 7,962

4,822 5,493 6,742 8,693 9,698 9,784 11,867

12,268 11,248 11,780 13,546 13,793 13,624 15,492

6,002 6,071 7,650 9,223 9,501 10,327 10,455

3,226 3,014 3,658 3,815 4,284 4,451 4,705

34,071 35,859 33,156 33,638 40,028 39,607 41,868

10,121 11,093 11,810 12,416 13,328 15,223 16,092

23,950 24,766 21,346 21,222 26,700 24,384 25,776

3,340 3,509 4,398 4,922 5,736 4,918 n/a

14,918 14,049 11,211 11,325 12,381 14,057 n/a

4,694 6,279 4,722 4,006 7,318 3,920 n/a

998 929 1,015 969 1,265 1,489 n/a

2,890 2,893 2,431 2,292 2,606 2,901 3,203

120,193 129,933 139,263 154,882 159,523 172,673 183,600

49,446 57,754 63,471 71,867 73,277 77,690 86,792

3,835 4,698 5,048 5,230 5,124 5,618 5,403

1,465 1,508 1,389 999 1,010 925 1,079

20,361 25,783 28,590 34,385 36,234 39,725 46,495

3,951 4,236 4,617 5,152 3,906 3,968 4,179

19,834 21,528 23,826 26,100 27,004 27,454 29,636

37,419 37,616 39,464 44,105 48,515 53,532 51,712

2,318 3,011 2,591 2,556 2,529 2,111 2,252

6,363 6,308 6,008 5,739 5,447 6,003 5,973

8,700 8,420 9,798 10,392 10,168 11,181 11,277

4,647 4,709 5,618 6,419 6,830 7,602 7,670

11,301 12,116 12,313 13,803 12,755 14,555 17,925

Type of Construction

Millions of Current Dollars

Total Construction

Private Construction

Residential Buildings

 New Housing Units

 1 Unit

 2 Units or more

 Improvements

 Nonresidential buildings

 Industrial

 Office

 Hotel, motels

 Other commercial

 Religious

 Educational

 Hospital and institutional

 Miscellaneous

 Farm nonresidential

 Public Utilities

 Telecommunications

 Other public utilities

 Railroads

 Electric light and power

 Gas

 Petroleum pipelines

 All other private

Public Construction

 Buildings

 Housing and redevelopment

 Industrial

 Educational

 Hospital

 Other

 Water supply facilities

 Miscellaneous public

 Highways and streets

 Military facilities

 Conservation and development

 Sewer systems
42

Rounding out the private construction component are farm non-residential, public

utilities, and “all other private.” These are generally of a non-residential nature, but are

not part of non-residential buildings. Farm non-residential construction includes

structures such as barns, storage houses, and fences. Land improvements such as

leveling, terracing, ponds, and roads are also a part of this subcomponent. Privately

owned public utilities construction is categorized by industry rather than function of the

building or structure. This subcomponent includes expenditures made by utilities for

telecommunications, railroads, petroleum pipelines, electric light and power, and natural

gas. “All other private” includes privately owned streets and bridges, sewer and water

facilities, airfields, and similar construction.

For public construction, there are two major components—building and non-building.

Both the two major components and the various subcomponents are shown as headings in

the first column of Tables 4-1 and 4-2. The building component contains subcomponents

similar to those for private construction, with educational buildings being the largest

subcomponent. Expenditures for the non-building component overwhelmingly consist of

outlays for highways and streets, with sewer systems being a distant second

subcomponent.

To get the sector totals, each subcomponent was assigned to a sector and summed. The

sector assignments are identical to those used in Chapman and Rennison.27 The sector

totals and the overall total are recorded in Tables 4-3 and 4-4. Reference to the tables

reveals that sector totals vary considerably, with residential being the largest and

industrial the smallest.

Table 4-3. Value of Construction Put in Place: Sector Totals and Sum Total in

Millions of Constant 1997 Dollars28

1994 1995 1996 1997 1998 1999 2000e

Residential 266,876 266,089 295,555 294,244 311,248 331,705 327,569

Commercial 152,521 165,828 184,237 205,703 215,774 221,308 236,036

Industrial 33,440 37,511 38,844 37,738 40,393 33,521 37,590

Public Works 118,434 117,111 115,034 118,944 125,459 128,671 127,762

Sum Total 571,271 586,539 633,670 656,629 692,874 715,205 728,957

Sector

Value of Construction Put in Place ($ Millions)

e e

e

 Estimated.

 

27 Chapman, Robert E., and Roderick Rennison. 1998. An Approach for Measuring Reductions in

Operations, Maintenance, and Energy Costs: Baseline Measures of Construction Industry Practices for the

National Construction Goals. NISTIR 6185. Gaithersburg, MD: National Institute of Standards and

Technology.

28 Note that due to rounding the values entered in the “Sum Total” row in Table 4-3, differ slightly from the

values entered in the “Total Construction” row in Table 4-1.
43

Table 4-4. Value of Construction Put in Place: Sector Totals and Sum Total in

Millions of Current Dollars29

1994 1995 1996 1997 1998 1999 2000e

Residential 242,709 252,049 286,163 294,244 319,731 354,444 363,768

Commercial 138,710 157,078 178,383 205,703 221,654 236,482 262,119

Industrial 30,412 35,532 37,609 37,738 41,494 35,819 41,744

Public Works 107,709 110,932 111,379 118,944 128,878 137,492 141,880

Sum Total 519,540 555,591 613,534 656,629 711,757 764,237 809,511

Sector

Value of Construction Put in Place ($ Millions)

e Estimated.

Reference to Table 4-3 reveals that the commercial sector is the only sector to have

grown consistently in real terms over the entire seven-year period. In real terms,

expenditures in the commercial sector grew from $152.5 billion in 1994 to $236.0 billion

in 2000, an increase of almost 55 %. Real expenditures for two of the four sectors,

industrial and public works, were essentially constant over the same seven-year period.

Real expenditures for the residential sector exhibited a cyclical pattern.

The data contained in Tables 4-3 and 4-4 provide the basis for calculating each sector’s

relative share of total construction expenditures. Each sector’s relative share of total

construction expenditures is shown graphically in pie chart form in Figure 4-1. It was

constructed using 1997 data from Table 4-4 (i.e., current dollar expenditures). Reference

to Figure 4-1 reveals that in 1997 the commercial sector accounted for 31 % of total

construction expenditures (i.e., 31 % of $656.6 billion). The commercial sector’s relative

share of total construction expenditures is exceeded only by the residential sector, which

constitutes 45 % of the total. In addition, the commercial sector’s relative share exceeds

the combined total for the industrial and public works sectors.

Figure 4-1 provides a useful perspective on the potential market for FIAPP products and

services. Consider the four following issues in developing a statement of market scope.

First, the use of FIAPP products and services is more likely in the non-residential sectors,

where construction establishments tend to be larger. Thus, slightly more than half of the

U.S. construction industry serves as a potential market for FIAPP products and services.

However, the relatively larger size of construction establishments engaged in nonresidential construction activities is only an indicator of market potential. Second, the

“owner” plays a vital role in choosing whether or not to employ FIAPP products and

services. In fact, the ultimate choice of which technologies to use—traditional or

 

29 Note that due to rounding the value entered in the “Sum Total” row in Table 4-4 differ slightly from the

values entered in the “Total Construction” row of Table 4-2.
44

FIAPP—rests with the owner. Third, the relative importance of cost—both first costs

and life-cycle costs—and schedule considerations are key drivers of whether or not to use

FIAPP products and services. These considerations tend to reflect more of an owner

perspective than a contractor perspective. However, much has been done in recent years

to bring these two perspectives into alignment. This brings us to the fourth issue.

Finally, the method of contracting and the use of strategic alliances between owners and

contractors affect the efficacy with which new technologies can be deployed.

Specifically, the engineer-procure-construct (EPC) method of contracting that is used

broadly within the industrial sector and, more recently, the design-build method of

contracting used in all three non-residential sectors promote a “partnership” environment

rather than the adversarial environment that often prevailed under the traditional designbid-build method of contracting. Furthermore, large industrial organizations (e.g., many

of the members of the Construction Industry Institute) have actively pursued strategic

alliances to promote increased cooperation and reduced confrontation between owners

and contractors.

Figure 4-1. 1997 Breakdown of $657 Billion Construction Market

4.2 Overview of the Commercial Sector

The commercial sector, defined economically, consists of establishments that provide

services. Defined in this way, the commercial sector is extremely varied. It includes

office buildings, service businesses (e.g., retail and wholesale stores, hotels and motels,

restaurants, and hospitals), as well as a wide range of facilities that would not be

considered “commercial” in a traditional sense (e.g., public schools, correctional

institutions, and religious and fraternal organizations).

Residential

45%

Industrial

6%

Public Works

18%

Commercial

31%
45

Expenditures by establishments in the commercial sector for the built environment

include construction expenditures (e.g., new construction and additions and alterations)

as well as expenditures for maintenance and repair activities, and for energy. The market

for FIAPP-related products and services both affects and is affected by each type of

expenditure. Consequently, it is instructive to first define what is included in each type

of expenditure and then examine the characteristics of commercial buildings that affect

these expenditures. This approach is aimed at producing a better understanding of the

market for FIAPP-related products and services within the commercial sector.

Construction expenditures include both new construction activities and additions and

alterations.

New construction activities include the complete original building of structures

and essential service facilities and the initial installation of integral equipment

(e.g., elevators and plumbing, heating, and air-conditioning supplies and

equipment).

Additions and alterations include construction work that adds to the value or

useful life of an existing building or structure, or which adapts a building or

structure to a new or different use. Included are major replacements of building

systems (e.g., installation of a new roof or heating system).

Maintenance and repair activities include incidental construction work that keeps a

building or structure in ordinary working condition.

Energy is defined as including all non-process or end-product related energy

consumption required to operate a building or structure. Energy consumption can be

categorized by energy source (e.g., electricity, gas, and oil) and by end-use (e.g., space

heating, cooling, and lighting).

Construction expenditures in 1997 for the commercial sector were $205.7 billion in

current dollars (see Table 4-4). Total expenditures include expenditures from

subcomponents classified by the Census under their headings of “private construction”

and “public construction.” The subcomponents that the Census classifies under the

private construction heading are: office, hotels and motels, other commercial, religious,

educational, hospital and institutional (i.e., health care), miscellaneous, and farm

nonresidential. The subcomponents included under the public sector heading are:

educational, hospital, and other. Because the commercial sector is so varied, it is useful

to group these subcomponents into a small number of key components. For convenience,

these subcomponents are grouped into four key components, three of which are fairly

homogeneous. The four key components are: office, educational, health care, and other.

The relative share of the overall commercial sector’s construction expenditures for each

of the four key components is shown graphically in pie chart form in Figure 4-2. Figure

4-2 was constructed using 1997 data from Table 4-4 (i.e., current dollar expenditures).

Reference to Figure 4-2 reveals that in 1997 the office buildings component accounted
46

for 17 % of the commercial sector’s construction expenditures. Educational and health

care account for 21 % and 9 %, respectively.

Although there are a number of data sets which allow in-depth analyses of the

commercial sector, the data associated with the Department of Energy’s (DOE’s)

Commercial Building Energy Consumption Survey (CBECS) is the source of preference

for summarizing the characteristics of the commercial sector’s stock of buildings. The

CBECS collects information on physical characteristics of commercial buildings,

building use and occupancy patterns, equipment use, conservation features and practices,

and types and uses of energy in buildings. The survey is conducted in two stages, the

Building Characteristics Survey and the Energy Suppliers Survey. Our focus is on the

Building Characteristics Survey. The types of buildings covered by the CBECS are far

more detailed than those appearing in the C30 publication. This enables us to examine

the commercial sector in greater detail than the breakdown shown in Figure 4-2.

Figure 4-2. 1997 Breakdown of $206 Billion Commercial Sector

Office

17%

Educational

21%

Hospital

9%

Other

53%

The most recent DOE Commercial Buildings Characteristics report30 provides detailed

information on the size, age, and other characteristics of commercial buildings. In 1995,

there were 4.58 million commercial buildings and 5.46 billion square meters (58.77

billion square feet) of commercial floorspace in the United States.31 The mean size of all

commercial buildings was 1,193 m2 (12,840 ft2). The DOE report grouped buildings into

eight size categories and into eight age categories. The vast majority of commercial

 

30 US Department of Energy. 1997. Commercial Buildings Characteristics 1995. DOE/EIA-E024695.

Washington, DC: Energy Information Administration.

31 Values expressed in square feet are denoted by ft2

. Values expressed in square meters are denoted by m2.
47

buildings were found in the smallest size categories, with more than half in the smallest

category and three-quarters in the two smallest categories. Most commercial buildings,

once constructed, are expected to last for decades or longer. New buildings are

constructed each year and older buildings are demolished, but the commercial buildings

stock at any point in time is dominated by older buildings. More than 70 % of all

commercial buildings and total floorspace were constructed prior to 1980, and more than

50 % of buildings and floorspace were constructed prior to 1970.

The DOE report also examined whether any changes in major characteristics had

occurred between 1989 and 1995. The report concluded that the profiles of major

characteristics of commercial buildings showed no statistically significant changes from

1989 to 1992 to 1995, the years in which the last three surveys were conducted.32

Significant changes between surveys would occur if characteristics in the newest

buildings (i.e., those constructed since the previous survey) were quite different, or if

changes were made to buildings in the existing stock. However, each three-year

increment of new buildings and floorspace was generally small compared to all buildings

and floorspace in a given category and the changes that did occur were not great enough

to be statistically significant.

The profiles of major characteristics which showed no significant changes included the

total number of buildings, the total amount of floorspace, the distribution of floorspace by

principal building activity (e.g., office buildings), the distribution of buildings by size of

building, and the distribution of floorspace by census region. The findings presented in

the DOE report and the characteristics just referenced are of particular importance in

defining the market for FIAPP-related products and services in the commercial sector for

two reasons.

First, the market for FIAPP-related products and services, or any new technology

intended for use in the built environment, is dominated by the characteristics of the

current building stock. Thus, the diffusion of new technologies in general, and FIAPP

technologies in particular, must include explicit reference to the current building stock

and not just to new construction. This statement is consistent with the assumption that

FIAPP-related products and services will be employed both as part of additions and

alterations to existing buildings and as part of new construction.

Second, DOE’s findings imply that both total floorspace for the entire commercial sector

and total floorspace by principal building activity have remained constant for an extended

period of time. If this trend continues, as seems likely, then total floorspace for each key

component will continue to remain constant. It is important to note that during the period

covered by the last survey the commercial sector experienced steady growth in

construction expenditures relative to the rest of the construction industry. Even during

this period of growth, total floorspace in the commercial sector remained constant.

 

32 DOE, Commercial Buildings Characteristics 1995, p. vii and p.10.
48

The choice to adopt a new technology is driven by a number of factors. However, two

factors that are of particular importance to decision makers considering investments in

new technologies in the commercial sector is the ability of those technologies to either

reduce costs or reduce cycle time. Clearly, cost reductions have an immediate impact on

the bottom line and thus are reflected on the “corporate” balance sheet. Cycle time

reductions bring the building into an income generating status sooner, thus impacting

profits. Although all owners and managers of commercial buildings are interested in the

potential for cost and/or cycle time reductions, those with the greatest incentive are the

owners/managers of relatively large buildings. Thus, large commercial buildings are

considered more likely candidates for early adoption of FIAPP technologies.

Consequently, this impact assessment adopts a conservative approach in defining the

scope of the market for FIAPP-related products and services to be the “large” buildings

component of the commercial sector. For purposes of this report, a large building is

defined as one whose floor area exceeds 4,645 m2

 (50,000 ft2). The next section defines

the size of the market for FIAPP-related products and services by detailing information

on the key characteristics of commercial buildings.

4.3 Characteristics of Commercial Buildings

The previous section concluded with a market scope statement. This section

demonstrates how that market scope statement is translated into a specific statement of

market size. To better understand both the scope and size of the CONSIAT market, it is

useful to examine in some detail the characteristics of commercial buildings.

In 1995, there were 4,579,000 commercial buildings in the United States. Collectively,

these 4,579,000 buildings had 5,460 million m2

 (58,772 million ft2) of floorspace.

Commercial buildings in the United States had a mean size of 1,193 m2

 (12,840 ft2).

Figures 4-3 through 4-8 provide detailed snapshots of the nation’s stock of commercial

buildings. In each figure, information is classified along one of two major dimensions,

either by building size, measured in terms of total floorspace, or by building age,

measured in terms of year of construction. Each set of figures (e.g., Figures 4-3, 4-4, and

4-5) uses the same bar chart format to facilitate comparisons of characteristics.

Figures 4-3, 4-4, and 4-5 record the distribution of the number of commercial buildings

and total floorspace by building size. All three figures use the same eight size categories

specified in the DOE report. The DOE size categories are specified in customary units;

they range from 1,001 to 5,000 ft2 (93.0 to 464.5 m2) for the smallest size category to

over 500,000 ft2 (over 46,451.5 m2) for the largest size category. The eight size

categories, as defined in this section, are used throughout this report.

Figure 4-3 records the distribution of the number of commercial buildings by building

size. Figure 4-3 shows clearly that smaller buildings dominate the key category of

commercial buildings. More than half of the stock of commercial buildings (2,399,000 of
49

the 4,579,000) is contained in the smallest size category and almost three-fourths

(3,434,000 of the 4,579,000) in the two smallest size categories. By contrast, only four

percent or 188,000 buildings are contained in the four largest size categories; the four

size categories deemed most likely to use CONSIAT products and services.

Figure 4-4 shows the distribution of total floorspace by building size. The floorspace of

commercial buildings first increases for the three smallest size categories and then trends

downward for the four largest size categories. Total floorspace for the four largest size

categories is 2,379 million m2

 (25,610 million ft2). Figure 4-4 shows that the largest

amount of floorspace is in the third smallest size category. The largest size category

contains the smallest number of buildings and the least amount of floorspace.

Figure 4-5 introduces an additional characteristic, the number of floors in the building.

This characteristic serves to sharpen the distinctions between the buildings in each size

category. Figure 4-5 uses the same classification scheme as employed in the DOE report.

DOE grouped commercial buildings into one of five categories, based on the number of

floors. These categories are one floor, two floors, three floors, four to nine floors, and ten

or more floors. Each floor category is coded by shading; a legend is provided on the

figure to match the floor category to a specific bar in each of the eight size categories.

Figure 4-5 shows clearly that commercial buildings with one and two floors dominate the

smaller size categories. Commercial buildings with one floor tend to decrease as the

building size gets larger. Figure 4-5 shows that buildings in the three smallest size

categories, which contain the largest number of commercial buildings as shown in Figure

4-3, are constructed largely with one and two floors. For the three smallest size

categories, the ratio of buildings with one floor to those with two floors is about 2:1.

Figures 4-6, 4-7, and 4-8 record the distribution of the number of commercial buildings

and total commercial floorspace by year of construction. All three figures use seven year

of construction (i.e., age) categories. It is important to note that these seven year of

construction categories differ from the eight year of construction categories specified in

the DOE report. This is because the last two DOE year of construction categories have

been combined. The last two DOE year of construction categories were 1990 to 1992

and 1993 to 1995. These categories were combined to form the 1990 to 1995 year of

construction category. The year of construction categories used are 1919 or before, 1920

to 1945, 1946 to 1959, 1960 to 1969, 1970 to 1979, 1980 to 1989, and 1990 to 1995. The

seven years of construction categories, as defined in this section, are used throughout this

report.

Figure 4-6 records the distribution of the number of commercial buildings by year of

construction. Figure 4-6 shows that most of the commercial buildings were constructed

between 1946 and 1989 (3,244,000 of 4,579,000) with the largest number in the 1946 to

1959 category (867,000 of 4,579,000). Approximately one-fifth of the stock was

constructed prior to 1946. The smallest number of commercial buildings was constructed

prior to 1920 (353,000 of 4,579,000).
50

Figure 4-3. Total Number of Commercial Buildings by Size Category: 1995

Figure 4-4. Total Commercial Floorspace by Building Size Category: 1995

Total Number of Commercial Buildings (1995) = 4,579,000

0

500

1,000

1,500

2,000

2,500

3,000

1,001 -

5,000

(93.0-

464.5)

5,001 -

10,000

(464.6-

929.0)

10,001 -

25,000

(929.1-

2,322.6)

25,001 -

50,000

(2,322.7-

4,645.1)

50,001 -

100,000

(4,645.2-

9,290.3)

100,001 -

200,000

(9,290.4-

18,580.6)

200,001 -

500,000

(18,580.7-

46,451.5)

Over

500,000

(Over

46,451.5)

Building Floors pace in S quare Feet (S quare Meters )

Number of Buildings (Thousands)

Total Floors pace of Commercial Buildings (1995) = 58,772 M illion Square Feet

 = 5,460 M illion Square M eters

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

1,001 -

5,000

(93.0-

464.5)

5,001 -

10,000

(464.6-

929.0)

10,001 -

25,000

(929.1-

2,322.6)

25,001 -

50,000

(2,322.7-

4,645.1)

50,001 -

100,000

(4,645.2-

9,290.3)

100,001 -

200,000

(9,290.4-

18,580.6)

200,001 -

500,000

(18,580.7-

46,451.5)

Over

500,000

(Over

46,451.5)

Building Floors pace in S quare Feet (S quare Meters )

Total Floorspace (Million 

Square Feet)

0

200

400

600

800

1,000

1,200

Total Floorspace (Million 

Square Meters)
51

Figure 4-5. Total Commercial Floorspace by Building Size Category and Number of Floors: 1995

0

1,0002,0003,0004,0005,0006,000

1,001 -

5,000

(93.0-

464.5)

5,001 -10,000

(464.6-

929.0)

10,001 -

25,000

(929.1-

2,322.6)

25,001 -

50,000

(2,322.7-

4,645.1)

50,001 -

100,000

(4,645.2-

9,290.3)

100,001 -

200,000

(9,290.4-

18,580.6)

200,001 -

500,000

(18,580.7-

46,451.5)

Over

500,000

(Over

46,451.5)

Building S ize/Floors pace in S quare Feet (S quare Meters )

Total Floorspace (Million Square Feet)

One Floor Two Floors Three Floors Four to Nine Floors Ten or M ore Floors
52

Figure 4-6. Total Number of Commercial Buildings by Year of Construction: 1995

Figure 4-7. Total Commercial Floorspace by Year of Construction: 1995

Total Number of Commercial Buildings (1995) = 4,579,000

0

100

200

300

400

500

600

700

800

900

1,000

1919

 or

 Before

1920-

1945

1946-

1959

1960-

1969

1970-

1979

1980-

1989

1990-

1995

Year of Cons truction

Number of Buildings (Thousands)

Total Floors pace of Commercial Buildings (1995) = 58,772 M illion Square Feet

 = 5,460 M illion Square M eters

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

1919

 or

 Before

1920-

1945

1946-

1959

1960-

1969

1970-

1979

1980-

1989

1990-

1995

Year of Cons truction

Total Floorspace (Million Square 

Feet)

0

200

400

600

800

1,000

1,200

Total Floorspace (Million Square 

Meters)
53

Figure 4-8. Total Commercial Floorspace by Year of Construction and Number of Floors: 1995

0

1,0002,0003,0004,0005,0006,0007,000

1919

 or

 Before

1920-

1945

1946-

1959

1960-

1969

1970-

1979

1980-

1989

1990-

1995

Year of Cons truction

Total Floorspace (Million Square Feet)

One FloorTwo FloorsThree FloorsFour to Nine FloorsTen or M ore Floors
54

Figure 4-7 records the distribution of floorspace by year of construction. Note that

approximately 75 % of the total floorspace was constructed between 1946 and 1989.

Approximately 18 % of the building stock was constructed prior to 1946. As clearly

shown in Figure 4-7, the amount of floorspace by year of construction increases from

1919 through 1989.

Figure 4-8 introduces information on the number of floors. As shown in Figure 4-8,

commercial buildings were constructed mainly with one and two floors during the 1946

to 1989 time period. The number of commercial buildings with one floor shows a

tendency to increase during this same time period. The floorspace of commercial

buildings with one floor is almost double the floorspace of commercial buildings with

two floors. Total floorspace in commercial buildings having ten or more floors tends to

increase from 1946 through 1989. Figure 4-8 shows that a large proportion of

commercial buildings constructed prior to 1920 were built with three to nine floors.

The material presented in this section demonstrated several ways in which the nation’s

5.46 billion m2

 (58.77 billion ft2) of commercial floorspace can be characterized. This

section’s approach to characterizing the market for FIAPP-related products and services

and detailing the market in terms of total floorspace of large commercial buildings, those

whose floor area exceeds 4,645 m2 (50, 000 ft2), sets the stage for the impact assessment.

These buildings account for 2.38 billion m2

 (25.61 billion ft2) or approximately 44 % of

the nation’s commercial floorspace. By focusing on total floorspace of large commercial

buildings as the potential target market, the calculation of FIAPP-related costs and

benefits is driven by floorspace-related considerations. This is fortuitous because the

commercial sector of the construction industry is geared towards processing information

presented in terms of per unit costs (e.g., $/m2 or $/ft2). As a consequence, most

published information on commercial construction expenditures for facility construction,

for maintenance and repair activities, and for energy is presented on a per unit basis.
55

5 Strategy for Identifying, Collecting, and Measuring FIAPP-Related

Benefits and Costs

The strategy outlined in this chapter was developed through an iterative process. First,

information was solicited from members of the BFRL CONSIAT team. This information

was used to develop candidate lists of key stakeholder classes (e.g., building owners) and

general types of FIAPP-related benefits and costs. Second, the lists were refined and

organized into a suite of “classification” hierarchies. Third, the classification hierarchies

were distributed to each of the BFRL CONSIAT project leaders and, upon their review of

the classification hierarchies, critiqued in a series of meetings with the project leaders.

The meetings with the BFRL CONSIAT project leaders also sought to identify subject

matter experts for follow-on discussions. Finally, subject matter experts from industry

and government were interviewed. These interviews were used to finalize the analysis

strategy and the classification hierarchies presented in this chapter as well as to collect

information on current industry practices and to identify additional data sources.

5.1 Identification of Key Stakeholders

Because individual stakeholders are affected in different ways by the introduction,

adoption, and use of FIAPP products and services, it is useful to first identify classes of

individual stakeholders and then classify them into stakeholder groups. By developing a

classification hierarchy of stakeholders, we are better able to understand and identify

both potential opportunities (i.e., real or perceived benefits and cost savings accruing to

that stakeholder) and potential barriers (i.e., real or perceived additional costs and benefit

reductions borne by that stakeholder) to the adoption of FIAPP products and services.

Since individual stakeholder classes evaluate the benefits and costs of FIAPP products

and services purely from their “stakeholder” viewpoint, it is important to reflect not only

that viewpoint, but the viewpoints of aggregations of stakeholder classes (i.e., a single

stakeholder group or a collection of stakeholder groups) and all stakeholder groups as

well. The viewpoint of the individual stakeholder is important because they make the

decision of whether or not to invest in FIAPP products and services. Examples of

individual stakeholder classes are building owners, engineering consultants, and trade

associations. A single stakeholder group is a special aggregation of individual

stakeholders classified according to a common theme. An example of a stakeholder

group is construction and associated support services. This stakeholder group contains

five classes of individual stakeholders: construction workers, general contractors,

specialty trade contractors, trade associations, and wholesale/retail trade/supply. A

collection of stakeholder groups is important because an individual stakeholder class may

be a key player in several stakeholder groups. The overall picture (i.e., all stakeholder

groups) is important because it reflects the benefits and costs of FIAPP products and

services to society. BFRL’s assessment of FIAPP-related impacts is undertaken from

society’s frame of reference. Thus, it includes all benefits and costs to whomsoever they

accrue.
56

Tables 5-1 and 5-2 identify the classes of individual stakeholders and the corresponding

stakeholder group(s) used in the assessment of FIAPP-related benefits and costs. Both

tables provide the same information, but are organized in different ways.

Table 5-1 is a hierarchy of stakeholders; it lists stakeholder groups with their

corresponding classes of individual stakeholders. It shows how the stakeholder groups

are formed. In Table 5-1, the six stakeholder groups are listed in a bold-italics typeface.

The classes of individual stakeholders are listed in alphabetical order beneath each

stakeholder group.

Table 5-2 is arranged as a checklist; it assigns each of the 31 classes of individual

stakeholders to its corresponding stakeholder group(s). Table 5-2 lists the classes of

individual stakeholders in alphabetical order to facilitate cross-referencing of individual

stakeholders and stakeholder groups. Note that an individual stakeholder class may be

associated with more than one stakeholder group. For example, trade associations are

associated with three stakeholder groups.

The analysis conducted in this report encompasses all stakeholder groups. However, if

analyses from the perspective of a single stakeholder or stakeholder group were desired,

Tables 5-1 and 5-2 could be used to structure these analyses (see Section 5.4). In such

cases, either Table 5-1 or Table 5-2 may be used to select which class (classes) of

individual stakeholders is (are) appropriate.

5.2 Classification of FIAPP-Related Benefits and Cost Savings

Stakeholders invest in FIAPP products and services because they anticipate receiving, in

present value terms, benefits or cost savings in excess of the costs or benefit reductions

associated with these investments. Table 5-3 provides a framework for one side of the

stakeholders investment decision problem. Namely, how to identify FIAPP-related

benefits and cost savings from society’s frame of reference (i.e., across all stakeholder

groups).

Table 5-3 is organized as a three-tiered hierarchy. Table 5-3 represents the culmination

of the Office of Applied Economics CONSIAT project team’s efforts to produce a

consensus on a comprehensive list of FIAPP-related benefits and cost savings.

The first tier of the hierarchy lists generic types of FIAPP-related benefits and cost

savings. Although the types of benefits and cost savings appearing in the first tier are

generic, the list is considered to be exhaustive. In addition, the generic types of benefits

and cost savings listed in the first tier are considered to be self-evident. The 15 first tier

elements are listed in a bold-italics typeface. Examples of first tier benefits and cost

savings are increased/new sales for system design/integration/optimization services,

lower first costs, and lower operations and maintenance costs.
57

Table 5-1. Hierarchy of FIAPP Stakeholders by Groups and Classes of Individual FIAPP StakeholdersBuilding Owners and Managers• Building Managers• Building OwnersCodes, Standards, and Support Services• Building Owners• Building Permitting and Inspection• Code Officials

• Code Organizations• Construction Products/Equipment Manufacturers• Professional Societies• Product Certification Services• Product Evaluation Services• Research Organizations

• Standards Organizations• Trade Associations

Construction and Associated Support Services• Construction Workers• General Contractors• Specialty Trade Contractors

• Trade Associations• Wholesale/Retail Trade/Supply

Other• Building Occupants• Special Interest Groups• Third Parties

Manufacturing Interest Group• Construction Products/Equipment Manuf• Customer Service Operations• Product/Equipment/Software Designers• Product/Equipment/Software Innovators• Product/Equipment/Software Marketing, 

Services

• Professional Societies• Research Organizations• Testing Laboratories• Testing Services• Trade AssociationsProfessional and Financial Services• Architects• Designers• Engineering Consultants• Insurance Companies• Investment Banking Services• Warranty Companies
58

Table 5-2. Assignment of Classes of Individual FIAPP Stakeholders to FIAPP Stakeholder Groups

Stakeholder Group

Individual Stakeholder Class Building Owners &

Managers

Codes,

Standards, &

SupportServices

Manufacturing

Interest Group

Construction

& Associated

SupportServices

Professional

& Financial

Services

Other

Architects 9Building Managers 9Building Occupants 9Building Owners 9 9Building Permitting andInspection

9

Code Officials 9Code Organizations 9Construction Products/EquipmentManufacturers

9 9

Construction Workers 9Customer Service Operations 9Designers 9Engineering Consultants 9General Contractors 9Insurance Companies 9Investment Banking Services 9Product Certification Services 9Product/Equipment/SoftwareDesigners

9
59

Table 5-2. Assignment of Classes of Individual FIAPP Stakeholders to FIAPP Stakeholder Groups (Conti

Stakeholder Group

Individual Stakeholder Class Building Owners &

Managers

Codes,

Standards, &

SupportServices

Manufacturing

Interest Group

Construction

& Associated

SupportServices

Professional

& Financial

Services

Other

Product/Equipment/SoftwareInnovators

9

Product/Equipment/SoftwareMarketing, Sales, andDistribution Services

9

Product Evaluation Services 9Professional Societies 9 9Research Organizations 9 9Special Interest Groups 9Specialty Trade Contractors 9Standards Organizations 9Testing Laboratories 9Testing Services 9Third Parties 9Trade Associations 99 9Warranty Companies 9Wholesale/Retail Trade/Supply 9
60

Table 5-3. FIAPP-Related Benefits (or Cost Savings) for All Stakeholders

Improved Company Capabilities and Flexibility for New Project Opportunities

• Access to Live Data Stream from Construction Site Allows Construction

Management to be Conducted Remote from the Job Site

 Automated Generation of Material Orders and Payments

 Pre-Programming and Simulating the Following Day’s Activities

for Optimum Efficiency

• Tele-Present Machine Operation Allows for Remote Operation of

Machinery

Improved Feedback Mechanisms on Performance of New Tools and Processes

Increased/New Sales for System Design/Integration/Optimization Services

• Automated Construction Machine Maintenance and Fleet Management

• Automated Construction Machine Programming and Task Execution

• Construction Simulation/Visualization

• Facility Operations

• Materials Management, Tracking, and Scheduling

Increased Net Income for Contractors

• Better Control of Cost Growth

• Better Design Evaluation and Checking

• Better Document Control

• Better Materials Management

• Better Scheduling of Construction-Site Operations

• Improvements in Productivity

• Smoother Start-Up Operations

Increased Revenues Due to Earlier Start-Up of Primary Functions

• Earlier Revenue Stream from Sale of Products/Services

• Faster Return on Investment

Increased Sales of Selected Product/Equipment Lines and Associated Services

Increased Sales of Construction Products/Equipment/Services with New

Features
61

Table 5-3. FIAPP-Related Benefits (or Cost Savings) for All Stakeholders

(Continued)

Lower First Costs

• Better Control of Cost Growth

 Earlier Bill of Materials and Procurement Analysis Reduces Risks

of Non-Availability of Parts

 Fewer Project Development and Scope Changes

 Less Design Rework

 Less Field Rework

• Better Component Selection Process

• Better Design Evaluation and Checking

• Better Document Control

• Better Materials Management

• Better Scheduling of Construction-Site Operations

• Early Payment on Bill of Materials Gets Discount Price

• Lower Financing Costs Due to Earlier Start-Up of Primary Functions

• Optimization of Project Team and Plan

 Better Coordination Between Owner, Prime Contractor, and

Subcontractors

 Better Opportunity for Supply Chain Management

 Earlier Optimization of Design and Project Plan

 Improved Procurement, Supply, and Contractor Management

• Smoother Start-Up Operations

Lower Operations and Maintenance Costs

• Availability of Electronic “As Built” Information Saves Time in TroubleShooting Maintenance Problems

• Availability of Online Information on the Building’s Characteristics

Promotes Use of Fact-Based Operations and Maintenance Programs

• Facilitates Cross-Training of Support Staff for Multi-Building Operations

• Improvements in Compliance Monitoring

• Increased Functionality and Performance

• Reduced Down Time

• Use of On-Part Information Systems (RFID) Reduces Certification and

Calibration Costs
62

Table 5-3. FIAPP-Related Benefits (or Cost Savings) for All Stakeholders

(Continued)

Lower Repair, Replacement, and Decommissioning Costs

• Availability of Electronic “As Built” Information Enables Faster Response

to and Resolution of Many Repair Problems

• Availability of Electronic “As Built” Information Reduces the Costs of

Addition and Modernization Projects

• Availability of Electronic “As Built” Information Reduces the Costs of

Decommissioning “Out-of-Date” Plant and Equipment

• Longer Equipment Life Due to Better Operating Conditions

• On-Part Information Systems (RFID) Identify “Generic” Replacement

Components and Sources

• Reduced Down Time

• Use of Electronic Data Interchange Promotes Faster Delivery of “Out-ofStock” Parts

Reductions in Construction-Related Accidents and Injuries

• Availability of Electronic “As Built” Information Increases Safety During

Normal Operations and Emergency Situations

• Fewer Lost Workdays

• Fewer Recordable Incidents

• Improved Safety Through Automated Tracking and Event Logging

 Fewer Machine-Worker Accidents

 Identification of High-Risk Employees

 Identification of High-Risk Machinery

• Less Down Time

• Lower Medical Costs

• Lower Workman’s Compensation Insurance Premiums

Reductions in Costs of Code Compliance Due to New Ways of Designing,

Constructing, and Maintaining Buildings

• New Ways of Communicating With Code Officials

 Automated Inspection Requests

• New Ways of Demonstrating Code Compliance

 Pre-Certified Automated Procedures

 Remote Certification (Tele-Presence Inspection)

• New Ways of Demonstrating Operating Compliance

 Autonomous Remote Sensing to Code Official
63

Table 5-3. FIAPP-Related Benefits (or Cost Savings) for All Stakeholders

(Continued)

Reductions in Delivery Time

• Better Control of Schedule Growth

 Automated Field Inspection Reports

 Automated Utility Avoidance

 Fewer Project Development and Scope Changes

 Less Design Rework

 Less Field Rework

 Earlier Bill of Materials and Procurement Analysis Reduces Risks

of Non-Availability of Parts

• Better Document Control

 Automated Updates of As-Built Information

• Better Scheduling of Construction-Site Operations

 Dynamic Database Allows for Identification of Non-Obvious

Critical Path Activities and Automated Rescheduling

 New Ideas/Task Sequencing Can be Explored with 3D Simulations

 Output of 3D Simulations Can be Used to Drive Machinery for

Automated Processes

• Earlier Start-Up of Primary Functions

• Faster Task Completion

 Ability to Employ Automated Processes and “Measureless”

Construction

 Automated Tracking of Parts and Processes

• Optimization of Project Team and Plan

 Better Coordination Between Owner, Prime Contractor, and

Subcontractors

 Better Opportunity for Supply Chain Management

 Earlier Optimization of Design and Project Plan

 Improved Procurement, Supply, and Contractor Management

• Smoother Start-Up Operations

Reductions in Warranty Costs

Reductions in Waste and Pollution
64

The second tier lists specific types of benefits and cost savings associated with its

“parent” first tier element. The second tier elements are listed in alphabetical order as a

series of bullets under the parent first tier element. An example of a second tier element

for lower first costs is better control of cost growth. Not all generic types of benefits and

cost savings have a second tier (e.g., reductions in warranty costs).

The third tier lists specific types of benefits and cost savings associated with its “parent”

second tier element. The third tier elements are listed in alphabetical order as a series of

bullets under the parent second tier element. Two of the third tier elements, fewer project

development and scope changes and less field rework, are concerned with better control

of cost growth. These cost savings are of central importance to BFRL and its industry

collaborators.

The classification hierarchy presented as Table 5-3 has been limited to three tiers.

Because Table 5-3 and Table 5-4 (see Section 5.3) are used to measure the “benefits” and

“costs” sides of FIAPP-related impacts, the end product of these classification hierarchies

is a collection of economic data. In the case of FIAPP-related benefits and cost savings,

the depth of the hierarchy (i.e., the number of tiers) is equal to three. In principle, the

depth of these data-related classification hierarchies could be equal to one, two, three, or

some number greater than three. The rule governing the depth of the hierarchy is how far

down in the hierarchy one must go until all lowest level elements in the hierarchy are

indicative of economic data. For FIAPP-related benefits and cost savings, three tiers

were considered adequate.

It is important to recognize that the benefits and cost savings listed in Table 5-3 might

accrue to any individual stakeholder (i.e., they are aggregated according to society’s

frame of reference). Thus, Table 5-3 is structured from “society’s” frame of reference

rather than from the perspective of a single stakeholder or stakeholder group. The main

purpose of Table 5-3 is to illustrate how BFRL approaches the assessment of the

“benefits” side of FIAPP-related impacts. Specifically, BFRL used this table to identify

the data needed to measure these impacts. For the impact assessment presented in this

report, Table 5-3 identifies the potential “benefits” data links. However, if the focus is on

an individual stakeholder or stakeholder group, it will be necessary to develop a

crosswalk between the generic types of benefits and cost savings listed in Table 5-3 and

the stakeholder groups listed in Table 5-1. This crosswalk is the subject of Section 5.4.

5.3 Classification of FIAPP-Related Cost Increases and Benefit Reductions

Costs are at the heart of any investments in new products.33 For the CONSIAT economic

impact assessment, costs are incurred at several points in the “product” life cycle.

Specifically, FIAPP-related costs include research costs, product development costs,

production costs, dissemination costs, and installation costs. In addition, a particular

vendor may experience benefit reductions due to reduced sales of some of its more

 

33 The word product is used generically to represent technologies, hardware (e.g., building systems,

subsystems, components, piece parts, and support equipment), software, and services.
65

“traditional” products. These cost increases and benefit reductions are summarized in

Table 5-4; they are organized as a three-tiered hierarchy.

The first tier of the hierarchy lists generic types of FIAPP-related cost increases and

benefit reductions. The list is considered to be exhaustive and self-evident. The six first

tier elements are listed in a bold-italics typeface. Examples of first tier cost increases and

benefit reductions are increased costs for new standards development, increased

investments by construction products/equipment manufacturers and hardware/software

developers, and new-technology introduction costs.

The last element, new-technology introduction costs, merits a closer examination. Ehlen

and Marshall34 define new-technology introduction costs as those costs covering the

activities that bring the material/product from the research laboratory to full field

implementation. New-technology introduction costs include the extra time and labor to

design, test, monitor, and use the new technology. Ehlen’s and Marshall’s research on

new-technology introduction costs is particularly relevant for this economic impact

assessment because they demonstrate that new-technology introduction costs disappear

once the designer is satisfied with the technology’s performance and service life, the

technology enters full implementation, and its application has become routine.35

The second tier lists specific types of cost increases and benefit reductions associated

with its “parent” first tier element. The second tier elements are listed in alphabetical

order as a series of bullets under the parent first tier element. One example of a second

tier element for increased investments by construction products/equipment manufacturers

is increased research and development costs. Another example of a second tier element

for new-technology introduction costs is increased training costs. Not all generic types of

costs and benefit reductions have a second tier (i.e., increased costs for new standards

development and reduced sales of selected product/equipment lines and associated

services).

The third tier elements are concerned with increased research and development costs and

with two of the four second tier elements for new-technology introduction costs—

increased marketing, advertising, and distribution costs by construction products/

equipment manufacturers and hardware/software developers and increased training costs.

The costs associated with these three second tier elements are of central importance to

both BFRL and its industry collaborators. Consequently, it was desirable to increase the

depth of the FIAPP-related costs and benefit reductions classification hierarchy to three.

Information on increased research and development costs is presented and discussed in

Section 6.3.2.1. Examples of increased research and development costs are increased

costs for product development and increased costs for product testing/simulation.

Information on new-technology introduction costs is presented and discussed in Section

6.3.2.2.

 

34 Ehlen, Mark A., and Harold E. Marshall. 1996. The Economics of New-Technology Materials: A Case

Study of FRP Bridge Decking. NISTIR 5864. Gaithersburg, MD: National Institute of Standards and

Technology.

35 Ibid., p. 15.
66

Table 5-4. FIAPP-Related Cost Increases (or Benefit Reductions) for All

Stakeholders

Increased Costs for New Standards Development

Increased Costs of Hardware and Software Installation to Provide

Infrastructure Support

• Additional Construction-Site Systems Infrastructure Needed to Monitor

and Control Systems and Components

• Additional Information Technology Costs for Program Management and

Procurement

• Increased Costs of Installing Hardware and Software for Use by Building

Owners and Managers

• Increased Costs of Installing Hardware and Software in Building Code

Offices

• Increased Costs to Modify the Current Inspection/Certification Process to

Accommodate New Hardware/Software Capabilities

Increased Costs to Properly Maintain Hardware and Software

• Additional Costs Associated with Parts Replacement and Software

Upgrades

• Additional Costs Associated with Periodic Calibration and Certification of

New Technology Products

• Additional Costs to Insure that the Current Information Transfer Protocols

Are Being Used

• Additional Costs to Insure that the Hardware and Software Incorporate the

Most Recent Set of Building Code Information

Increased Investments by Construction Products/Equipment Manufacturers

and Hardware/Software Developers

• Additional Costs for New Intellectual/Material Inputs

• Conversion Costs for Installing New Production Processes in Existing

Facilities

• Increased Costs for Copyright/Trademark Registration/Defense

• Increased Research and Development Costs

 Increased Costs for Product Development

 Increased Costs for Product Testing/Simulation
67

Table 5-4. FIAPP-Related Cost Increases (or Benefit Reductions) for All

Stakeholders (Continued)

New-Technology Introduction Costs

• Increased Costs of Adapting New Construction Technologies, Products,

Equipment, and Practices to Industry Use

• Increased Marketing, Advertising, and Distribution Costs by Construction

Products/Equipment Manufacturers and Hardware/Software Developers

 Increased Costs Associated with Market Positioning Efforts

 Increased Costs to Develop New Distribution and Service Channels

 Intermediate Requirement to Maintain Redundant Services and

Distribution Channels

• Increased Risk Exposure and Uncertainty Due to Construction with New

Technologies, Products, Equipment, or Practices

• Increased Training Costs

 Increased Costs for Instruction on How to Incorporate New

Technologies, Products, Equipment, and Practices into the Design

Process

 Increased Costs for Training Building Owners and Managers on

New Operations and Maintenance Processes and Techniques

 Increased Costs for Training Construction Workers on New

Construction Processes and Techniques

 Increased Costs for Training Public Officials on Hardware/Software

Capabilities

Reduced Sales of Selected Product/Equipment Lines and Associated Services
68

5.4 How FIAPP-Related Benefits and Costs Accrue to Stakeholders

Recall that BFRL’s assessment of FIAPP-related impacts is undertaken from society’s

frame of reference. Thus, it includes all benefits and costs to whomsoever they accrue.

Although this is the traditional approach for public-sector economic impact studies, it is

too broad for most stakeholder groups. This is because most stakeholder groups want to

evaluate the pros and cons of “their” investments in FIAPP products and services. In

addition, the traditional approach employed in public-sector studies complicates the data

collection effort. Basically, the higher the level of abstraction, the more difficult it

becomes to define data “categories” and collect the types of data that lead to meaningful

results. Consequently, this study develops crosswalks between stakeholder groups and

FIAPP-related benefits and cost savings and FIAPP-related cost increases and benefit

reductions. The two crosswalks are presented as Table 5-5 and Table 5-6. Table 5-5 lists

key types of benefits and cost savings by stakeholder group; Table 5-6 lists key types of

costs and benefit reductions by stakeholder group.

The two crosswalks serve three purposes. First, they define in an unambiguous manner

all of the potential data categories from which to collect economic data. In fact, each data

category may be specified as a unique combination of stakeholder group and type of

benefit or type of cost. Second, the crosswalks promote a priority-setting process for

identifying what specific types of data to collect and where to collect them. For example,

if we know that two stakeholder groups—building owners and managers and professional

and financial services—are beneficiaries of lower operations and maintenance costs (see

the cells beneath the “stakeholder group” column headings in Table 5-5 with check marks

(9)), then we can focus our “operations and maintenance cost” data collection effort on

these two stakeholder groups. Thus, the data collection strategy, stated in its simplest

terms, is to limit the data collection effort to those cells of Table 5-5 and Table 5-6 with

check marks (9). This priority-setting approach to data collection is employed

throughout the next three chapters. Finally, the crosswalks provide the means through

which an individual stakeholder or stakeholder group may evaluate the pros and cons of

investing in FIAPP products and services. Thus, the crosswalks not only greatly simplify

the current economic impact assessment they also provide the framework for identifying

key data elements and for specifying a data collection strategy for individual

stakeholders.

The third purpose of the crosswalks is best understood by considering a specific

stakeholder group, say building owners and managers. If building owners and managers

are considering investing in a specific FIAPP product versus a traditional product, they

need to know if the life-cycle cost over the proposed study period of the FIAPP product is

less than that of the traditional product.

The first step in this “decision problem” is to identify the types of benefits and the types

of costs. The “benefits” accruing to and the “costs” borne by building owners and

managers are recorded in the first “stakeholder group” column of Tables 5-5 and 5-6,
69

respectively. Reference to Table 5-5 shows that building owners and managers benefit

from all but six of the 15 types of benefits and cost savings. Examples of specific types

of benefits and cost savings accruing to building owners and managers are lower first

costs, lower operations and maintenance costs, and reductions in delivery time.

Reference to Table 5-6 shows that building owners and managers bear four types of

increased costs. They are the costs associated with new standards development,

increased costs of hardware and software to provide infrastructure support, increased

costs to properly maintain hardware and software, and new-technology introduction

costs. The second step is to compile a list of the types of benefits and the types of costs

for which data are available and are relevant (i.e., data that allow comparisons between

the products being considered). The third step is to collect the economic data. The

economic data collected in the third step are used to support a life-cycle cost analysis of

the products being considered. Finally, evaluate the economic performance of each

product being considered. This is done by calculating the life-cycle cost for each product

and selecting the one that minimizes the life-cycle cost over the proposed study period.

The same procedure can be used for an individual stakeholder class. First, select the

individual stakeholder class. Then, refer to Table 5-2 to identify the appropriate

stakeholder group(s). Finally, follow the procedure just described to determine whether

or not that stakeholder should invest in the FIAPP product under consideration.
70

Table 5-5. Types of FIAPP-Related Benefits (or Cost Savings) Classified by Stakeholder Group

Stakeholder Group

Type of Benefit or

Cost Saving Building Owners &

Managers

Codes,

Standards, &

SupportServices

Manufacturing

Interest Group

Construction

& Associated

SupportServices

Professional

& Financial

Services

O

Improved Company Capabilities andFlexibility for New Project Opportunities

9 99

Improved Feedback Mechanisms onPerformance of New Tools and Processes

9 99

Increased/New Sales for System Design/Integration/Optimization Services

9 99

Increased Net Income for Contractors 9 9Increased Revenues Due to Earlier Start-Upof Primary Functions

9

Increased Sales of Selected Product/Equipment Lines and Associated Services

9 99

Increased Sales of Construction Products/Equipment/Services with New Features

9 9 99

Lower First Costs 99 9 9 9Lower Operations and Maintenance Costs 9 9Lower Repair, Replacement, andDecommissioning Costs

9 9 99

Reductions in Construction-Related Accidentsand Injuries

9 9 9

Reductions in Costs of Code Compliance Dueto New Ways of Designing, Constructing, andMaintaining Buildings

9 9 9 9 9

Reductions in Delivery Time 9 9 9 9 9Reductions in Warranty Costs 9 99Reductions in Waste and Pollution 9 99 
71

Table 5-6. Types of FIAPP-Related Cost Increases (or Benefit Reductions) Classified by Stakeholder Gro

Stakeholder Group

Type of Cost Increase or

Benefit Reduction Building Owners &

Managers

Codes,

Standards, &

SupportServices

Manufacturing

Interest Group

Construction

& Associated

SupportServices

Professional

& Financial

Services

O

Increased Costs for New StandardsDevelopment

9 9 9 9 9

Increased Costs of Hardware and Software toProvide Infrastructure Support

99 9 9

Increased Costs to Properly MaintainHardware and Software

99 9 9 9

Increased Investments by ConstructionProducts/Equipment Manufacturers

9

New-Technology Introduction Costs 9 9 9 9 9Reduced Sales of Selected Product Lines andServices

9 9 9
72
73

6 Data and Assumptions for the CONSIAT Economic Impact

Assessment

This chapter describes the data and assumptions used to evaluate the economic impacts

expected from the adoption and use of FIAPP products and services in commercial buildings.

The goal of this chapter is fourfold. First, it establishes the sources and validity of the data

used in the CONSIAT economic impact assessment. Second, it defines the base case and the

FIAPP alternative. Third, it produces estimated values for key sets of benefits and costs.

Fourth, it documents the process by which key assumptions were established, including how

the values of key parameters were set.

6.1 Data Sources

Establishing the sources and validity of the data used in the CONSIAT economic impact

assessment is essential if readers are to be able to follow the analysis, gain insights useful

for their own applications, and reproduce our results. This section describes the three

groups of data upon which the economic impact assessment is based. The material

presented in this section is intended to establish an audit trail which readers can follow to

gain access to the same information used in the CONSIAT economic impact assessment.

6.1.1 Baseline Measures of Construction Industry Practices

The Construction and Building Subcommittee of the National Science and Technology

Council has established seven National Construction Goals in collaboration with a broad

cross section of the construction industry.36 Data describing current practices of the US

construction industry are needed to establish baselines against which the industry can

measure its progress towards achieving the seven National Construction Goals. The

seven Goals are: (1) reductions in the delivery time of constructed facilities; (2)

reductions in operations, maintenance, and energy costs; (3) increases in occupant

productivity and comfort; (4) reductions in occupant-related illnesses and injuries; (5)

reductions in waste and pollution; (6) increases in the durability and flexibility of

constructed facilities; and (7) reductions in construction worker illnesses and injuries.

Baseline measures and measures of progress will be produced for each National

Construction Goal in each of the four key construction industry sectors. The four sectors

are: (1) residential; (2) commercial; (3) industrial; and (4) public works. Industry

performance in 1994 is used as the reference point from which the values of the baseline

measures are calculated.

 

36 Wright, Richard N., Arthur H. Rosenfeld, and Andrew J. Fowell. 1995. Construction and Building:

Federal Research and Development in Support of the US Construction Industry. Washington, DC: National

Science and Technology Council.
74

Two reports by Chapman and Rennison provide detailed sets of baseline measures for

National Construction Goal 1,37 reductions in delivery time, and National Construction

Goal 2,38 reductions in operations, maintenance, and energy costs. A third report by

Chapman provides a detailed set of baseline measures for National Construction Goal 7,39

reductions in construction worker illnesses and injuries. Goals 1, 2, and 7 were identified

as the highest priority National Construction Goals by the construction industry.

The baseline measures for Goals 1, 2, and 7 for the commercial sector were the starting

point for collecting the data and information needed to conduct the CONSIAT economic

impact assessment. Specifically, the values of the baseline measures are reference data

against which the values contained in this report can be compared. In addition, all three

reports provided extensive cross-referencing of data to sources. This enabled the current

effort to quickly and efficiently retrieve data and information focused exclusively on the

commercial sector. The remainder of this section is devoted to the description of these

data sources and the key data sets associated with these data sources.

6.1.2 The CII Benchmarking and Metrics Database

Information from CII is used to produce estimates for four key data items: (1) reductions

in first costs; (2) reductions in delivery time; (3) reductions in construction-related

accidents; and (4) higher net income for contractors. The first data item is needed to

estimate the percent cost savings for a typical commercial sector project. The second

data item provides the basis for estimating the potential for increased revenues due to

earlier start-up of operations. The third data item is needed to estimate cost savings

resulting from improved safety performance. The fourth data item provides the basis for

estimating the effects on contractor margins due to better control over cost growth during

the project delivery process. Anecdotal information is also presented which is useful in

estimating a fifth data item—reductions in maintenance and repair costs.

CII is an internationally-recognized research consortium focused on advancing the capital

projects industry. CII draws its membership primarily from companies involved in the

operation or construction of industrial facilities. CII membership is nearly equally split

between owner members and contractor members. CII data are used in this document

because CII has committed itself to an annual cycle of surveying its member companies,

collecting data on an individual project basis, analyzing these data, and publishing its

findings. Many projects for which data are being collected are commercial buildings.

 

37 Chapman, Robert E., and Roderick Rennison. 1998. An Approach for Measuring Reductions in Delivery

Time: Baseline Measures of Construction Industry Practices for the National Construction Goals. NISTIR

6189. Gaithersburg, MD: National Institute of Standards and Technology. 38 Chapman, Robert E., and Roderick Rennison. 1998. An Approach for Measuring Reductions in

Operations, Maintenance, and Energy Costs: Baseline Measures of Construction Industry Practices for the

National Construction Goals. NISTIR 6185. Gaithersburg, MD: National Institute of Standards and

Technology.

39 Chapman, Robert E. 2000. An Approach for Measuring Reductions in Construction Worker Illnesses and

Injuries: Baseline Measures of Construction Industry Practices for the National Construction Goals.

NISTIR 6473. Gaithersburg, MD: National Institute of Standards and Technology.
75

Research by the author indicates that CII is one of the few organizations in the US that is

systematically collecting construction project data in a manner conducive to estimating

the benefits and costs of employing innovative methods for using existing design and

information technologies. CII has agreed to provide NIST with aggregated data from its

database, which will enable NIST to develop an extensive set of benefit and cost

measures associated with the use of design and information technologies.40 At the same

time, NIST’s analyses of the CII data will provide CII with valuable insights into the

performance of its member companies, which will be of direct benefit to its membership.

All information presented in this subsection is based on the results of a research

collaboration between NIST and CII.41 The focus of this research collaboration was on

quantifying the value of using design/information technologies within the non-residential

sectors of the construction industry. Although the evolution and deployment of design/

information technologies will undoubtedly play an important role in the future of the

construction industry, many stakeholders are unsure of the economic value of using these

technologies. A detailed, authoritative, and readily accessible set of information is

needed to enable construction industry stakeholders to make cost-effective investment

decisions among established, new, and innovative design/information technologies. The

CII Benchmarking and Metrics database, which is composed exclusively of actual project

execution experiences, is the product from which this set of information was developed.

This collaborative research effort uses the CII Benchmarking and Metrics database to

evaluate the use of design/information technology and relate its use to project

performance. Results from this collaborative research effort are used to identify,

document, and develop estimates for the benefits and costs of using FIAPP products and

services in commercial buildings.

The CII Benchmarking and Metrics Committee42 established the CII Benchmarking and

Metrics database in 1996. The CII Benchmarking and Metrics database is based on

survey data collected from CII member companies. The Benchmarking and Metrics

Committee is responsible for the design of the survey instrument, the training of

benchmarking associates from member companies, and the compilation and analysis of

respondent data.

The survey instrument focuses on information on project size, cost, schedule, overall

performance, as well as on details of project execution. The survey instrument is

designed to collect information both on performance metrics—cost, schedule, and

safety—and on the use of CII-endorsed best practices. Perhaps most importantly, CII’s

 

40 All data provided to NIST by CII have been aggregated in a manner that precludes identification of an

individual company’s or project’s performance.

41 Thomas, Stephen R., Candace L Macken, and Sang-Hoon Lee. 2001. Impacts of Design/Information

Technology on Building and Industrial Projects. NIST GCR 01-828. Gaithersburg, MD: National Institute

of Standards and Technology.

42 The Benchmarking and Metrics Committee was chartered by CII’s Board of Advisors in November

1993. The Benchmarking and Metrics Committee is composed of representatives from both owner and

contractor companies; it met for the first time in February 1994.
76

analysis of respondent data seeks to quantify the impacts of best practice usage on the

values of performance metrics (e.g., how the use of best practices translates into

reductions in project delivery time). Detailed information is collected on 6 CII-endorsed

best practices: (1) safety;43 (2) pre-project planning;44 (3) team building;45 (4)

constructability;46 (5) project change management;47 and (6) design/information

technology.48 These data are used to construct a series of indices for measuring the

degree of usage both for individual best practices (e.g., design/information technology)

and for the overall set. Having data which links best practice use (e.g., design/

information technology) to project outcomes (e.g., reductions in project delivery time) is

a valuable tool for identifying performance improvement opportunities.

Information from 566 projects totaling $35.7 billion (installed cost) has been collected,

compiled, analyzed, and made available to NIST.49 Figure 6-1 summarizes the project

data received from both CII owners and contractors. Note that nearly two-thirds of the

projects came from owners.

The project data analyzed in this report are classified into two construction industry

groups: (1) commercial buildings and (2) industrial facilities.50 Figure 6-2 reports the

distribution of projects in the database by industry group. Data on both owner respondent

projects and contractor respondent projects are shown in Figure 6-2. The industrial

facilities group comprises approximately 80 % of the database. In all, there are 473

projects in the industrial facilities group. The remaining 93 projects are commercial

buildings.

 

43 Safety practices include the site-specific program and efforts to create a project environment and state of

consciousness embracing the concept that all accidents are preventable and that zero accidents is an

obtainable goal.

44 Pre-project planning involves the process of developing sufficient strategic information with which

owners can address risk and decide to commit resources to maximize the chance for a successful project. 45 Team building is a process that brings together a diverse group of project participants and seeks to

resolve differences, remove roadblocks, and proactively build and develop the group into an aligned,

focused, and motivated work team that strives for a common mission for shared goals, objectives, and

priorities.

46 Constructability practices seek to achieve overall project objectives through the optimum use of

construction knowledge and experience in planning, design, procurement, and field operations.

Constructability is achieved through the effective and timely integration of construction input into planning

and design as well as field operations.

47 Project change management practices seek to promote a balanced change culture, recognize change,

evaluate change, implement change, and continuously improve from lessons learned.

48 Design/information technology practices involve the use of data integration programs, 3D CAD

modeling, electronic data interchange (EDI), and bar coding.

49 Contractor data was used only for those projects on which contractors performed both design and

construction tasks. Although the CII Benchmarking and Metrics database contains data for four versions of

its questionnaire, only data from versions 2.0, 3.0, and 4.0 were included. The version 1.0 questionnaire

did not address design/information technology use.

50 The data used in this report have been aggregated to promote a more straightforward analysis of the

impacts of design/information technology use. CII classifies project data into four categories: (1)

buildings; (2) heavy industrial; (3) light industrial; and (4) infrastructure. This report combines data on

heavy and light industrial projects and excludes data on infrastructure projects. The infrastructure project

grouping has been excluded due to its small sample size.
77

Figure 6-1. CII Database by Respondent Type

220

346

0

50

100

150

200

250

300

350

400

Owners Contractors

Respondent

Number of Projects

Figure 6-2. CII Database by Industry Type

86

260

7

213

0

50

100

150

200

250

300

Buildings Industrial

Industry Group

Number of Projects

Owner Contractor
78

The CII database currently represents a broad range of project size as measured by cost.

As shown in Figure 6-3, approximately one-half of the projects have a cost of less than

$15 million, slightly more than one-fourth have a cost between $15 and $50 million, and

slightly less than one-fourth have a cost in excess of $50 million. The individual project

costs range from slightly below $5 million to in excess of $500 million, with an average

cost of approximately $63 million. Data on both owner and contractor respondent

projects are shown in Figure 6-3.

Projects in the CII database can be identified and categorized by the nature of the project.

Project nature indicates to which of the three categories a project belongs: (1) grassroots;

(2) addition; and (3) modernization. The survey instrument defined grass roots as a new

facility. An addition was defined as a new facility component that ties in to an existing

facility, often intended to expand capacity or commercial floorspace. Modernization was

defined as a facility for which a substantial amount of the equipment or structure is

replaced or modified, and which may expand commercial floorspace. For purposes of

this document, grassroots projects are classified under the heading of new construction,

and addition and modernization projects are classified under the heading of additions and

alterations. Figure 6-4 shows how the projects in the database are distributed among the

three categories of project nature. The projects are approximately equally distributed

among all three categories. Data on both owner respondent projects and contractor

respondent projects are shown in Figure 6-4.

The results of the statistical analyses of the CII project data are summarized in Table 6-1.

The table records two cost metrics, two schedule metrics, two safety metrics, and a

project budget factor. The two cost metrics are project cost growth51 and construction

cost growth; they are calculated for owner projects only. The two cost metrics provide

the basis for estimating reductions in first costs. The two schedule metrics are total

project duration52 (in weeks) and construction phase duration (in weeks); they provide the

basis for estimating the potential for increased revenues due to early start-up. The two

safety metrics are the recordable incidence rate53 (RIR) and the lost workday case

incidence rate54 (LWCIR); they provide the basis for estimating cost savings due to

improvements in safety performance. The project budget factor55 is calculated for

contractor projects only; it is used to estimate the effects on contractor margins due to

better control over cost growth during the project delivery process.

 

51 Project cost growth equals {(actual total project cost – initial predicted project cost) / initial predicted

project cost}, where actual total project cost equals total installed cost at turnover to the user (excluding

land costs), and initial predicted project cost equals the project’s budget at the start of detailed design. 52 Total project duration equals the elapsed time from the start of detailed design to turnover to the user.

53 The RIR represents the number of injuries and illnesses per 100 full-time workers. It is calculated as

(N/EH)x200,000, where N = the number of injuries and illnesses, EH = the total hours worked by all

employees during the calendar year, and 200,000 = the base for 100 full-time workers (working 40 hours

per week, 50 weeks per year).

54 The LWCIR is a measure of more serious injuries; it records those cases which result in days away from

work or restricted work activity.

55 Project budget factor equals {(actual total project cost)/(initial predicted project cost + approved

changes)}, where actual total cost equals the total cost of the final scope of work, initial predicted project

cost equals the cost estimate used as the basis for the contract award, and approved changes equal the

estimated cost of owner-approved changes.
79

Figure 6-3. CII Database by Cost Category

182

91

73

84

71 65

0

25

50

75

100

125

150

175

200

< $15 $15 - $50 > $50

Cost Category ($Million)

Number of Projects

Owner Contractor

Figure 6-4. CII Database by Project Nature

95

111

140

84 90

46

0

20

40

60

80

100

120

140

160

Addition Grass Roots Modernization

Project Nature

Number of Projects

Owner Contractor
80

Performance improvements due to the extensive use of design/information technology

practices (Column 3) are computed as the difference between average project

performance for that metric (Column 1) and the value of that metric for that subset of

projects which made extensive use of design/information technology practices (Column

2). The calculated values recorded in Column 3 of Table 6-1 provide the starting point

for estimating FIAPP-related benefits and cost savings derived in Subsection 6.3.1.

Anecdotal information collected as part of a series of in-depth analyses of a select set of

exemplary projects provided the basis for estimating reductions in maintenance and

repair costs (see Subsection 6.3.1). These data were collected as part of a previous

research collaboration between NIST and CII.56

Table 6-1. Summary of Selected Results from the Statistical Analyses of the CII

Project Data

Design/Information Technology Use

Metric Average

Col. (1)

Extensive

Col. (2)

Measured

Performance

Improvement

Col. (3)

Project Cost

Growth

1.0 % -1.0 % 2.0 %

Construction Cost

Growth

5.3 % 1.8 % 3.5 %

Total Project

Duration

147 weeks 125 weeks 22 weeks

Construction Phase

Duration

87 weeks 80 weeks 7 weeks

Recordable

Incidence Rate

3.520 2.588 0.932

Lost Workday Case

Incidence Rate

0.718 0.407 0.311

Project Budget

Factor

0.959 0.945 0.014

6.1.3 Other Data Sources

In addition to the information extracted from the CII Benchmarking and Metrics

database, information on the value of construction put in place, maintenance and repair

costs, safety statistics, and commercial sector net income figures were needed. The focus

of this subsection is on identifying the data sources for these key data items. How this

information is used to develop estimates of benefits and cost savings in the commercial

sector is described in detail in Subsection 6.3.1.

 

56 Thomas, Stephen R. 1999. Impacts of Design/Information Technology on Project Outcomes. NIST

GCR 99-786. Gaithersburg, MD: National Institute of Standards and Technology.
81

Data from the US Bureau of the Census

The value of construction put in place in the commercial sector is based on data

published in the Current Construction Reports series C30 publication (see Table 4-3 in

Section 4.1). The C30 publication provides a combined estimate of the value of new

construction put in place plus the value of additions and alterations put in place. This

value was $205.7 billion in 1997 for the commercial sector.

Reference to Table 4-3 reveals that the value of construction put in place in the

commercial sector (measured in constant 1997 dollars) has increased steadily over the

period 1994 through 2000. However, over extended periods, the value of construction

put in place in the commercial sector (measured in constant 1997 dollars) fluctuates

according to the business cycle. Rather than trying to model the business cycle over an

extended period of time into the future, this study adopts a conservative approach. It uses

a single mid-range figure for the value of construction put in place in the commercial

sector. Specifically, the estimate for the value of construction put in place in the

commercial sector used in this study is held constant at the 1997 level (i.e., $205.7 billion

expressed in constant 1997 dollars).

The Statistical Abstract of the United States was used as the source of data for estimating

the average cost per m2

 (ft2) for a typical commercial building. The estimate is based on

published data for new construction and additions.57 Data over a 10-year period between

1988 and 1997 were used to produce the estimate. The estimated value, expressed in

1997 dollars, is $1,050.88/m2 ($97.63/ft2). The estimated cost per m2 is used in

conjunction with the value of construction put in place and the diffusion model (see

Subsection 6.4.4) to estimate in any given year the amount of commercial floorspace

employing FIAPP products and services.

Data from the International Facilities Management Association

The International Facilities Management Association (IFMA) is an association serving

the facility management profession. IFMA has carried out a number of benchmarking

studies covering both the commercial and industrial sectors.

IFMA’s Research Report #13,58 published in 1994 is the result of a 1993 survey of IFMA

members. The report presents benchmarking data derived from 283 questionnaires.

While the IFMA report presents summary data based on a relatively small sample size; it

is one of the few reports which analyzes the relationship between maintenance and repair

costs. In addition, the IFMA report contains information on both the mean values (i.e.,

average values) and the distribution (i.e., range of values and the ordering of these

 

57 The Statistical Abstract of the United States reports both the value of construction and the net increase in

the amount of floorspace for selected commercial building types for new construction and additions

combined. Since alterations tend not to increase existing floorspace, the Statistical Abstract only reports

the value of alterations.

58 International Facilities Management Association. 1994. Benchmarks II. Research Report #13. Houston,

TX: International Facilities Management Association.
82

values) of each type of cost (i.e., repair costs and maintenance costs). This information is

used to estimate the annual maintenance and repair costs for commercial buildings. This

value, expressed in 1997 dollars is $23.90/m2 ($2.22/ft2).

Data from the US Bureau of Labor Statistics

The US Bureau of Labor Statistics (BLS) disseminates data in a continuous series of

annual releases from the BLS safety and health statistical series. The BLS Survey of

Occupational Injuries and Illnesses provides data on injuries and illnesses that are

derived from population samples. In cooperation with State agencies, BLS collects

information from employers59 on the number and incidence of nonfatal work-related

injuries and illnesses. Each year the Survey provides estimates by industry and by State

of the number of workplace injuries and illnesses, and also by the number of injuries and

illnesses that involve lost work time. The average number of days away from work and

the percent distribution of days away from work by industry are also given.

By recording the days away from work, the Survey provides a measure of the

“seriousness” of injuries and illnesses. For workers with injuries and illnesses involving

time away from work, the Survey estimates the number and percent distribution of

injuries and illnesses by occupation, sex, age, race, and length of service. Numbers,

percent distributions, and incidence rates are also calculated by detailed nature of injury

and illness, part of body affected, source of the injury or illness, and type of event or

exposure leading to the incident. Cross tabulations of the worker characteristics and

injury/illness circumstances are also available. The median and percent distribution of

days away from work are estimated for each worker and case characteristic.

BLS data on the recordable incidence rate (RIR) and the lost workday case incidence rate

(LWCIR) are used to construct key trends in safety performance. Information on trends

is necessary because safety performance in the construction industry has been improving

over the last decade. Consequently, it is necessary to separate safety improvements due

to industry-wide trends from improvements due to the use of FIAPP products and

services.

US Internal Revenue Service

The US Internal Revenue Service (IRS) publishes financial data for all business

enterprises. These data appear in the Statistics of Income, Corporation Income Tax

Returns, and the Statistics of Income Bulletin. IRS data on net income60 is used as one

component in the estimation procedure for placing a dollar value on potential increases in

revenues due to early start-up (i.e., reductions in delivery time). IRS data from 1997 for

corporate net income for services, wholesale and retail trade, and finance, insurance, and

real estate, were used to estimate the average weekly net income of $1.78/m2 ($0.17/ft2).

 

59 Construction establishments with no employees (i.e., self-employed construction workers) are not

covered by the Survey. 60 Net income equals total taxable receipts less business deductions. Net income is before income tax.
83

6.2 Defining the Base Case and the FIAPP Alternative

The purpose of this section is to define the base case and the FIAPP alternative to the

base case. This “definition step” is done to draw two key distinctions between the base

case and the FIAPP alternative (i.e., the two configurations). These distinctions are

important because they facilitate the estimation of the benefits and costs covered in

Section 6.3.

It is anticipated that FIAPP products and services will be employed in both the

construction of new commercial buildings and for the renovation of existing commercial

buildings. Verification that the FIAPP products and services employed are performing

“as stipulated” is done as part of a formal project execution process. If the FIAPP

alternative is not chosen, the same process applies for commercial buildings employing

the base case. Thus, for new commercial buildings, either the base case or the FIAPP

alternative is employed during “grass roots” construction. Similarly, for existing

commercial buildings, either the base case or the FIAPP alternative is employed while the

facility is undergoing renovation.

Both the base case and the FIAPP alternative (i.e., both configurations) have features

against which costs, savings, and performance are measured. These features include the

equipment and software required for design, construction, and facility operations. It is

important to recognize that both configurations must meet all facility-related performance

requirements. This “performance requirement” constraint is needed to ensure that both

configurations are reliable, serviceable, safe, and at a minimum, neutral with regard to

design aesthetics.61 The performance requirement applies both to either configuration

employed during the construction of a new commercial building and to either

configuration employed during the renovation of an existing commercial building.

Throughout the remainder of this report, the term base case is used to represent the

configuration that maintains the status quo (i.e., the “average” use of traditional design,

information, and construction technologies). The FIAPP alternative is that collection of

products and services (i.e., configuration) that provides equivalent or enhanced

performance for all features of the base case while satisfying the definition of a FIAPP

given in Section 3.1.

Based on the definitions of the base case and the FIAPP alternative, there are two key

differences between the two configurations. First, the degree to which construction

activities (e.g., materials management) and facility service features (e.g., maintenance

and repair procedures) are integrated, automated, and controlled is significantly higher in

the FIAPP alternative. The second difference is that the FIAPP alternative has the

potential to achieve enhanced performance for selected construction activities (see Table

 

61 For more information on how to specify performance requirements, see Chapter 2 of Fuller and Petersen

(Fuller, Sieglinde K., and Stephen R. Petersen. 1996. Life-Cycle Costing Manual for the Federal Energy

Management Program. NIST Handbook 135. Gaithersburg, MD: National Institute of Standards and

Technology).
84

6-1) and facility service features (see Subsection 6.3.1.2). These differences, although

interrelated, are crucial in structuring differences in costs (e.g., due to the installation of

additional equipment and software to generate improved systems integration, automation,

and control) and savings (e.g., reductions in construction-related accidents and

maintenance and repair cost savings due to the availability of electronic “as-built”

information) between the two configurations. Quantitative measures of these differences

are developed in Section 6.3.

6.3 Estimating Significant FIAPP-Related Benefits and Costs

This section develops estimates of the key benefits and costs that are the focus of the

CONSIAT economic impact assessment. These benefits and costs are well-defined

subsets of the comprehensive lists of benefits and costs presented in Chapter 5.

It is important to recognize that every effort has been made to capture and record any

cost-related information affecting the users of FIAPP products and services. Similarly,

considerable effort went into documenting and estimating BFRL’s CONSIAT-related

investments. Relatively less effort went into estimating the full range of FIAPP-related

benefits and cost savings. We focused on what we judged the most substantial and

measurable benefits, which we termed the “significant few” benefits. Thus, the return on

BFRL’s CONSIAT-related investments is expected to be very conservative (i.e., the

values presented in this report are lower bounds on the potential range of returns on

BFRL’s CONSIAT-related investments).

6.3.1 Benefits and Cost Savings

The enhanced performance of the FIAPP alternative vis-à-vis the base case produces five

types of benefits and cost savings. These benefits and cost savings are: (1) lower first

costs; (2) lower maintenance and repair costs; (3) fewer construction-related accidents;

(4) reductions in delivery time; and (5) higher net income for contractors. Lower first

costs are registered through a reduction in total installed costs (i.e., all project-related

costs with the exception of land costs). Lower maintenance and repair costs are

registered through reductions in future costs. Lower first costs and lower maintenance

and repair costs, as measured in this study, accrue to building owners and operators.

Fewer construction-related accidents are registered through reductions in direct jobsite

costs (e.g., medical costs), indirect jobsite costs (e.g., lost productivity of the crew due to

the accident), and liability costs (e.g., claims costs). Reductions in delivery time are

registered through increased opportunities for rental income and product sales. Higher

net income is registered through the contractor’s increased capability to control cost

growth during the project delivery process. The first three types listed—lower first costs,

lower maintenance and repair costs, and fewer construction-related accidents—are

readily classified as cost savings. Reductions in delivery time are classified as a benefit,

rather than as cost savings, because they create the potential for increased sales revenues
85

due to earlier start-up of operations. Higher net income is classified as a benefit because

it increases the contractor’s profit margin.

Although the FIAPP alternative is expected to result in fewer/shorter business

interruptions due to building-related problems (e.g., faster turnarounds due to the

availability of electronic “as-built” information), no estimates of these, potentially

significant, benefits and cost savings are included in the current CONSIAT economic

impact assessment. Although subject matter experts have reached consensus on the

generic types of benefits and cost savings due to fewer/shorter business interruptions (see

Table 5-3 in Section 5.2), no such consensus emerged on how to quantify these benefits

and cost savings. Plans for incorporating such estimates in a future economic impact

assessment are described in Section 9.2. It is important to recognize that although the

benefits and cost savings due to fewer/shorter business interruptions are not included in

this assessment, the costs of installing, operating, and maintaining the equipment and

software required to achieve these benefits and cost savings are included. This

contributes to the conservative outcome of the CONSIAT economic impact assessment.

6.3.1.1 Reduced First Costs

Information compiled from the CII Benchmarking and Metrics database was used to

establish values both for reductions in project cost growth and reductions in construction

cost growth (see Table 6-1). These reductions were 2.0 % for project cost growth and 3.5

% for construction cost growth. The data used to compute both sets of reductions are

from owner projects only. This is because owners bear the full cost of the project,

including the contractor’s profit margin. This approach ensures that any reductions in

first costs are modeled independently from contractor project data.

The value used in the CONSIAT economic impact assessment is based on the 2.0 %

reduction in project cost growth. There are two reasons for selecting this value. First, it

is more conservative than the 3.5 % reduction in construction cost growth. Thus, it is

more in keeping with the conservative approach employed in this impact assessment.

Second, the definition of the project cost growth metric makes use of both actual total

project cost and initial predicted project cost. Recall that actual total project cost equals

the project’s total installed cost at turnover to the user (excluding land costs) and initial

predicted project cost equals the project’s budget at the start of detailed design.

The project cost growth metric, as defined by CII, may be used as an estimator of the

percent reduction in total installed cost due to the use of FIAPP products and services.62

 

62 Consider the case where all base case projects and FIAPP alternative projects have the same initial

predicted project cost. In this case, all differences in project cost growth are due to differences in total

installed cost. Thus, a 2.0 % reduction in project cost growth between the set of FIAPP alternative projects

and the set of base case projects results in a 2.0 % reduction in total installed costs for the FIAPP

alternative vis-à-vis the base case.
86

Therefore, the use of FIAPP products and services is estimated to reduce total installed

costs for a typical commercial sector project vis-à-vis the base case by 2.0 %.

The 2.0 % reduction in total installed cost employed in this impact assessment is

considered very conservative (i.e., cost reductions due to the use of the FIAPP alternative

are likely to be significantly higher). The basis for the previous statement is due to a CII

study on the impacts of information management in the engineer-procure-construct (EPC)

process.63 The CII study of the EPC process used an activity-based costing approach,

coupled with Monte Carlo simulation, to quantify the benefits of employing traditional

methods of information management on EPC projects. The study concluded “aggressive

information management strategies for design related activities may yield as much as 10

% improvement in total elapsed time (calendar time) required to fully execute a typical

EPC process. These same strategies will most likely yield a reduction in overall labor

costs of approximately 2 to 3 %. Similarly, aggressive information management

strategies for materials management related activities may yield as much as 3 %

reduction in elapsed time and 7 % reduction in execution costs. When combined,

aggressive information management strategies may result in reductions as much as 14 %

for elapsed time and 8 % for execution costs.”64

It is important to note that not all commercial sector projects will employ the FIAPP

alternative. Thus, the 2.0 % reduction in first costs will only accrue to those commercial

sector projects which actually employ the FIAPP alternative. Information on the annual

proportion of commercial sector construction-related investments (i.e., expenditures for

new construction projects and for additions and alterations) that employ the FIAPP

alternative are based on the diffusion model (see Subsection 6.4.4). Annual estimates

showing how reductions in first costs contribute to cost savings nationwide are given in

Section 7.2, where all key components are laid out in a spreadsheet format.

6.3.1.2 Reduced Maintenance and Repair Costs

FIAPP products and services will lower the costs of maintaining and repairing

commercial buildings. How much these costs are reduced depends on a number of

factors, such as, the ability to maintain electronic “as built” information in a form that

will save time in troubleshooting maintenance problems, the ability to make available

online information on the building’s characteristics to promote the use of fact-based

maintenance programs, and the ability to use electronic data interchange to promote

faster delivery of “out-of-stock” parts. The use of selected FIAPP products and services

during the post “start-up” phase of the project life cycle will allow building support

systems and equipment to operate and be maintained under near optimal conditions for

extended periods of time. As a result, equipment life will be extended, fewer

replacements will be required, and replacement costs will decline.

 

63 Construction Industry Institute. 1998. Cost and Schedule Impacts of Information Management. Research

Summary 125-1. Austin, TX: Construction Industry Institute.

64 Ibid. p. 11.
87

To develop a range of estimates for annual maintenance and repair cost savings, industry

experts and building managers and operators were interviewed. The general consensus

was that the use of selected FIAPP products and services during the post start-up phase

would enable both smaller crew sizes and the ability to handle more building-related

service requests with a given size of maintenance staff. To a certain extent, some

potential maintenance and repair cost savings would be offset by expenditures for

training for the building’s maintenance staff. However, once trained, maintenance staff

can be more easily moved from one “FIAPP” location to another—a potential cost

saving. Finally, the building owner/operator would incur costs to maintain an “up-todate” set of electronic “as built” information. Since these costs are expected to rise over

time, they tend to “degrade” out year cost savings. Based on these inputs, maintenance

and repair cost savings were estimated to range from 5 to 15 %. This range of values is

considered very conservative (i.e., savings are likely to be greater), since a previous study

focusing on a subset of the FIAPP suite of technologies produced the same range of

values.65 This range of values is used to specify the range of values for maintenance and

repair cost savings in the sensitivity analysis (see Chapter 8).

The baseline value for annual maintenance and repair cost savings used in the economic

impact assessment is 10 %. This translates into an annual maintenance and repair cost

savings of $2.39/m2 ($0.22/ft2). Because these cost savings are expected to degrade over

time, they are reduced by 10 % per year following their installation. Degradation over

time is modeled through the use of a deflator.

The majority of reductions in maintenance and repair costs normally do not occur in the

same year in which the capital investment is made, as is the case for reductions in first

costs. To address this issue, this study assumes: (1) savings accrue only to that

proportion of commercial floorspace that previously employed FIAPP products and

services; and (2) future FIAPP-related savings in maintenance and repair costs are

brought back to the year in which the capital investment is made. Thus, the proportion of

commercial floorspace that has employed FIAPP products and services will rise

gradually over time, creating an opportunity for growth in savings. Similarly, for each

year’s set of capital investments that employ FIAPP products and services, all FIAPPrelated savings in maintenance and repair costs are brought back through the combined

application of the discount rate and the deflator. Annual estimates showing how

reductions in maintenance and repair costs contribute to cost savings nationwide are

given in Section 7.2, where all key components are laid out in a spreadsheet format.

6.3.1.3 Reductions in Construction-Related Accidents

Information compiled from the CII Benchmarking and Metrics database (see Table 6-1)

was used to establish the baseline values both for reductions in the recordable incidence

rate (RIR) and the lost workday case incidence rate (LWCIR). These reductions were

0.932 for the RIR and 0.311 for the LWCIR. Because the RIR also includes cases which

 

65 Van Tienhoven, C. J. 1996. The Benefits of STEP. Report No. IS96-014. The Hague: Shell Information

Services.
88

result in lost workdays (i.e., are included in the LWCIR calculation), it is necessary to

“net out” the LWCIR to achieve a number which corresponds to cases without lost

workdays. The resultant figure is 0.621 (i.e., 0.932 minus 0.311); it is used as the

baseline value for reductions in construction-related accidents that do not result in any

lost workdays. This newly defined term is referred to as the “net” RIR.

The incidence rate for construction-related accidents has been declining in recent years.

Figure 6-5 shows that both the RIR and the LWCIR have declined between 1989 and

1997. During this period, the RIR has declined from 14.3 to 9.5, a compound rate of

improvement of 5.25 % per annum. During the same period, the LWCIR has declined

from 6.8 to 4.4, a compound rate of improvement of 5.6 %. Because these industry-wide

trends are delivering improved safety performance, any measures of “improved” safety

performance due to FIAPP products and services must incorporate these trends into its

analysis of cost savings. The approach employed in this impact assessment (see Section

7.2) makes explicit these industry-wide trends. This is done by reducing the 0.621 figure

for improvements in the “net” RIR by 5.25 % for each year beginning in 1998 and the

0.311 figure for improvements in the LWCIR by 5.6 % for each year beginning in 1998.

Figure 6-5. Recordable Incidence Rate and Lost Workday Case Incidence Rate for

 Years 1989-1997

It is important to recognize that the reductions in construction-related accidents due to the

FIAPP alternative are likely to be higher than the “absolute” measured performance

improvement of the “net” RIR value of 0.621 and the LWCIR value of 0.311 resulting

from the projects in the CII Benchmarking and Metrics database. This is because the

12.20 11.80

9.90 9.50

6.10 5.80 5.50 5.50

14.30 14.20

13.00 13.10

10.60

6.80 6.79

4.90

4.50 4.40

0

2

4

6

8

10

12

14

16

1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

RIR

LWCIR
89

“percentage” improvement in the computed value of the RIR is more than 25 % (compare

Columns 3 and 1 of Table 6-1 for the RIR) and the improvement in the computed value

of the LWCIR is more than 40 % (compare Columns 3 and 1 of Table 6-1 for the

LWCIR). If these improvement figures are applied to the data recorded in Figure 6-5, the

reductions in the RIR and LWCIR, and hence the “net” RIR, would be much greater.

The reason for the differences is due to the superior safety performance of CII member

organizations. Once again, the conservative approach to estimating benefits and cost

savings employed in this impact assessment caused us to choose the lower values as the

basis for computing cost savings due to reductions in construction-related accidents under

the FIAPP alternative.

Information on the costs of construction-related accidents is needed to translate the

baseline values for reductions in the “net” RIR and the LWCIR into dollar terms. Two

studies which provide detailed information on the costs of construction-related accidents

are Design for Safety66 and Hinze and Applegate’s survey article.67 Data compiled from

these studies and updated to 1997 dollars are summarized in Table 6-2. The table

separates accident costs into three categories: (1) direct jobsite costs; (2) indirect jobsite

costs; and (3) estimated liability costs. Direct jobsite costs include the medical costs for

the injured worker and several incidental items. Direct jobsite costs are covered by

worker’s compensation insurance. Indirect jobsite costs include the lost productivity of

the crew due to the accident, the additional cost of a replacement worker, time expended

to complete forms related to the injury, and the cost for damage to materials and

equipment. Liability costs are associated with any claims related to the injury. Claims

costs can vary considerably. Hinze and Applegate calculated that claims costs are often

10 to 20 times the dollar value of direct jobsite costs for lost workday cases.68 The

figures recorded in Table 6-2 are on the low end of the recommended ratios given in

Hinze and Applegate. Thus, any FIAPP-related cost savings due to improved safety

performance are likely to be very conservative.

Table 6-2. Average Costs of Construction Site Injuries: 1997

Job Costs

Type of Injury Direct Indirect

Estimated

Liability

Costs

Total Cost to

Employer

No Lost Workdays $650 $550 $300 $1,500

Lost Workday $8,700 $2,000 $20,800 $31,500

Reference to Table 6-2 reveals that the total cost to the employer of a lost workday case

is about 20 times higher than an accident which does not result in a lost workday (i.e.,

$31,500 versus $1,500). Cost savings due to reductions in construction-related accidents

 

66 Construction Industry Institute. 1996. Design for Safety. Research Summary 101-1. Austin, TX:

Construction Industry Institute.

67 Hinze, J., and L. Applegate. 1991. “Costs of Construction Accidents,” Journal of Construction

Engineering and Management, Vol. 117, No. 3, pp. 537-550. 68 Ibid. pp. 544-545.
90

accrue both to building owners and to contractors engaged in the construction of those

buildings. Annual estimates showing how reductions in construction-related accidents

contribute to cost savings nationwide are given in Section 7.2.

6.3.1.4 Reductions in Delivery Time

Information compiled from the CII Benchmarking and Metrics database was used to

establish the values for reductions in delivery time. These values were 22 weeks for total

project duration and 7 weeks for construction phase duration. The data used to compute

both sets of reductions are from owner projects only. This is because owners participate

in all phases of the project delivery process.

The value used in the CONSIAT economic impact assessment is based on the figure for

reductions in the duration of the construction phase rather than for reductions in total

project duration. The decision to use the 7 week figure for reductions in delivery time

was motivated by a desire to maintain a conservative approach to estimating FIAPPrelated benefits.

A company’s desire to reduce delivery time is driven by the potential for increased sales

of its products and services in the marketplace. In the case of new products, getting to

the marketplace before the competition may translate into a substantial gain in market

share for that company’s line of products. Because companies are assumed to be profit

maximizers, reductions in delivery time offer the potential to increase profits. Therefore,

to place a value on reductions in delivery time, it is necessary to employ a metric that

closely approximates the profitability of the commercial sector. The “profitability”

metric employed in this study is net income. This study uses net income, rather than

business receipts or value added, because it best reflects profitability in the commercial

sector. In addition, net income results in a more conservative estimate of the value of

reductions in delivery time. The Internal Revenue Service publishes annual estimates of

net income for the commercial sector. Net income for the commercial sector in 1997 was

$505 billion.69 This translates into an average weekly net income of $1.78/m2

 ($0.17/ft2).

These estimates are used to translate reductions in delivery time into a dollar

denominated value.

The net income figure, expressed in 1997 dollars, is held constant throughout the study

period. This assumption is very conservative because net income has tended to increase

over time. The net income figure is combined with the diffusion model to estimate the

benefits nationwide due to reductions in delivery time (see Section 7.2).

6.3.1.5 Higher Net Income for Contractors

Information compiled from the CII Benchmarking and Metrics database and Statistics of

Income from the Internal Revenue Service was used as the basis for estimating higher net

 

69 The $505 billion net income figure for calendar year 1997 is based on filings by corporations only.

Excluded are filings by sole proprietorships and partnerships.
91

income for contractors. Because higher net income serves to increase a contractor’s

profit margin, only data from contractor projects are used. This approach ensures that

any improvements in contractor performance are modeled independently from owner

project data. In this way, improvements in contractor performance, say due to better cost

control stemming from process improvements and increased productivity, result in

improvements in profits to contractors rather than reductions in total installed cost to the

owner.

Contractor data from the CII Benchmarking and Metrics database was used to construct a

budget factor. The budget factor, as defined by CII, tracks deviations between the

contractor’s cost estimate used as the basis for the contract award for the proposed scope

of work and the contractor’s total cost for the final scope of work. Since changes in the

scope of work sometimes occur, the CII budget metric explicitly accounts for ownerauthorized changes. Because the contractor’s margin enters as a multiplicative factor,

resulting in the contract award amount, reductions in the calculated value of the budget

factor increase the contractor’s profit margin (i.e., their costs were lower than those upon

which the contract award was based). Conversely, increases in the calculated value of the

budget factor reduce the contractor’s profit margin. Analyses of data for CII contractors

performing both design and construction tasks resulted in an average improvement of

0.014 for the computed value of the budget factor metric (see Table 6-1). This metric’s

performance improvement translates into an improvement of at least 1.4 % in the

contractor’s profit margin.

In order to estimate a value for higher net income, it is first necessary to factor out an

estimate of the contractor’s profit margin from the value of construction put in place.

This is accomplished by dividing the net income figure for the construction industry by

the value of construction put in place. Based on net income figures from the most recent

years available from the Statistics of Income, an average profit margin of less than 5 %

can be expected. Note that this figure encompasses the entire construction industry (e.g.,

it contains the residential sector where many firms are sole proprietorships or

partnerships). According to industry experts, profit margins for the non-residential

sectors of the construction industry (i.e., where most firms are corporations) tend to be

very tight. This is borne out by the proportion of construction industry net income due to

corporations (i.e., 0.3 to 0.4) versus sole proprietorships and partnerships. Thus, an

assumed profit margin of 5 % can be considered to be on the high side for the average

commercial sector contractor. In keeping with the conservative approach of this

economic impact assessment, the base against which higher net income is computed is

estimated as the value of construction put in place divided by 1.05. Annual estimates of

higher net income nationwide are given in Section 7.2.

6.3.2 Cost Increases and Benefit Reductions

Two types of costs—new-technology introduction costs and increased research and

development costs—are central to this economic impact assessment. The first type of

costs, new-technology introduction costs, result in higher costs to commercial building
92

owners and managers and to contractors. Understanding the types of costs that affect

commercial building owners, managers, and contractors is necessary in order to estimate

annual values of net savings on a national level. These estimates affect not only the

present value of net savings nationwide, but the estimated return on BFRL’s CONSIATrelated investments as well. The second type of costs, increased research and

development costs, focuses only on BFRL’s CONSIAT-related investments. No

estimates of the investments required to develop, test, and market FIAPP products and

services by the vendor tier (see Figure 3-2) are included in this subsection. Plans for

incorporating these costs in a future economic impact assessment are described in Section

9.2.

6.3.2.1 New-Technology Introduction Costs

If commercial building owners, managers, and contractors employ the FIAPP alternative

rather than the base case, they can expect to bear three types of additional costs (see

Table 5-6). These costs are: (1) higher evaluation costs; (2) increased costs of adapting

new building products and services to industry use; and (3) increased training costs.

These three costs may be classified as new-technology introduction costs. Ehlen and

Marshall70 define new-technology introduction costs as those costs covering the activities

that bring the material/product from the research laboratory to full field implementation.

New-technology introduction costs include the extra time and labor to design, test,

monitor, and use the new technology. Ehlen’s and Marshall’s research on newtechnology introduction costs is particularly relevant for this economic impact assessment

because they demonstrate that new-technology introduction costs disappear once the

designer is satisfied with the technology’s performance, the technology enters full

implementation, and its application has become routine.71

The establishment of the Virtual FIAPP Testbed will enable manufacturers to bring actual

products and equipment that they have under development, obtain assistance in testing

and evaluating their performance, and perform interoperability tests with other

manufacturers. Thus, new-technology introduction costs are expected to decline over

time. However, in keeping with the conservative approach employed in this economic

impact assessment, these costs are held constant throughout the study period. An

additional cost equal to $32.29/m2 ($3.00/ft2

) is assigned when a commercial construction

project employs the FIAPP alternative. As more information becomes available, the

estimated cost per square meter will be revised (see Section 9.2).

6.3.2.2 Increased Research and Development Costs

BFRL launched a multidisciplinary CONSIAT research effort in Fiscal Year (FY) 1998.

This effort and the eight projects that support the overall CONSIAT effort are described

 

70 Ehlen, Mark A., and Harold E. Marshall. 1996. The Economics of New-Technology Materials: A Case

Study of FRP Bridge Decking. NISTIR 5864. Gaithersburg, MD: National Institute of Standards and

Technology.

71 Ibid. p. 15.
93

in Chapter 3. Because FIAPP products and services are targeted for demonstration in

2004 and commercial availability in 2005, BFRL’s highest level of investment is for

FY2004 through FY2006. Beginning in FY2007, BFRL’s CONSIAT-related

investments will decline rapidly. Beginning in 2010, BFRL moves out of the CONSIAT

major product and into a long-term basic and applied research mode. Consequently,

these costs are not considered part of the CONSIAT impact assessment. By 2010,

BFRL’s research in this area has returned to its long-term base level of funding of

$2,200,000.

It is also important to recognize that BFRL’s research on the application of the ISO’s

Standard for the Exchange of Product (STEP) model data for the process plant industries

was crucial to the establishment of its overall CONSIAT effort. Consequently, BFRL’s

STEP-related investments between FY1994 and FY1997 are included as part of its

CONSIAT-related investments. FY1994 was chosen as the starting point, since by that

time BFRL’s STEP-related research had reached a high-level of maturity.

BFRL’s CONSIAT-related investments are summarized in Table 6-3. The first two

columns of the table record actual investments by Fiscal Year in thousands of dollars for

Fiscal Years FY1994 through FY2000 and estimated investments for FY2001. The

second two columns of the table record estimated investments by Fiscal Year in

thousands of dollars for Fiscal Years FY2002 through FY2009. Note that all values

recorded in Table 6-3 are on a Fiscal Year basis. Because the vast majority of BFRL’s

investment costs are staff-related costs, it is straightforward to convert Fiscal Year dollars

to calendar year dollars. This conversion is necessary, because the values presented in

Chapters 7 and 8 are on a calendar year basis. For example, the estimated FY2002

investment is $3,000,000. Of the $3,000,000 total, 25 %, or $750,000, is allocated to

calendar year 2001, and 75 %, or $2,250,000, is allocated to calendar year 2002.

Table 6-3. BFRL Investment Costs by Fiscal Year

Fiscal Year

1994 – 2001

BFRL

Investment Costs

(In $K)

Fiscal Year

2002 - 2009

BFRL Estimated

Investment Costs

(In $K)

1994 1,326a 2002 3,000

1995 1,507a 2003 3,500

1996 1,487a 2004 4,000

1997 1,733a 2005 4,000

1998 2,344a 2006 4,000

1999 2,018a 2007 3,500

2000 1,917a 2008 3,000

2001 2,256e 2009 2,500

a = actual investment costs; e = estimated investment costs
94

6.4 Key Assumptions and Analysis Issues

A clear statement of the assumed values of key sets of parameters underlying the analysis is

vital to understanding how the analysis was conducted. The assumptions covered in this

section focus on the setting of the assumed values of the following key sets of parameters: (1)

the base year; (2) the starting and ending points in the study period; (3) the discount rate; (4)

the process by which FIAPP products and services diffuse into the marketplace; and (5) the

process by which BFRL’s contribution is measured. The assumed values of these five key

sets of parameters figure prominently in evaluating the economic impacts of FIAPP products

and services. Documenting the assumptions and the rationale behind the setting of the

assumed values of these key sets of parameters is necessary to ensure that: (1) all costs and

savings are discounted to an equivalent time basis for purpose of comparison; and (2) readers

can follow the flow of the analysis, gain insights useful for their own applications, and

reproduce our results.

The base year establishes the anchor point for all cost and savings calculations. The starting

and ending points in the study period define both the scope of the study period—those years

over which costs and savings are tabulated—and the length of the study period—a key

parameter in the AIRR calculation. Because cash flows, both costs and savings, are

distributed throughout the study period, the choice of the discount rate is of central

importance to the analysis. The diffusion process is the critical link between potential cost

savings (see Subsection 6.3.1) and cost savings nationwide (see Section 7.2). The model of

the diffusion process presented in Subsection 6.4.4 provides the basis for calculating year-byyear savings following the introduction of FIAPP products and services. Because BFRL’s

CONSIAT-related research is expected to speed up the introduction of FIAPP products and

services into the commercial marketplace, a process for evaluating the “value” of BFRL’s

contribution is needed. This process is described in Subsection 6.4.6.

In addition to the five key sets of parameters used to make explicit the assumptions of the

economic impact assessment, there are issues linking the baseline analysis to the

sensitivity analysis. These “analysis issues” are concerned with the discount rate, the

diffusion process, measuring BFRL’s contribution, and dealing with uncertainty. The

first three analysis issues provide the necessary “direct” linkage between the baseline

analysis and the sensitivity analysis. They are crucial in measuring how variations about

the baseline input values affect the economic outcome measures. The last analysis issue,

dealing with uncertainty, is the core concept in structuring the sensitivity analysis. This

analysis issue is discussed in Subsection 6.4.5.

6.4.1 Base Year for Computing Benefits and Costs

The base year for computing all FIAPP-related costs and savings is 1997. There are two

reasons, one primary and one secondary, why 1997 was selected as the base year.
95

(1) 1997 marks the year in which BFRL formed an integrated CONSIAT project team.

BFRL is working towards a prototype suite of FIAPP systems and technologies being

tested and deployed in a full-scale demonstration project by 2004. Thus, by using

1997 as the base year, this economic impact study maintains its ex ante (i.e.,

prospective) nature while still being rooted in the present.

(2) 1997 is a year for which authoritative and comprehensive construction industry cost

data are available. Thus, cost conversions for previous years may be accomplished

through the use of a well-defined cost index to equate them to constant 1997 dollars.

6.4.2 Length of the Study Period

The study period begins in 1993 and ends in 2017. Thus, the length of the study period is

25 years. Any costs and/or savings that occur after 2017 are not included. Two factors

were instrumental in determining the beginning and end of the study period.

(1) The study period begins in 1993, which is when BFRL launched its research on the

application of ISO’s Standard for the Exchange of Product (STEP) model data for the

process plant industries. BFRL’s research in this area was instrumental in the

creation of PlantSTEP, Inc. in December 1994.72 However, major investments in the

overall CONSIAT effort did not begin until 1997, when BFRL formed an integrated

CONSIAT project team. BFRL’s CONSIAT-related investments will continue at a

fairly high level until 2007, at which point they will rapidly decline.

(2) The end of the study period is 2017. By 2004, BFRL will be completing a full-scale

demonstration project. By 2005, the first commercial applications of FIAPP products

and services are anticipated (i.e., applications other than in demonstration projects).

Thus, 2005 marks the point at which FIAPP products and services penetrate the

commercial marketplace. By 2017, the use of FIAPP products and services is

expected to be widespread (i.e., at least 50 % of the potential commercial marketplace

will have been penetrated).

6.4.3 Discount Rate

The baseline analysis for the CONSIAT economic impact assessment uses a real rate of

7 % to convert dollar amounts to present values. This rate is specified in Section 8.b of

OMB Circular A-9473 as the rate for all benefit-cost analyses of public investments and

regulatory programs that provide benefits or incur costs to the general public. The use of

 

72 PlantSTEP is an industrial consortium of companies that own, design, build, operate, and maintain

process plants and companies that supply equipment, materials, and information technology for the process

and construction industries. The primary focus of PlantSTEP is to develop and support implementation of

data exchange standards based on STEP.

73 Executive Office of the President. 1992. OMB Circular A-94. Washington, DC: Office of Management

and Budget.
96

a 7 % real discount rate also facilitates comparisons of the results of the CONSIAT

baseline analysis with the results of the baseline analyses of the previous economic

impact assessments. For purposes of this analysis, all CONSIAT-related research costs

are classified as a public investment. The benefits that accrue to the public are in the

form of cost savings and increased sales revenues due to reductions in delivery time.

OMB recommends that separate analyses be used to evaluate the sensitivity of key

economic measures to variations in the discount rate.74 The sensitivity analysis presented

in Chapter 8 evaluates the implications of raising the discount rate to 10 % or lowering

the discount rate to 2 %. The 2 % to 10 % range of values for the real discount rate was

chosen to bracket the historical values of real treasury interest rates. These rates are

periodically updated by OMB and published in Appendix C of OMB Circular A-94; they

apply to government lease-purchase and cost-effectiveness analyses. Although these

rates do not apply to regulatory analyses or benefit-cost analyses of public investments,

they provide a useful frame of reference for establishing minimum and maximum values

for the real discount rate. All values of the discount rate used in this report are real rates,

since constant dollar estimates of benefits and costs are used.

6.4.4 Diffusion Process

Facts and data are essential components in any rigorous analysis. Factual information on

the commercial sector was tabulated from published sources (see Section 4.2). These

data provide the basis for estimating the “potential” benefits and cost savings associated

with the use of the FIAPP products and services in commercial buildings (see Subsection

6.3.1). However, to develop realistic estimates of cost savings nationwide, it is also

necessary to generate estimated values for the annual proportion of commercial buildings

employing FIAPP products and services for new construction activities and for additions

and alterations. To generate estimates of cost savings nationwide, information on

potential benefits and cost savings and on cost increases and benefit reductions (e.g.,

new-technology introduction costs) must be coupled with a model of the diffusion

process. Much of the discussion in this subsection and in Section 7.2 of the next chapter

is aimed at establishing an audit trail for how the values of the key parameters of the

diffusion process were established and employed in the economic impact assessment.

The focus of this subsection is on how the diffusion process is modeled (i.e., the form of

the model and its key parameter values). Section 7.2 focuses on how the diffusion model

is employed in the economic impact assessment.

An economy is not affected in any material way by a new technology until the use or

ownership of that technology is widespread. This spread of a new technology is a topic

usually referred to as technological diffusion. It is modeled via a diffusion process. The

underlying basis for the study of technological diffusion is to rationalize why, if a new

technology is superior, it is not taken up immediately by all potential users.

The empirical analysis of diffusion processes is a vast and complex subject. Although a

 

74 Ibid., p. 7.
97

full treatment of the topic is beyond the scope of this report, four factors affecting the

diffusion process are worth noting. Readers interested in thorough treatments of this

important subject, including case studies, are referred to the books by Stoneman75 and

Mansfield.76

First, new technology and its adoption involve uncertainty. Thus, the attitude of decision

makers to uncertainty needs to be considered. The degree of uncertainty may be related

to the level of use of the new technology and to how learning proceeds.

Second, how learning proceeds affects the diffusion process in a number of ways. It can

involve learning about the existence of a new technology or learning about its true

characteristics. For example, firms might learn about how to use the new technology to

produce new or current products at lower cost. For a given initial state of knowledge, the

faster that learning occurs, the higher the rate of diffusion.

Third, during a diffusion process, how learning proceeds may not be the only factor

changing. The good itself may be improving. This improvement may have a doubleedged effect on diffusion: a direct effect, stimulating greater use; and an indirect effect,

whereby expectations of future advances may lead to the postponement of adoption.

Fourth, to a large degree the adoption decision for the firm will be related to expected

profitability, which in turn will be dependent upon a number of factors. Thus differences

between firms will be important, as may be the behavior of the industry supplying any

new goods. The market structure of the user and supplying industries (i.e., situations

involving imperfect competition) are also important.

The most widely accepted model of technology diffusion was developed by Edwin

Mansfield. Consequently, the Mansfield model is employed in the CONSIAT economic

impact assessment. The Mansfield model estimates the proportion of potential users who

have adopted the new technology by time t. The mathematical representation of the

model is

[ ] 1 ( ) ( ) 1 − − = + t P t e α β

where

P(t) = the proportion of potential users who have adopted the new technology by

time t,

e = Euler’s number, the base of the natural system of logarithms,

 

75 Stoneman, Paul. 1983. The Economic Analysis of Technological Change. New York: Oxford University

Press.

76 Mansfield, Edwin. 1995. Innovation, Technology and the Economy: Selected Essays of Edwin Mansfield.

2 vols. Economists of the Twentieth Century Series. Aldershot, UK: Elgar.
98

α = the location parameter, and

β = the shape parameter ( β > 0 ).

A plot of P(t) produces an S-shaped logistics curve, which is asymptotic to 0 as the value

of t gets small and to 1 as the value of t gets large. Because the diffusion of a new

technology may not achieve 100 % penetration of the marketplace, P(t) must be modified

to reflect the level at which the potential market is saturated. The version of the

Mansfield model employed in this report uses a subscript η to designate the market

saturation level. The mathematical representation of the model is

[ ] 1 ( ) ( ) 1 − − = + t P t e α β

η η

where

Pη(t) = the proportion of potential users who have adopted the new technology by

time t,

η = the market saturation level,

e = Euler’s number, the base of the natural system of logarithms,

α = the location parameter, and

β = the shape parameter ( β > 0 ).

An extensive review of the economics literature on the diffusion process produced

candidate values for α and β. Readers interested in case studies based on the Mansfield

model that are useful in specifying values for α and β are referred to Mansfield’s

collection of articles.77 An additional factor used to specify the values of α and β is the

length of time it takes for Pη(t) to reach 50 % of its potential market. Due to the

relationship between the Mansfield model and the logistics distribution, the value at

which Pη(t) reaches 50 % of its potential market has a closed-form relationship based

solely on the values of α and β. If we assume t = 1 is the time at which the technology is

first introduced, then α /β is the number of years it takes that technology to reach 50 % of

its potential market. In order to get a meaningful value of t, it is necessary to constrain α

to be positive (i.e., α > 0 ).

The values of the ratio α /β vary from 4 years to 16 years in a wide range of articles

published in the economics literature (see Mansfield,78 Mansfield et al,

79 and Simon80 ).

 

77 Mansfield, Innovation, Technology and the Economy, Vol. II, pp. 3-83. 78 Ibid., pp. 63-72. 79 Mansfield, Edwin, John Rapoport, Anthony Romeo, Edmond Villani, Samuel Wagner, and Frank Husic.

1977. The Production and Application of New Industrial Technology. New York: W. W. Norton &

Company, Inc.
99

Consequently, this report uses a value of 8 for the ratio α /β as its baseline value. The

corresponding baseline values for α and β are 4.0 and 0.5, respectively.

The estimated value for η was set equal to 0.4. Thus, the baseline value for η is 0.4.

This means that FIAPP products and services will eventually be employed in projects

totaling 40 % of the value of construction put in place for large commercial buildings.

Recall that a large commercial building is defined as a building with more than 4,645.1

m

2

 of floor area (50,000 ft2). The estimated value for η for this class of commercial

buildings is based on data contained in the 1995 CBECS. Specifically, the value of η is

set to approximate the average proportion of floorspace in large commercial buildings

employing either an energy management and control system81 (EMCS) or energy

efficient motors.82 These technologies were selected because they are mature (i.e., their

use is sufficiently widespread to “approximate” market saturation) and they embody a

combination of hardware and software technologies that have significant information

resource management requirements.

From the discussion that follows, it should be clear that this estimate is rather

conservative. Thus, the estimated savings nationwide and the value of BFRL’s

contribution may be considered to be lower bound estimates.

Penetration of the market by FIAPP products and services for commercial sector projects

will be driven by two mutually reinforcing factors. These factors are: (1) the desire of the

owners of large commercial buildings to use information technology as a tool for

reducing both life-cycle costs and cycle time; and (2) the need for contractors to improve

their productivity and profitability in an increasingly competitive global marketplace.

The owners of commercial buildings have a tremendous incentive to improve the delivery

process for construction projects. This point is underscored by a Business Roundtable

report that summarizes data on over 60 major companies’ project systems.83 When

examining the relative performance of these systems, some important trends appear.

Most important from a business perspective, the gap between the best and

the worst has widened over the past several years. Some companies have

learned how to acquire consistent and significant comparative advantages

from their capital project systems, while others find themselves

increasingly at a disadvantage. ...

 80 Simon, P. 1975. Models of Process Diffusion and Entry in the U.S. Chemical Industry. Ph.D. dissertation,

University of Pennsylvania.

81 An EMCS is an energy management feature that uses mini/micro-computers, instrumentation, control

equipment, and software to manage a building’s use of energy for heating, ventilation, air conditioning,

lighting, and/or business-related processes. These systems can also manage fire control, safety, and

security.

82 A demand-side management program designed to promote high-efficiency motors, adjustable speed

drives, or variable speed motors to ensure that the motor’s speed and size is properly matched to the load

placed on the motor.

83 The Business Roundtable. 1997. The Business Stake in Effective Project Systems. Washington, DC: The

Business Roundtable.
100

In relative cost performance, the best company is spending 72 cents of the

industry average dollar for the same functional scope. The fastest

company takes only 70 % as long as the industry average to bring a project

from a business idea to a facility in production.84

Although the results published by The Business Roundtable are associated with industrial

facilities, research by CII has demonstrated similar trends for commercial buildings.

Specifically, comparisons between the best performing 25 % of commercial building

projects and the worst performing 25 % show differences in cost growth of 7.7 % and

construction phase duration of more than 30 %.85 Another CII study showed that CII

owners had a significant financial incentive to partner with CII contractors in order to

reduce cycle time and achieve better control over cost growth.86

Contractors who wish to survive and prosper will be pushed to employ design and

information (i.e., FIAPP-like) technologies from two sources. First, the owners of large

commercial buildings will increasingly demand the use of these technologies as a

requirement for partnering (i.e., owners are committed to the use of these technologies

both in their core business processes and in their supply chain management process).

This point was touched on previously from the owner’s perspective and will not be

explored further here. Second, contractors have experienced a significant, sustained

decline in productivity vis-à-vis the rest of the economy.87 This decline in productivity

has adversely affected profitability, causing profit margins to shrink. Thus, contractors

see the use of FIAPP products and services as a competitive edge for their business,

enabling them to both increase net income and reduce project duration. The former

directly impacts profitability, whereas the latter creates the opportunity for a greater

volume of work.

Because large contractors dominate the markets for engineering design and construction

of large commercial buildings, their adoption and use of FIAPP products and services are

likely to be both rapid and extensive. The basis for the previous statement may be seen

by noting that the top 50 contractors in 1997 installed $28.7 billion worth of commercial

sector projects.88 Although these figures represent global revenues, a significant share of

these commercial sector projects was domestic.

The specification of the baseline values of the diffusion model is not complete until a

time of first use is made explicit. As noted earlier, the time of first use corresponds to the

value at which t = 1. The baseline value for the time of first use is based on the

assumption that the demonstration project will be completed in 2004. Once the

 

84 Ibid. pp. 2-3. 85 Thomas, Macken, and Lee, Impacts of Design/Information Technology on Building and Industrial

Projects. 86 Thomas, Stephen R. 1998. Benchmarking and Metrics Summary for 1997. BMM 97-1. Austin, TX:

Construction Industry Institute.

87 Teicholz, Paul M. “Reverse Productivity Declines,” ENR, Vol. 243, No. 23, December 13, 1999, p. 59. 88 Tulacz, Gary J. “Top 400 Contractors,” ENR Sourcebook, November 1998, pp. 4-10.
101

demonstration project has been completed, FIAPP products and services will become

available commercially. Thus, the baseline value for the time of first use is 2005.

The values of α and β specify the rate of adoption of FIAPP products and services in

commercial buildings, whereas the value of η specifies the size of the potential market

for these products and services. Consequently, once the time of first use is made explicit,

it becomes possible to estimate the annual proportion of construction-related expenditures

in the commercial sector covered by FIAPP products and services. For any given year,

this amount is equal to the product of Pη(t) for that year and the total value of

construction-related expenditures in the commercial sector (i.e., $205.7 billion) and the

constant of proportionality for large commercial buildings (i.e., 0.44). Table 6-4 records

the value of Pη(t) for values of t from 0 to 16 (i.e., from 2004 to 2020).89 Note that the

years shown on the table extend past the end of the study period.

Table 6-4. Baseline Case of Pη(t) (α = 4.0, β = 0.5, η = 0.4)

Year t Pη(t)

2004 0 0.0000

2005 1 0.0078

2006 2 0.0190

2007 3 0.0303

2008 4 0.0477

2009 5 0.0730

2010 6 0.1076

2011 7 0.1510

2012 8 0.2000

2013 9 0.2490

2014 10 0.2924

2015 11 0.3270

2016 12 0.3523

2017 13 0.3697

2018 14 0.3810

2019 15 0.3883

2020 16 0.3928

 

89 The values of Pη (t) corresponding to years 2004 (i.e., t = 0) and 2005 (i.e., t = 1) have been adjusted to

reflect the assumption that FIAPP products and services are not expected to be available in 2004 and may

not be available at the start of 2005. This is accomplished via a multiplicative adjustment factor, see

Equation 7.1 in Section 7.2, that takes on a value of 0 for t < 1, a value of 0.667 for t = 1, and a value of 1.0

for t ≥ 2.
102

The diffusion model, as specified above and used in the baseline analysis, is plotted in a

graphical form in Figure 6-6. The trace of Pη(t) is shown as a solid line in Figure 6-6.

The vertical axis of Figure 6-6 records the values of Pη(t). The values on the vertical axis

ranges from 0 to η. The horizontal axis of Figure 6-6 records the values of t and the

years for which the value of Pη(t) is calculated. Recall that in the baseline analysis t = 1

corresponds to the year 2005.90 Note that the years shown on the horizontal axis extend

past the end of the study period. This is done to show that Pη(t) does not approach the

market saturation level, η, until well after the study period is over. Thus, substantial cost

savings due to the use of FIAPP products and services will continue to accrue well after

the end of the study period. Once again, this leads to the conclusion that the estimated

savings nationwide are a lower-bound estimate.

Figure 6-6. Baseline Case of Pη(t) by t(year)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0(2004)1(2005)2(2006)3(2007)4(2008)5(2009)6(2010)7(2011)8(2012)9(2013)

10(2014)11(2015)12(2016)13(2017)14(2018)15(2019)16(2020)

t(year)

Pη(t)

Much of the sensitivity analysis is concerned with the diffusion model (see Chapter 8).

As such, ranges of values were specified for α, β, η, and the time of first use. The ranges

for α and β were selected based on values of α and β published in the economics

literature and their implications for the values of the ratio α /β also published in the

economics literature. The range of values for α used in the sensitivity analysis is a low of

3 and a high of 5 (i.e., 3 ≤ α ≤ 5). The range of values for β used in the sensitivity

analysis is a low of 0.4 and a high of 0.6 (i.e., 0.4 ≤ β ≤ 0.6). These ranges of values for

 

90 As noted earlier, the value of Pη(t) for t = 1 is multiplied by 0.667 to reflect the likelihood that FIAPP

products and services may not be available at the start of the first year.
103

α and β result in ranges for the ratios α /β which are consistent with the values published

in the economics literature (i.e., 5.0 ≤ α /β ≤ 12.5).

The range of values for η is based on information published in the 1995 CBECS. These

values range from a low of 30 % to a high of 50 % (i.e., 0.3 ≤ η ≤ 0.5). The range of

values for the time of first use are based on “targeted” times (i.e., years) at which FIAPP

products and services will be available commercially. These times range from a low of

2004 to a high of 2007. That is, the earliest date at which FIAPP products and services

are commercially available is in the year 2004 and the latest date is 2007. The alternative

times of first use are specified by a discrete distribution, also known as the multinomial

distribution. The discrete probabilities for each year are: 2004, 0.125; 2005, 0.5; 2006,

0.25; and 2007, 0.125.

6.4.5 Dealing with Uncertainty

Uncertainty enters into a benefit-cost analysis in three main ways. First, the value of cash

flows (i.e., benefits, costs, and savings) may not be known with certainty. For example, a

new technology may not be well understood by many potential users, implying that their

benefits of adopting the technology may be subject to considerable variability.

Consequently, decision makers are presented with a range of potential benefit values

(e.g., high, moderate, and low). As the technology becomes better known, this range of

values may be reduced (i.e., uncertainty, in the form of benefit variability, is being

reduced with time as new information becomes available). In addition, variations in the

discount rate affect the present value of any cash flows which do not occur in the base

year.

Second, the timing of cash flows may not be known with certainty. In the case of a new

technology, the process by which the technology diffuses to firms and households may

take many time paths.91 For example, one time path might imply slow adoption at first

followed by a period of rapid adoption. Such might be the case if, shortly after

introduction, the technology were adopted as a standard. Alternatively, the new

technology might enjoy a brief period of rapid adoption followed by a relatively long

period of slow adoption. Such might be the case if, after introducing the new technology,

there were a series of product improvements that caused many potential users to adopt a

“wait and see” attitude.

Third, the value, timing, and magnitude of cash flows may not be known with certainty.

This “composite” source of uncertainty is more complex than the two cases just

 

91 The time paths by which a new technology may diffuse have several characteristics that are important.

First, there is a time of first use (i.e., when the technology is introduced to the market place). If the time of

first use is considered fixed, then it is the same for all that technology’s time paths. Second, for each time

path, there is a rate of adoption; the rate of adoption affects the slope of the time path. It is important to

recognize that the slope of the time path need not be the same at different points on the time path. Finally,

there is a level of adoption that prevails when the market reaches saturation.
104

discussed. It includes three issues related to the time path overlaid by variability in

benefits, costs, and savings. The three time path issues are related to the time of first use

(i.e., when the technology is introduced to the market place), the rate of adoption over the

time path, and the level of adoption that prevails when the market reaches saturation.

Although the introduction of a new technology can be expected to result in variability of

benefits, costs, and savings for users which adopt it (i.e., there is some uncertainty about

the values of these cash flows and, via the discount rate, their present values), the case at

hand is more complex. Variations in the time of first use and the rate of adoption are the

principal sources of variability in the timing of cash flows. Variations in the level of

adoption enter as factors affecting both the values and the magnitudes of cash flows.

This is because the level of adoption comes into play as a multiplicative factor applied to

any given time path. While different times of first use and rates of adoption affect the

timings of cash flows, different adoption levels affect the values (i.e., due to its being

overlaid by the variability in benefits, costs, and savings) and magnitudes (i.e., due to its

affect on the size of the potential market) of these cash flows. Consider the case of the

direct benefits to users from adopting a new technology. Other things being equal, higher

levels of adoption result in larger benefit streams and higher variability (i.e., a wider

range of values) of those benefit streams across all time paths than do lower levels of

adoption.

6.4.6 Measuring BFRL’s Contribution

This section describes the process used to measure the “value” of BFRL’s contribution to

the development of FIAPP products and services for use in commercial buildings. It

begins with a review of the nature of BFRL’s contribution.

BFRL’s contribution serves two vital roles. One is that of a facilitator, and the other is

that of a developer of key FIAPP enabling technologies. Both roles are crucial if

commercial products and services are to be developed in a timely manner.

BFRL’s role as facilitator has three facets. First, BFRL is participating in a consortium

of facility owners, contractors, construction product and equipment manufacturers, and

service providers interested in producing, testing, demonstrating, and buying and selling

FIAPP products and services. Second, BFRL is participating in the Virtual FIAPP

Testbed to facilitate the development and evaluation of new products and systems by

manufacturers and external service providers. Third, BFRL is working towards an

operational prototype FIAPP being tested and deployed in a full-scale demonstration

project by 2004.

BFRL’s role as developer of key FIAPP enabling technologies is extensive and

pervasive; it spans all eight projects in the integrated CONSIAT project team (see Section

3.2). However, three areas of BFRL’s research and development effort are particularly

important, since they provide platforms on which vendors can develop commercial

products and services. First, BFRL is developing and testing standard communication

protocols for the open exchange of information. Second, BFRL is developing advanced
105

measurement technologies. Third, BFRL is developing interoperability testing

procedures to facilitate the development and introduction of FIAPP products and services

into the marketplace.

This review of the nature of BFRL’s contribution makes it clear that BFRL is a catalyst in

the development of FIAPP products and services. Does this mean that FIAPP products

and services would not be developed without BFRL’s participation? The answer to that

question is an unequivocal “No.” Eventually, FIAPP products and services would

become commercially available. Would they have the same capabilities? The answer to

that question is a qualified “Probably not.” The reasoning stems from the fact that the

nature of BFRL’s dual role is one that few organizations can fill. Consider the case of an

enabling technology. Few if any vendors will invest in enabling technologies, since they

can not adequately recapture their investment. In fact, other vendors might be able to

employ the enabling technology to develop their own proprietary products. BFRL and

NIST do not have this problem, since a key part of their mission is to promote

competitiveness through the development of enabling technologies. A similar reasoning

holds for BFRL’s role as a facilitator. Thus, BFRL’s contribution both serves to speed up

the introduction of FIAPP products and services and to result in products and services

with better understood properties and, in all likelihood, better capabilities. The remainder

of this section focuses on how to measure the value of BFRL’s contribution in speeding

up the introduction of FIAPP products and services.

Because BFRL’s research effort is expected to result in a faster introduction of FIAPP

products and services into the commercial marketplace, those savings which would have

been foregone in the event of a delay are attributable to BFRL. Information from subject

matter experts and similar economic impact assessments92, 93 was used to develop an

estimate of how much the commercial introduction of FIAPP products and services

would have been delayed, were it not for BFRL’s dual role as a facilitator and developer

of key FIAPP enabling technologies. Without BFRL’s involvement, the commercial

introduction of FIAPP products and services is estimated to occur in 2009, a delay of four

years (i.e., commercial introduction in 2009 rather than in 2005). Therefore, any savings

prior to the “delayed” introduction of FIAPP products and services in 2009 would have

been foregone. Such an accounting framework may be handled through use of a 0-1

weighting factor. For those years in which savings are attributable to BFRL, the

weighting factor takes on a value of 1. For all years after the “delayed” introduction of

FIAPP products and services in 2009, the weighting factor takes on a value of 0.

An important part of the sensitivity analysis is concerned with measuring changes in the

value of BFRL’s contribution. The value of BFRL’s contribution is measured through

the use of a 0-1 weighting factor tied to BFRL’s efforts to speed up the commercial

introduction of FIAPP products and services. These efforts vary in their funding

requirements, depending on whether commercial introduction is targeted for 2004, 2005,

2006, or 2007. The estimated BFRL investment costs, as a function of the year targeted

 

92 Chapman and Weber, A Case Study of the Fire Safety Evaluation System, pp. 31-42. 93 Chapman, Robert E. 1999. Benefits and Costs of Research: A Case Study of Cybernetic Building Systems.

NISTIR 6303. Gaithersburg, MD: National Institute of Standards and Technology.
106

for first commercial use, are summarized in Table 6-5. Reference to the table shows that

a very aggressive level of funding is required if FIAPP products and services are to

become available in 2004. However, if these products become available in 2004, then the

BFRL weighting factor takes on a value of 1 for 2004 through 2008 (i.e., a “delay” of

five years is avoided) and a value of 0 from 2009 until the end of the study period. On

the other hand, if funding were held constant, then commercial introduction is not

expected to occur until 2007. In this scenario, the BFRL weighting factor would take on

a value of 1 for 2007 and 2008 (i.e., a “delay” of two years is avoided) and a value of 0

from 2009 until the end of the study period.

Table 6-5. Estimated Investment Costs as a Function of the Year of First

Commercial Use

BFRL Estimated Investment Costs (In $K) as a

Fiscal Year Function of the Year of First Commercial Use

2004 2005 2006 2007

2002 5,000 3,000 2,500 2,200

2003 6,000 3,500 3,000 2,200

2004 6,000 4,000 3,500 2,200

2005 6,000 4,000 4,000 3,000

2006 5,000 4,000 4,000 4,000

2007 3,500 3,500 4,000 4,000

2008 3,000 3,000 3,500 4,000

2009 2,500 2,500 3,000 3,000

Each of the four scenarios (i.e., year targeted for first commercial use) is examined in

Chapter 8. A two-stage analysis is employed. The first stage analysis focuses on

measuring the return to the public from each scenario, assuming that the first commercial

use of FIAPP products and services occurs in the year targeted. The second stage

evaluates the consequences of targeting commercial introduction for one year but not

achieving it until later. This analysis examines the critical relationship between risk and

return for each scenario. The second stage analysis employs both deterministic and

Monte Carlo techniques to measure the expected outcomes associated with no lag, a one

year lag, and a two year lag between the year targeted for commercial introduction and

the year in which commercial introduction was achieved. The alternative lags are

specified by a discrete distribution. The discrete probabilities for each lag are: no lag,

0.6; a one year lag, 0.25; and a two year lag, 0.15.
107

7 Baseline Analysis of Economic Impacts

The baseline analysis presented in this chapter is the reference point for the CONSIAT

economic impact assessment. Recall that in the baseline analysis, all data entering into

the calculations are set at their likely values (see Section 2.1.1). Throughout this report,

likely value and baseline value are used interchangeably. Thus, the baseline values

represent a fixed state of analysis. The term baseline analysis is used to denote a

complete analysis in all respects but one; it does not address the effects of uncertainty.

Sensitivity analysis measures the impact on project outcomes of changing the values of

one or more key variables about which there is uncertainty. Sensitivity analysis is the

subject of Chapter 8.

The results of the baseline analysis portion of the CONSIAT economic impact

assessment are presented for two basic cases (see Exhibit 7-1). First, are the cost savings

nationwide achievable through the use of FIAPP products and services in commercial

buildings. Second, are the cost savings attributable to BFRL and the return on BFRL’s

CONSIAT-related investment costs.

Key economic measures show the present value of savings (PVS), the present value of

net savings (PVNS), the savings-to-investment ratio (SIR), and the adjusted internal rate

of return (AIRR) that are attributable to BFRL’s CONSIAT-related research,

development, and deployment efforts (see Chapter 3). These values are derived by

measuring how cost savings nationwide would have been reduced if BFRL had not been

involved in the development of FIAPP products and services (see Section 6.4.6).

The results of the baseline analysis demonstrate that the use of FIAPP products and

services will generate substantial cost savings to commercial building owners and

managers and to contractors engaged in the construction of those buildings. The present

value of savings nationwide expected from the use of FIAPP products and services is

nearly $1.4 billion (measured in 1997 dollars). Furthermore, because of BFRL’s

involvement, FIAPP products and services are expected to be commercially available in

2005. If BFRL had not participated in the development of FIAPP products and services,

the commercial introduction of FIAPP products and services is expected to be delayed

until 2009. Consequently, potential cost savings accruing to commercial building owners

and managers and to contractors over the period 2005 through 2008 would have been

foregone. The present value of these cost savings is approximately $120 million. These

cost savings measure the value of BFRL’s contribution for its CONSIAT-related

investment costs of approximately $29.1 million. Stated in present value terms, every

public dollar invested in BFRL’s CONSIAT-related research, development, and

deployment efforts is expected to generate $4.13 in cost savings to the public (i.e., an SIR

of 4.13). The annual percentage yield (AIRR) from BFRL’s CONSIAT-related

investments over the study period is 13.3 %.
108

Exhibit 7-1. Summary of Economic Impacts of BFRL Research on Construction

Systems Integration and Automation Technologies in Commercial Buildings

1.a Significance of Research Effort:

Owners of commercial buildings and contractors engaged in the

construction of those facilities are pressing for reductions in delivery time

as a means of improving their competitive positions. Owner concerns over

both the first costs and life-cycle costs of commercial buildings and

tightening profit margins for contractors are also affecting the competitive

positions of each stakeholder. One means of improving the competitive

positions of each commercial sector stakeholder is through the

development, adoption, and use of fully-integrated and automated project

process (FIAPP) products and services. Characteristics of FIAPP products

and services include one-time data entry; interoperability with design,

construction, and operation processes; and user-friendly input/output

techniques. BFRL’s focused research efforts, its collaboration with the

Construction Industry Institute (CII), and its participation in the FIATECH

Consortium are designed to deliver FIAPP products and services to CII

members and the rest of the construction industry.

BFRL’s focused research on Plant STEP, construction metrology, and

economic analysis led BFRL to form an integrated CONSIAT project team

in 1997. In addition, BFRL is uniquely positioned to collaborate with

industry on the development of FIAPP products and services and to

provide a forum for conducting interoperability testing. BFRL is working

towards a prototype FIAPP being tested and deployed by 2004. To achieve

this goal, BFRL is working with building owners, contractors, equipment

and systems manufacturers and service providers, software developers,

building managers, trade associations, professional societies, standards

organizations, university researchers, and other government agencies.

Without BFRL’s participation, it is likely that the introduction of FIAPP

products and services will be delayed for at least four years.

1.b Key Points:

• Pressure to reduce

delivery time and lifecycle costs has

created a potential

market for FIAPP

products and services.

• BFRL is uniquely

positioned to

collaborate with

industry on the

development of

FIAPP products and

services and to

provide a forum for

conducting

interoperability

testing.

• Without BFRL’s

participation, it is

likely that the

introduction of FIAPP

products and services

will be delayed for at

least four years.

2. Analysis Strategy: How Key Measures are Estimated

The objective of the study is to (1) evaluate, for the period 1993 through 2017, the net cost savings

due to the adoption and use of FIAPP products and services in commercial buildings, and (2) estimate

BFRL’s contribution to these net cost savings. The approach is to estimate in 1997 present value

(PV) dollars:

Present Value Cost Savings Nationwide in commercial buildings that employ FIAPP products and

services. PV cost savings nationwide are estimated for each year from 1993 to 2017 and summed.

Present Value Savings (PVS) attributable to BFRL by including the savings only for those years

that accrued due to BFRL’s participation (i.e., 1993 to 2008).

Present Value Net Savings (PVNS) attributable to BFRL by subtracting from BFRL PVS the

present value of BFRL’s investment costs (PV Costs). A PVNS >0 indicates an economically

worthwhile project.

Two additional measures are also estimated:

Savings-to-Investment Ratio (SIR) attributable to BFRL by taking the ratio of BFRL PVS to

BFRL PV Costs. A ratio >1 indicates an economically worthwhile project.

Adjusted Internal Rate of Return (AIRR), the annual rate of return over the study period on

BFRL’s investment. An AIRR > the discount rate indicates that the project is economically

worthwhile.
109

Exhibit 7-1. Summary of Economic Impacts of BFRL Research on Construction

Systems Integration and Automation Technologies in Commercial Buildings

(continued)

2. Analysis Strategy: Data and Assumptions

• The period over which costs and savings are measured begins in 1993 and ends in 2017. Hence

the length of the study period is 25 years.

• The base year is 1997, and all amounts are calculated in PV 1997 dollars.

• The discount rate is 7 % (real), which is the discount rate currently in effect for government

projects.

• Estimates of cost savings associated with the adoption and use of FIAPP products and services are

based on construction industry data and information provided by industry experts.

• Without BFRL’s participation, the introduction of FIAPP products and services will be delayed

by four years.

3.b Key Results:

1997 Dollars

($ amounts in millions)

Cost Savings Nationwide:

 $1,377.9

Savings Attributable to

BFRL:

PVS $120.5

PV Costs $29.144

PVNS $91.3

SIR 4.13

AIRR 13.3 %

3.a Calculation of Savings, Costs, and Additional Measures

Savings and Costs

Present Value Cost Savings Nationwide (PVCSN):

Sum from 1993 to 2017 of present value of cost savings nationwide

by year

= $1,377.9 million

Present Value Savings (PVS) Attributable to BFRL:

Sum from 1993 to 2008 of present value of cost savings nationwide

by year

= $120.5 million

Present Value Investment Costs (PV Costs) to BFRL:

Sum from 1993 to 2017 of present value of investment cost to BFRL

by year

= $29.144 million

Present Value Net Savings (PVNS) Attributable to BFRL:

Difference between present value savings (PVS) attributable to

BFRL and present value of investment costs (PV Costs) to BFRL

 = $120.5 - $29.144 = $91.3 million

Additional Measures

SIR of BFRL Contribution:

Savings-to-Investment Ratio on BFRL investment

 = $120.5/$29.144 = 4.13

AIRR of BFRL Contribution:

Adjusted Internal Rate of Return on BFRL investment

 = (1+ 0.07) * 4.131/25 – 1 = 0.133

3.c Traceability:

ASTM Discount Factor

Tables (PVCSN and PVS)

ASTM E 917 (PV Costs)

ASTM E 1074 (PVNS)

ASTM E 964 (SIR)

ASTM E 1057 (AIRR)
110

7.1 BFRL Summary Impact Statement

Exhibit 7-1 is a summary impact statement, covering the background, approach, and

results of the baseline analysis. Exhibit 7-1 utilizes the framework introduced in Chapter

2 (see Exhibit 2-1).

7.2 Cost Savings Nationwide

This section combines three types of information presented in Sections 6.3 and 6.4 to

generate a baseline estimate of cost savings nationwide. These three types of information

are related to: (1) the diffusion model developed in Section 6.4.4; (2) the cost savings due

to reductions in first costs, maintenance and repair costs, and construction-related

accidents and the increases in net income for owners and contractors due to reductions in

delivery time and higher contractor profit margins; and (3) new-technology introduction

costs. These three types of information are combined via three sets of calculations to

estimate “annual” cost savings to the nation. Estimates are produced for each year from

1993 to 2017. Each year’s cost savings is then discounted to a present value and summed

to get the present value of cost savings nationwide. The present value of cost savings

nationwide is a key indicator of the merits of employing FIAPP products and services in

commercial buildings. The results of the baseline analysis show that cost savings

nationwide are nearly $1.4 billion ($1,378 million in present value 1997 dollars). Each

set of calculations used to produce the estimate of cost savings nationwide is summarized

through a table and described in the text that follows.

Table 7-1 summarizes information derived from the diffusion model. To help readers

reproduce the values recorded in Columns 2 through 5 of Table 7-1, the formula used to

estimate the annual value for each column heading is given (see Equations 7.1 through

7.4). In each formula, t corresponds to the Year (see Column 1 of Table 7-1) for which

values are calculated (i.e., t = 1 corresponds to Year = 2005). Terms used in each

formula are defined the first time they appear. Note that several equations use the same

term (e.g., the value of commercial sector construction employing FIAPP products and

services, VIPCF(t), appears in Equations 7.2, 7.3, and 7.4).

Column 1 of Table 7-1 lists each year of the study period from 1993 through 2017.

Column 2 of Table 7-1 records information on the calculated values of the diffusion

model, Pη(t), for each year. Recall that t = 1 corresponds to the year in which FIAPP

products and services are expected to become commercially available (i.e., t = 1

corresponds to the year 2005). Thus, Pη(t) = 0 for all values of t less than 1. Recall that

in the first year (i.e., t = 1) the “unadjusted” value of Pη(t) is multiplied by 0.667 to

reflect the likelihood that FIAPP products and services may not be available at the start of

the first year. No adjustments are made to the values of Pη(t) for values of t ≥ 2. In

Column 3, the diffusion model is combined with information on the 1997 value of

construction put in place for the commercial sector as a whole and the proportion of

commercial sector floorspace associated with large commercial buildings (i.e., those
111

whose floorspace exceeds 4,645.1 m2 (50,000 ft2)) to generate annual estimates of the

value of commercial sector construction put in place which employs FIAPP products and

services. Estimates are given for each year from 1993 to 2017. To get the annual value

of construction put in place which employs FIAPP products and services, VIPCF(t), the

value of Pη(t) in Column 2 is multiplied by the 1997 value of construction put in place for

the commercial sector as a whole and the proportion of commercial floorspace associated

with large commercial buildings. The value of commercial sector construction put in

place, expressed in 1997 dollars, is held constant at $205.7 billion throughout the period

during which FIAPP products and services diffuse into the marketplace (i.e., 2005

through 2017). The proportion of commercial floorspace associated with large

commercial buildings is held constant at 0.44 (i.e., 44 %) throughout the period during

which FIAPP products and services diffuse into the marketplace (i.e., 2005 through

2017). Columns 4 and 5 record estimates of the amount of commercial floorspace

receiving FIAPP installations in millions of m2, UCM(t), (Column 4) and millions of ft2,

UCF(t), (Column 5).

Proportion of Commercial Sector Investments Covered by

FIAPP Products and Services

( ) { [ ] 1 }* ( ) 1 ( ) P t e A t t − − = + α β

η η 7.1

where

( ) [ ] 1

1 − − + t e α β η = the “unadjusted” value of Pη(t); and

A(t) = the adjustment factor, where A(t) = 0 for t < 1,

 A(t) = 0.667 for t = 1, and A(t) = 1.0 for t ≥ 2.

Value of FIAPP-Related Investments In Millions of 1997 Dollars

VIP (t) P (t) *VIP (t) * F (t) CF = η C C 7.2

where

VIPC (t) = the value of commercial sector construction put in place in

 millions of 1997 dollars ($205,703); and

FC(t) = the proportion of commercial floorspace associated with large

 commercial buildings (0.44).
112

New FIAPP Installations in Millions of Square Meters

UCM (t) = VIPCF (t)/CPSM 7.3

where

CPSM = the average cost per square meter for commercial floorspace

 ($1,050.88/m2

).

New FIAPP Installations in Millions of Square Feet

UCF (t) = VIPCF (t)/CPSF 7.4

where

CPSF = the average cost per square foot for commercial floorspace

 ($97.63/ft2

).

Table 7-2 summarizes how baseline cost savings by category and in total are calculated.

The years for which cost savings are calculated are listed in Column 1 of Table 7-2. The

years run from 1993 until 2017 (i.e., the entire study period). The table records

information on five categories of cost savings: (1) first cost savings; (2) maintenance and

repair cost savings; (3) savings due to reductions in two types of construction-related

accidents; (4) reductions in delivery time; and (5) higher net income for contractors.

Annual values for each category of cost savings are recorded in Column 2 for first costs,

Column 3 for maintenance and repair costs, Column 4 for accidents avoided which do not

result in any lost workdays, Column 5 for accidents avoided which do result in lost

workdays, Column 6 for reductions in delivery time, and Column 7 for higher net income

for contractors. Note that no cost savings for any category occur until 2005, the year in

which FIAPP products and services first become commercially available.

To help readers reproduce the values recorded in Table 7-2, the formula used to estimate

the annual cost savings for each category of cost savings is given (see Equations 7.5

through 7.10). In each formula, t corresponds to the Year (see Column 1 of Table 7-2)

for which savings are calculated (i.e., t = 1 corresponds to Year = 2005). The nature of

the anticipated cost savings for each category is designated with a subscript, depending

on whether they result in cost avoidance (i.e., savings) or in higher net income (i.e.,

benefits). The subscript S is used to denote savings; these values appear in Columns 2

through 5. They are calculated through application of Equations 7.5 through 7.8. The

subscript B is used to denote benefits; these values appear in Columns 6 and 7. They are

calculated through application of Equations 7.9 and 7.10.
113

Table 7-1. Baseline Values for the Diffusion Models and of FIAPP-Related

Investments and Installations by Year: 1993 – 2017

Square Meters Square Feet

Col. (1) Col. (2) Col. (3) Col. (4) Col. (5)

1993 0 0 0 0

1994 0 0 0 0

1995 0 0 0 0

1996 0 0 0 0

1997 0 0 0 0

1998 0 0 0 0

1999 0 0 0 0

2000 0 0 0 0

2001 0 0 0 0

2002 0 0 0 0

2003 0 0 0 0

2004 0 0 0 0

2005 0.0078 707.8 .7 7.3

2006 0.0190 1,717.0 1.6 17.6

2007 0.0303 2,746.3 2.6 28.1

2008 0.0477 4,315.6 4.1 44.2

2009 0.0730 6,604.5 6.3 67.6

2010 0.1076 9,736.7 9.3 99.7

2011 0.1510 13,668.4 13.0 140.0

2012 0.2000 18,101.9 17.2 185.4

2013 0.2490 22,535.3 21.4 230.8

2014 0.2924 26,467.0 25.2 271.1

2015 0.3270 29,599.2 28.2 303.2

2016 0.3523 31,888.1 30.3 326.6

2017 0.3697 33,457.4 31.8 342.7

Year

Proportion of 

Commercial 

Sector 

Investments 

Covered by 

FIAPP Products 

and Services 

Pη(t)

Value of FIAPPRelated 

Investments (In 

Millions of 1997 

Dollars)

New FIAPP Installations in 

Millions of 
114

To facilitate cross-referencing between Table 7-2 and Equations 7.5 through 7.10, a short

hand notation for each column heading is used. Specifically, the year-by-year savings

(benefits) for each category are: RFCS(t) for Column 2; RMRCS(t) for Column 3;

NETRIRS(t) for Column 4; LWCIRS(t) for Column 5; RDTB(t) for Column 6; and

HCNIB(t) for Column 7. Terms used in each formula are defined the first time they

appear. Note that several equations use the same term.

Reductions in First Costs

RFCS (t) = VIPCF (t) *0.02 7.5

where

 0.02 = the expected reduction in first costs expressed as a decimal.

Reductions in Maintenance and Repair Costs

RMRC (t) U # (t) * MRC # (t) *UPV (t) *0.1 m

S = C C 7.6

where

# = M for m2

 and F for ft2;

MRCC#(t) = the per unit maintenance and repair cost in the commercial sector in

 year t (in 1997 dollars);

UPV m(t) = the “modified” uniform present value factor94 for year t; and

0.1 = the expected reduction in maintenance and repair costs expressed as a

 decimal.

 

94















 



 





+

+ − 















−

+ =

N−t

m

d

g

d g

g UPV t

1

1 * 1 1 ( )

where

d = the real discount rate of 7 % expressed as a decimal (0.07);

g = the negative escalation rate of 10 % expressed as a decimal (-0.10)—it is used to model how

cost savings are expected to degrade over time; and

N = the value of t corresponding to the year 2018.

Including an escalation rate is the reason for “modifying” the uniform present value factor. The term g is

used to designate the escalation rate in the formula for the “modified” uniform present value factor that

appears above. The engineering economics literature uses the term e to designate the escalation rate (see

Fuller and Petersen, Life-Cycle Costing Manual, p. D-4.). The term g is used here instead of e to avoid

confusion with Euler’s number, which appears in Equation 7.1.
115

Reductions in Construction-Related Accidents

Resulting in No Lost Workdays: NETRIR

[ ] 1 ( ) 0.621*(1.0525) * ( ) * ( ) * ( ) *(200,000) − − NETRIR t = P t F t CWH t C C

y

S η

* 0.0015 7.7

where

0.621 = the expected reduction in “NETRIR” expressed as a decimal;

1.0525 = deflator used to adjust for improved RIR-related safety performance

 of 5.25 % per year;

y = Year – 1997;

CWHC(t) = the number of craft workhours in the commercial sector95 in year t;

200,000 = the base for computing safety-related measures (100 full time workers

 working 40 hours per week, 50 weeks per year); and

0.0015 = the value of avoiding an accident which results in no lost workdays

 (in millions of 1997 dollars).

Reductions in Construction-Related Accidents

Resulting in One or More Lost Workdays: LWCIR

[ ] 1 ( ) (0.311) *(1.056) * ( ) * ( ) * ( ) *(200,000) − − LWCIR t = P t F t CWH t C C

y

S η

* 0.0315 7.8

where

0.311 = the expected reduction in LWCIR expressed as a decimal;

1.056 = deflator used to adjust for improved LWCIR-related safety

 performance of 5.6 % per year; and

0.0315 = the value of avoiding an accident which results in one or more lost

 workdays (in millions of 1997 dollars).

 

95 The estimated number of craft workhours in the commercial sector assumes that craft workhours are

directly proportional to the value of construction put in place. Since the value of commercial construction

put in place is approximately 31 % of the total for the entire construction industry, the number of craft

workhours in the commercial sector was taken to be 31 % of the construction industry total in 1997.
116

Table 7-2. Baseline Cost Savings by Category and in Total in Millions of 1997 Dollars by Year: 1993-2017

Cases With No Lost 

Workdays

Lost Workday 

Cases

Col. (1) Col. (2) Col. (3) Col. (4) Col. (5) Col. (6) Col. (7) Col. (8) (2)+(3)+(4)+(5)+

1993 0 0 0 0 0 0 01994 0 0 0 0 0 0 01995 0 0 0 0 0 0 01996 0 0 0 0 0 0 01997 0 0 0 0 0 0 01998 0 0 0 0 0 0 01999 0 0 0 0 0 0 02000 0 0 0 0 0 0 02001 0 0 0 0 0 0 02002 0 0 0 0 0 0 02003 0 0 0 0 0 0 02004 0 0 0 0 0 0 02005 14.157 7.622 .035 .354 8.384 9.438 39.9892006 34.340 18.078 .080 .813 20.337 22.893 96.5412007 54.927 28.132 .121 1.232 32.530 36.618 153.5602008 86.312 42.744 .181 1.833 51.117 57.541 239.7272009 132.090 62.752 .263 2.656 78.228 88.060 364.0492010 194.734 87.847 .368 3.709 115.328 129.822 531.8082011 273.368 115.533 .491 4.930 161.898 182.245 738.4652012 362.037 140.746 .618 6.183 214.412 241.358 965.3542013 450.707 157.072 .731 7.289 266.925 300.471 1,183.1952014 529.341 159.138 .816 8.107 313.495 352.894 1,363.7902015 591.985 144.282 .867 8.585 350.595 394.657 1,490.9702016 637.763 112.290 .887 8.759 377.706 425.175 1,562.5802017 669.148 63.991 .884 8.703 396.293 446.098 1,585.117

Total Cost Savi

Year (In Millions 

Dollars)

Annual Cost Savings By Category

Savings Due to Reductions in 

Construction-Related Accidents (In 

Millions of 1997 Dollars)

Year Savings Due to Reductions in First 

Costs (In Millions of

1997 Dollars)

Savings Due to 

Reductions in 

Maintenance and 

Repair Costs (In 

Millions of 1997 

Dollars)

Benefits Due to 

Reductions in 

Delivery Time (In 

Millions of 1997 

Dollars)

Benefits From 

Higher Net Income 

for Contractors (In 

Millions of 1997 

Dollars)
117

Reductions in Delivery Time

RDTB (t) = UC# (t) *WNIC# (t) *7 7.9

where

WNIC#(t) = weekly net income per unit of floor area in the commercial

 sector in year t (in 1997 dollars); and

7 = the expected reduction in delivery time expressed in weeks.

Higher Contractor Net Income

( ) [ ] ( ) *(1.05) *0.014 −1 HCNI t = VIP t B CF 7.10

where

1.05 = deflator used to translate total installed cost to contractor cost; and

0.014 = the expected increase in contractor net income expressed as a decimal.

In addition to annual cost savings by category, Table 7-2 also contains total cost savings

by year. These cost savings are recorded in Column 8. Total cost savings for each year

equal the sum of each category’s cost savings for that year. Total cost savings,

denominated in millions of 1997 dollars, increase steadily between 2005 and 2017.

Table 7-3 summarizes how the present values of net cost savings nationwide by year and

in total are calculated. The table also includes information on total cost savings,

additional FIAPP-related installation costs, net cost savings, and the discount factor

needed to translate yearly net cost savings into yearly present value cost savings

nationwide. The years for which present values are calculated are listed in Column 1 of

Table 7-3. The years run from 1993 until 2017 (i.e., the entire study period). Column 2

of Table 7-3 contains total cost savings by year in millions of 1997 dollars. The total cost

savings for each year is transferred from the respective row of Column 8 of Table 7-2.

The additional cost to install FIAPP products and services for each year is recorded in

Column 3 of Table 7-3. This cost equals the product of the additional cost to building

owners and contractors of $32.29/m2

 ($3.00/ft2) for commercial buildings that employ

FIAPP products and services. The amount of commercial floorspace that employs FIAPP

products and services in millions of m2

 (ft2) is contained in Column 4 (Column 5) of

Table 7-1 for each year of the specific calculation.
118

Table 7-3. Baseline Computation of Present Value Cost Savings Nationwide in

Millions of 1997 Dollars: 1993-2017

Year

Total Cost 

Savings by 

Year

New 

Technology 

Introduction 

Costs

Net Cost 

Savings

Single 

Present 

Value 

Factor by 

Year

Present Value 

of Net Cost 

Savings 

Nationwide 

by Year

Col. (1) Col. (2) Col. (3) Col. (4)

(2) - (3) Col. (5) Col. (6)(4) x (5)

1993 0 0 0 1.311 0

1994 0 0 0 1.225 0

1995 0 0 0 1.145 0

1996 0 0 0 1.070 0

1997 0 0 0 1.000 0

1998 0 0 0 0.935 0

1999 0 0 0 0.873 0

2000 0 0 0 0.816 0

2001 0 0 0 0.763 0

2002 0 0 0 0.713 0

2003 0 0 0 0.666 0

2004 0 0 0 0.623 0

2005 39.989 21.750 18.239 0.582 10.615

2006 96.541 52.760 43.781 0.544 23.814

2007 153.560 84.391 69.169 0.508 35.162

2008 239.727 132.611 107.117 0.475 50.890

2009 364.049 202.944 161.105 0.444 71.532

2010 531.808 299.191 232.616 0.415 96.528

2011 738.465 420.006 318.459 0.388 123.504

2012 965.354 556.239 409.116 0.362 148.282

2013 1,183.195 692.472 490.723 0.339 166.225

2014 1,363.790 813.286 550.504 0.317 174.275

2015 1,490.970 909.533 581.437 0.296 172.026

2016 1,562.580 979.867 582.713 0.277 161.125

2017 1,585.117 1,028.087 557.030 0.258 143.947

TOTAL 1,377.926
119

The difference between total cost savings and the additional costs to employ FIAPP

products and services equals net cost savings. Column 4 of Table 7-3 records net cost

savings for each year in millions of 1997 dollars. Note that net cost savings increase

steadily. The calculated value of the single present value factor for each year is recorded

in Column 5 of Table 7-3. All entries are calculated using a real discount rate of 7 % (see

Section 6.4.3). Because 1997 is the base year, the single present value factor takes on a

value of 1.0 for that year. For years prior to 1997, the single present value factor is

greater than 1.0. For years following 1997, the single present value factor is less than

1.0. The single present value factor for any given year, Year, equals (1.07)1997-Year where

1993 ≤ Year ≤ 2017. The present value of net cost savings nationwide by year is

recorded in Column 6 of Table 7-3. It equals the product of the net cost savings, in

Column 4, and the single present value factor, in Column 5, for that year. Note that the

present value of net cost savings nationwide increases steadily.

Because the entries in Column 6 are in present value terms, they can be summed to get

total cost savings nationwide over the entire study period. Total cost savings nationwide

resulting from the three sets of baseline analysis calculations are nearly $1.4 billion

($1,378 million in present value 1997 dollars); see the bottom of Column 6 in Table 7-3.

Reference to Table 7-3 demonstrates the magnitude of the savings to the nation from

using FIAPP products and services in the commercial sector. These cost savings

nationwide also provide a basis for measuring the value of BFRL’s contribution.

7.3 Measuring the Value of BFRL’s Contribution and the Return on BFRL’s

CONSIAT-Related Investments

Measuring the value of BFRL’s contribution to the development of FIAPP products and

services and the return on its CONSIAT-related investments is the focus of this section.

Information on BFRL’s CONSIAT-related research, development, and deployment

efforts—in terms of its dollar investments—over the 25-year period from 1993 to 2017

are first presented. These figures demonstrate not only a significant, up-front research

commitment by BFRL, but also a continued effort as FIAPP products and services move

into the commercial marketplace. Next, the likely delay in the commercial availability of

FIAPP products and services is addressed. Because of BFRL’s participation, FIAPP

products and services are expected to be commercially available in 2005. If BFRL had

not participated in the development of FIAPP products and services, the commercial

introduction of FIAPP products and services is expected to be delayed until 2009.

Finally, a full array of economic measures summarizes the importance of BFRL’s

contribution to the development of FIAPP products and services for use in commercial

buildings. These measures include the present value of savings (PVS), the present value

of net savings (PVNS), the savings-to-investment ratio (SIR), and the adjusted internal

rate of return (AIRR) that are attributable to BFRL’s CONSIAT-related research,

development, and deployment efforts.
120

Table 7-4. Summary of BFRL Research Investments: 1993-2017

Year

Annual Dollar 

Amount (In 

Millions of 

Current 

Dollars)

Conversion 

Factor by 

Year 

(Current 

Dollars to 

1997 Dollars)

Investment 

Costs by 

Year (In 

Millions of 

1997 Dollars)

Single 

Present Value 

Factor by 

Year

Present Value 

of Investment 

Costs by Year 

(In Millions of 

1997 Dollars)

Col. (1) Col. (2) Col. (3) Col. (4)

(2) x (3) Col. (5) Col. (6)

(4) x (5)

1993 .332 1.111 .369 1.311 .483

1994 1.371 1.083 1.485 1.225 1.819

1995 1.502 1.053 1.582 1.145 1.811

1996 1.548 1.023 1.584 1.070 1.694

1997 1.886 1.000 1.886 1.000 1.886

1998 2.263 0.985 2.228 0.935 2.083

1999 1.992 0.963 1.919 0.873 1.676

2000 2.002 0.932 1.866 0.816 1.523

2001 2.442 0.932 2.276 0.763 1.736

2002 3.125 0.932 2.913 0.713 2.077

2003 3.625 0.932 3.379 0.666 2.251

2004 4.000 0.932 3.728 0.623 2.322

2005 4.000 0.932 3.728 0.582 2.170

2006 3.875 0.932 3.612 0.544 1.965

2007 3.375 0.932 3.146 0.508 1.599

2008 2.875 0.932 2.680 0.475 1.273

2009 1.875 0.932 1.748 0.444 .776

2010 0 0.932 0 0.415 0

2011 0 0.932 0 0.388 0

2012 0 0.932 0 0.362 0

2013 0 0.932 0 0.339 0

2014 0 0.932 0 0.317 0

2015 0 0.932 0 0.296 0

2016 0 0.932 0 0.277 0

2017 0 0.932 0 0.258 0

TOTAL 29.144 a

 The dollar amounts for 1993 through 2000 are in millions of current dollars. The dollar

amounts for 2001 through 2017 are in millions of 2000 dollars.
121

Table 7-4 summarizes information on BFRL’s CONSIAT-related investments. Column 1

of the table records the year in which CONSIAT-related investments were made.

Column 2 records the value (in millions of current dollars) by year of investment for each

year between 1993 and 2017. For example, in 1993 the investment was $332,000 (in

1993 dollars), in 1994 the investment was $1,371,000 (in 1994 dollars), and in 1995 the

investment was $1,502,000 (in 1995 dollars). For 2001 through 2017, the entries in

Column 2 are in millions of 2000 dollars. Investments over the 1991 to 1996 time period

cover STEP-related and construction metrology-related research. Investments beginning

in 1997 include research, development, and deployment efforts aimed at producing

FIAPP products and services. Because the values for 1993 through 2000 in Column 2 are

in current dollars by year and the values for 2001 through 2017 are in 2000 dollars, it is

necessary to convert them to constant 1997 dollars and then convert them to present value

(i.e., time equivalent) dollars. This involves a two-step process. First, each year’s

current dollar investment is converted to a “real” investment in 1997 constant dollars

through application of the Consumer Price Index (CPI). The conversion factors, for each

year, are shown in Column 3 of Table 7-4. The constant 1997 dollar values (in millions

of dollars) are the year-by-year products of the entries in Column 2 and Column 3. These

values are shown in Column 4. The values in Column 4 are converted into present value

terms through the use of a single present value factor, based on a real discount rate of 7

%. The value of each year’s single present value factor is given in Column 5. The

present values in millions of 1997 dollars are recorded in Column 6; they are the year-byyear products of the entries in Column 4 and Column 5.

Because entries in Column 6 are in present value terms, they can be summed to get the

present value of BFRL’s CONSIAT-related investments. The present value of BFRL’s

CONSIAT-related investments, PV Costs, totals $29.144 million; this value is recorded at

the bottom of Column 6.

Table 7-5 provides the information needed to calculate the present value of savings

attributable to BFRL. The years for which present values are calculated are listed in

Column 1 of Table 7-5. The years run from 1993 until 2017 (i.e., the entire study

period). The present value of cost savings nationwide by year is recorded in Column 2 of

Table 7-5. The present value of cost savings nationwide for each year is transferred from

the respective row of Column 6 of Table 7-3. BFRL’s dual role as a facilitator and

developer of key FIAPP enabling technologies is expected to speed up the introduction of

FIAPP products and services into the commercial marketplace. Without BFRL’s

participation, the introduction of FIAPP products and services into the commercial

marketplace would likely have been delayed. Information from subject matter experts

and similar economic impact assessments suggest a range of values from two to five

years for the likely delay.96 The selected baseline value for the delay is four years (see

Section 6.4.6). Because BFRL’s research, development, and deployment efforts are

expected to result in faster introduction of FIAPP products and services, those savings

which would have been foregone in the event of a delay are attributable to BFRL.

 

96 Chapman, A Case Study of Construction Systems Integration and Automation Technologies in Industrial

Facilities, pp. 98-101.
122

Therefore, any savings over the first four years (starting with 2005), prior to the

“delayed” introduction of FIAPP products and services in 2009, would have been

foregone. Such an accounting framework may be handled through use of a 0-1 weighting

factor. For those years in which savings are attributable to BFRL, the weighting factor

takes on a value of 1. The year-by-year values of the BFRL baseline weighting factor are

given in Column 3 of Table 7-5. The present value of savings attributable to BFRL is the

product of each year’s present value of cost savings nationwide in Column 2 and the

value of the BFRL baseline weighting factor in Column 3. The present value of savings

attributable to BFRL on a year-by-year basis is given in Column 4 of Table 7-5.

Because entries in Column 4 are in present value terms, they can be summed to get the

present value of savings attributable to BFRL. The present value of savings attributable

to BFRL, PVS, totals $120.482 million; this value is recorded at the bottom of Column 6.

Given the values of PV Costs and PVS attributable to BFRL, it is now possible to derive

three other economic impact measures. These measures are: (1) present value of net

savings (PVNS) attributable to BFRL; (2) the savings-to-investment ratio (SIR) on

BFRL’s CONSIAT-related investments; and (3) the adjusted internal rate of return

(AIRR) on BFRL’s CONSIAT-related investments. Multiple measures are particularly

useful because they each provide a different perspective—PVNS is a magnitude, the SIR

is a ratio, and the AIRR is a rate of return—and promote consistency in evaluating

BFRL’s contribution.

The PVNS attributable to BFRL, expressed in millions of present value 1997 dollars and

based on the approach outlined in Section 2.2.1, is equal to:

PVNS = PVS – PV Costs

= $120.482 - $29.144

= $91.337 million

Utilizing the approach outlined in Section 2.2.2, the SIR on BFRL’s CONSIAT-related

investments is equal to:

SIR = PVS / (PV Costs)

= $120.482 / $29.144

= 4.13
123

Table 7-5. Estimated Cost Savings in Millions of 1997 Dollars Attributable

to BFRL

Year

Present Value of 

Cost Savings 

Nationwide by 

Year

BFRL Baseline 

Weighting Factor 

(4-Year Delay)

Present Value of 

Cost Savings by 

Year Attributable 

to BFRL

Col. (1) Col. (2) Col. (3) Col. (4)

(2) x (3)

1993 0 1 0

1994 0 1 0

1995 0 1 0

1996 0 1 0

1997 0 1 0

1998 0 1 0

1999 0 1 0

2000 0 1 0

2001 0 1 0

2002 0 1 0

2003 0 1 0

2004 0 1 0

2005 10.615 1 10.615

2006 23.814 1 23.814

2007 35.162 1 35.162

2008 50.890 1 50.890

2009 71.532 0 0

2010 96.528 0 0

2011 123.504 0 0

2012 148.282 0 0

2013 166.225 0 0

2014 174.275 0 0

2015 172.026 0 0

2016 161.125 0 0

2017 143.947 0 0

TOTAL 120.482
124

Utilizing the approach outlined in Section 2.2.3, the AIRR on BFRL’s CONSIAT-related

investments is equal to:

AIRR = (1 + 0.07) * 4.131/25 - 1

= 0.133

= 13.3 %

The values of the five economic impact measures derived in Chapter 7 are the baseline

values that appear in Section 3.b of Exhibit 7-1. These values also figure in the

sensitivity analysis, which is the subject of the next chapter.
125

8 Sensitivity Analysis of Economic Impacts

The CONSIAT economic impact assessment described in this report was carried out in

two stages. In the first stage, a baseline analysis was performed. The data and

assumptions underlying the baseline analysis were described in Chapter 6; the results of

the baseline analysis were presented in Chapter 7.

In the second stage, eleven variables were varied both singly and in combination

according to an experimental design. The sensitivity analysis uses the same data and

assumptions as the baseline analysis for its starting point. Information on how the

deviations about the baseline values for each of the eleven input variables were specified

and how the range of values for each variable was determined is described and

documented in Chapter 6. The sensitivity analysis described in this chapter is based on

Monte Carlo techniques. The objective of the sensitivity analysis is to evaluate how

uncertainty in the values of each of the eleven input variables, both singly and in

combination, translates into changes in each of six key economic measures. The six

economic measures evaluated in the sensitivity analysis are: (1) the present value of

savings nationwide; (2) the present value of savings due to BFRL; (3) the present value

of BFRL’s CONSIAT-related investment costs; (4) the present value of net savings due

to BFRL; (5) the savings-to-investment ratio on BFRL’s CONSIAT-related investments;

and (6) the adjusted internal rate of return on BFRL’s CONSIAT-related investments.

Three of these measures are particularly helpful in understanding BFRL’s contribution,

since each measure provides a different perspective. The first, the present value of net

savings due to BFRL is a magnitude measure; it shows a net dollar value to the public net

of BFRL’s CONSIAT-related investments. The second, the savings-to-investment ratio

on BFRL’s CONSIAT-related investments is a multiplier; it shows, in present value

terms, how many dollars the public receives for each public dollar spent. The third, the

adjusted internal rate of return on BFRL’s CONSIAT-related investments is a rate of

return; it shows the return on the public monies going into the development of CONSIAT

products and services throughout the 25-year study period.

8.1 Methodology

Because the values of many variables that enter into the CONSIAT economic impact

assessment are not known with certainty, it is advisable to select a small set of variables

whose impact is likely to be substantial and subject them to a sensitivity analysis.

Variations in the values of these input variables translate into the value of each outcome

(e.g., the SIR) in such a manner that the impacts of uncertainty can be measured

quantitatively.

Sensitivity analysis may be divided into two polar cases: (1) deterministic; and (2)

probabilistic. Deterministic sensitivity analyses are the most straightforward. Their

advantage is that they are easy to apply and the results are easy to explain and
126

understand. Their disadvantage is that they do not produce results that can be tied to

probabilistic levels of significance (i.e., the probability that the SIR is less than 1.0).

For example, a deterministic sensitivity analysis might use as inputs a pessimistic value, a

value based on a measure of central tendency (e.g., mean or median), and an optimistic

value for the variable of interest. Then an analysis could be performed to see how each

outcome (e.g., the SIR) changes as each of the three chosen values for the selected input

is considered in turn, while all other input variables are maintained at their baseline

values. A deterministic sensitivity analysis can also be performed on different

combinations of input variables. That is, several variables are altered at once and then an

outcome measure is computed. This is the approach used in two of the previous

economic impact assessments.97

In a probabilistic sensitivity analysis, a small set of key input variables is varied either

singly or in combination according to an experimental design. In most cases,

probabilistic sensitivity analyses are based on Monte Carlo techniques, or some other

form of simulation. The major advantage of probabilistic sensitivity analysis is that it

permits the effects of uncertainty to be rigorously analyzed. For example, not only the

expected value of each economic measure can be computed but also the variability of that

value. In addition, probabilistic levels of significance can be attached to the computed

values of each economic measure. The disadvantage of a probabilistic sensitivity

analysis is that it requires many calculations carried out according to an experimental

design, and is therefore practical only when used with a computer.

The approach selected for this study makes use of works by McKay, Conover, and

Beckman98 and by Harris;99 it is based on the method of model sampling. Model

sampling provides the basis for many probabilistic sensitivity analyses. Model sampling

is a procedure for sampling from a stochastic process to determine, through multiple

trials, the characteristics of a probability distribution. This approach was used in two of

the previous economic impact assessments.100

The method of model sampling was implemented through application of the @RISK

software product.101 This software product is an add-in for spreadsheets. For the case at

hand, selected columns of the spreadsheet were associated with one or more of the eleven

input variables. The @RISK software product allows the user to specify a unique

probability distribution for each input variable. Specification of the experimental design

 

97 See Chapman and Fuller, Two Case Studies in Building Technology, and Chapman and Weber, A Case

Study of the Fire Safety Evaluation System. 98 McKay, M. C., W. H. Conover, and R.J. Beckman. 1979. “A Comparison of Three Methods for Selecting

Values of Input Variables in the Analysis of Output from a Computer Code.” Technometrics (Vol. 21): pp.

239-245.

99 Harris, Carl M. 1984. Issues in Sensitivity and Statistical Analysis of Large-Scale, Computer-Based

Models. NBS GCR 84-466. Gaithersburg, MD: National Bureau of Standards. 100 See Chapman, A Case Study of Cybernetic Building Systems, and Chapman, A Case Study of

Construction Systems Integration and Automation Technologies in Industrial Facilities. 101 Palisade Corporation. 1997. Guide to Using @RISK: Risk Analysis and Simulation Add-In for Microsoft

Excel or Lotus 1-2-3. Newfield, NY: Palisade Corporation.
127

involves defining which variables are to be simulated and the number of simulations.

Throughout this sensitivity analysis, 1,000 simulations were run for each input variable or

combination of input variables under analysis. The number of simulations was chosen to

ensure that values in the tails of the distribution for each input variable would be selected

for inclusion in the analysis. When the @RISK software product is executed, it randomly

samples from the parent probability distribution for each input variable of interest (i.e.,

the input variable(s) specified by the experimental design).

In reality, the exact nature of the parent probability distribution for each input variable is

unknown. Estimates of the parameters (e.g., mean and variance) of the parent probability

distribution can be made and uncertainty can be reduced by investigation and research.

However, uncertainty can never be eliminated completely. The true specification of the

parent probability distribution can only be known after CONSIAT products and services

have been operating in the marketplace for an extended period of time. Therefore, in

order to implement the procedure without undue attention to the characterization of the

parent probability distribution, it was decided to focus on only three probability

distributions. These probability distributions are: (1) the triangular; (2) the uniform; and

(3) the discrete or multinomial. Readers interested in learning more about these

probability distributions, including variate relationships, estimation procedures, and

random number generation, are referred to Evans, Hastings, and Peacock.102

One reason for using these three probability distributions is that they are all defined over

a finite interval. Furthermore, the specification of each probability distribution is

accomplished with as few as two data points. The triangular distribution is widely used

in simulation modeling; its specification requires three data points, the minimum value,

the most likely value, and the maximum value. The triangular distribution is used

whenever the range of input values is continuous and a clustering about some central

value is expected. Discussions with subject matter experts and reference to selected

publications indicated eight input variables for which clustering about a central value was

to be expected. Once the triangular distribution was selected for these eight input

variables, all three values were derived through investigation and discussions with subject

matter experts. The uniform distribution is also widely used in simulation modeling; its

specification requires only two data points, the minimum value and the maximum value.

In addition, all values between the minimum and maximum are equally likely. The

uniform distribution is used whenever the range of input values is continuous but no a

priori reason can be given for expecting clustering about some central value. It was used

for one input variable (see Section 8.2). The discrete distribution is used whenever the

range of input values is discrete; it was used for two input variables (see Section 8.2).

8.2 Key Variables

Information on the eleven input variables that are the focus of the sensitivity analysis is

presented in this section. The eleven variables are: (1) alpha, α, the location parameter in

 

102 Evans, Merran, Nicholas Hastings, and Brian Peacock. 1993. Statistical Distributions. New York, NY:

John Wiley & Sons, Inc.
128

the diffusion model; (2) beta, β, the shape parameter in the diffusion model; (3) eta, η,

the market saturation level in the diffusion model; (4) the discount rate; (5) first cost

savings; (6) maintenance and repair cost savings; (7) reductions in delivery time; (8)

higher contractor net income; (9) new-technology introduction costs; (10) the time of first

use; and (11) the length of the delay.

Table 8-1 summarizes information on each of the eleven input variables. The table

includes information on the type of probability distribution used to model variations

about the baseline value for each input variable, the baseline value for each input

variable, and the minimum and maximum values for each input variable.

Table 8-1. Baseline and Extreme Values of the Eleven Input Variables Used in the

Sensitivity Analysis

Baseline Minimum Maximum

(1) Alpha Uniform 435

(2) Beta Triangular 0.5 0.4 0.6

(3) Eta Triangular 0.4 0.3 0.5

(4) Discount Rate Triangular 0.07 0.02 0.10

(5) First Cost Savings Triangular 0.020 0 0.035

(6) Maintenance and

 Repair Cost Savings Triangular 0.10 0.05 0.15

(7) Reductions in

 Delivery Time Triangular 7 0 22

(8) Higher Contractor

 Net Income Triangular 0.014 0 0.037

(9) New-Technology

 Introduction Costs Triangular

$32.29

($3.00)

$21.53

($2.00)

$43.05

($4.00)

(10) Time of First Use Discrete 2005 2004 2007

(11) Delay Discrete 0 0 2

Variable Name

Probability 

Distribution

Setting and Value

Reference to the entries under the heading Probability Distribution shows that all but

three of the eleven input variables use the triangular distribution to model variations

about the baseline value for that variable. One of these variables, α, employs the uniform

distribution. The input variable, α, is modeled with a uniform distribution because a

review of the economics literature on the diffusion process produced no a priori reason

for expecting a clustering of values around a value of 4.0. The economics literature was,

however, useful in specifying the range about the baseline value of α. The other two

input variables, the time of first use and the length of delay, employ the discrete

distribution. Both of these variables designate a year. For example, the time of first use

(i.e., when CONSIAT products and services first become available commercially) either

occurs in 2005 (i.e., the year corresponding to the baseline value) or in some other year.
129

Thus, the discrete distribution is the most meaningful way to model when CONSIAT

products and services first become available commercially.

The next three headings record, for each input variable, its setting (i.e., baseline,

minimum, and maximum) and value. For each input variable, the baseline value is

recorded first. For example, the baseline value for the discount rate is 7 % (real); it is

recorded in decimal form as 0.07. Two other values for the discount rate, 2 % and 10 %,

are selected to bracket the baseline value. These values are recorded in decimal form as

0.02 and 0.10, respectively.

8.3 Sensitivity Results

The results of the sensitivity analysis are summarized in a series of tables and figures.

Two sets of results are presented. The first set covers the case where each of the eleven

input variables is varied singly. The first set of results is designed to show the effect of

each input on each of the economic measures. This is done by varying each input

variable singly while holding the other ten input variables at their baseline values. These

results are summarized in Tables 8-2 through 8-12. Table 8-13 summarizes the results of

a deterministic sensitivity analysis for the combined effects of the time of first use and

the length of delay. Table 8-13 has three parts: (1) Part A, where each time of first use is

evaluated assuming no delay; (2) Part B, where each time of first use is evaluated

assuming a one-year delay; and (3) Part C, where each time of first use is evaluated

assuming a two-year delay. The second set covers the case where all eleven input

variables are varied in combination. The second set of results is designed to produce a

data set that facilitates an in-depth analysis of the results, and promotes an understanding

of what these results mean. These results are summarized in Tables 8-14 and 8-15 and in

Figures 8-1 through 8-6. To facilitate comparisons among each of the Monte Carlo

simulations, Tables 8-2 through 8-12 and Table 8-14 use the same presentation format.

Table 8-15 summarizes in tabular form the results plotted in Figures 8-1 through 8-6.

8.3.1 Changing One Input

Tables 8-2 through 8-12 report a series of statistical measures for each economic

measure. To facilitate comparisons among the economic measures, a shorthand notation

for each is used. The present value of savings nationwide over the entire study period is

denoted by PVSALL. The present value of savings due to BFRL is denoted by PVSBFRL.

The present value of BFRL’s CONSIAT-related investment costs is denoted by PVCBFRL.

The present value of net savings due to BFRL is denoted by PVNSBFRL. The savings-toinvestment ratio on BFRL’s CONSIAT-related investments is designated by SIRBFRL.

The adjusted internal rate of return on BFRL’s CONSIAT-related investments is

designated by AIRRBFRL. The statistical measure and its corresponding value are

recorded under the heading Statistical Measure. Seven statistical measures are reported

to characterize the results of each Monte Carlo simulation. The calculation of these

statistical measures is based on a “sample of 1,000 observations” produced by each
130

Monte Carlo simulation. These statistical measures are: (1) the minimum; (2) the 25th

percentile, denoted by 25 %; (3) the 50th percentile (i.e., the median), denoted by 50 %;

(4) the 75th percentile, denoted by 75 %; (5) the maximum; (6) the mean; and (7) the

standard deviation. The minimum and the maximum define the range of values for the

results from each of the Monte Carlo simulations. The 50th percentile and the mean are

measures of central tendency. The 25th and 75th percentiles define the interquartile range,

a range that includes the middle 50 % of the observations. The interquartile range is also

a crude measure of central tendency. The standard deviation measures the variability of

the results from each of the Monte Carlo simulations. It is important to recognize that the

values reported for PVSALL, PVSBFRL, PVCBFRL, and PVNSBFRL are all in millions of

1997 dollars.

The results presented in Tables 8-2, 8-3, and 8-4 are related to the values of the

parameters in the diffusion model (see Section 6.4.4). Each parameter, α, β, and η is

analyzed in turn.

Table 8-2 shows how variations about the baseline value for α (i.e., α = 4.0) affect each

economic measure. The parameter α was selected for evaluation because it is the

location parameter for the diffusion model. The effect of α is as follows: lower values of

α produce a thicker tail immediately following the introduction of CONSIAT products

and services into the marketplace (i.e., higher values of Pη(t), whenever t is small),

whereas higher values of α produce a thinner tail (i.e., lower values of Pη(t), whenever t

is small). Reference to Table 8-2 reveals that α exerts a strong effect on five of the six

economic measures. For example, the minimum value of PVSALL is less than one half of

the highest value. Although the present value of savings nationwide is strongly affected

by changes in the value of α, the measures of BFRL’s influence are affected to a far

greater degree. The reason is due to the way in which BFRL’s influence is measured.

Because those savings occurring in the first four years are attributable to BFRL, higher

values of α reduce these savings and lower values of α increase these savings over the

value calculated in the baseline analysis. For example, the minimum value of SIRBFRL is

one-sixth the highest value, and PVNSBFRL varies by more than a factor of 10. Note that

PVCBFRL is unaffected by changes in the value of α. Thus, the standard deviation for

PVCBFRL is 0.0. Consequently, the standard deviation for PVSBFRL and the standard

deviation for PVNSBFRL are equal (i.e., $66.5 million in 1997 dollars).

Table 8-2. Summary Statistics Due to Changes in the Input Variable Alpha

Statistical Measure

Economic

Measure Minimum 25% 50% 75% Maximum Mean Standard

Deviation

PVSALL 871.406 1,119.611 1,382.380 1,655.858 1,980.009 1,391.566 313.721

PVSBFRL 46.898 76.927 121.359 184.499 285.684 134.644 66.544

PVCBFRL 29.144 29.144 29.144 29.144 29.144 29.144 0.0

PVNSBFRL 17.753 47.783 92.214 155.354 256.540 105.500 66.544

SIRBFRL 1.609 2.640 4.164 6.331 9.802 4.620 2.283

AIRRBFRL 0.091 0.112 0.133 0.152 0.172 0.132 0.023
131

Table 8-3 shows how variations about the baseline value for β (i.e., β = 0.5) affect each

economic measure. The parameter, β, was selected for evaluation because it specifies the

rate of change for the diffusion model. The effect of β is as follows: higher values of β

produce a higher rate of adoption of CONSIAT products and services in the marketplace

immediately following the introduction of these products and services (i.e., higher values

of Pη(t), whenever t is small), whereas lower values of β produce a lower rate of adoption

(i.e., lower values of Pη(t), whenever t is small). Reference to Table 8-3 reveals that β

exerts a moderate to strong effect on five of the six economic measures. For example, the

range of values for PVSALL is slightly in excess of $750 million in 1997 dollars.

Although the present value of savings nationwide is strongly affected by changes in the

value of β, the measures of BFRL’s influence are affected to a much lesser degree than

for changes in α. The reason is due to the way in which BFRL’s influence is measured.

Because those savings occurring in the first four years are attributable to BFRL, lower

values of β reduce these savings and higher values of β increase these savings over the

value calculated in the baseline analysis. However, these differences are less than those

associated with α, because α affects the thickness of the lower tail of Pη(t), whereas β

only affects the rate of change of the slope of the tail in the period immediately following

the introduction of CONSIAT products and services. For example, the minimum value

of SIRBFRL is slightly less than half the highest value, and PVNSBFRL varies by a factor of

slightly more than two. Note that PVCBFRL is unaffected by changes in the value of β.

Thus, the standard deviation for PVCBFRL is 0.0. Consequently, the standard deviation

for PVSBFRL and the standard deviation for PVNSBFRL are equal (i.e., $13.7 million in

1997 dollars).

Table 8-3. Summary Statistics Due to Changes in the Input Variable Beta

Statistical Measure

Economic

Measure Minimum 25% 50% 75% Maximum Mean Standard

Deviation

PVSALL 973.785 1,258.309 1,382.033 1,492.397 1,737.736 1,373.051 161.564

PVSBFRL 91.943 110.950 120.830 130.782 158.063 121.205 13.741

PVCBFRL 29.144 29.144 29.144 29.144 29.144 29.144 0.0

PVNSBFRL 62.799 81.805 91.685 101.637 128.918 92.061 13.741

SIRBFRL 3.155 3.807 4.146 4.487 5.423 4.159 0.471

AIRRBFRL 0.120 0.129 0.133 0.136 0.145 0.132 0.005

Table 8-4 shows how variations about the baseline value for η (i.e., η = 0.4) affect each

economic measure. The parameter, η, was selected for evaluation because it specifies the

level at which the market for CONSIAT products and services saturates. The effect of η

is as follows: higher values of η produce a higher level of adoption of CONSIAT

products and services in the marketplace towards the latter part of the study period (i.e.,

higher values of Pη(t), for all values of t, especially whenever t is large), whereas lower

values of η produce a lower level of adoption (i.e., lower values of Pη(t)). Reference to

Table 8-4 reveals that η exerts a moderate to strong effect on five of the six economic

measures. For example, the range of values for PVSALL (i.e., Maximum – Minimum) is
132

slightly less than $700 million in 1997 dollars. Although the present value of savings

nationwide is strongly affected by changes in the value of η, the measures of BFRL’s

influence are affected to a lesser degree than for changes in α. The reason, once again, is

due to the way in which BFRL’s influence is measured. Because those savings occurring

in the first four years are attributable to BFRL, lower values of η reduce these savings

and higher values of η increase these savings over the value calculated in the baseline

analysis. However, these differences are less than those associated with α, because α

affects the thickness of the lower tail of Pη(t), whereas η affects the level at which the

market saturates. Thus, the influence of η on the years immediately following the

introduction of CONSIAT products and services is modest. Consequently, the range of

values for the measures of BFRL’s influence due to variations about the baseline value

for η tend to be about the same as for β, and significantly narrower than for α.

Table 8-4. Summary Statistics Due to Changes in the Input Variable Eta

Statistical Measure

Economic

Measure Minimum 25% 50% 75% Maximum Mean Standard

Deviation

PVSALL 1,040.260 1,277.849 1,378.806 1,478.809 1,714.327 1,380.564 140.930

PVSBFRL 90.957 111.731 120.559 129.302 149.895 120.712 12.323

PVCBFRL 29.144 29.144 29.144 29.144 29.144 29.144 0.0

PVNSBFRL 61.813 82.587 91.414 100.158 120.751 91.568 12.323

SIRBFRL 3.121 3.834 4.137 4.437 5.143 4.142 0.423

AIRRBFRL 0.120 0.129 0.133 0.136 0.142 0.132 0.005

Table 8-5 shows how variations about the baseline value of the discount rate (7 % (real))

affect each economic measure. The discount rate affects calculations in a number of

ways. For example, BFRL’s CONSIAT-related investment costs, PVCBFRL, are affected

by the discount rate. The present value of savings nationwide, PVSALL, and the present

value of savings due to BFRL, PVSBFRL, are also affected by the discount rate. Reference

to Table 8-5 reveals that PVSALL is more sensitive to changes in the discount rate than are

the key measures of BFRL’s influence. This is because savings do not begin until 2005,

whereas the base year is 1997. Thus, savings occurring in the out years (e.g., 2009 and

beyond) benefit from a lower discount rate and are penalized by a higher discount rate.

This explains the wide range in computed values for PVSALL, a range that exceeds $2.1

billion in 1997 dollars. On the other hand, BFRL’s CONSIAT-related investments are

largely clustered around 1997, and BFRL’s savings occur between 2005 and 2008 (i.e.,

much earlier than the bulk of the savings used to calculate PVSALL). This explains why

the key measures of BFRL’s influence are less sensitive to changes in the discount rate

than is PVSALL.

The results presented in Tables 8-6, 8-7, 8-8, and 8-9 address the “savings/benefits” side

of the analysis. Tables 8-6 and 8-7 are related to the values of percent cost savings.

Tables 8-8 and 8-9 are related to increases in benefits, which in both cases are due to

increases in net income.
133

Table 8-5. Summary Statistics Due to Changes in the Input Variable Discount Rate

Statistical Measure

Economic

Measure Minimum 25% 50% 75% Maximum Mean Standard

Deviation

PVSALL 887.466 1,290.033 1,514.077 1,874.513 3,012.035 1,615.876 449.220

PVSBFRL 88.427 115.014 128.762 149.751 209.877 133.853 25.984

PVCBFRL 26.348 28.685 29.828 31.501 35.907 30.178 2.065

PVNSBFRL 62.078 86.329 98.934 118.250 173.970 103.675 23.920

SIRBFRL 3.356 4.010 4.317 4.754 5.845 4.399 0.540

AIRRBFRL 0.097 0.118 0.128 0.136 0.153 0.127 0.012

Table 8-6 summarizes the results of the Monte Carlo simulation of variations about the

baseline value of first cost savings. Reference to Table 8-6 reveals considerable variation

about the values calculated in the baseline analysis for five of the six economic measures.

For example, the range of values for PVSALL (i.e., Maximum – Minimum) exceeds $2.2

billion in 1997 dollars. Four of the five measures of BFRL’s influence are strongly

affected by changes in the input variable first cost savings. More importantly, however,

three measures of BFRL’s influence—PVNSBFRL, SIRBFRL, and AIRRBFRL—indicate that

its CONSIAT-related investments may not be cost effective (i.e., cost effectiveness

requires PVNSBFRL > 0.0, SIRBFRL > 1.0, and AIRRBFRL > 0.07). The value of PVCBFRL

is not affected by the input variable first cost savings.

Table 8-6. Summary Statistics Due to Changes in the Input Variable First Cost

Savings

Statistical Measure

Economic

Measure Minimum 25% 50% 75% Maximum Mean Standard

Deviation

PVSALL 112.605 893.983 1,253.274 1,596.071 2,326.055 1,241.567 484.500

PVSBFRL 28.410 85.267 111.411 136.355 189.472 110.559 35.255

PVCBFRL 29.144 29.144 29.144 29.144 29.144 29.144 0.0

PVNSBFRL -0.734 56.123 82.267 107.211 160.328 81.415 35.255

SIRBFRL 0.975 2.926 3.823 4.679 6.501 3.794 1.210

AIRRBFRL 0.069 0.117 0.129 0.138 0.153 0.126 0.016

Table 8-7 summarizes the results of the Monte Carlo simulation of variations about the

baseline value of maintenance and repair cost savings. Table 8-7 reveals modest

variations about the values calculated in the baseline analysis for five of the six economic

measures. For example, the minimum value for the PVSALL is $1,187.9 million, and the

maximum is $1,576.7 million, a range of slightly less than $400 million (measured in

1997 dollars). Whereas, the minimum value for the PVSBFRL is $97.4 million, and the

maximum is $144.6 million, a range of approximately $45 million (measured in 1997

dollars). The value of PVCBFRL is not affected by the input variable maintenance and

repair cost savings.
134

Table 8-7. Summary Statistics Due to Changes in the Input Variable Maintenance

and Repair Cost Savings

Statistical Measure

Economic

Measure Minimum 25% 50% 75% Maximum Mean Standard

Deviation

PVSALL 1,187.911 1,318.587 1,377.409 1,435.741 1,576.729 1,376.502 82.187

PVSBFRL 97.428 113.282 120.419 127.496 144.601 120.309 9.971

PVCBFRL 29.144 29.144 29.144 29.144 29.144 29.144 0.0

PVNSBFRL 68.284 84.138 91.274 98.351 115.457 91.164 9.971

SIRBFRL 3.343 3.887 4.132 4.375 4.962 4.128 0.342

AIRRBFRL 0.123 0.130 0.132 0.135 0.141 0.132 0.004

Table 8-8 summarizes the results of the Monte Carlo simulation of variations about the

baseline value of reductions in delivery time. Reference to Table 8-8 reveals

considerable variation about the values calculated in the baseline analysis for five of the

six economic measures. The pattern of results presented in Table 8-8 is similar to the one

resulting from first cost savings (see Table 8-6). For example, the range of values for

PVSALL (i.e., Maximum – Minimum) exceeds $2.2 billion in 1997 dollars. As was the

case for first cost savings, four of the five measures of BFRL’s influence are strongly

affected by changes in the input variable reductions in delivery time. For example, the

value calculated in the baseline analysis for the SIRBFRL is 4.13. In Table 8-8, the

minimum value for the SIRBFRL is 2.346, and the maximum value is 8.000, a relative

difference of a factor of more than three-to-one. The value of PVCBFRL is not affected by

the input variable reductions in delivery time.

Table 8-8. Summary Statistics Due to Changes in the Input Variable Reductions in

Delivery Time

Statistical Measure

Economic

Measure Minimum 25% 50% 75% Maximum Mean Standard

Deviation

PVSALL 661.751 1,275.050 1,602.790 2,005.168 2,926.232 1,652.937 502.951

PVSBFRL 68.369 112.996 136.844 166.123 233.144 140.493 36.597

PVCBFRL 29.144 29.144 29.144 29.144 29.144 29.144 0.0

PVNSBFRL 39.225 83.851 107.699 136.979 204.000 111.348 36.597

SIRBFRL 2.346 3.877 4.695 5.700 8.000 4.821 1.256

AIRRBFRL 0.107 0.130 0.138 0.147 0.163 0.138 0.012

Table 8-9 summarizes the results of the Monte Carlo simulation of variations about the

baseline value of higher contractor net income. Table 8-9 reveals considerable variations

about the values calculated in the baseline analysis for five of the six economic measures.

The value of PVCBFRL is not affected by the input variable higher contractor net income.

Reference to Table 8-9 reveals that the range of values for PVSALL (i.e., Maximum –

Minimum) exceeds $2.2 billion in 1997 dollars. Note that the ranges of values and the

standard deviations for the five economic measures for which variations do occur for this
135

input variable are approximately equal to those for first cost savings and less than those

for reductions in delivery time (see Tables 8-6 and 8-8).

Table 8-9. Summary Statistics Due to Changes in the Input Variable Higher

 Contractor Net Income

Statistical Measure

Economic

Measure Minimum 25% 50% 75% Maximum Mean Standard

Deviation

PVSALL 530.369 1,223.724 1,512.503 1,908.493 2,766.567 1,565.514 481.060

PVSBFRL 58.809 109.261 130.274 159.088 221.526 134.131 35.004

PVCBFRL 29.144 29.144 29.144 29.144 29.144 29.144 0.0

PVNSBFRL 29.665 80.117 101.130 129.944 192.382 104.987 35.004

SIRBFRL 2.018 3.749 4.470 5.459 7.601 4.602 1.201

AIRRBFRL 0.100 0.128 0.136 0.145 0.160 0.136 0.012

The results presented in Table 8-10 address the “costs” side of the analysis. Table 8-10

summarizes the results of the Monte Carlo simulation of variations about the baseline

value of new technology introduction costs. Table 8-10 reveals strong variations about

the values calculated in the baseline analysis for five of the six economic measures. The

value of PVCBFRL is not affected by the input variable new technology introduction costs.

Reference to Table 8-10 reveals that the range of values for PVSALL (i.e., Maximum –

Minimum) exceeds $1.2 billion in 1997 dollars. One of the economic measures of

BFRL’s influence is the SIR. For this economic measure, the value calculated in the

baseline analysis is 4.13. In Table 8-10, the minimum value for the SIRBFRL is 2.604, and

the maximum value is 5.740 (i.e., the baseline value of the SIRBFRL ± 1.5). Note that the

ranges of values and the standard deviations for the five economic measures for which

variations do occur for the “costs” side of the analysis are less than those for three of the

four input variables associated with the “savings/benefits” side of the analysis (i.e., first

cost savings, reductions in delivery time, and higher contractor net income (see Tables 8-

6, 8-8, and 8-9)). Thus, the economic measures are “less sensitive” to variations in the

“costs” side of the analysis than they are to the “savings/benefits” side of the analysis.

Table 8-10. Summary Statistics Due to Changes in the Input Variable New-

 Technology Introduction Costs

Statistical Measure

Economic

Measure Minimum 25% 50% 75% Maximum Mean Standard

Deviation

PVSALL 765.223 1,183.489 1,396.944 1,596.607 2,021.082 1,386.715 276.424

PVSBFRL 75.898 106.333 121.865 136.394 167.281 121.121 20.114

PVCBFRL 29.144 29.144 29.144 29.144 29.144 29.144 0.0

PVNSBFRL 46.754 77.189 92.721 107.250 138.137 91.977 20.114

SIRBFRL 2.604 3.649 4.181 4.680 5.740 4.156 0.690

AIRRBFRL 0.112 0.127 0.133 0.138 0.147 0.132 0.008
136

Table 8-11 shows how variations about the baseline value for the time of first use (i.e., t =

1 in the year 2005) affect each economic measure. The alternative times of first use are

specified by a discrete distribution (see Table 8-1). The discrete probabilities for each

year are: 2004, 0.125; 2005, 0.5; 2006, 0.25; and 2007, 0.125. The time of first use

affects all six economic measures. It exerts a moderate to strong influence on the present

value of savings nationwide, PVSALL, since it determines the number of years over which

cost savings can accrue. This is because the end of the study period is fixed at 2017.

Thus, if the year of first use is 2007, there are fewer years over which savings can accrue

than for the baseline value (i.e., 2005). Notice that the measures of BFRL’s influence are

strongly affected. This is because BFRL’s contribution is measured in terms of the

savings occurring in those years before CONSIAT products and services would become

available commercially were it not for BFRL’s influence (i.e., 2009). The year of first

use defines when savings begin. Also, because BFRL is targeting the introduction of

CONSIAT products and services for a particular year, BFRL’s investment cost stream,

PVCBFRL, varies as a function of the target date. Thus, the differences from the value

calculated in the baseline analysis are due to the timing of the savings and BFRL’s

investment cost stream, which, through the discount rate, affects the values of PVSBFRL

and PVCBFRL. Variations in the value of PVSBFRL and PVCBFRL are responsible for

variations in the values of PVNSBFRL, SIRBFRL, and AIRRBFRL.

Table 8-11. Summary Statistics Due to Changes in the Input Variable Time of

 First Use

Statistical Measure

Economic

Measure Minimum 25% 50% 75% Maximum Mean Standard

Deviation

PVSALL 853.341 1,098.833 1,377.926 1,377.926 1,690.445 1,292.270 240.842

PVSBFRL 29.310 64.186 120.482 120.482 208.436 108.465 52.134

PVCBFRL 27.069 28.872 29.144 29.144 34.878 29.656 2.228

PVNSBFRL 2.241 35.314 91.337 91.337 173.558 78.810 50.146

SIRBFRL 1.083 2.223 4.134 4.134 5.976 3.568 1.468

AIRRBFRL 0.073 0.105 0.133 0.133 0.149 0.121 0.023

Table 8-12 shows how variations about the baseline value for the length of the delay

affect each economic measure. The alternative numbers of years for the length of the

delay are specified by a discrete distribution (see Table 8-1). The discrete probabilities

for each length of delay are: 0 years, 0.6; 1 year, 0.25; and 2 years, 0.15. The length of

delay affects five of the six economic measures. The value of PVCBFRL is not affected by

the input variable length of delay. Because the baseline analysis assumed no delay, the

computed value for each economic measure equals the value calculated in the baseline

analysis for three of the six statistical measures (i.e., the 50th percentile, the 75th

percentile, and the maximum). By virtue of the way the discrete distribution is specified

for the length of the delay, the 25th percentile is associated with a one-year delay and the

minimum is associated with a two-year delay. Reference to Table 8-12 reveals that with

the exception of first cost savings (see Table 8-6) the minimum value for four of the five
137

measures of BFRL’s influence—PVSBFRL, PVNSBFRL, SIRBFRL, and AIRRBFRL—is lower

than the corresponding minimum value for each of these variables examined previously.

Although the minimum values for these four measures of BFRL’s influence are smaller

for this input variable than for nine of the other ten, in every case BFRL’s contribution is

positive (i.e., PVNSBFRL > 0.0, SIRBFRL > 1.0, and AIRRBFRL > 0.07). The length of delay

affects the calculated values of the economic measures in a way that is similar to the year

of first use. This is because BFRL’s contribution is measured in terms of the savings

occurring in those years before CONSIAT products and services would become available

commercially were it not for BFRL’s influence (i.e., 2009). Thus, a two-year delay,

measured against the baseline year of first use of 2005, delays the commencement of the

savings/benefits stream until 2007. Reference to Table 8-11 reveals that the minimum

values for PVSALL and PVSBFRL are the same as in Table 8-12. In the case of Table 8-11,

these values correspond to a “no delay” (i.e., the baseline value for the length of delay)

and a year of first use of 2007.

Table 8-12. Summary Statistics Due to Changes in the Input Variable Length of

 Delay

Statistical Measure

Economic

Measure Minimum 25% 50% 75% Maximum Mean Standard

Deviation

PVSALL 853.341 1,098.833 1,377.926 1,377.926 1,377.926 1,231.876 195.960

PVSBFRL 29.310 64.186 120.482 120.482 120.482 93.174 35.500

PVCBFRL 29.144 29.144 29.144 29.144 29.144 29.144 0.0

PVNSBFRL 0.165 35.041 91.337 91.337 91.337 64.030 35.500

SIRBFRL 1.006 2.202 4.134 4.134 4.134 3.197 1.218

AIRRBFRL 0.070 0.104 0.133 0.133 0.133 0.116 0.023

Table 8-13 summarizes the results of a deterministic sensitivity analysis for the combined

effects of the “targeted” time of first use and the length of delay. Table 8-13 has three

parts: (1) Part A, where each targeted time of first use is evaluated assuming no delay; (2)

Part B, where each targeted time of first use is evaluated assuming a one-year delay; and

(3) Part C, where each targeted time of first use is evaluated assuming a two-year delay.

The results presented in Table 8-13 differ significantly from those presented in all but one

(see Table 8-6) of the previous tables in an important way. Namely, the minimum values

of four of the key measures of BFRL’s influence (i.e., PVSBFRL, PVNSBFRL, SIRBFRL, and

AIRRBFRL

103) indicate that BFRL’s CONSIAT-related investments may not be cost

effective. To place the previous remark in context, it is important to recognize that this

outcome corresponds to situations where a non-zero delay is present. For example,

reference to Part A of Table 8-13 reveals that for all targeted times of first use all

economic measures indicate that BFRL’s CONSIAT-related investments are cost

effective. Reference to Parts B and C reveals that a delay of one year for the 2007

 

103 The value of the AIRR is only defined for cases where the computed value of the SIR is non-negative.

If the computed value of SIRBFRL is negative, then the value of AIRRBFRL is listed as n.a. Similarly, if the

computed value of SIRBFRL is zero or positive but after taking the 1/Lth root (see Equation 2.5) the imputed

value of AIRRBFRL is negative, then the value of AIRRBFRL is listed as n.a.
138

targeted time of first use and of two years for the 2006 targeted time of first use will

result in a negative PVNSBFRL. What Table 8-13 does not show is the probability that the

computed value of an economic measure will indicate that BFRL’s CONSIAT-related

investment will not be cost effective. To estimate the probability that BFRL’s

CONSIAT-related investment will not be cost effective requires a more comprehensive

analysis. Specifically, Table 8-13 demonstrates that the combined effects of the targeted

year of first use and the length of the delay are sufficient to lead to “undesirable”

outcomes. However, what about other combinations of input variables? Could

combinations of all eleven inputs lead to more extreme outcomes or will they offset the

“undesirable” outcomes illustrated in Table 8-13? This analysis “topic” is the subject of

Subsection 8.3.2

Table 8-13. Results of the Deterministic Sensitivity Analysis for the Combined

 Effects Due to Changes of the Input Variables “Targeted” Time of

 First Use and the Length of the Delay

Part A: No Delay

Year of First Commercial Use Economic

Measure 2004 2005 2006 2007

PVSALL 1,690.445 1,377.926 1,098.833 853.341

PVSBFRL 208.436 120.482 64.186 29.310

PVCBFRL 34.878 29.144 28.872 27.069

PVNSBFRL 173.558 91.337 35.314 2.241

SIRBFRL 5.976 4.134 2.223 1.083

AIRRBFRL 0.149 0.133 0.105 0.073

Part B: One-Year Delay

Year of First Commercial Use Economic

Measure 2004 2005 2006 2007

PVSALL 1,377.926 1,098.833 853.341 646.198

PVSBFRL 120.482 64.186 29.310 12.514

PVCBFRL 34.878 29.144 28.872 27.069

PVNSBFRL 85.604 35.041 0.438 -14.555

SIRBFRL 3.454 2.202 1.015 0.462

AIRRBFRL 0.124 0.104 0.071 0.037

Part C: Two-Year Delay

Year of First Commercial Use Economic

Measure 2004 2005 2006 2007

PVSALL 1,098.833 853.341 646.198 469.371

PVSBFRL 64.186 29.310 12.514 0.0

PVCBFRL 34.878 29.144 28.872 27.069

PVNSBFRL 29.308 0.165 -16.358 -27.069

SIRBFRL 1.840 1.006 0.433 0.0

AIRRBFRL 0.096 0.070 0.035 n.a.
139

8.3.2 Changing All Eleven Inputs in Combination

Table 8-14 summarizes the results of the Monte Carlo simulation in which all eleven of

the input variables were varied in combination. Reference to Table 8-14 reveals that the

present value of net savings due to BFRL, PVNSBFRL, can be negative. This implies that

there is some non-zero probability that BFRL’s CONSIAT-related investments are not

cost effective. However, on the opposite extreme, PVNSBFRL may exceed $800 million in

1997 dollars, and SIRBFRL reaches 26.6.

A closer examination of Table 8-14 reveals several interesting outcomes. First, the range

of values—the difference between the minimum and maximum—is very wide. For

example, the minimum value of PVSALL is approximately -$1.35 billion, whereas the

maximum is almost $8.0 billion. Second, the computed value of the mean equals or

exceeds the computed value of the median for five of the six economic measures. This is

because a small number of very large observations are pulling up the computed value of

the mean. Third, the computed value of the standard deviation for each of the six

economic measures is higher than the corresponding value for variations in a single input

variable. This is also due to a small number of very large observations. Finally, the

computed values of both the mean and the median of each of the five economic measures

of BFRL’s contribution are lower than the corresponding baseline value. This is due to

the way the length of the delay enters into the calculations. In the baseline analysis, no

delay was assumed. Thus, if CONSIAT products and services were targeted for

commercialization in 2005, they were assumed to enter the market in 2005. In the

sensitivity analysis, CONSIAT products and services enter the market in the year targeted

60 % of the time, one year after the year targeted 25 % of the time, and two years after

the year targeted 15 % of the time. As was seen in Part C of Table 8-13, a delay of two

years may lead to commercialization in 2009, the year in which commercialization is

assumed to take place without BFRL’s participation. Thus, although the nation reaps

substantial cost savings associated with the use of CONSIAT products and services, none

of these savings are attributed to BFRL. This is due to our conservative approach to

measuring BFRL’s impact, in which zero weight is assigned to any savings accruing in

2009 or thereafter.

Table 8-14. Summary Statistics Due to Changes in All of the Input Variables

Statistical Measure

Economic

Measure Minimum 25% 50% 75% Maximum Mean Standard

Deviation

PVSALL -1,353.334 833.362 1,461.006 2,274.788 7,979.868 1,686.191 1,221.494

PVSBFRL -38.580 32.322 78.666 164.442 860.753 120.427 124.971

PVCBFRL 24.529 28.503 30.125 32.289 43.889 30.719 3.140

PVNSBFRL -71.204 2.530 48.726 132.643 828.414 89.708 123.768

SIRBFRL -1.183 1.076 2.611 5.258 26.617 3.810 3.773

AIRRBFRL n.a. 0.063 0.105 0.135 0.226 0.104 0.046
140

The fact that the range of outcomes is so wide suggests that an in-depth examination of

the results of this Monte Carlo simulation is warranted. We now turn to this in-depth

examination.

The graphical results of the sensitivity analysis where all eleven input variables were

varied in combination are shown in Figures 8-1 through 8-6. The figures were

constructed by first sorting the values of each economic measure from smallest to largest.

The resultant cumulative distribution function (CDF) was then plotted. In each figure,

the vertical axis records the probability that the economic measure (e.g., SIRBFRL) is less

than or equal to a specified value. The values recorded on the horizontal axis cover the

range of values encountered during this Monte Carlo simulation.

The tabular results of the sensitivity analysis are recorded in Table 8-15. The table lists

each of the calculated percentiles from the resultant CDF. The range of percentiles

included in the table go from the 1st to the 99th. For purposes of this analysis, the 0th

percentile is set equal to the minimum value, and the 100th percentile is set equal to the

maximum value (see Table 8-14). This enables a close coupling of the values recorded in

Table 8-15 and the values used to plot each figure.

Table 8-15 includes for each percentile the computed value for PVSALL, PVSBFRL,

PVCBFRL, PVNSBFRL, SIRBFRL, and AIRRBFRL. The percentiles are computed based on all

1,000 data points (i.e., observations) for each economic measure. The percentiles are

estimated by first ordering each economic measure and then applying a statistical

procedure. Readers interested in procedures for estimating percentiles are referred to the

text by Ott.104

Figure 8-1 shows how present value cost savings nationwide, PVSALL, varies when all

eleven input variables are varied in combination. In analyzing Figure 8-1, it is useful to

keep in mind that the value of PVSALL resulting from the baseline analysis was $1,377.9

million. As was seen in Table 8-14, the median value of the 1,000 observations was

$1,461.0 million, more than $80 million more than the value of PVSALL calculated in the

baseline analysis. What the figure shows clearly is the considerable degree to which

PVSALL varies—both above and below the median value.

To best understand the implications of these variations, it is useful to refer both to Figure

8-1 and the entries under the PVSALL column heading of Table 8-15. First, the lower

limit shown on Figure 8-1 is -$1,353.3 million. However, the 1st percentile for PVSALL is

-$225.7 million. Thus, only 10 observations out of 1,000 are clustered between -$1,353.3

and the 1st percentile (-$225.7 million). Second, the CDF increases at a steady, almost

linear rate between the 9th percentile ($388.1 million) and the 60th percentile ($1,731.1

million). Third, above the 60th percentile, the CDF increases at a decreasing rate. This is

shown by the way in which the CDF tails off as the calculated value of PVSALL gets

 

104 Ott, Lyman. 1984. An Introduction to Statistical Methods and Data Analysis. Boston, MA: Duxbury

Press.
141

large. Finally, the maximum value of PVSALL is almost $8.0 billion. However, the 99th

percentile is $5.6 billion. Thus, only 10 observations out of 1,000 account for

approximately $2.4 billion in the total range of values for PVSALL. This implies that the

trace of the CDF for PVSALL is positively skewed.

Figure 8-2 shows how present value savings due to BFRL, PVSBFRL, varies when all

eleven input variables are varied in combination. In analyzing Figure 8-2, it is useful to

keep in mind that the value of PVSBFRL resulting from the baseline analysis was $120.5

million. As was seen in Table 8-14, the median value of the 1,000 observations was

more than $40 million less than the value of PVSBFRL calculated in the baseline analysis.

Figure 8-2 exhibits a pattern similar to the pattern seen in Figure 8-1. There is, however,

one important difference between the traces of the CDFs in the two figures. Figure 8-2 is

more positively skewed than Figure 8-1 (compare the upper tails of the two CDF traces).

To best understand the implications of these variations, it is useful to refer both to Figure

8-2 and the entries under the PVSBFRL column heading of Table 8-15. First, note that the

lower limit shown on Figure 8-2 extends to -$38.6 million. Reference to Table 8-15

reveals that the 1st percentile for PVSBFRL is still negative (-$0.006 million) and that the

2nd percentile becomes $0.0. However, by the 3rd percentile, the computed value of

PVSBFRL becomes positive ($3.4 million). Thus, less than 30 observations out of 1,000

result in no savings attributable to BFRL. Stated another way, there is at least a 97 %

probability that BFRL’s CONSIAT-related efforts will produce positive and measurable

cost savings to building owners, managers, and contractors. Second, the CDF increases

at a steady, almost linear rate between the 3rd percentile ($3.4 million) and the 50th

percentile ($78.7 million). Third, above the 50th percentile, the CDF increases at a

decreasing rate. This is shown by the way in which the CDF tails off as the calculated

value of PVSBFRL gets large. Finally, the maximum value of PVSBFRL is approximately

$860 million. However, the 99th percentile is $564.6 million. Thus, only 10 observations

out of 1,000 account for almost $300 million in the total range of values for PVSBFRL.

Figure 8-3 shows how the present value of BFRL’s CONSIAT-related investment costs,

PVCBFRL, varies when all eleven input variables are varied in combination. Because the

only variables that produce variations in PVCBFRL are the discount rate and the “targeted”

time of first use, the shape of Figure 8-3 differs from the shapes of all of the other figures

presented in this section.

Reference to the figure and to Tables 8-14 and 8-15 reveals that the CDF for PVCBFRL

exhibits not only a long tail on the upper end but a significant lower tail as well. From

Table 8-14 we see that the mean and median are relatively close in value, and are nearly

equal to the baseline value. An examination of Figure 8-3 reveals that the trace of the

CDF for PVCBFRL is nearly linear between the 15th percentile ($27.837 million) and the

60th percentile ($30.785 million). Below the 15th percentile and above the 60th percentile

the traces exhibit similar patterns of non-linearity.
142

Table 8-15. Percentiles for Statistical Measures Due to Changes in All of the Input

 Variables

PVSALL PVSBFRL PVCBFRL PVNSBFRL SIRBFRL AIRRBFRL

1ST -225.729 -0.006 25.778 -30.674 0.000 n.a.

2ND -41.299 0.000 26.177 -28.284 0.000 n.a.

3RD 7.326 3.386 26.403 -26.880 0.117 n.a.

4TH 60.528 4.453 26.726 -23.490 0.161 n.a.

5TH 128.929 6.141 26.851 -22.325 0.218 0.000

6TH 189.997 8.227 26.906 -20.806 0.289 0.006

7TH 247.089 9.625 27.037 -19.361 0.343 0.014

8TH 326.500 11.082 27.111 -18.239 0.373 0.018

9TH 388.069 12.037 27.186 -17.239 0.415 0.022

10TH 425.319 12.805 27.309 -16.260 0.440 0.027

11TH 444.672 14.099 27.417 -14.970 0.483 0.029

12TH 471.215 15.671 27.503 -14.023 0.522 0.032

13TH 503.377 16.752 27.638 -12.792 0.566 0.037

14TH 532.046 17.293 27.756 -11.432 0.600 0.039

15TH 562.980 19.362 27.837 -10.292 0.660 0.040

16TH 593.427 20.952 27.890 -8.698 0.697 0.044

17TH 619.940 21.950 27.926 -7.682 0.743 0.045

18TH 633.675 23.583 28.003 -6.497 0.787 0.048

19TH 672.176 25.755 28.063 -4.058 0.872 0.050

20TH 709.518 26.528 28.140 -3.294 0.895 0.052

21ST 731.911 27.604 28.216 -2.077 0.931 0.055

22ND 774.909 28.659 28.267 -1.371 0.952 0.057

23RD 792.420 29.981 28.354 -0.039 0.999 0.058

24TH 814.590 31.189 28.425 1.246 1.044 0.061

25TH 833.362 32.322 28.503 2.530 1.076 0.063

26TH 859.438 33.460 28.582 3.650 1.128 0.065

27TH 872.718 34.848 28.659 6.078 1.194 0.067

28TH 887.213 36.567 28.714 7.581 1.257 0.068

29TH 923.801 38.756 28.785 8.589 1.287 0.070

30TH 952.205 40.061 28.843 9.357 1.318 0.072

31ST 986.646 42.133 28.893 11.883 1.388 0.074

32ND 1,010.696 43.876 28.946 13.566 1.426 0.075

33RD 1,033.603 45.176 28.975 15.100 1.486 0.076

Economic Measure

Percentile
143

Table 8-15. Percentiles for Statistical Measures Due to Changes in All of the Input

 Variables (continued)

PVSALL PVSBFRL PVCBFRL PVNSBFRL SIRBFRL AIRRBFRL

34TH 1,046.883 46.449 29.029 16.249 1.545 0.079

35TH 1,070.716 47.684 29.063 17.035 1.585 0.081

36TH 1,110.502 48.877 29.095 18.974 1.625 0.083

37TH 1,130.370 50.126 29.187 20.422 1.685 0.084

38TH 1,164.760 51.413 29.256 21.354 1.741 0.086

39TH 1,190.948 54.965 29.312 23.974 1.806 0.088

40TH 1,211.380 55.724 29.412 26.639 1.867 0.090

41ST 1,240.338 58.498 29.500 28.712 1.926 0.091

42ND 1,249.820 60.979 29.579 30.615 2.023 0.093

43RD 1,269.751 62.406 29.659 32.459 2.081 0.094

44TH 1,292.194 63.796 29.718 34.279 2.152 0.096

45TH 1,316.128 66.401 29.776 36.296 2.201 0.097

46TH 1,354.130 67.980 29.882 38.302 2.309 0.099

47TH 1,381.995 70.572 29.964 40.252 2.350 0.101

48TH 1,401.785 72.098 30.018 41.822 2.452 0.102

49TH 1,438.790 75.518 30.066 45.152 2.520 0.103

50TH 1,461.006 78.666 30.125 48.726 2.611 0.105

51ST 1,496.820 82.533 30.179 52.217 2.702 0.106

52ND 1,516.942 84.214 30.251 54.835 2.753 0.107

53RD 1,536.935 85.979 30.322 56.218 2.830 0.109

54TH 1,563.003 88.821 30.388 57.352 2.912 0.110

55TH 1,587.752 91.915 30.442 60.810 2.999 0.111

56TH 1,617.477 93.547 30.522 62.599 3.109 0.112

57TH 1,646.228 95.526 30.586 64.991 3.192 0.113

58TH 1,674.037 98.315 30.652 67.234 3.237 0.114

59TH 1,701.748 101.084 30.707 71.079 3.299 0.115

60TH 1,731.099 104.975 30.785 73.647 3.366 0.117

61ST 1,753.054 109.272 30.866 78.204 3.444 0.117

62ND 1,801.729 112.352 30.967 81.261 3.543 0.119

63RD 1,839.372 114.522 31.052 84.390 3.760 0.121

64TH 1,880.123 118.033 31.149 87.588 3.866 0.121

65TH 1,904.534 121.706 31.232 90.902 3.922 0.123

66TH 1,939.521 124.543 31.342 93.475 4.019 0.124

Economic Measure

Percentile
144

Table 8-15. Percentiles for Statistical Measures Due to Changes in All of the Input

 Variables (continued)

PVSALL PVSBFRL PVCBFRL PVNSBFRL SIRBFRL AIRRBFRL

67TH 1,985.279 127.782 31.427 97.138 4.122 0.125

68TH 2,031.487 132.953 31.544 102.062 4.238 0.126

69TH 2,076.201 138.767 31.639 107.066 4.343 0.128

70TH 2,121.199 143.661 31.776 112.370 4.482 0.129

71ST 2,159.779 148.656 31.877 115.407 4.700 0.130

72ND 2,189.824 151.448 31.962 121.007 4.858 0.132

73RD 2,215.715 154.286 32.113 124.117 4.962 0.132

74TH 2,254.461 159.857 32.199 128.528 5.128 0.134

75TH 2,274.788 164.442 32.289 132.643 5.258 0.135

76TH 2,333.132 170.876 32.352 140.356 5.449 0.136

77TH 2,375.492 177.504 32.493 146.334 5.652 0.138

78TH 2,396.502 184.394 32.654 150.944 5.872 0.140

79TH 2,455.405 191.449 32.808 156.944 6.059 0.142

80TH 2,491.533 196.332 33.029 166.205 6.258 0.143

81ST 2,592.521 202.451 33.193 171.075 6.477 0.145

82ND 2,629.810 207.046 33.407 176.372 6.689 0.146

83RD 2,695.161 220.082 33.514 187.193 6.964 0.148

84TH 2,752.600 230.660 33.651 198.535 7.238 0.149

85TH 2,814.330 239.170 33.958 206.527 7.427 0.150

86TH 2,914.070 246.576 34.106 216.067 7.799 0.152

87TH 2,979.936 256.283 34.288 223.212 8.031 0.153

88TH 3,055.092 264.734 34.497 231.137 8.452 0.155

89TH 3,163.703 270.142 34.838 237.038 8.702 0.158

90TH 3,261.409 282.190 35.125 250.085 8.921 0.160

91ST 3,371.450 293.051 35.411 263.269 9.207 0.163

92ND 3,493.785 311.375 35.691 278.865 9.805 0.164

93RD 3,614.232 328.511 35.950 295.711 10.474 0.167

94TH 3,797.795 347.591 36.382 316.748 10.871 0.169

95TH 4,063.257 387.208 36.769 353.696 11.962 0.173

96TH 4,298.030 416.571 37.371 381.411 12.468 0.180

97TH 4,514.824 451.341 38.179 420.650 13.230 0.184

98TH 5,022.385 508.469 39.030 476.173 14.922 0.189

99TH 5,608.432 564.584 40.677 534.159 16.380 0.197

Economic Measure

Percentile
145

Figure 8-1. Present Value of Cost Savings Nationwide in Millions of 1997 Dollars

0.000.100.200.300.400.500.600.700.800.901.00

-1,500

-1,000

-5000

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

5,500

6,000

6,500

7,000

Present Value of Cost Savings Nationwide in Millions of 1997 Dollars

Cumulative Probability
146

Figure 8-2. Present Value of Cost Savings Attributable to BFRL in Millions of 1997 Dollars

0.000.100.200.300.400.500.600.700.800.901.00

-100 0 100 200 300 400 500 600 700 800 

Present Value of Cost Savings Attributable to BFRL in Millions of 1997 Doll

Cumulative Probability
147

Figure 8-3. Present Value of BFRL’s Investment Costs in Millions of 1997 Dollars

0.000.100.200.300.400.500.600.700.800.901.00

24.0 26.0 28.0 30.0 32.0 34.0 36.0 38.0 40.0 4

Present Value of BFRL's Investm ent Costs in M illions of 1997 Doll

Cumulative Probability
148

Figure 8-4 shows how present value net savings due to BFRL, PVNSBFRL, varies when all

eleven input variables are varied in combination. In analyzing Figure 8-4, it is useful to

keep in mind that the value of PVNSBFRL resulting from the baseline analysis was $91.3

million. As was seen in Table 8-14, the median value of the 1,000 observations was

more than $40 million less than the value of PVNSBFRL calculated in the baseline

analysis. Also, Figure 8-4 exhibits a pattern similar to the pattern seen in Figure 8-2.

Note that both Figure 8-2 and Figure 8-4 are highly, positively skewed (compare the

upper tails of the two CDF traces). In addition, both figures are defined over nearly

identical ranges of values. This similarity is to be expected since the only difference

between PVSBFRL (see Figure 8-2) and PVNSBFRL is PVCBFRL (see Figure 8-3). Recall

that PVCBFRL was defined over a relatively narrow range of values. Thus, throughout the

range of values over which PVSBFRL and PVNSBFRL are defined, the value of PVCBFRL

acts much like a constant term.

As was the case for the previous figures, it is useful to refer both to Figure 8-4 and the

entries under the PVNSBFRL column heading of Table 8-15. First, note that the lower

limit shown on Figure 8-4 extends below $0.0. Reference to Table 8-15 reveals that the

1st percentile for PVNSBFRL is still negative (-$30.7 million). However, by the 24th

percentile, the computed value of PVNSBFRL becomes positive ($1.2 million). Thus, less

than 240 observations out of 1,000 are negative. Stated another way, there is at least a 76

% probability that BFRL’s CONSIAT-related investments are cost effective. Second, the

CDF increases at a steady, almost linear rate between the 5th percentile ($-22.3 million)

and the 50th percentile ($48.7 million). Third, above the 50th percentile, the CDF

increases at a decreasing rate. This is shown by the way in which the CDF tails off as the

calculated value of PVNSBFRL gets large. Finally, the maximum value of PVNSBFRL is

nearly $830 million. However, the 99th percentile is $534.2 million. Thus, only 10

observations out of 1,000 account for approximately $300 million in the total range of

values for PVNSBFRL.

Because there are so many similarities—very low values, very high values, and the CDF

traces—between the results of the sensitivity analysis for PVSBFRL and PVNSBFRL, it is

useful to analyze the underlying characteristics of both the upper and lower tails of the

two CDF traces. This analysis was facilitated through the use of the @RISK software

product. Specifically, the @RISK software product enables the random draw for each

input variable for each of the 1,000 simulations to be stored in a spreadsheet. This

produces a one-to-one correspondence between each simulation’s input set and the

resultant values of the economic measures.

As might be expected, the factors that contribute to very low values of PVSBFRL and

PVNSBFRL differ from those that contribute to very high values of PVSBFRL and

PVNSBFRL. Consider first the very low values of PVSBFRL and PVNSBFRL (i.e., the 50

lowest values). Two factors, operating in combination, contribute to very low values of

PVSBFRL and PVNSBFRL. These factors are “later” values for the “targeted” time of first

use and the length of the delay. For example, of the 50 lowest values, 13 had no delay,

all but four of which had a “targeted” time of first use in either 2006 or 2007, whereas 26

had a two-year delay. Five other factors had a strong influence: (1) reductions in first
149

cost, if its value is less than the 10th percentile of its parent CDF; (2) reductions in

delivery time, if its value is less than the 10th percentile of its parent CDF; (3)

maintenance and repair cost savings, if its value is less than the 10th percentile of its

parent CDF; (4) higher contractor net income, if its value is less than the 10th percentile

of its parent CDF; and (5) new-technology introduction costs, if its value is greater than

the 90th percentile of its parent CDF. Very high values (i.e., the 50 highest values) were

associated with two factors operating in combination—“earlier” values for the “targeted”

time of first use and the length of the delay. For example, 44 of the 50 highest values had

no delay and a “targeted” time of first use of 2004 or 2005. Four other factors had a

strong influence: (1) reductions in first cost, if its value is greater than the 90th percentile

of its parent CDF; (2) higher contractor net income, if its value is greater than the 90th

percentile of its parent CDF; (3); reductions in delivery time, if its value is greater than

the 90th percentile of its parent CDF; and (4) α, if its value is less than the 10th percentile

of its parent CDF.

Figure 8-5 shows how the savings-to-investment ratio on BFRL’s CONSIAT-related

investments, SIRBFRL, varies when all eleven input variables are varied in combination.

In analyzing Figure 8-5, it is useful to keep in mind that the value of SIRBFRL resulting

from the baseline analysis was 4.134. As was seen in Table 8-14, the median value of the

1,000 observations was 2.611. Also, Figure 8-5 exhibits a pattern similar to the pattern

seen in Figure 8-2. Note that both Figure 8-5 and Figure 8-2 are highly, positively

skewed (compare the upper tails of the two CDF traces). This similarity in shapes is to

be expected since SIRBFRL is the ratio of PVSBFRL to PVCBFRL. Recall that PVCBFRL was

defined over a relatively narrow range of values. Thus, the value of PVCBFRL acts very

much like a constant term. Although the shapes of the two distributions are similar, the

ranges of values are specified in different units.

As was the case for the previous figures, it is useful to refer both to Figure 8-5 and the

entries under the SIRBFRL column heading of Table 8-15. First, note that the lower limit

shown on Figure 8-5 extends to –1.183. Reference to Table 8-15 reveals that the 1st

percentile for SIRBFRL is 0.0. However, by the 24th percentile, the computed value of

SIRBFRL exceeds 1.0 (1.044). Thus, less than 240 observations out of 1,000 are less than

1.0. Stated another way, based on the calculated value of SIRBFRL, there is at least a 76 %

probability that BFRL’s CONSIAT-related investments are cost effective. Second, the

CDF increases at a steady, almost linear rate between the 5th percentile (0.218) and the

40th percentile (1.867). Third, above the 40th percentile, the CDF increases at a

decreasing rate. This is shown by the way in which the CDF tails off as the calculated

value of SIRBFRL gets large. Finally, the maximum value of SIRBFRL is 26.617. However,

the 99th percentile is 16.380. Thus, only 10 observations out of 1,000 account for

approximately 10 units in the total range of values for SIRBFRL.

Figure 8-6 shows how the adjusted internal rate of return on BFRL’s CONSIAT-related

investments, AIRRBFRL, varies when all eleven input variables are varied in combination.

In analyzing Figure 8-6, it is useful to keep in mind that the value of AIRRBFRL resulting

from the baseline analysis was 0.133. As was seen in Table 8-14, both the mean (0.104)

and median (0.105) values of the 1,000 observations were nearly equal. Figure 8-6
150

exhibits a pattern different from those seen in the other figures. Note that Figure 8-6 is

negatively skewed (compare the lower and upper tails of the CDF trace). Although the

values for AIRRBFRL are a monotonic transformation of the values for SIRBFRL, the shapes

of the two CDFs are quite dissimilar. This is because the AIRRBFRL is functionally

related to (SIRBFRL)

1/25. This relationship is highly non-linear, explaining why the two

CDF traces are so dissimilar.

As was the case for the previous figures, it is useful to refer both to Figure 8-6 and the

entries under the AIRRBFRL column heading of Table 8-15. First, note that the lower

limit shown on Figure 8-6 is 0.0. This is because a value of AIRRBFRL less than 0.0 has

no economic meaning. Such cases are designated by the term n.a. in Tables 8-14 and 8-

15. Reference to Table 8-15 reveals that the 1st percentile through the 4th percentile for

AIRRBFRL is n.a. This entry is reflected by the “step-up” in the CDF at 0.0. By the 25th

percentile, the computed value of AIRRBFRL exceeds the average value for the 1,000

“draws” from the parent CDF for the real discount rate of 0.0619. Thus, less than 250

observations out of 1,000 produce a value for the AIRRBFRL less than the discount rate.105

Stated another way, based on the calculated value of AIRRBFRL, there is at least a 75 %

probability that BFRL’s CONSIAT-related investments are cost effective. Second, the

CDF increases at a steady, almost linear rate between the 15th percentile (0.040) and the

85th percentile (0.150). Third, below the 15th percentile, the CDF increases at an

increasing rate. Finally, above the 85th percentile, the CDF increases at a decreasing rate.

Note that the values of PVNSBFRL, SIRBFRL, and AIRRBFRL all indicated the regions of the

appropriate CDF trace where BFRL’s CONSIAT-related investments were cost effective.

In each case these economic measures defined very nearly the same break-even point in

each of the CDF traces. The break-even point corresponds to a value of each economic

measure just below the 24th or 25th percentile of its CDF. This point is noteworthy, since

each measure provides a different perspective, but produces essentially the same end

result in terms of identifying the break-even point.

 

105 Note that the 29th percentile had a value of 0.07 (i.e., a value that equals the baseline value for the

discount rate).
151

Figure 8-4. Present Value of Net Savings Attributable to BFRL in Millions of 1997 Dollars

0.000.100.200.300.400.500.600.700.800.901.00

-100 0 100 200 300 400 500 600 700 

Present Value of Net Savings Attributable to BFRL in Millions of 1997 Dollars

Cumulative Probability
152

Figure 8-5. Savings to Investment Ratio on BFRL’s Research and Development Investment

0.000.100.200.300.400.500.600.700.800.901.00

-2 0 2 4 6 8 10 12 14 16 18 20 22 24 

Savings to Investment Ratio on BFRL's Research and Development Investm

Cumulative Probability
153

Figure 8-6. Adjusted Internal Rate of Return on BFRL’s Research and Development Investment

0.000.100.200.300.400.500.600.700.800.901.00

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 

Adjusted Internal Rate of Return on BFRL's Research and Development Inve

Cumulative Probability
154
155

9 Summary and Suggestions for Further Research

9.1 Summary

A formal resource allocation process for funding research is needed in both the public

and private sectors. Research managers need guidelines for research planning so that

they can maximize the payoffs from their limited resources. Furthermore, quantitative

descriptions of research impacts have become a basic requirement in many organizations

for evaluating budget requests.

There are several reasons for measuring the economic impacts of a federal laboratory’s

research program. First, economic impact studies are a management tool; they help set

priorities and point to new research opportunities. Second, as federal laboratories

become more customer oriented, by revealing the “voice of the customer,” such studies

will strengthen the ties to industry and identify opportunities for leveraging federal

research investments. Finally, changing requirements, such as the Government

Performance and Results Act, will affect how federal research funds are allocated.

Increasingly, federal agencies and laboratories that fail to demonstrate that their research

efforts complement those of industry and that they are having a positive impact on

society will be at a disadvantage when competing for federal research funds.

The National Institute of Standards and Technology (NIST), a scientific research agency

of the U.S. Department of Commerce’s Technology Administration, is improving its

resource allocation process by doing “microstudies” of its research impacts on society.

This report is the fifth in a series of impact studies prepared by BFRL.106 It focuses on

BFRL’s construction systems integration and automation technologies (CONSIAT)

program. The CONSIAT program is an interdisciplinary research effort within BFRL—

in collaboration with the Construction Industry Institute, the private sector, other federal

agencies, and other laboratories within NIST—to develop key enabling technologies,

standard communication protocols, and advanced measurement technologies needed to

 

106The first report in the series focuses on two building technology applications: (1) ASHRAE Standard 90-

75 for residential energy conservation; and (2) 235 shingles, an improved asphalt shingle for sloped roofing

(see Chapman, Robert E., and Sieglinde K. Fuller. 1996. Benefits and Costs of Research: Two Case

Studies in Building Technology. NISTIR 5840. Gaithersburg, MD: National Institute of Standards and

Technology). The second report focuses on a fire technology application: the Fire Safety Evaluation

System for health care facilities (see Chapman, Robert E., and Stephen F. Weber. 1996. Benefits and

Costs of Research: A Case Study of the Fire Safety Evaluation System. NISTIR 5863. Gaithersburg, MD:

National Institute of Standards and Technology). The third report focuses on the research, development,

deployment, and adoption and use of cybernetic building systems in office buildings (see Chapman, Robert

E. 1999. Benefits and Costs of Research: A Case Study of Cybernetic Building Systems. NISTIR 6303.

Gaithersburg, MD: National Institute of Standards and Technology). The fourth report focuses on the

research, development, and deployment, and adoption and use of construction systems integration and

automation technologies in industrial facilities (see Chapman, Robert E. 2000. Benefits and Costs of

Research: A Case Study of Construction Systems Integration and Automation Technologies in Industrial

Facilities. NISTIR 6501. Gaithersburg, MD: National Institute of Standards and Technology).
156

deliver fully-integrated and automated project process (FIAPP) products and services to

the construction industry.

BFRL’s CONSIAT program is aimed at producing a suite of products and services that

integrate a wide variety of planning, design, and construction activities. The goal of

BFRL’s CONSIAT program is to produce FIAPP products and services that will result in

significant reductions in both the delivery time of constructed facilities and the life-cycle

costs of those facilities. These products and services are being developed for use by

building owners and operators, construction contractors, architects, engineers, and other

providers of professional services.

This case study of BFRL’s CONSIAT-related research, development, and deployment

effort illustrates how to apply in practice a series of standardized methods, referred to as

economic measures, to evaluate and compare the economic impacts of alternative

research investments. It is presented in sufficient detail to understand the basis for the

economic impact assessment and to reproduce the results. It is based on past, ongoing,

and planned research efforts. Thus, it includes CONSIAT-related investment costs that

have already occurred along with estimates of future investment costs and cost savings

due to the use of FIAPP products and services.

The CONSIAT economic impact assessment was carried out in two stages. In the first

stage, a baseline analysis was performed. In the baseline analysis, all input variables

used to calculate the economic measures are set at their likely values. It is important to

recognize that the term baseline analysis is used to denote a complete analysis in all

respects but one; it does not address the effects of uncertainty. In the second stage,

eleven input variables were varied both singly and in combination according to an

experimental design. Monte Carlo simulations are employed to evaluate how changing

the value of these variables affects the calculated values of the economic measures.

The results of the baseline analysis demonstrate that the use of FIAPP products and

services will generate substantial cost savings to commercial building owners and

managers and to contractors engaged in the construction of those facilities. The present

value of savings nationwide expected from the use of FIAPP products and services is

nearly $1.4 billion (measured in 1997 dollars). Furthermore, because of BFRL’s

involvement, FIAPP products and services are expected to be commercially available in

2005. If BFRL had not participated in the development of FIAPP products and services,

the commercial introduction of FIAPP products and services is expected to be delayed

until 2009. Consequently, potential cost savings accruing to commercial building owners

and managers and to contractors over the period 2005 through 2008 would have been

foregone. The present value of these cost savings is approximately $120 million. These

cost savings measure the value of BFRL’s contribution for its CONSIAT-related

investment costs of approximately $29.1 million. Stated in present value terms, every

public dollar invested in BFRL’s CONSIAT-related research, development, and

deployment effort is expected to generate $4.13 in cost savings to the public. The annual

percentage yield (AIRR) from BFRL’s CONSIAT-related investments over the study

period is 13.3 %.
157

The objective of the sensitivity analysis was to evaluate how uncertainty in the values of

each of the eleven input variables, both singly and in combination, translates into changes

in each of the six economic measures. The six economic measures evaluated in the

sensitivity analysis are: (1) the present value of savings nationwide, PVSALL; (2) the

present value of savings due to BFRL, PVSBFRL; (3) the present value of BFRL’s

CONSIAT-related investment costs, PVCBFRL; (4) the present value of net savings due to

BFRL, PVNSBFRL; (5) the savings-to-investment ratio on BFRL’s CONSIAT-related

investments, SIRBFRL; and (6) the adjusted internal rate of return on BFRL’s CONSIATrelated investments, AIRRBFRL. The major advantage of the sensitivity analysis is that it

produces results that can be tied to probabilistic levels of significance for each economic

measure (e.g., the probability that PVNSBFRL is greater than or equal to zero, SIRBFRL is

greater than or equal to 1.0, or AIRRBFRL is greater than or equal to the discount rate,

each of which would indicate that BFRL’s CONSIAT-related investments were cost

effective).

The results of the sensitivity analysis serve to validate the results of the baseline analysis.

For example, each Monte Carlo simulation in which a single input variable was varied

produced 1,000 observations for each of the six economic measures. Ten of the 11 such

simulations produced values for the median and mean that were nearly identical to the

corresponding value calculated in the baseline analysis for that measure. The final Monte

Carlo simulation, in which all eleven of the input variables were varied in combination,

also produced 1,000 observations for each of the six economic measures. In this case, the

median value for each economic measure was less than the corresponding value

calculated in the baseline analysis for that measure. In addition, the results from this

Monte Carlo simulation reveal that the present value of net savings due to BFRL,

PVNSBFRL, can be negative. This implies that there is some non-zero probability that

BFRL’s CONSIAT-related investments are not cost effective. However, on the opposite

extreme, PVNSBFRL may exceed $800 million in 1997 dollars.

The fact that the range of values for an economic measure is so wide prompted an indepth examination of the results of this Monte Carlo simulation for three of the six

economic measures. These measures are particularly helpful in understanding BFRL’s

contribution, since each measure provides a different perspective. The first, the present

value of net savings due to BFRL, is a magnitude measure; it shows a dollar value to the

public net of BFRL’s CONSIAT-related investments. The second, the savings-toinvestment ratio on BFRL’s CONSIAT-related investments, is a multiplier; it shows, in

present value terms, how many dollars the public receives for each public dollar spent.

The third, the adjusted internal rate of return on BFRL’s CONSIAT-related investments,

is a rate of return; it shows the annual return on the public monies going into the

development of FIAPP products and services throughout the 25-year study period.

For each of the three economic measures, less than 250 observations out of 1,000 were

responsible for the observed “uneconomical” outcome. Stated another way, there is at

least a 75 % probability that BFRL’s CONSIAT-related investments are cost effective.
158

This underscores the importance of using multiple measures that ensure consistency in

decision making.

9.2 Suggestions for Further Research

The background work for this report uncovered additional areas of research that might be

of value to government agencies and other institutions that are concerned with an

efficient allocation of their research budgets. These areas of research are concerned with:

(1) the development of a standard classification of research benefits and costs; (2) factors

affecting the diffusion of new technologies; (3) conducting ex ante evaluations with

scheduled follow-ups; and (4) evaluations based on multiattribute decision analysis.

9.2.1 The Development of a Standard Classification of Research Benefits and

Costs

A survey by the Civil Engineering Research Foundation shows that expenditures for

research and development efforts in the areas of construction, building, and disaster

mitigation technologies were over $2.1 billion in 1992.107 Private industry, trade

association, university, and government research bodies would like to know what are the

economic impacts of these investments. The standardized evaluation methods employed

in this report are appropriate for measuring these economic impacts. However, there is

no systematic and comprehensive classification of research benefits and costs to guide

analysts who must identify the benefits and costs associated with new construction,

building, and disaster mitigation technologies that are used in these standardized

evaluation methods. Such a classification, if developed, refined, and adopted as a

standardized classification, could be used in several ways.108 First, the classification will

help researchers and research managers identify potential benefits and costs associated

with candidate research projects and thereby help them choose those with maximum net

benefits (maximum net savings). Second, the classification will provide a standardized

basis for identifying benefits and costs in research proposals. Finally, the classification

will make possible a consistent treatment of benefits and costs in ex ante evaluations of

new technologies and in ex post evaluations of completed building- and fire-related

research projects.

 

107Civil Engineering Research Foundation. 1993. A Nationwide Survey of Civil Engineering-Related

R&D. Report no. 93-5006. Washington, DC: Civil Engineering Research Foundation. 108Although the standardized classification would be focused on identifying benefits and costs associated

with building- and fire-related research projects, it would be generic to the extent that scientific research in

general produces types of benefits and costs that are similar across technology areas. Thus the standardized

classification will be applicable to many non-building- and non-fire-related technologies as well.
159

9.2.2 Factors Affecting the Diffusion of New Technologies

Reliable estimates of the data input values for the standardized evaluation methods

cannot be made without some relatively sound basis for predicting the rate of diffusion

and the ultimate level of adoption of a new technology. The rate of diffusion and the

ultimate level of adoption of a new technology depend on many factors. Uncertainty

about how a new technology will perform affects both its rate of diffusion and its

ultimate level of adoption.

Two factors over which a research laboratory exerts some control and which have the

potential to reduce uncertainty about new technologies are: (1) the research laboratory’s

information dissemination efforts; and (2) the research laboratory’s participation in

standards-making organizations. Additional research on these two factors is warranted

for a number of reasons. First, the characteristics of information are changing

dramatically. With the advent of the World Wide Web and the increased acceptance of

electronic media, the fruits of research may be quickly and widely disseminated. The

reliance on printed reports sent to a targeted audience as the sole vehicle for

communication is being eclipsed by other means of information dissemination. This

transition needs to be studied to ensure that the information dissemination strategy that

emerges is tailored to the needs of the research laboratory’s customer base. Second,

research results in the form of technical reports often provide the basis for standards.

Consequently, information dissemination efforts may be used to leverage private-sector

activities aimed at standardization. Finally, standards are an important means for

disseminating information on expected levels of performance and for measuring key

performance characteristics (e.g., through the use of standard practices, specifications,

and test methods). For new technologies, acceptance by a standards-making organization

should lead both to higher rates of diffusion and to higher levels of adoption.

Consequently, research on how a research laboratory’s participation in standards-making

organizations affects the rates of diffusion and levels of adoption of new technologies

will enable it to improve the efficiency with which it allocates staff and other resources to

these activities.

9.2.3 Conducting Ex Ante Evaluations with Scheduled Follow-ups

From an analysis perspective, an ex ante evaluation of a new technology poses several

challenges which are absent in an ex post evaluation of a completed research project. The

biggest challenge involves the diffusion of a new technology (i.e., predicting the rate of

diffusion and the ultimate level of adoption). Although two of the factors affecting the

diffusion of a new technology were discussed in the previous suggestion for further

research, much can be learned about the diffusion process by performing ex ante

evaluations with the understanding that scheduled follow-up evaluations will be

conducted.

The follow-up evaluation focuses on answering several key questions. These questions

are aimed at learning more about the research laboratory’s role and ability to move
160

research results towards the market place and about the way in which firms and

households (i.e., the intended users of the new technology) adopt and make use of the

new technology. First, did the new technology become available to the intended users

when anticipated in the ex ante evaluation? Second, is the new technology being adopted

at the rate anticipated? Third, are the users that adopt the new technology experiencing

the types of changes anticipated (e.g., cost savings, increased durability, and increased

reliability)? Finally, are the types of users that adopt the new technology the same as

anticipated? If these questions are asked and the answers are reviewed, critiqued, and fed

back to research managers, ex ante evaluations will become a key link in the research

laboratory’s continuous improvement efforts.

Because ex ante evaluations are more complex than ex post evaluations, it is not always

possible to quantify all of the relevant benefits and costs going into the evaluation. Such

was the case in this economic impact assessment. Specifically, estimates of the cost

savings due to fewer/shorter interruptions of building operations due to building-related

problems (e.g., faster turnarounds due to electronic “as-built” information) are not

included. Similarly, estimates of the investments required to develop, test, and market

FIAPP products and services by the vendor tier (see Figure 3-2) are not included. These

challenges and others (e.g., improved estimates of new-technology introduction costs) are

being addressed through BFRL’s Office of Applied Economics’ efforts to monitor

outcomes and compile information on CONSIAT-related impacts in preparation for the

follow-up CONSIAT economic impact assessment.

9.2.4 Evaluations Based on Multiattribute Decision Analysis

Many research investment alternatives differ in characteristics that decision makers

consider important but that are not readily expressed in monetary terms. Because the

standardized evaluation methods employed in this report consider only monetary benefits

and monetary costs associated with alternative research investments, their application

does not reflect the importance of these non-financial characteristics to the decision

maker. When non-financial characteristics are important, decision makers need a method

that accounts for these characteristics (also called attributes) when choosing among

alternative research investments. A class of methods that can accommodate nonmonetary benefits and costs is multiattribute decision analysis.109

The analytical hierarchy process (AHP) is one of a set of multiattribute decision analysis

methods that considers non-financial characteristics in addition to common economic

evaluation measures when evaluating project alternatives. The AHP has several

important strengths: (1) it is well-known and well-reviewed in the literature; (2) it

includes an efficient attribute weighting process; (3) it incorporates hierarchical

descriptions of attributes; (4) its use is facilitated by available software; and (5) it has

 

109For more information on multiattribute decision analysis, see Norris, Gregory A., and Harold E.

Marshall. 1995. Multiattribute Decision Analysis Method for Evaluating Buildings and Building Systems.

NISTIR 5663. Gaithersburg, MD: National Institute of Standards and Technology.
161

been accepted by ASTM as a standard practice for investments related to buildings and

building systems.110

The AHP and its associated software represent a powerful and versatile management tool.

How to apply this management tool most productively in a research environment

suggests additional research in two areas. First, what will be the relationship between the

AHP software and the standard classification proposed earlier? Second, how will the

AHP be used to assess fit to mission, to set priorities, or to evaluate performance against

some other management goal? If research is conducted on the two topics just outlined,

the AHP-based tool which emerges will provide a format for: (1) efficiently and reliably

screening and selecting among alternative research investments (e.g., by embedding

information from the standard classification of research benefits and costs, information

on fit to mission, and on research priorities); (2) selecting research projects for in-depth

analyses, either of the ex ante or ex post type of evaluation; and (3) selecting and

scheduling follow-up evaluations.

 

110American Society for Testing and Materials. 1998. Standard Practice for Applying Analytical

Hierarchy Process (AHP) to Multiattribute Decision Analysis of Investments Related to Buildings and

Building Systems. E 1765. West Conshohocken, PA: American Society for Testing and Materials.
162
163

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