Benefits and Costs of Research: A Case Study of

Construction Systems Integration and

Automation Technologies in Industrial Facilities

Robert E. Chapman

June 2000

Office of Applied Economics

Building and Fire Research Laboratory

National Institute of Standards and Technology

 

U.S. DEPARTMENT OF COMMERCE

William M. Daley, Secretary

TECHNOLOGY ADMINSTRATION

Dr. Cheryl L. Shavers, Under Secretary of Commerce for Technology

NATIONAL INSTITUTE OF STANDARDS AND TECHNOLGOY

Raymond G. Kammer, Director

NISTIR 6501

U.S. DEPARTMENT OF COMMERCE Office of Applied Economics

Technology Administration Building and Fire Research Laboratory

National Institute of Standards and Technology Gaithersburg, Maryland 20899

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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) major product. The CONSiAT major product 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 industrial facility 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 in excess of $2.0

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 industrial facility 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 $150 million. These

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

investment costs of approximately $30.1 million. Stated in present value terms, every

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

deployment effort is expected to generate $4.95 in cost savings to the public.

Keywords

Building economics; construction; delivery time; economic analysis; impact evaluation;

industrial facilities; integration and automation; life-cycle costing; safety
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v

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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Acknowledgments

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, commercial/institutional, and public works 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 Dr. Harold E. Marshall and Ms. Barbara C. Lippiatt of BFRL’s Office of Applied

Economics for the thoroughness of their reviews and for their many insights and to Ms.

Catherine J. Linthicum 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, Dr.

Laurence Pfeffer, 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. Thanks are also due to Dr. Ruth Haines of

NIST’s Manufacturing Extension Partnership Program for her stimulating insights on the

linkages between construction and manufacturing. 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

Acknowledgments......................................................................................................... vii

List of Tables................................................................................................................ xii

List of Figures.............................................................................................................. xiv

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............................ 14

3 Building and Fire Research Laboratory’s CONSiAT Major Product....................... 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
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3.3 Key Components of BFRL’s CONSiAT Major Product..................................... 21

3.3.1 Project Information Management System Technologies.............................. 25

3.3.2 Advanced Graphical User Interfaces for Construction Project Delivery

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

3.3.3 Product Data Standards for the Process Plant Industries.............................. 28

3.3.4 Site Measurement System Interoperability and Communication Standards . 29

3.3.5 Non-Intrusive Scanning Technologies for Construction Status Assessment 31

3.3.6 Real-Time Construction Component Tracking............................................ 33

3.3.7 Economic Analysis of FIAPP Products and Services.................................. 34

4 Market for FIAPP Products and Services ............................................................... 35

4.1 Value of Construction Put in Place .................................................................... 35

4.2 Characteristics of the Industrial Sector............................................................... 43

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

Costs...................................................................................................................... 47

5.1 Identification of Key Stakeholders..................................................................... 47

5.2 Classification of FIAPP-Related Benefits and Cost Savings............................... 48

5.3 Classification of FIAPP-Related Cost Increases and Benefit Reductions............ 56

5.4 How FIAPP-Related Benefits and Costs Accrue to Stakeholders ....................... 60

6 Data and Assumptions for the CONSiAT Economic Impact Assessment................ 65

6.1 Data Sources...................................................................................................... 65

6.1.1 Baseline Measures of Construction Industry Practices................................ 65

6.1.2 The CII Benchmarking and Metrics Database............................................. 66

6.1.3 Other Data Sources..................................................................................... 72

6.2 Defining the Base Case and the FIAPP Alternative............................................ 75

6.3 Estimating Significant FIAPP-Related Benefits and Costs................................. 76

6.3.1 Benefits and Cost Savings .......................................................................... 76

6.3.1.1 Reduced First Costs ............................................................................... 77

6.3.1.2 Reduced Maintenance and Repair Costs................................................. 78

6.3.1.3 Reductions in Construction-Related Accidents....................................... 80

6.3.1.4 Reductions in Delivery Time.................................................................. 82

6.3.1.5 Higher Net Income for Contractors ........................................................ 83

6.3.2 Cost Increases and Benefit Reductions ....................................................... 84

6.3.2.1 New-Technology Introduction Costs...................................................... 85

6.3.2.2 Increased Research and Development Costs........................................... 85

6.4 Key Assumptions and Analysis Issues............................................................... 87

6.4.1 Base Year for Computing Benefits and Costs............................................. 87

6.4.2 Length of the Study Period......................................................................... 88

6.4.3 Discount Rate............................................................................................. 88

6.4.4 Diffusion Process....................................................................................... 89

6.4.5 Dealing with Uncertainty............................................................................ 97

6.4.6 Measuring BFRL’s Contribution ................................................................ 98
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7 Baseline Analysis of Economic Impacts............................................................... 103

7.1 BFRL Summary Impact Statement .................................................................. 106

7.2 Cost Savings Nationwide................................................................................. 106

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

CONSiAT-Related Investments....................................................................... 114

8 Sensitivity Analysis of Economic Impacts............................................................ 119

8.1 Methodology ................................................................................................... 119

8.2 Key Variables.................................................................................................. 121

8.3 Sensitivity Results........................................................................................... 123

8.3.1 Changing One Input ................................................................................. 123

8.3.2 Changing All Eleven Inputs in Combination............................................. 133

9 Summary and Suggestions for Further Research................................................... 149

9.1 Summary ......................................................................................................... 149

9.2 Suggestions for Further Research..................................................................... 151

9.2.1 The Development of a Standard Classification of Research Benefits and

Costs........................................................................................................ 152

9.2.2 Factors Affecting the Diffusion of New Technologies .............................. 152

9.2.3 Conducting Ex Ante Evaluations with Scheduled Follow-ups ................... 153

9.2.4 Evaluations Based on Multiattribute Decision Analysis............................ 154

References .................................................................................................................. 155

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.
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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 1992 Dollars.. 38

Table 4-2. Value of Construction Put in Place in Millions of Current Dollars ............ 39

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

Millions of Constant 1992 Dollars............................................................. 40

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

Millions of Current Dollars....................................................................... 40

Table 5-1. Hierarchy of FIAPP Stakeholders by Groups and Classes of Individual

FIAPP Stakeholders.................................................................................. 49

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

Stakeholder Groups................................................................................... 50

Table 5-3. FIAPP-Related Benefits (or Cost Savings) for All Stakeholders................ 52

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

Stakeholders ............................................................................................. 58

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

Stakeholder Group.................................................................................... 62

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

by Stakeholder Group ............................................................................... 63

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

Project Data .............................................................................................. 72

Table 6-2. Average Costs of Construction Site Injuries: 1997.................................... 82

Table 6-3. BFRL Investment Costs by Fiscal Year .................................................... 86

Table 6-4. Baseline Case of Pη(t) (α = 4.0, β = 0.5, η = 0.5) and Bη(t) (α ́= 6.0,

β = 0.5, η = 0.5)........................................................................................ 96

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

Commercial Use ..................................................................................... 100
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Table 7-1. Baseline Values for the Diffusion Models and of FIAPP-Related

Investments by Year: 1993 - 2017........................................................... 107

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

Dollars by Year: 1993-2017.................................................................... 109

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

Millions of 1997 Dollars: 1993-2017 ...................................................... 113

Table 7-4. Summary of BFRL Research Investments: 1993-2017............................ 115

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

BFRL...................................................................................................... 117

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

Sensitivity Analysis ................................................................................ 122

Table 8-2. Summary Statistics Due to Changes in the Input Variable Alpha ............ 124

Table 8-3. Summary Statistics Due to Changes in the Input Variable Beta............... 125

Table 8-4. Summary Statistics Due to Changes in the Input Variable Eta ................ 126

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

Rate ....................................................................................................... 127

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

Savings................................................................................................... 127

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

and Repair Cost Savings ......................................................................... 128

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

Delivery Time......................................................................................... 128

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

Contractor Net Income............................................................................ 129

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

New-Technology Introduction Costs....................................................... 129

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

Use ......................................................................................................... 130

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

Delay ...................................................................................................... 131
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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 ..................................................... 132

Table 8-14. Summary Statistics Due to Changes in All of the Input Variables........... 133

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

Variables................................................................................................. 136

List of Figures

Figure 3-1. Information Flows Model for Industrial Facilities.................................... 19

Figure 3-2. Schematic Diagram of BFRL’s CONSiAT Major Product....................... 23

Figure 4-1. 1997 Breakdown of $618B Construction Market..................................... 41

Figure 6-1. CII Database by Respondent Type........................................................... 69

Figure 6-2. CII Database by Industry Type ................................................................ 69

Figure 6-3. CII Database by Cost Category................................................................ 71

Figure 6-4. CII Database by Project Nature ............................................................... 71

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

Years 1989-1997 ..................................................................................... 81

Figure 6-6. Baseline Case of Pη(t) and Bη(t) by t(year) .............................................. 97

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

Dollars................................................................................................... 139

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

Dollars................................................................................................... 140

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

Dollars................................................................................................... 141

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

Dollars................................................................................................... 145

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

Investment............................................................................................. 146
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Figure 8-6. Adjusted Internal Rate of Return on BFRL’s Research and Development

Investment............................................................................................. 147

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 Industrial

Facilities................................................................................................. 104
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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

CDF Cumulative Distribution Function

CII Construction Industry Institute

CONSiAT Construction Systems Integration and Automation Technologies

CPI Consumer Price Index

ECS Emissions Control System

EDI Electronic Data Interchange

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

IT Information Technology

LCC Life-Cycle Cost
xvii

LIDAR LIght 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

PDU Protocol Data Unit

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

ROI Return on Investment

SIC Standard Industrial Classification

SIR Savings-to-Investment Ratio

STEP Standard for the Exchange of Product

TV Terminal Value

VRML Virtual Reality Modeling Language
xviii
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Executive Summary

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

BFRL’s construction systems integration and automation technologies (CONSiAT) major

product. The CONSiAT major product 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 in industrial facilities.

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

are most appropriate for measuring the benefits (cost savings) 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 seven key areas of research. The CONSiAT effort within BFRL is

 

i

 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

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).
xx

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 major product.

The goal of BFRL’s CONSiAT major product 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.

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/institutional, industrial, and public works. Information on the

industrial 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 industrial sector. Detailing the

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

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

alterations and maintenance and repair activities as well.

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

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

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

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 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 industrial facilities. 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 discount rate

is 7 % (real). The base year is 1997, and all dollar amounts are calculated in present

value 1997 dollars
xxi

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.

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 industrial

facility 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 in excess of $2.0 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 industrial facility 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 $150 million. These cost savings measure the value of BFRL’s

contribution for its CONSiAT-related investment costs of approximately $30.1 million.

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

research, development, and deployment efforts is expected to generate $4.95 in cost

savings to the public (i.e., an SIR of 4.95). The annual percentage yield (AIRR) from

BFRL’s CONSiAT-related investments over the study period is 14.1 %.

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
xxii

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 and mean values for each economic measure were 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. On the opposite extreme,

however, PVNSBFRL may reach nearly $1.0 billion in 1997 dollars.

The range of values for an economic measure is so wide that it prompted an in-depth

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 160 observations out of 1,000 were

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

least an 84 percent probability that BFRL’s CONSiAT-related investments are cost

effective. This underscores the importance of using multiple measures that 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. 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 lifecycle 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

 

1Reich, Robert W. 1989. “The Quiet Path to Technological Preeminence.” Scientific American (October):

pp. 41-47.

2Good, 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 fourth in a series of impact studies prepared by BFRL.8 It focuses on

BFRL’s construction systems integration and automation technologies (CONSiAT) major

 

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.

4Marshall, 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

product. The CONSiAT major product 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 major product 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 major product.

The goal of BFRL’s CONSiAT major product 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/institutional, 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 industrial facilities only (i.e., original equipment manufacturing

facilities and other types of industrial facilities manufacturing products and/or

commodities). The decision to focus first on the industrial sector was based both on the

desire of key industrial sector stakeholders to partner with BFRL and on data-related

issues. A subsequent impact assessment focusing on the commercial/institutional sector

is planned as a follow-up to the industrial impact assessment.

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 major product’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 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).
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 industrial facilities. The

approach is to present all CONSiAT-related information in sufficient detail both to

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

reader 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 industrial sector 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 major product. Both the

overall CONSiAT research and development effort and the seven 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 industrial facilities are necessary to measure the economic impacts of fullyintegrated 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 industrial

facilities 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 twopage 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 major product in industrial facilities. 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 fullyintegrated and automated project process systems and technologies in industrial facilities.

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

The starting point for conducting an economic impact assessment is referred to as the

baseline analysis. 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. Consequently, 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.

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 Marshall10 and the subsequent video11 and workbook.12

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 ASTM standard practices.13 Detailed descriptions of each of the standardized

methods are given in Chapman and Fuller.14 Readers interested in an excellent, in-depth

survey covering these as well as other methods are referred to Ruegg and Marshall.15

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);

Bt

a*

 = the benefits for alternative a* in year t;

 

10Marshall, 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.

11Marshall, 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.

12Marshall, 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.

13American Society for Testing and Materials (ASTM). Fourth Edition, 1999. ASTM Standards on

Building Economics. West Conshohocken, PA: American Society for Testing and Materials.

14Chapman and Fuller, Two Case Studies in Building Technology, pp. 27-37.

15Ruegg, Rosalie T. and Harold E. Marshall. 1990. Building Economics: Theory and Practice. New York:

Chapman and Hall.
7

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).16

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

PVNS method.

 

16If 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).
10

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

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.17 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 Methods18

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. But among the methods are

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.

 

17Chapman and Fuller, Two Case Studies in Building Technology, pp. 35-37.

18For 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.19 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,

 

19If 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

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

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.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.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:

v Benefit-to-Cost Ratio (BCR) or

Savings-to-Investment Ratio (SIR)

v Adjusted Internal Rate of Return

(AIRR)
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:

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

all contributors to the research effort under study was determined;

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

under study, any users of the new technology under study, and any third

parties affected by either the research effort or the use of the new

technology was determined;

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

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

technology under study, and any third parties affected by either the

research effort or the use of the new technology was determined;

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

organization’s contribution was determined;

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

contribution was determined;

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

organization’s contribution was determined; and

(7) any additional measures were calculated and how the research

organization’s contribution was determined.

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. At this point, we note that it is essential to 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:
15

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

(b) the adjusted internal rate of return.

Summaries (e.g., tables, graphs, comparative statistics) of the following information

should also be reported:

(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.
16
17

3 Building and Fire Research Laboratory’s CONSiAT Major Product

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. 20 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 industrial facility; it spans the entire 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

 

20 Construction Industry Institute. 1999. 1999 Strategic Plan. Austin, TX: Construction Industry Institute.
18

be subdivided into sub-activities and used to highlight information flows between subactivities. 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 an industrial 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 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 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 an industrial

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 Industrial Facilities

Owners and

Operators

-----------------------------------------------------------------------------------------------------------------------------------------------------------------

Architects and Engineers,

Constructors, and Suppliers

and Fabricators

Business

 Need

 Project Management Activities

Produce and Maintain

Financial Viability

Plan and Control

Project

Obtain Agreement

With Regulatory Bodies

Regulatory

Bodies

Produce Conceptual

Facility Design

Review and Approve

Detailed Facility

Design

Commission

Facility

Operate and

Maintain Facility

Decommission

Facility

Produce Conceptual

Engineering Design

Produce Detailed

Facility Design

Prepare Site,

Construct Facility,

Pre-commission

Service Facility

and Equipment

Demolish Facility

and Restore Site

Suppliers and

Fabricators 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 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 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 and: achieve mutually beneficial results by pooling talent; substantially leverage

their scarce R&D dollars; spread risks and achieve robust solutions; 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 Major Product

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 facility

owners, contractors, equipment and systems manufacturers and service providers,

software developers, 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 insure 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

for earthwork, foundation, and the structural steel delivery process and the pipe spools
22

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

seven key projects (see Sections 3.3.1 through 3.3.7). In addition, the overall effort

includes a Virtual FIAPP Testbed and a series of full-scale field demonstration projects.

A schematic for how the seven 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

seven 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

Product 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 Major Product

Virtual FIAPP Testbed

Site Measurement

System Interoperability

and Communications

Standards

Non-Intrusive

Scanning

Technologies

Vendors:

Commercial

Products and

Services

Product Data

Standards for

Process Industries

Advanced

Graphical User

Interfaces

Vendors:

Commercial

Products and

Services

Market

Adoption

Field

Demonstration

Projects

Economic Analysis

of FIAPP Products

and Services

...

Real-Time

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 facility owners, designers, construction products and

equipment manufacturers and suppliers, contractors, and facility managers. Protocols

being developed include:

ß Data exchange protocol for piping design, fabrication, installation, and inspection;

ß Identification and fiducial marking protocols for discrete components; and

ß LiveView construction site management system interoperability protocols.

• Develop advanced measurement technologies, including:

ß Ability to compare “as-is” and “as-designed” geometry, location, and orientation

of construction objects incorporating advanced VRML (Virtual Reality Modeling

Language) techniques;

ß Ability to characterize LIDAR (LIght Detection And Ranging) measurement

accuracy as a function of range, surface texture, and reflectivity for a range of

conditions anticipated at construction sites;

ß Mobile scanning system with wireless transport of range data to a remote site;

ß Robust post-processing technique for registration and volumetric calculations of

LIDAR-acquired terrain data; and

ß 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 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 major product 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 major product emerge, the need to

merge these two approaches to the integration and management of project information
26

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.

In the first year, this project will deal 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

two-dimensional drawings and documents. These mechanisms have been carried directly
27

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 major product 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, tentatively 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. The STEP geometry representations chosen by AISC will be

mapped 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 the Process Plant Industries

The U.S. process plant industries are 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 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 CADD and CAM technologies and progressing toward achievement of

FIAPP capabilities. This project works with the chemical, pharmaceutical, power,

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.

Industry studies show that the piping delivery process is often one of the most inefficient

of all construction activities. In a CII study,21 the average amount of rework on piping

systems for industrial projects exceeds 3 percent of the project’s total installed cost.

Beyond documenting procedures for reducing potential rework costs, this project will

investigate improvements in the fabrication, delivery, and installation of piping systems.

 

21 Construction Industry Institute. 1989. Costs of Quality Deviations in Design and Construction. RS10-1.

Austin, TX: Construction Industry Institute.
29

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

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.

3.3.4 Site Measurement System Interoperability and Communication Standards

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 and 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 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. This

project seeks to develop the standard interoperability language and protocols that will

make possible this transfer of information.

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
30

computer, or on different ones. Also, ideally, this protocol should be designed to provide

maximum flexibility of physical networks capable of realizing the protocol.

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 LIDAR. 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.

Unlike modern manufacturing plants (and our most complex laboratory experiments) the

every day construction site represents a highly unstructured environment. Stated simply:

there can be no wires leading to the thousands of sensors, displays, and machines which

must be integrated to make the system work. Current technologies to realize a wireless

solution are low bandwidth, especially when compared to modern wire-based systems.

Thus a standard protocol will have to take into account lower bandwidth availability yet

anticipate improvements in wireless technology. BFRL has already begun looking at the

issues surrounding wireless data communication from the construction site.23

Our present effort, focused around the BFRL Construction Metrology and Automation

Group’s LiveView protocol, deals with interoperability protocols. Consider a field agent

that provides a state update (who/what/where/when) for a static object encountered (e.g.,

reading the bar code from a girder on a construction site). After the field agent has

identified the object and determined the object’s position, it is necessary to broadcast any

updates to the data consumers. In this case, the facilities for entity creation and managing

entity information, provide a common data exchange to enable communication between

the field agent and the data consumers.

Development work on LiveView proposed for FY 2000 includes:

• The creation of a partial LiveView implementation that can be used to enable initial

full-scale tests at construction sites employing a limited number of sensor systems

for the purposes of tracking excavation and the erection of steel frame structures.

• Development of recommended practice documents for applying LiveView to

construction automation and metrology tasks. These documents will provide

system integrators with a complete picture of how a working system is put

 

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.

23 Pfeffer, L.E., and Latimer, D.T., “Toward Open Network Data-Exchange Protocols for Construction

Metrology and Automation: LiveView,” Proceedings of the International Conference on Automation and

Robotics in Construction (ISARC-99), Madrid, Spain, September 22-24, 1999.
31

together. Additionally, such a document provides vendors of specific systems a

model to follow for how their products could be used. Specifically, in FY 2000,

this will include the introduction of component tracking via GPS, fanning laser,

and LIDAR; excavation tracking and general field range data via LIDAR and

GPS; and bar code and RFID (radio frequency identification) related information.

• Field testing of LiveView at an actual construction site on the NIST grounds.

Tentatively, pending contract issuance, this will be the 205 ECS.

Future work will seek to provide “Quality of Data” estimates with measurements.

Currently, due to the nature of simulation in IEEE 1278, which assumes perfect accuracy

of reporting positions, if an entity reports a position, there is no reason to question the

potential error in that message. However, field methods for measuring position have

limited accuracy. The data quality might be represented with a simple confidence-band,

or a more complex function. Issues in how to represent and transport information of this

nature need to be investigated further. Finally, the issue of security and access rights in

LiveView will have to be addressed. Current research in security for distributed systems

needs to be evaluated and applied to the LiveView system. Ultimately this will lead to the

proposal of LiveView as an IEEE standard.

3.3.5 Non-Intrusive Scanning Technologies for Construction Status Assessment

Recent evaluations by the construction industry indicate that timely knowledge of project

status—where things are, what has been done, what needs to be done—is the single most

important issue facing construction managers today. For example, excavation companies

spend considerable sums on site layout and verification. Each verification (stakeout)

requires a survey team to be hired. A waiting line for surveying services is not

uncommon. During the wait, money is lost. If an error is discovered, the re-work can

multiply the cost significantly. Excavation is just one example of a broad class of

construction status monitoring tasks that are complicated by the amorphous nature of the

item to be tracked. Concrete placement, paving operations, and determining quantities of

raw materials such as sand and gravel are all further extensions to this theme. Even more

powerful, however, will be the ability to automatically capture the “as-built” condition of

an existing structure, or to capture and decipher a complex construction operation as it

happens and to provide real-time feedback to those conducting the operations. All of

these are complex situations where traditional metrology techniques are simply not

effective, due to the massive quantities of data needed to describe the environment. This

project focuses on the use of new fast laser ranging technologies and three-dimensional

analysis to automatically and 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 major product and the National Construction

Goal of reducing delivery time by 50 percent.

BFRL’s Construction Metrology and Automation Group in collaboration with NIST’s

Information Technology Laboratory and BFRL’s Computer Integrated Construction
32

Group has made significant strides during FY1999 in the use of interactive LIDAR 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, registering the live

data with extant data from other, previous LIDAR 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.

NIST is focusing significant research efforts towards becoming a world leader in laserbased site metrology. BFRL’s work has been focused on the development of an

automated system for the assessment of earthmoving activities at a construction site. The

approach involves capturing multiple LIDAR scene images that completely cover all

viewable facets of the terrain. A typical LIDAR image looks much like a computer

screen image, and in fact is acquired in a similar fashion—by sweeping the laser beam

across the site in a line and then stepping the lines down through the field of view in a

raster-like fashion. Each line may represent thousands of range measurements as well as

returned intensity measurements. Unlike traditional survey instruments, there are no

retro-reflector stations involved with LIDAR surveys. The laser beam reflects off dirt

and construction components and is received at the instrument that utilizes sensitive

coherent radiation sensors to detect the returned signal. Once a complete circuit of an

earthmoving site has been made the various LIDAR images are registered to the site

coordinate system. The result is a point cloud of three-dimensional locations which

discretely (but at very high density) defines the geometry of the construction site. These

points can subsequently be meshed and, through various mathematical approaches, used

to derive cut and fill requirements, quantities of material placed or removed, and rates of

material removal, all of which are of significant interest to excavation subcontractors,

primes, and owners. Although the laboratory demonstrations utilized a small sand pile,

the same technology and algorithms will work on a construction site measuring hundreds

of meters on a side. This technology has drawn international interest and collaborative

projects are currently being planned with several companies.

Development work proposed for FY 2000 includes:

• Develop more robust processing of scan data to deal with most terrain features such

as vertical surfaces and undercuts.

• Conduct site visits with earthmoving subcontractors, general site contractors, and

facility owners to determine their information specific management needs with regard

to excavation work at a construction site.

• Develop a mobile scanning system with wireless transport of geometric data to a

remote database.

Future work will focus on a demonstration of a complete prototype information

management system integrated with automated earthwork tracking systems and a
33

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 Real-Time Construction Component Tracking

Inordinate 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 site operating

on a field-portable computer as the field agent interface. Peripherals, such as a laserbased real-time spatial positioning system, a bar code and RFID scanning system, and a

wireless data link, are integrated seamlessly through the browser. The field inspector

uses either the bar code scanner or RFID reader to determine the part identification. Then

an interactive session begins in which the field computer queries, by means of the

wireless link, a remote job site database that then returns information concerning the part.

The returned information also includes a three-dimensional model of the component. In

the current NIST laboratory work, the new component location data is automatically 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 every component. 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.

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

industry partners to ensure that the standards being developed are responsive to industry

needs and are compatible with other industry standardization efforts.

The technology developed in this project will be tested at the Building 205 ECS project

on the NIST, Gaithersburg campus.
34

3.3.7 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) will conduct an ex ante (i.e., prospective)

economic impact assessment of BFRL’s CONSiAT-related research, monitor outcomes,

and conduct a follow-up economic impact assessment. The subject of this report is the ex

ante economic impact assessment. OAE will also design and create a database for

compiling information on CONSiAT-related impacts. Once the database is in place,

OAE will monitor outcomes and compile information on CONSiAT-related impacts in

preparation for the follow-up economic impact assessment.

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. Once the software package has been finalized, OAE will seek

out a private-sector collaborator to market, distribute, and maintain the decision support

software package.
35

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 two 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/institutional,

industrial, and public works. Data from the five year period 1993 through 1997 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 places special emphasis on identifying and detailing the key characteristics of

the industrial sector. Detailing the key characteristics of the industrial sector is crucial,

because investments in FIAPP products and services affect not only new construction

activities but additions and alterations and maintenance and repair activities as well.

Ways in which these key characteristics affect the calculation of FIAPP-related benefits

and costs are discussed in Chapter 6.

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

 

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).

25 US Department of Commerce. 1998. Current Construction Reports: Value of Construction Put in

Place. C30. Washington, DC: US Bureau of the Census.
36

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).

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 1993 through 1997. Table 4-1

records annual values in millions of constant 1992 dollars. Table 4-2 records annual

values in millions of current dollars.26 Reference to Table 4-1 reveals that total

 

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
37

construction expenditures in real terms have increased modestly over the five-year period

(i.e., from $461.1 billion to $520.1 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.

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.

 

Costing Manual for the Federal Energy Management Program. NIST Handbook 135. Gaithersburg, MD:

National Institute of Standards and Technology.
38

Table 4-1. Value of Construction Put in Place in Millions of Constant 1992 Dollars

1993 1994 1995 1996 1997

Total construction 461,078 480,620 478,069 506,655 520,117

Private construction 347,851 367,247 360,040 385,967 395,321

Residential buildings 200,502 218,005 201,677 220,017 221,546

New housing units 137,243 153,250 142,413 153,966 156,038

1 unit 126,960 140,416 126,773 136,516 137,156

2 or more units 10,283 12,833 15,640 17,450 18,882

Improvements 63,259 64,755 59,264 66,052 65,508

Nonresidential buildings 106,729 111,416 120,627 131,188 139,067

Industrial 25,554 26,803 29,043 28,503 26,440

Office 20,197 20,553 22,891 24,329 27,631

Hotels, motels 4,405 4,308 6,351 9,521 10,741

Other commercial 31,292 34,756 38,098 42,042 42,748

Religious 3,748 3,584 3,864 3,955 4,951

Educational 4,484 4,471 4,908 5,880 7,101

Hospital and institutional 12,050 11,377 10,051 10,280 11,576

Miscellaneous 5,000 5,565 5,421 6,677 7,880

Farm nonresidential 3,271 2,990 2,692 3,319 3,329

Public utilities 34,120 32,074 32,401 29,286 29,448

Telecommunications 9,468 9,785 10,073 10,245 9,918

Other public utilities 24,652 22,289 22,328 19,041 19,529

Railroads 3,056 3,186 3,201 3,894 4,321

Electric light and power 15,096 13,877 12,656 9,914 10,545

Gas 5,536 4,308 5,637 4,330 3,820

Petroleum pipelines 965 918 834 903 843

All other private 3,229 2,763 2,643 2,156 1,931

Public construction 113,227 113,373 118,029 120,688 124,796

Buildings 46,813 45,728 49,683 51,119 53,515

Housing and redevelopment 3,833 3,495 3,928 3,958 4,055

Industrial 1,658 1,358 1,348 1,214 842

Educational 18,465 18,838 20,800 21,035 22,786

Hospital 3,579 3,663 3,871 4,050 4,247

Other 19,279 18,373 19,737 20,863 21,585

Highways and streets 34,164 36,219 35,303 36,483 38,605

Military facilities 2,405 2,196 2,728 2,317 2,223

Conservation and development 5,771 5,996 5,779 5,335 4,841

Sewer systems 8,622 8,199 8,557 9,260 8,951

Water supply facilities 4,868 4,237 4,695 5,187 5,393

Miscellaneous public 10,583 10,799 11,284 10,987 11,267

Constant (1992) Dollars

VALUE OF CONSTRUCTION PUT IN PLACE (SERIES C30)

Type of Construction
39

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

1993 1994 1995 1996 1997

Total construction 478,648 519,539 538,134 583,638 618,217

Private construction 362,688 399,346 407,477 446,306 471,159

Residential buildings 210,455 238,874 230,688 256,460 265,610

New housing units 144,071 167,919 162,898 179,448 187,075

1 unit 133,282 153,838 145,009 159,124 164,444

2 or more units 10,788 14,081 17,889 20,324 22,631

Improvements 66,384 70,955 67,790 77,012 78,535

Nonresidential buildings 110,635 120,285 135,022 150,350 165,146

Industrial 26,482 28,947 32,505 32,657 31,394

Office 20,920 22,178 25,613 27,886 32,816

Hotels, motels 4,565 4,648 7,112 10,912 12,752

Other commercial 32,453 37,551 42,654 48,188 50,763

Religious 3,887 3,869 4,326 4,534 5,885

Educational 4,649 4,822 5,493 6,742 8,437

Hospital and institutional 12,492 12,268 11,248 11,780 13,741

Miscellaneous 5,188 6,002 6,071 7,650 9,358

Farm nonresidential 3,392 3,226 3,014 3,804 3,956

Public utilities 34,925 34,071 35,859 33,261 34,188

Telecommunications 9,619 10,121 11,093 11,772 11,626

Other public utilities 25,306 23,950 24,766 21,489 22,562

Railroads 3,108 3,340 3,509 4,398 5,059

Electric light and power 15,567 14,918 14,049 11,211 12,144

Gas 5,645 4,694 6,279 4,865 4,390

Petroleum pipelines 986 998 929 1,015 969

All other private 3,281 2,890 2,893 2,431 2,258

Public construction 115,960 120,193 130,657 137,333 147,058

Buildings 48,559 49,446 55,700 58,659 63,603

Housing and redevelopment 4,011 3,835 4,491 4,614 4,861

Industrial 1,718 1,465 1,508 1,389 998

Educational 19,129 20,361 23,278 24,112 27,065

Hospital 3,710 3,951 4,332 4,638 5,042

Other 19,991 19,834 22,089 23,907 25,637

Highways and streets 34,299 37,419 38,498 41,243 45,197

Military facilities 2,453 2,318 3,011 2,634 2,620

Conservation and development 5,937 6,363 6,368 6,011 5,658

Sewer systems 8,863 8,700 9,435 10,433 10,463

Water supply facilities 5,085 4,647 5,283 5,964 6,339

Miscellaneous public 10,765 11,301 12,362 12,388 13,177

Current Dollars in Millions

VALUE OF CONSTRUCTION PUT IN PLACE (SERIES C30)

Type of Construction
40

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 1992 Dollars28

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

Millions of Current Dollars29

Reference to Table 4-3 reveals that the commercial/institutional sector is the only sector

to have grown consistently in real terms over the entire five-year period. In real terms,

expenditures in the commercial/institutional sector grew from $125.8 billion in 1993 to

$164.6 billion in 1997, an increase of almost 31 percent. Real expenditures for two of the

four sectors, industrial and public works, were essentially constant over the same fiveyear period. Real expenditures for the residential sector exhibited a cyclical pattern.

 

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 “Total – All Sectors” row in Table 4-3, differ slightly

from the values entered in the “Total Construction” row in Table 4-1.

29 Note that due to rounding the value entered in the “Total-All Sectors” row in Table 4-4 differ slightly

from the values entered in the “Total Construction” row of Table 4-2.

1993 1994 1995 1996 1997

Residential 204,335 221,500 205,605 223,975 225,601

Commercial/Institutional 125,770 128,478 138,684 151,951 164,575

Industrial 27,212 28,161 30,391 29,717 27,282

Public Works 103,763 102,483 103,390 101,011 102,658

Total - All Sectors 461,080 480,622 478,070 506,654 520,116

Value of Construction Put in Place ($ Millions) Sector

1993 1994 1995 1996 1997

Residential 214,466 242,709 235,179 261,074 270,471

Commercial/Institutional 130,376 138,710 155,230 174,153 195,452

Industrial 28,200 30,412 34,013 34,046 32,392

Public Works 105,608 107,709 113,709 114,365 119,900

Total - All Sectors 478,650 519,540 538,131 583,638 618,215

Value of Construction Put in Place ($ Millions) Sector
41

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/institutional sector accounted for 32

percent of total construction expenditures (i.e., 32 percent of $618.2 billion). The

commercial/institutional sector’s relative share of total construction expenditures is

exceeded only by the residential sector, which constitutes 44 percent of the total. In

addition, the commercial/institutional sector’s relative share exceeds the combined total

for the industrial and public works sectors.

Figure 4-1. 1997 Breakdown of $618B Construction Market

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

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

Residential

44%

Industrial

5%

Public Works

19%

Commercial/

Institutional

32%
42

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.

The choice to adopt a new technology is driven by a number of factors. These factors are

discussed in some detail in Section 6.4.5. However, two factors that are of particular

importance to decision makers considering investments in new technologies are the

ability of those technologies to reduce costs and to reduce the delivery time of

constructed facilities. Clearly, cost reductions have an immediate impact on the bottom

line and thus are reflected on the “corporate” balance sheet. In the case of industrial

facilities, construction expenditures and expenditures for facility operations and for

maintenance and repair activities are dwarfed by manufacturing/process-related

expenditures (e.g., production workers’ wages).30 Thus, any investments which have the

potential both to reduce construction expenditures and expenditures for facility

operations and for maintenance and repair activities and to reduce manufacturing/

process-related expenditures will have a doubly beneficial impact on the bottom line.

Conversely, any investments that result in reductions in delivery time will bring the

facility on-line sooner and into a revenue-generating status. Reductions in delivery time

may also translate into a significant advantage for a manufacturing establishment’s

products/commodities. Manufacturing establishments that are consistently able to reduce

the delivery time of their constructed facilities will more likely be the first to market their

new products/commodities. This strategy is likely to result in increased market share, a

significant market advantage for any manufacturing establishment.

As is shown in Chapter 5, FIAPP products and services offer such opportunities in the

industrial sector. Although such a doubly beneficial impact will probably result for the

other non-residential sectors of the construction industry, the industrial sector is

considered a more likely candidate for early adoption of FIAPP technologies.

Consequently, this impact assessment adopts a conservative approach in defining the

scope of the market for FIAPP products and services to be the industrial sector. The next

section defines the size of the market for FIAPP products and services by detailing

information on the industrial sector.

 

30 Wright, Richard N., Arthur H. Rosenfeld, and Andrew J. Fowell. 1995. National Planning for

Construction and Building R&D. NISTIR 5759. Gaithersburg, MD: National Institute of Standards and

Technology.
43

4.2 Characteristics of the Industrial Sector

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.

The industrial sector, defined in economic terms, consists of establishments that

manufacture products and/or commodities. Defined in this way, the industrial sector is

extremely varied. It includes chemical manufacturing, oil refining, pulp and paper

production, pharmaceuticals manufacturing, electronics manufacturing, automotive

manufacturing, consumer products manufacturing, and miscellaneous manufacturing.

Expenditures by establishments in the industrial sector for the built environment include

construction expenditures (e.g., new construction and additions and alterations) as well as

expenditures for facility operations and for maintenance and repair activities. The market

for FIAPP 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 industrial facilities that affect these expenditures.

This approach is aimed at producing a better understanding of the market for FIAPP

products and services within the industrial 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.

Facility operations include all non-process or end-product related activities required to

operate a building or structure (e.g., energy and water consumption, trash removal/

environmental costs, cleaning services/janitorial, and security services/life safety costs),

with the exception of maintenance and repair activities. In some cases, fixed operations

components may also be included (e.g., real estate and other taxes, insurance, and leasing

expenses).

Maintenance and repair activities include incidental construction work that keeps a

building or structure in ordinary working condition.

New construction expenditures in 1997 for the industrial sector were $32.4 billion in

current dollars (see Table 4-4). Total construction expenditures include both new

construction expenditures and expenditures for additions and alterations. Unfortunately,

the C30 report figures do not include an estimate of expenditures for additions and
44

alterations for any of the non-residential sectors. However, data from the 1992 Census of

the Construction Industry can be used to estimate a value for expenditures for additions

and alterations in 1997. Data from the 1992 Census of the Construction Industry reveal

that expenditures for additions and alterations within the industrial sector average 45 %

of expenditures for new construction. Applying this percentage to the 1997 value in

Table 4-4 produces the desired estimate. Thus, expenditures for additions and alterations

in the industrial sector in 1997 are estimated to be $14.6 billion.

A report by Chapman and Rennison presented and analyzed information on operations,

maintenance, and energy costs.31 The industrial sector figured prominently in these

analyses. Readers interested in an in-depth discussion and analysis of operations,

maintenance, and energy costs in the industrial sector are referred to the report by

Chapman and Rennison.32

Based on data presented in Table 7-9 of Chapman and Rennison,33 estimates of industrial

facility operations costs34 and the total cost for repair of industrial buildings and

structures can be produced. These estimates are based on data from the 1992 Census of

the Construction Industry updated to 1997. Each estimate may be framed as a percentage

of expenditures for new construction. The resultant estimate of industrial facility

operations costs updated to 1997 is $5.2 billion. The resultant estimate of the total cost

for repair of industrial buildings and structures updated to 1997 is $6.4 billion.

Information on maintenance and repair costs per unit of floor area is available from a

variety of sources. However, information from the International Facilities Management

Association (IFMA) provides guidance on the relative costs of repair versus preventive

maintenance. IFMA is an association serving the facility management profession. IFMA

has carried out a number of benchmarking studies covering both the commercial/

institutional and industrial sectors. IFMA’s Research Report #13,35 published in 1994 is

the result of a 1993 survey of IFMA members. The report presents benchmarking data

derived from 283 survey questionnaires. Combining this information36 with the

estimated costs for repair of industrial buildings and structures produces an estimate of

the costs of preventive maintenance for industrial buildings and structures. The resultant

estimate of the costs of preventive maintenance for industrial buildings and structures for

1997 is $3.8 billion.

Total expenditures for maintenance and repairs may be estimated in either of two ways.

First, sum the estimated 1997 expenditures for repair of industrial buildings and the

 

31 Chapman and Rennison, An Approach for Measuring Reductions in Operations, Maintenance, and

Energy Costs.

32 Ibid., pp. 183-225.

33 Ibid., pp. 222-223.

34 Because it is difficult to separate manufacturing-related consumption of energy and water from facilityrelated consumption, all costs for energy and water consumption are allocated to manufacturing-related

activities.

35 International Facilities Management Association. 1994. Benchmarks II. Research Report #13. Houston,

TX: International Facilities Management Association.

36 Ibid., p. 35.
45

estimated 1997 expenditures for preventive maintenance to get the estimated value of

expenditures for maintenance and repairs in the industrial sector in 1997. The resultant

sum is $10.2 billion. Second, use data from the 1992 Census of the Construction Industry

to determine for the industrial sector what percentage of expenditures for new

construction is spent for expenditures on maintenance and repairs and apply it to the 1997

value in Table 4-4. These data reveal that expenditures for maintenance and repairs

within the industrial sector average 38 % of expenditures for new construction. Thus,

expenditures for maintenance and repairs in the industrial sector in 1997 by the second

method are estimated to be $12.3 billion.

Reference to Table 4-3 reveals that new construction expenditures for the industrial

sector—measured in constant 1992 dollars—have hovered around $30 billion per year in

recent years. Because expenditures for additions and alterations, for facility operations,

and for maintenance and repair activities may be estimated as “fixed proportions” of

expenditures for new construction, investments in and expenditures for buildings and

structures in the industrial sector—expressed in constant dollar terms—are remaining

fairly constant. This finding is of particular importance in defining the size of the market

for FIAPP products and services for two reasons.

First, the market for FIAPP 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 products

and services will be employed both as retrofits to existing buildings and as initial

installations in new buildings.

Second, this finding implies that the sum total of all construction/facility-related

expenditures for the entire industrial sector have remained fairly constant for an extended

period of time. If this trend continues, as seems likely, then total expenditures will

continue to remain constant. Thus, the potential market for FIAPP products and services

in the industrial sector may be modeled as a constant dollar volume for which FIAPP

products and services compete with traditional products and services.

This section’s approach to characterizing the market for FIAPP products and services and

detailing the market in terms of total dollar expenditures for construction, facility

operations, and maintenance and repair activities sets the stage for the impact assessment.

By focusing on total dollar expenditures as the potential target market, the calculation of

FIAPP-related costs and benefits is driven by expenditure-related considerations. This is

fortuitous because the industrial sector is geared towards processing information

presented in terms of total expenditures (e.g., total installed cost). By casting the

economic impact assessment in terms of total expenditures, this study will be able to

denominate all key inputs in terms of percent changes from a baseline (e.g., percent

reduction in total installed cost). This approach will promote a better understanding of

the findings of this study and its implications for the construction industry.
46
47

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.
48

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.
49

Table 5-1. Hierarchy of FIAPP Stakeholders by Groups and Classes of Individual FIAPP Stakeholders

Building Owners and Managers

• Building Managers

• Building Owners

Codes, 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 Manufacturers

• Customer Service Operations

• Product/Equipment/Software Designers

• Product/Equipment/Software Innovators

• Product/Equipment/Software Marketing, Sales, and Distribution

Services

• Professional Societies

• Research Organizations

• Testing Laboratories

• Testing Services

• Trade Associations

Professional and Financial Services

• Architects

• Designers

• Engineering Consultants

• Insurance Companies

• Investment Banking Services

• Warranty Companies
50

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

Stakeholder Group

Individual Stakeholder Class Building

Owners &

Managers

Codes,

Standards, &

Support

Services

Manufacturing

Interest Group

Construction

& Associated

Support

Services

Professional

& Financial

Services

Other

Architects  ̧

Building Managers  ̧

Building Occupants  ̧

Building Owners  ̧  ̧

Building Permitting and

Inspection

 ̧

Code Officials  ̧

Code Organizations  ̧

Construction Products/Equipment

Manufacturers

 ̧  ̧

Construction Workers  ̧

Customer Service Operations  ̧

Designers  ̧

Engineering Consultants  ̧

General Contractors  ̧

Insurance Companies  ̧

Investment Banking Services  ̧

Product Certification Services  ̧

Product/Equipment/Software

Designers

 ̧
51

Table 5-2. Assignment of Classes of Individual FIAPP Stakeholders to FIAPP Stakeholder Groups (Continued)

Stakeholder Group

Individual Stakeholder Class Building

Owners &

Managers

Codes,

Standards, &

Support

Services

Manufacturing

Interest Group

Construction

& Associated

Support

Services

Professional

& Financial

Services

Other

Product/Equipment/Software

Innovators

 ̧

Product/Equipment/Software

Marketing, Sales, and

Distribution Services

 ̧

Product Evaluation Services  ̧

Professional Societies  ̧  ̧

Research Organizations  ̧  ̧

Special Interest Groups  ̧

Specialty Trade Contractors  ̧

Standards Organizations  ̧

Testing Laboratories  ̧

Testing Services  ̧

Third Parties  ̧

Trade Associations  ̧  ̧  ̧

Warranty Companies  ̧

Wholesale/Retail Trade/Supply  ̧
52

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

ß Automated Generation of Material Orders and Payments

ß 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
53

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

(Continued)

Lower First Costs

• Better Control of Cost Growth

ß Earlier Bill of Materials and Procurement Analysis Reduces Risks

of Non-Availability of Parts

ß Fewer Project Development and Scope Changes

ß Less Design Rework

ß 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

ß Better Coordination Between Owner, Prime Contractor, and

Subcontractors

ß Better Opportunity for Supply Chain Management

ß Earlier Optimization of Design and Project Plan

ß 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
54

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

ß Fewer Machine-Worker Accidents

ß Identification of High-Risk Employees

ß 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

ß Automated Inspection Requests

• New Ways of Demonstrating Code Compliance

ß Pre-Certified Automated Procedures

ß Remote Certification (Tele-Presence Inspection)

• New Ways of Demonstrating Operating Compliance

ß Autonomous Remote Sensing to Code Official
55

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

(Continued)

Reductions in Delivery Time

• Better Control of Schedule Growth

ß Automated Field Inspection Reports

ß Automated Utility Avoidance

ß Fewer Project Development and Scope Changes

ß Less Design Rework

ß Less Field Rework

ß Earlier Bill of Materials and Procurement Analysis Reduces Risks

of Non-Availability of Parts

• Better Document Control

ß Automated Updates of As-Built Information

• Better Scheduling of Construction-Site Operations

ß Dynamic Database Allows for Identification of Non-Obvious

Critical Path Activities and Automated Rescheduling

ß New Ideas/Task Sequencing Can be Explored with 3D Simulations

ß Output of 3D Simulations Can be Used to Drive Machinery for

Automated Processes

• Earlier Start-Up of Primary Functions

• Faster Task Completion

ß Ability to Employ Automated Processes and “Measureless”

Construction

ß Automated Tracking of Parts and Processes

• Optimization of Project Team and Plan

ß Better Coordination Between Owner, Prime Contractor, and

Subcontractors

ß Better Opportunity for Supply Chain Management

ß Earlier Optimization of Design and Project Plan

ß Improved Procurement, Supply, and Contractor Management

• Smoother Start-Up Operations

Reductions in Warranty Costs

Reductions in Waste and Pollution
56

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, to two, to

three, or to 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.37 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

 

37 The word product is used generically to represent technologies, hardware (e.g., building systems,

subsystems, components, piece parts, and support equipment), software, and services.
57

“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 Marshall38 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.39

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.

 

38 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.

39 Ibid., p. 15.
58

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

ß Increased Costs for Product Development

ß Increased Costs for Product Testing/Simulation
59

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

ß Increased Costs Associated with Market Positioning Efforts

ß Increased Costs to Develop New Distribution and Service Channels

ß 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

ß Increased Costs for Instruction on How to Incorporate New

Technologies, Products, Equipment, and Practices into the Design

Process

ß Increased Costs for Training Building Owners and Managers on

New Operations and Maintenance Processes and Techniques

ß Increased Costs for Training Construction Workers on New

Construction Processes and Techniques

ß Increased Costs for Training Public Officials on Hardware/Software

Capabilities

Reduced Sales of Selected Product/Equipment Lines and Associated Services
60

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

( ̧)), 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 ( ̧). 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,

respectively. Reference to Table 5-5 shows that building owners and managers benefit
61

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.
62

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, &

Support

Services

Manufacturing

Interest Group

Construction

& Associated

Support

Services

Professional

& Financial

Services

Other

Improved Company Capabilities and

Flexibility for New Project Opportunities

 ̧  ̧  ̧

Improved Feedback Mechanisms on

Performance of New Tools and Processes

 ̧  ̧  ̧

Increased/New Sales for System Design/

Integration/Optimization Services

 ̧  ̧  ̧

Increased Net Income for Contractors  ̧  ̧

Increased Revenues Due to Earlier StartUp of Primary Functions

 ̧

Increased Sales of Selected Product/

Equipment Lines and Associated Services

 ̧  ̧  ̧

Increased Sales of Construction Products/

Equipment/Services with New Features

 ̧  ̧  ̧  ̧

Lower First Costs  ̧  ̧  ̧  ̧  ̧

Lower Operations and Maintenance Costs  ̧  ̧

Lower Repair, Replacement, and

Decommissioning Costs

 ̧  ̧  ̧  ̧

Reductions in Construction-Related

Accidents and Injuries

 ̧  ̧  ̧  ̧

Reductions in Costs of Code Compliance

Due to New Ways of Designing,

Constructing, and Maintaining Buildings

 ̧  ̧  ̧  ̧  ̧  ̧

Reductions in Delivery Time  ̧  ̧  ̧  ̧  ̧  ̧

Reductions in Warranty Costs  ̧  ̧  ̧

Reductions in Waste and Pollution  ̧  ̧  ̧  ̧
63

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

Stakeholder Group

Type of Cost Increase or

Benefit Reduction Building

Owners &

Managers

Codes,

Standards, &

Support

Services

Manufacturing

Interest Group

Construction

& Associated

Support

Services

Professional

& Financial

Services

Other

Increased Costs for New Standards

Development

 ̧  ̧  ̧  ̧  ̧  ̧

Increased Costs of Hardware and

Software to Provide Infrastructure

Support

 ̧  ̧  ̧  ̧

Increased Costs to Properly Maintain

Hardware and Software

 ̧  ̧  ̧  ̧  ̧

Increased Investments by Construction

Products/Equipment Manufacturers

 ̧

New-Technology Introduction Costs  ̧  ̧  ̧  ̧  ̧  ̧

Reduced Sales of Selected Product Lines

and Services

 ̧  ̧  ̧  ̧
64
65

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 the industrial sector.

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.40 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/institutional; (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.

 

40 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.
66

Two reports by Chapman and Rennison provide detailed sets of baseline measures for

National Construction Goal 1,41 reductions in delivery time, and National Construction

Goal 2,42 reductions in operations, maintenance, and energy costs. A third report by

Chapman provides a detailed set of baseline measures for National Construction Goal 7,43

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 industrial 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

industrial 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 three key data items: (1) reductions

in construction costs; (2) reductions in delivery time; and (3) reductions in constructionrelated accidents. The first data item is needed to estimate the percent cost savings for a

typical industrial sector project. The second data item provides the basis for estimating

the potential for increased sales revenues due to earlier start-up of production operations.

The third data item is needed to estimate cost savings resulting from improved safety

performance. Anecdotal information is also presented which is useful in estimating a

fourth 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 chemical manufacturing, oil refining, pulp and paper, or

similar 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.

 

41 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.

42 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.

43 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 (in press). Gaithersburg, MD: National Institute of Standards and Technology.
67

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.44 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.45 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 the industrial sector.

The CII Benchmarking and Metrics Committee46 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

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

 

44 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.

45 Thomas, Stephen R. 1999. Impacts of Design/Information Technology on Project Outcomes. NIST GCR

99-786. Gaithersburg, MD: National Institute of Standards and Technology.

46 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.
68

reductions in project delivery time). Detailed information is collected on 6 CII-endorsed

best practices: (1) safety;47 (2) pre-project planning;48 (3) team building;49 (4)

constructability;50 (5) project change management;51 and (6) design/information

technology.52 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 297 projects totaling $13.0 billion (installed cost) has been collected,

compiled, analyzed, and made available to NIST.53 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 Benchmarking and Metrics Committee uses four construction industry groups and

allows for categorization of the database by these groups. The four industry groups are:

(1) buildings; (2) heavy industrial; (3) infrastructure; and (4) light industrial. 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

heavy industrial group comprises approximately two thirds of the database. The

remainder of the projects is distributed among the other three industry groups as follows:

50 building projects; 14 infrastructure projects; and 39 light industrial projects.

Throughout this document buildings are classified under the commercial/institutional

sector, both heavy industrial projects and light industrial projects are classified under the

industrial sector, and infrastructure projects are classified under the public works sector.

 

47 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.

48 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.

49 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.

50 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.

51 Project change management practices seek to promote a balanced change culture, recognize change,

evaluate change, implement change, and continuously improve from lessons learned.

52 Design/information technology practices involve the use of data integration programs, 3D CAD

modeling, electronic data interchange (EDI), and bar coding.

53 Information compiled from the CII Benchmarking and Metrics database was limited to data from US

domestic projects. 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

three versions of its questionnaire, only data from versions 2.0 and 3.0 were included. The version 1.0

questionnaire did not address design/information technology use.
69

Figure 6-1. CII Database by Respondent Type

114

183

0

25

50

75

100

125

150

175

200

Owners Contractors

Respondent

Number of Projects

Figure 6-2. CII Database by Industry Type

25

13

101

44

14

1

93

6

0

20

40

60

80

100

120

Buildings Heavy Industrial Infrastructure Light Industrial

Industry Group

Number of Projects

Owner Contractor
70

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, one-third have a cost between $15 and $50 million, and one-sixth 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 $45 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. Modernization was defined as a facility for

which a substantial amount of the equipment or structure is replaced or modified, and

which may expand capacity. 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, and two safety metrics. The

two cost metrics are project cost growth54 and construction cost growth. The two

schedule metrics are total project duration55 (in weeks) and construction phase duration

(in weeks). The two safety metrics are the recordable incidence rate56 (RIR) and the lost

workday case incidence rate57 (LWCIR).

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).

 

54 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.

55 Total project duration equals the elapsed time from the start of detailed design to turnover to the user.

56 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).

57 The LWCIR is a measure of more serious injuries; it records those cases which result in days away from

work or restricted work activity.
71

Figure 6-3. CII Database by Cost Category

101

51

31

56

34

24

0

20

40

60

80

100

120

<$15 $15-$50 >$50

Cost Category ($Million)

Number of Projects

Owner Contractor

Figure 6-4. CII Database by Project Nature

73

58

52

26 30

58

0

10

20

30

40

50

60

70

80

90

100

Addition Grass Roots Modernization

Project Nature

Number of Projects

Owner Contractor
72

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

-2.0 % -3.4 % 1.4 %

Construction Cost

Growth

-0.2 % -4.7 % 4.5 %

Total Project

Duration

96 weeks 80 weeks 16 weeks

Construction Phase

Duration

58 weeks 50 weeks 8 weeks

Recordable

Incidence Rate

2.184 1.439 0.745

Lost Workday Case

Incidence Rate

0.585 0.238 0.347

Source: Thomas, Impacts of Design/Information Technology on Project Outcomes, pp. 3-12.

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 costs, repair

costs, safety statistics, the book value and replacement cost of industrial facilities, and net

income from manufacturing 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 industrial sector is described in detail in

Subsection 6.3.1.

Data from the US Bureau of the Census

The value of construction put in place in the industrial sector is based on data published

in the Current Construction Reports series C30 publication (see Table 4-4 in Section 4.1)

supplemented by information from the 1992 Census of the Construction Industry (see

Section 4.2). The C30 publication provides an estimate of the value of new construction

put in place ($32.4 billion in 1997). Information from the 1992 Census of the

Construction Industry is used to estimate the value of construction put in place for

additions and alterations as a “fixed proportion” of expenditures for new construction.

The resultant value for additions and alterations is $14.6 billion (measured in 1997

dollars). Thus, the value of construction put in place (i.e., new construction plus

additions and alterations) for the industrial sector is $47.0 billion (measured in 1997

dollars).
73

The US Bureau of the Census also carries out a number of surveys that are specific to the

industrial sector. Two of these surveys used in preparing this report are the Census of

Manufacturers and the Annual Survey of Manufacturers.

The Census of Manufacturers provides detailed information on selected characteristics of

the industrial sector. The survey is carried out every five years in years ending with 2

and 7; data are currently available for the 1992 Census. The 1992 Census of

Manufacturers includes all establishments with one paid employee or more, which are

primarily engaged in manufacturing. This includes approximately 380,000

establishments. Manufacturing is defined as the mechanical or chemical transformation

of substances or materials into new products. The assembly of component parts of

products is also considered to be manufacturing. The 1992 Census of Manufacturers

covers 20 major industry groups with two-digit SIC codes 20 through 39 inclusive.

The Annual Survey of Manufacturers presents manufacturing establishments statistical

data for years when the Census of Manufacturers is not carried out. It is a less detailed

sample survey of establishments with payroll.

Two key data elements are obtained from the surveys. These data elements are the cost

of purchased services for the repair of industrial facilities and other structures and the

gross book value of industrial facilities and other structures. The first data element is a

direct measure of annual repair costs for industrial facilities. It is used as the starting

point for estimating potential annual repair cost savings due to the use of FIAPP products

and services. The first data element is also used, in conjunction with data from the

International Facilities Management Association, as the basis for estimating annual

maintenance costs for industrial facilities. The second data element is used as the basis

for estimating the replacement cost of industrial facilities, a term which is used as a

deflator in the estimation procedure for placing a dollar value on potential increases in

sales revenue due to early start-up (i.e., reductions in delivery time).

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/institutional 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

values) of each type of cost (i.e., repair costs and maintenance costs). This information

may be used to develop a “weighting” factor for allocating aggregated costs into each of

 

58 International Facilities Management Association. 1994. Benchmarks II. Research Report #13. Houston,

TX: International Facilities Management Association.
74

the two cost categories. This factor is used to derive directly from Census data on the

cost of purchased services for the repair of industrial facilities and other structures an

estimate of annual maintenance costs for industrial facilities (see Section 4.2).

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 illness. 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

sales revenue due to early start-up (i.e., reductions in delivery time).

 

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.
75

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 industrial facilities and for the renovation of existing industrial

facilities. 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 industrial facilities employing the

base case. Thus, for new industrial facilities, either the base case or the FIAPP

alternative is employed during “grass roots” construction. Similarly, for existing

industrial facilities, 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 industrial facility and to either configuration

employed during the renovation of an existing industrial facility.

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

6-1) and facility service features (see Subsection 6.3.1.2). These differences, although

 

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).
76

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. Lower first costs are

registered through a reduction in a typical industrial sector project’s 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 facility 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 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 due to

earlier start-up of production operations. Higher net income is classified as a benefit

because it increases the contractor’s profit margin.
77

Although the FIAPP alternative is expected to result in fewer/shorter interruptions of

process operations due to facility-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 interruptions of process

operations (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 interruptions of process

operations 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 decision was made to maintain the conservative approach 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 1.4 % for project cost growth and 4.5

% 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 1.4 %

reduction in project cost growth. There are two reasons for selecting this value. First, it

is more conservative than the 4.5 % 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

Therefore, the use of FIAPP products and services is estimated to reduce total installed

costs for a typical industrial sector project vis-à-vis the base case by 1.4 %.

 

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 1.4 % reduction in project cost growth between the set of FIAPP alternative projects

and the set of base case projects results in a 1.4 % reduction in total installed costs for the FIAPP

alternative vis-à-vis the base case.
78

The 1.4 % 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

percent 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 two to three percent. Similarly, aggressive information

management strategies for materials management related activities may yield as much as

three percent reduction in elapsed time and seven percent reduction in execution costs.

When combined, aggressive information management strategies may result in reductions

as much as 14 percent for elapsed time and eight percent for execution costs.”64

It is important to note that not all industrial sector projects will employ the FIAPP

alternative. Thus, the 1.4 % reduction in first costs will only accrue to those industrial

sector projects which actually employ the FIAPP alternative. Information on the annual

proportion of industrial sector construction-related investments (i.e., expenditures for new

construction projects and for facility renovation projects) 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 industrial

facilities. 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 facility’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-ofstock” parts. The use of selected FIAPP products and services during the post “start-up”

phase of the project life cycle will allow facility 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.

To develop a range of estimates for annual maintenance and repair cost savings, industry

experts and facility managers and operators were interviewed. The general consensus

was that the use of selected FIAPP products and services during the post start-up phase

 

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.
79

would enable both smaller crew sizes and the ability to handle more facility-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 facility’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 facility owner/operator would incur costs to maintain an “up-to-date”

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

%. Because these cost savings are expected to degrade over time, they are reduced by 10

% per year following their installation.

To estimate how much annual maintenance and repair costs will be reduced, we begin

with Census data and information from IFMA (see Subsection 6.1.3) to derive an

estimate of annual maintenance and repair expenditures for the industrial sector. The

resultant figure is $10.2 billion (in 1997 dollars). In reality, only a small portion of the

industrial sector total for maintenance and repair expenditures can be saved through the

use of selected FIAPP products and services. This is because the $10.2 billion figure is

for all facilities in the industrial sector, whereas FIAPP products and services are only

employed in a fraction of each year’s construction-related investments (see Subsection

6.4.4). In addition, 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 these issues, this study assumes: (1) savings accrue only to that

proportion of the industrial sector that previously employed FIAPP products and services;

and (2) no FIAPP-related savings in maintenance and repair costs occur in the year in

which the capital investment is made. Thus, the proportion of the industrial sector’s

stock of facilities 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 FIAPP-related savings

in maintenance and repair costs occur in the future. 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.

 

65 Van Tienhoven, C. J. 1996. The Benefits of STEP. Report No. IS96-014. The Hague: Shell Information

Services.
80

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

0.745 for the RIR and 0.347 for the LWCIR. Because the RIR also includes cases which

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 0.398 (i.e., 0.745 minus 0.347); 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 0.398 figure

for improvements in the “net” RIR by 5.25 % for each year beginning in 1998 and the

0.347 figure for improvements in the LWCIR by 5.6 % for each year beginning in 1998.

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 0.398 and the LWCIR value of 0.347 resulting

from the projects in the CII Benchmarking and Metrics database. This is because the

“percentage” improvement in the computed value of the RIR is more than 30 % (compare

Columns 3 and 1 of Table 6-1 for the RIR) and the improvement in the computed value

of the LWCIR is nearly 60 % (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.
81

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

 Years 1989-1997

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.

 

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.

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
82

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

accrue both to industrial facility owners and to contractors engaged in the construction of

those facilities. 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 16 weeks for total

project duration and 8 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 8 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 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 industrial 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 industrial 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 industrial sector (i.e., manufacturing). These estimates are used to translate

reductions in delivery time into a dollar denominated value.
83

FIAPP-related reductions in delivery time in any one year only affect a small proportion

of the industrial sector’s output. This study models this outcome in two stages. First, the

“maximum potential” proportion is set equal to the ratio of the total value of industrial

facilities put in place in any given year and the replacement cost of the nation’s stock of

industrial facilities.69 The maximum potential proportion is estimated to be 0.031; it is

assumed to decline at a rate of 2.3 % throughout the study period. The decline is due to

growth in the industrial sector’s gross stock of fixed private capital.70 Second, the

proportion of the total value of industrial facilities put in place that employ the FIAPP

alternative in any given year is estimated via the diffusion model (see Subsection 6.4.4).

Because the diffusion model captures how FIAPP products and services move into the

marketplace, the proportion of the industrial sector’s projects employing FIAPP products

and services increases over time.

To complete the calculation, an additional data item is needed, an estimate of the value of

a week’s reduction in delivery time to the industrial sector. Recall that the CII

Benchmarking and Metrics database provides an estimate of the expected number of

weeks reduction (i.e., 8 weeks) associated with the FIAPP alternative. The value of a

week’s reduction in delivery time is estimated by dividing the industrial sector’s net

income figure for 199771 (i.e., $288.5 billion) by 52, the number of weeks in a year. The

resultant figure is $5,548 million (in 1997 dollars). Each year’s 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 multiplied by each of the two proportions described above to estimate

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

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

 

69 Replacement cost is estimated as the industrial sector’s gross stock of fixed private capital facilities and

structures (see U.S. Bureau of Economic Analysis, Survey of Current Business, September 1995). Due to

changes in reporting (e.g., net versus gross figures) for the industrial sector’s stock of fixed private capital,

the 1997 estimate was based on 1994 data rather than on later data. The data from 1994 was updated to

1997 to estimate the industrial sector’s gross stock of fixed private capital facilities and structures in 1997.

70 The annual rate of growth in the industrial sector’s gross stock of fixed private capital facilities and

structures is based on time-series data (expressed in 1987 dollars) between 1980 and 1994 (see U.S. Bureau

of the Census, Statistical Abstract of the United States: 1997, p. 554).

71 The actual net income figure for 1997 was not available at the time this study was being conducted.

Consequently, the most recent figure that was available (i.e., 1995) was converted to 1997 dollars and used

as an estimate for the 1997 net income figure.
84

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

0.013 for the computed value of the budget factor metric.72 This metric’s performance

improvement translates into an improvement of at least 1.3 % 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

industrial 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 industrial facility

owners and managers and to contractors. Understanding the types of costs that affect

industrial facility 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 CONSiAT-

 

72 Thomas, Impacts of Design/Information Technology on Project Outcomes, p. 11.
85

related 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 industrial facility 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

Marshall73 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.74

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, higher costs in the form of 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. Specifically, an additional cost equal to 0.5 % of total installed cost is

assigned when an industrial facility construction project employs the FIAPP alternative.

Discussions with industry experts were used to specify a very conservative value (i.e.,

high value) for this additional cost. As more information becomes available, the

estimated value of 0.5 % of total installed cost 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 seven projects that support the overall CONSiAT effort are described

in Chapter 3. Because FIAPP products and services are targeted for demonstration in

 

73 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.

74 Ibid., p. 15.
86

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 FY1999 and estimated investments for FY2000 and

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

FY2001 investment is $2,500,000. Of the $2,500,000 total, 25 %, or $625,000, is

allocated to calendar year 2000, and 75 %, or $1,875,000, is allocated to calendar year

2001.

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 1,326a2002 3,000

1995 1,507a2003 3,500

1996 1,487a2004 4,000

1997 1,733a2005 4,000

1998 2,344a2006 4,000

1999 2,018a2007 3,500

2000 2,105e2008 3,000

2001 2,500e2009 2,500

a = actual investment costs

e = estimated investment costs
87

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.

(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
88

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 the latest 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 1994.75 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-9476 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

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

 

75 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.

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

and Budget.
89

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.77 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

industrial 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 industrial facilities (see Subsection 6.3.1). However, to

develop realistic estimates of cost savings nationwide, it is also necessary to generate

estimated values for the following three factors: (1) the overall rate of adoption of FIAPP

products and services in industrial facilities; (2) the annual proportion of industrial facilities

employing FIAPP products and services for new construction activities and for additions and

alterations; and (3) the cumulative proportion of industrial facilities covered by FIAPP

products and services. To generate estimates of cost savings nationwide, information on

potential benefits and cost savings 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 these three factors 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

full treatment of the topic is beyond the scope of this report, four factors affecting the

 

77 Ibid., p. 7.
90

diffusion process are worth noting. Readers interested in thorough treatments of this

important subject, including case studies, are referred to the books by Stoneman78 and

Mansfield.79

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 ea b

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,

a = the location parameter, and

 

78 Stoneman, Paul. 1983. The Economic Analysis of Technological Change. New York: Oxford University

Press.

79 Mansfield, Edwin. 1995. Innovation, Technology and the Economy: Selected Essays of Edwin Mansfield.

2 vols. Economists of the Twentieth Century Series. Aldershot, UK: Elgar.
91

b = the shape parameter ( b > 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 h to designate the market

saturation level. The mathematical representation of the model is

[ ]

1 ( )

( ) 1

− −

= +

t

P t e

a b

h h

where

Ph(t) = the proportion of potential users who have adopted the new technology by

time t,

h = the market saturation level,

e = Euler’s number, the base of the natural system of logarithms,

a = the location parameter, and

b = the shape parameter ( b > 0 ).

An extensive review of the economics literature on the diffusion process produced

candidate values for a and b. Readers interested in case studies based on the Mansfield

model that are useful in specifying values for a and b are referred to Mansfield’s

collection of articles.80 An additional factor used to specify the values of a and b is the

length of time it takes for Ph(t) to reach 50 % of its potential market. Due to the

relationship between the Mansfield model and the logistics distribution, the value at

which Ph(t) reaches 50 % of its potential market has a closed-form relationship based

solely on the values of a and b. If we assume t = 1 is the time at which the technology is

first introduced, then a /b 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 a

to be positive (i.e., a > 0 ).

The values of the ratio a /b vary from 4 years to 16 years in a wide range of articles

published in the economics literature (see Mansfield,81 Mansfield et al,

82 and Simon83 ).

 

80 Mansfield, Innovation, Technology and the Economy, Vol. II, pp. 3-83.

81 Ibid., pp. 63-72.

82 Mansfield, Edwin, John Rapport, 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.
92

Consequently, this report uses a value of 8 for the ratio a /b as its baseline value. The

corresponding baseline values for a and b are 4.0 and 0.5, respectively.

The estimated value for h was set equal to 0.5. Thus, the baseline value for h is 0.5.

This means that FIAPP products and services will eventually be employed in projects

totaling 50 % of the value of construction put in place in the industrial sector. 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 industrial sector projects

will be driven by two mutually reinforcing factors. These factors are: (1) the desire of

manufacturers (i.e., the owners of industrial facilities) to use information technology as a

tool for reducing both 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 industrial facilities 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.84 When

examining the relative performance of these systems, some important trends appear.

“The best company transforms a 15 % return on investment (ROI) project, based on

average performance, into a 22.5 % ROI project. In contrast, the poorest performers turn

that same project into a 9 % ROI.

“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. ...

“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. The company with the best track record in starting up and getting on-spec

product from new facilities achieves 6 % more product from facilities than the industry

average.

“When these three performance factors are combined, an astounding 10 % improvement

in ROI can be achieved. This means that based on extraordinary project performance, a

15 % ROI project can be transformed into a 25 % ROI winner.

 

83 Simon, P. 1975. Models of Process Diffusion and Entry in the U.S. Chemical Industry. Ph.D. dissertation,

University of Pennsylvania.

84 The Business Roundtable. 1997. The Business Stake in Effective Project Systems. Washington, DC: The

Business Roundtable.
93

“However, one company is actually achieving 75 % of that possible gain. Several others

are achieving substantial boosts in ROI just by the manner in which they organize and

execute their project work. Conversely, some major U.S. manufacturers are doing

projects so poorly they regularly transform an average 15 % ROI project into a 9 %

project.’’ 85

These same themes—reduced costs and shorter cycle times—resonate with a key subset

of the industrial sector, chemical manufacturers. The American Chemical Society’s

Technology Vision 2020 86 identifies two distinct areas that are targeted for significant

performance improvement—areas that will benefit from FIAPP products and services.

These areas are: (1) supply chain management; and (2) engineering design and

construction. It is important to note that the goals set forth in Technology Vision 2020

have been endorsed by a broad cross section of the U.S. chemical industry. For example,

key organizational players involved in the production of Technology Vision 2020 were

the American Chemical Society, the American Institute of Chemical Engineers, the

Chemical Manufacturers Association, the Council for Chemical Research, and the

Synthetic Organic Chemical Manufacturers Association. Thus, one can conclude that

chemical manufacturers represent a focused and motivated market for FIAPP products

and services.

While the chemical industry has concentrated on science and production, an area that has

received less attention than it merits is that of the supply chain. The supply chain is

defined as the critical linkages between the supplier and the producer, and the producer

and the customer. Thus, the delivery process for construction projects is a key

component of the supply chain. As the chemical industry becomes increasingly global,

issues related to the supply chain are increasingly critical to industrial competitiveness.

Today, many estimate that the costs associated with supply chain issues represent about

10 % of the sales value of delivered products domestically and as much as 40 %

internationally.87 Clearly, this represents an area of opportunity for increasing

competitiveness.

The American Chemical Society’s Technology Vision 2020 has a goal that “the supply

chain functions will operate in an environment of seamless coordination of orders,

production, and distribution across countries and continents.” 88 To achieve this vision,

the chemical industry plans to sponsor an effort to drive information systems toward

harmonized communications, data transmission, and information processing in support of

global supply chain activities.

The revenue-generating capability of the chemical industry is derived from its ability to

deliver chemicals and materials that satisfy customers’ needs in a timely and cost-

 

85 Ibid. pp. 2-3.

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

Society.

87 Ibid. p. 39.

88 Ibid. p.41.
94

effective manner. Consequently, maintaining and improving the competitiveness of the

U.S. chemical industry will require advances in engineering design and construction.

“The building of new plants continues to be in a state of change. Improvements will

result from both innovations and the better application of old technologies. Future

conditions will cause this change to continue. (1) Owners will often partner with the

engineering firms, construction companies, and major equipment suppliers when building

new plants. (2) Standard, prefabricated modular components designed for industry-wide

requirements will be increasingly used. (3) Pressure to shorten the time needed to design

and build a plant will force significant change in the way this process is carried out. (4)

The design of a plant will be done globally to minimize the cost. (5) The use of

electronic footprints of existing plants will enable plant designs to be done more quickly.

“Plants will need to be designed and built in a much shorter time. Technology is needed

to automate the design process through modular concepts in easy-to-use software. In

addition, compatible communications systems between major equipment suppliers and

their design firms will allow for rapid translation of concepts into innovative plant

structures. Partnering is required to get adequate response from the supplier to cut the

building time. Dynamic simulation will allow for integrated design with verification of

structural integrity. This will shorten start-up time and eliminate overdesign.” 89

Contractors who wish to survive and prosper will be pushed to employ design and

information (i.e., FIAPP-like) technologies from two sources. First, industrial facility

owners 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

manufacturing processes and in their supply chain management process). This point was

discussed previously from the owner’s perspective and will not be explored further here.

Second, contractors have experienced a significant, sustained decline in productivity visà-vis the rest of the economy.90 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 industrial facilities, 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 10 contractors in 1997 installed $23.7 billion worth of industrial sector

projects.91 Although these figures represent global revenues, a significant share of these

industrial 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

 

89 Ibid. p.60.

90 Teicholz, Paul M. “Reverse Productivity Declines,” ENR, Vol. 243, No. 23, December 13, 1999, p. 59.

91 Tulacz, Gary J. “Top 400 Contractors,” ENR Sourcebook, November 1998, pp. 4-10.
95

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

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 a and b specify the rate of adoption of FIAPP products and services in

industrial facilities, whereas the value of h 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 industrial sector covered by FIAPP products and services. For any given year, this

amount is equal to the product of Ph(t) for that year and the total value of constructionrelated expenditures in the industrial sector (i.e., $47.0 billion). Table 6-4 records the

value of Ph(t) for values of t from 0 to 16 (i.e., from 2004 to 2020). Note that the years

shown on the table extend past the end of the study period.

A secondary diffusion process specifies how FIAPP products and services penetrate the

stock of industrial facilities. The secondary diffusion process is used only in the

estimation of reductions in maintenance and repair costs. The need for a secondary

diffusion process is driven by the way in which additions and alterations affect the

industrial base. For example, a $50 million modernization project could reduce future

maintenance and repair costs for a $250 million industrial facility. Thus, a given dollar

amount for additions and alterations has a greater impact on the industrial base (i.e., it

affects a greater dollar value) than does the same dollar amount for new construction.

However, because the value of the industrial base is so much greater than the value of

construction put in place in any given year, it will take longer for FIAPP products and

services to reach saturation (i.e., 50 % of the value of the industrial base, which is

assumed to be equal to its replacement cost).

The secondary diffusion model, Bh(t), where “B” is used to indicate that this diffusion

model applies to the industrial base, uses the same values for b and h as does Ph(t) but a

different value for the location parameter. The new location parameter, a ́, is set equal

to 6. This means that it will take 12 years for FIAPP products and services to penetrate

25 % of the industrial base. Table 6-4 records the value of Bh(t) for values of t from 0 to

16. Comparisons between Ph(t) and Bh(t) reveal that Bh(t+4) equals Ph(t). This is

because the only difference between Ph(t) and Bh(t) is the value of the location parameter.

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 Ph(t) is shown as a solid line in Figure 6-6.

The trace of Bh(t) is shown as a dashed line in Figure 6-5. The figure includes both a left

and a right vertical axis. The left vertical axis of Figure 6-6 records the values of Ph(t).

The right vertical axis of Figure 6-6 records the values of Bh(t). The values on both

vertical axes range from 0 to h. The horizontal axis of Figure 6-6 records the values of t

and the years for which the values of Ph(t) and Bh(t) are calculated. Recall that in the

baseline analysis t = 1 corresponds to the year 2005. Note that the years shown on the

horizontal axis extend past the end of the study period. This is done to show that Ph(t)
96

and Bh(t) do not approach the market saturation level, h, 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.

Much of the sensitivity analysis is concerned with the diffusion model (see Chapter 8).

As such, ranges of values were specified for a, b, h, and the time of first use. The ranges

for a and b were selected based on values of a and b published in the economics

literature and their implications for the values of the ratio a /b also published in the

economics literature. The range of values for a used in the sensitivity analysis is a low of

3 and a high of 5 (i.e., 3 ≤ a ≤ 5). The range of values for a ́ is tied to the range of

values of a. In particular a ́ is equal to a + 2. The range of values for b used in the

sensitivity analysis is a low of 0.4 and a high of 0.6 (i.e., 0.4 ≤ b ≤ 0.6). These ranges of

values for a and b and a ́ and b result in ranges for the ratios a /b and a ́/b which are

consistent with the values published in the economics literature (i.e., 5.0 ≤ a /b ≤ 12.5)

and (i.e., 8.33 ≤ a ́/b ≤ 17.5).

Table 6-4. Baseline Case of Pη(t) (α = 4.0, β = 0.5, η = 0.5) and Bη(t) (α ́= 6.0,

β = 0.5, η = 0.5)

Year t Pη(t) Bη(t)

2004 0 0.0000 0.0000

2005 1 0.0147 0.0020

2006 2 0.0237 0.0033

2007 3 0.0379 0.0055

2008 4 0.0596 0.0090

2009 5 0.0912 0.0147

2010 6 0.1345 0.0237

2011 7 0.1888 0.0379

2012 8 0.2500 0.0596

2013 9 0.3112 0.0912

2014 10 0.3655 0.1345

2015 11 0.4088 0.1888

2016 12 0.4404 0.2500

2017 13 0.4621 0.3112

2018 14 0.4763 0.3655

2019 15 0.4853 0.4088

2020 16 0.4910 0.4404

The range of values for h is based on information provided by industrial sector experts.

These values range from a low of 30 % to a high of 70 % (i.e., 0.3 ≤ h ≤ 0.7).
97

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.

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

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

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)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

Bη(t)

P (t) B (t) η η

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.
98

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.92 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

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 industrial facilities. It begins

with a review of the nature of BFRL’s contribution.

 

92 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.
99

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 seven 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 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 assessments93, 94 was used to develop an

 

93 Chapman and Weber, A Case Study of the Fire Safety Evaluation System, pp. 31-42.
100

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

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

2001 4,000 2,500 2,200 2,200

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

 

94 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.
101

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

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.
102
103

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 industrial facilities.

Second, are the cost savings attributable to BFRL and the return on BFRL’s CONSiATrelated 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 industrial facility 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 in excess of

$2.0 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 industrial facility 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 $150 million. These

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

investment costs of approximately $30.1 million. Stated in present value terms, every

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

deployment efforts is expected to generate $4.95 in cost savings to the public (i.e., an SIR

of 4.95). The annual percentage yield (AIRR) from BFRL’s CONSiAT-related

investments over the study period is 14.1 %.
104

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

Systems Integration and Automation Technologies in Industrial Facilities

1.a Significance of Research Effort:

Owners of industrial facilities 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 industrial facilities and tightening

profit margins for contractors are also affecting the competitive positions of

each stakeholder. One means of improving the competitive positions of

each industrial 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 onetime 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 facility owners, contractors, equipment

and systems manufacturers and service providers, software developers,

facility operators, 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 industrial facilities, 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 industrial facilities 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.
105

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

Systems Integration and Automation Technologies in Industrial Facilities

(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).

• 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.a Calculation of Savings, Costs, and Additional Measures

Savings and Costs

Present Value Cost Savings Nationwide:

Sum from 1993 to 2017 of present value of cost savings nationwide

by year

= $2,043.2 million

Present Value Savings (PVS) Attributable to BFRL:

Sum from 1993 to 2008 of present value of cost savings nationwide

by year

= $149.0 million

Present Value Investment Costs (PV Costs) to BFRL:

Sum from 1993 to 2017 of present value of investment cost to BFRL

by year

= $30.096 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

 = $149.0 - $30.096 = $118.9 million

Additional Measures

SIR of BFRL Contribution:

Savings-to-Investment Ratio on BFRL investment

 = $149.9/$30.096 = 4.95

AIRR of BFRL Contribution:

Adjusted Internal Rate of Return on BFRL investment

 = (1+ 0.07) * 4.951/25 – 1 = 0.141

3.b Key Results:

1997 Dollars

($ amounts in millions)

Cost Savings Nationwide:

 $2,043.2

Savings Attributable to

BFRL:

PVS $149.0

PV Costs $30.096

PVNS $118.9

SIR 4.95

AIRR 14.1%
106

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 primary and secondary diffusion models 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) newtechnology 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 industrial facilities. The results of the baseline analysis

show that cost savings nationwide exceed $2.0 billion ($2,043 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 models. Column 1 of the

table lists each year of the study period from 1993 through 2017. Columns 2 and 3 of

Table 7-1 record information on the calculated values of the primary diffusion model,

Ph(t), and the secondary diffusion model, Bh(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, Ph(t) = 0 and

Bh(t) = 0 for all values of t less than 1. Next, the primary diffusion model is combined

with information on the 1997 value of construction put in place for the industrial sector as

a whole to generate annual estimates of the value of construction put in place for that

proportion of the industrial sector 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, the value of Ph(t)

in Column 2 is multiplied by the 1997 value of construction put in place for the industrial

sector as a whole. This value, expressed in 1997 dollars, is held constant at $47.0 billion

throughout the period during which FIAPP products and services diffuse into the

marketplace (i.e., 2004 through 2017). The secondary diffusion model specifies how

FIAPP products and services penetrate the stock of industrial facilities. The secondary

diffusion process is used only in the estimation of reductions in maintenance and repair

costs. Recall that the need for a secondary diffusion process is driven by the way

additions and alterations affect the industrial base (see Section 6.4.4).
107

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

 Investments by Year: 1993 - 2017

Year

Proportion of 

Industrial Sector 

Investments 

Covered by 

FIAPP Products 

and Services 

Pη(t)

Proportion of 

Industrial Base 

Covered by 

FIAPP Products 

and Services 

Bη(t)

Value of FIAPPRelated 

Investments (In 

Millions of 1997 

Dollars)

Col. (1) Col. (2) Col. (3) Col. (4)

1993 0 0 0

1994 0 0 0

1995 0 0 0

1996 0 0 0

1997 0 0 0

1998 0 0 0

1999 0 0 0

2000 0 0 0

2001 0 0 0

2002 0 0 0

2003 0 0 0

2004 0 0 0

2005 0.0147 0.0020 688.8

2006 0.0237 0.0033 1,114.5

2007 0.0379 0.0055 1,782.7

2008 0.0596 0.0090 2,801.3

2009 0.0912 0.0147 4,287.0

2010 0.1345 0.0237 6,320.1

2011 0.1888 0.0379 8,872.2

2012 0.2500 0.0596 11,750.0

2013 0.3112 0.0912 14,627.8

2014 0.3655 0.1345 17,179.9

2015 0.4088 0.1888 19,213.0

2016 0.4404 0.2500 20,698.7

2017 0.4621 0.3112 21,717.3
108

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 saving for each category of cost savings is given (see Equations 7.1

through 7.6). 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 7.1 through 7.4. The

subscript, B, is used to denote benefits; these values appear in Columns 6 and 7. They are

calculated through application of Equations 7.5 and 7.6.

To facilitate cross-referencing between Table 7-2 and Equations 7.1 through 7.6, 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 which have not already been

defined are defined the first time they appear (e.g., VIPIS(t) in Equation 7.1). Note that

several equations use the same term (e.g., Ph(t) appears in every equation and VIPIS(t)

appears in both Equations 7.1 and 7.6).

Reductions in First Costs

RFC (t) [P (t) *VIP (t)]*(0.014)

S h IS = 7.1

where VIPIS(t) = the value of construction put in place in the industrial sector in

 year t (in millions of 1997 dollars); and

 0.014 = the expected reduction in first costs expressed as a decimal.
109

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)+(6)+(7)

1993 0 0 0 0 0 0 0

1994 0 0 0 0 0 0 0

1995 0 0 0 0 0 0 0

1996 0 0 0 0 0 0 0

1997 0 0 0 0 0 0 0

1998 0 0 0 0 0 0 0

1999 0 0 0 0 0 0 0

2000 0 0 0 0 0 0 0

2001 0 0 0 0 0 0 0

2002 0 0 0 0 0 0 0

2003 0 0 0 0 0 0 0

2004 0 0 0 0 0 0 0

2005 9.644 0 .025 .440 16.974 8.528 35.611

2006 15.603 .268 .038 .675 26.846 13.799 57.229

2007 24.957 .703 .058 1.022 41.975 22.071 90.785

2008 39.218 1.450 .086 1.520 64.476 34.682 141.432

2009 60.018 2.807 .125 2.204 96.454 53.077 214.684

2010 88.482 5.381 .175 3.076 139.000 78.249 314.363

2011 124.211 10.392 .234 4.089 190.742 109.846 439.514

2012 164.500 20.169 .294 5.129 246.931 145.476 582.499

2013 204.789 38.736 .348 6.046 300.498 181.106 731.523

2014 240.518 72.078 .389 6.725 344.991 212.703 877.403

2015 268.982 127.214 .413 7.122 377.144 237.875 1,018.750

2016 289.782 209.299 .423 7.265 397.173 256.270 1,160.212

2017 304.043 317.509 .421 7.219 407.349 268.881 1,305.422

Total Cost Savings by 

Year (In Millions of 1997 

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)
110

Reductions in Maintenance and Repair Costs

( ) {[ ( ) *(0.02 ( 1)) *(1.023) ]*

Y

S IS MRC t B t RMRC t

− = + h −

 ( ) *(1.1) * (0.1)

( 1)

1 



















−

− −

=

∑

j

t

j

Ph t j 7.2

where MRCIS(t) = the value of maintenance and repair costs in the industrial sector

 in year t (in millions of 1997 dollars);

 0.02 = net additions to the industrial base due to the value of new

 construction put in place expressed as a decimal;

 Y = Year – 1997;

 1.023 = deflator used to adjust for the long-term growth in the industrial

 base of 2.3 %;

 1.1 = deflator used to adjust for “degraded” performance over time of

 10 % per year beginning the year after construction; and

 0.1 = the expected reduction in maintenance and repair costs

 expressed as a decimal.

Reductions in Construction-Related Accidents

Resulting in No Lost Workdays: NETRIR

[ ]*(0.0015) (0.398) *(1.0525) * ( ) * ( ) *(200,000)

( ) = − −1

P t CWH t NETRIR t

IS

Y

S h

 7.3

where 0.398 = 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;

 CWHIS(t) = the number of craft workhours in the industrial sector 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).
111

Reductions in Construction-Related Accidents

Resulting in One or More Lost Workdays: LWCIR

( ) [(0.347) *(1.056) * ( ) * ( ) *(200,000) ]*(0.0315)

− −1 = P t CWH t LWCIR t

IS

Y

S h

 7.4

where 0.347 = 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).

Reductions in Delivery Time

RDT (t) [(0.0313) *(1.023) * P (t) *WNI (t)]*8

IS

Y

B h

− = 7.5

where 0.0313 = net additions to the industrial base due to the combined value of

 new construction and additions and alterations expressed as a

 decimal;

 WNIIS(t) = weekly net income in the industrial sector in year t (in millions

 of 1997 dollars); and

 8 = the expected reduction in delivery time expressed in weeks.

Higher Contractor Net Income

( ) [ ( ) * ( ) *(1.05) ]*(0.013)

−1 = P t VIP t HCNI t B h IS 7.6

where 1.05 = deflator used to translate total installed cost to contractor cost;

 and

 0.013 = the expected increase in contractor net income expressed as a

 decimal.
112

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 0.5 % of total installed cost and the value of construction put in

place which employ FIAPP products and services. The value of construction put in place

which employ FIAPP products and services is contained in Column 4 of Table 7-1 for

each year of the specific calculation. 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 compound

amount 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 compound amount factor takes on a value of 1.0 for that year. For years

prior to 1997, the single compound amount factor is greater than 1.0. For years following

1997, the single compound amount factor is less than 1.0. The single compound amount

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 compound

amount 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 exceed $2.0 billion ($2,043

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 industrial sector. These cost savings

nationwide also provide a basis for measuring the value of BFRL’s contribution.
113

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 

Compound 

Amount 

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 35.611 3.444 32.167 0.582 18.721

2006 57.229 5.573 51.656 0.544 28.097

2007 90.785 8.913 81.872 0.508 41.619

2008 141.432 14.006 127.426 0.475 60.539

2009 214.684 21.435 193.249 0.444 85.805

2010 314.363 31.601 282.763 0.415 117.336

2011 439.514 44.361 395.153 0.388 153.247

2012 582.499 58.750 523.749 0.362 189.831

2013 731.523 73.139 658.384 0.339 223.018

2014 877.403 85.899 791.504 0.317 250.570

2015 1,018.750 96.065 922.685 0.296 272.989

2016 1,160.212 103.494 1,056.718 0.277 292.191

2017 1,305.422 108.587 1,196.836 0.258 309.285

TOTAL 2,043.250
114

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

effort—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. 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 industrial facilities.

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 1999. 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 2000 through 2017, the entries in

Column 2 are in millions of 1999 dollars. Investments over the 1993 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 1999 in Column 2 are

in current dollars by year and the values for 2000 through 2017 are in 1999 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 compound amount factor, based on a real discount rate

of 7 %. The value of each year’s single compound amount factor is given in Column 5.

The present values in millions of 1997 dollars are recorded in Column 6; they are the

year-by-year 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 $30.096 million; this value is recorded at

the bottom of Column 6.
115

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

Note: The dollar amounts for 1993 through 1999 are in millions of current dollars. The

dollar amounts for 2000 through 2017 are in millions of 1999 dollars.

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 

Compound 

Amount 

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 2.039 0.965 1.967 0.873 1.718

2000 2.204 0.965 2.126 0.816 1.735

2001 2.625 0.965 2.532 0.763 1.932

2002 3.125 0.965 3.014 0.713 2.149

2003 3.625 0.965 3.496 0.666 2.330

2004 4.000 0.965 3.858 0.623 2.403

2005 4.000 0.965 3.858 0.582 2.245

2006 3.875 0.965 3.738 0.544 2.033

2007 3.375 0.965 3.255 0.508 1.655

2008 2.875 0.965 2.773 0.475 1.317

2009 1.875 0.965 1.809 0.444 .803

2010 0 0.965 0 0.415 0

2011 0 0.965 0 0.388 0

2012 0 0.965 0 0.362 0

2013 0 0.965 0 0.339 0

2014 0 0.965 0 0.317 0

2015 0 0.965 0 0.296 0

2016 0 0.965 0 0.277 0

2017 0 0.965 0 0.258 0

TOTAL 30.096
116

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. 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.

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 $148.997 million; this value is recorded at the bottom of Column 4.

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.

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

= $148.977 - $30.096

= $118.881 million
117

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 18.721 1 18.721

2006 28.097 1 28.097

2007 41.619 1 41.619

2008 60.539 1 60.539

2009 85.805 0 0

2010 117.336 0 0

2011 153.247 0 0

2012 189.831 0 0

2013 223.018 0 0

2014 250.570 0 0

2015 272.989 0 0

2016 292.191 0 0

2017 309.285 0 0

TOTAL 148.977
118

Utilizing the approach outlined in Section 2.2.2, the SIR on BFRL’s CONSiAT-related

investments is equal to:

SIR = PVS / (PV Costs)

= $148.977 / $30.096

= 4.95

Utilizing the approach outlined in Section 2.2.3, the AIRR on BFRL’s CONSiAT-related

investments is equal to:

AIRR = (1 + 0.07) * 4.951/25 - 1

= 0.141

= 14.1%

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.
119

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 the 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
120

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.95

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

Beckman96 and by Harris;97 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 one of

the previous economic impact assessments.98

The method of model sampling was implemented through application of the @RISK

software product.99 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

involves defining which variables are to be simulated and the number of simulations.

 

95 See Chapman and Fuller, Two Case Studies in Building Technology, and Chapman and Weber, A Case

Study of the Fire Safety Evaluation System.

96 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.

97 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.

98 See Chapman, A Case Study of Cybernetic Building Systems.

99 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.
121

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.100

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, a, the location parameter in

the diffusion model; (2) beta, b, the shape parameter in the diffusion model; (3) eta, h,

 

100 Evans, Merran, Nicholas Hastings, and Brian Peacock. 1993. Statistical Distributions. New York, NY:

John Wiley & Sons, Inc.
122

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 4 3 5

(2) Beta Triangular 0.5 0.4 0.6

(3) Eta Triangular 0.5 0.3 0.7

(4) Discount Rate Triangular 0.07 0.02 0.10

(5) First Cost Savings Triangular 0.014 0 0.045

(6) Maintenance and

 Repair Cost Savings Triangular 0.10 0.05 0.15

(7) Reductions in

 Delivery Time Triangular 8 0 16

(8) Higher Contractor

 Net Income Triangular 0.013 0 0.031

(9) New-Technology

 Introduction Costs Triangular 0.005 0 0.010

(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, a, employs the uniform

distribution. The input variable, a, 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 a. 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.

It does not occur in year 2005.5. Thus, the discrete distribution is the most meaningful
123

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
124

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, a, b, and h is

analyzed in turn.

Table 8-2 shows how variations about the baseline value for a (i.e., a = 4.0) affect each

economic measure. The parameter, a, was selected for evaluation because it is the

location parameter for the diffusion model. The effect of a is as follows: lower values of

a produce a thicker tail immediately following the introduction of CONSiAT products

and services into the marketplace (i.e., higher values of Ph(t), whenever t is small),

whereas higher values of a produce a thinner tail (i.e., lower values of Ph(t), whenever t

is small). Reference to Table 8-2 reveals that a exerts a strong effect on five of the six

economic measures. For example, the minimum value of PVSALL is only one third of the

highest value. Although the present value of savings nationwide is strongly affected by

changes in the value of a, 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 a

reduce these savings and lower values of a increase these savings over the value

calculated in the baseline analysis. For example, the minimum value of SIRBFRL is onesixth the highest value, and PVNSBFRL varies by more than a factor of 10. Note that

PVCBFRL is unaffected by changes in the value of a. 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., $84.5 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 1,248.776 1,624.427 2,032.286 2,485.172 3,112.868 2,077.268 535.216

PVSBFRL 57.880 94.643 147.367 222.565 356.658 166.290 84.466

PVCBFRL 30.096 30.096 30.096 30.096 30.096 30.096 0.0

PVNSBFRL 27.783 64.547 117.271 192.469 326.561 136.193 84.466

SIRBFRL 1.923 3.145 4.897 7.395 11.851 5.525 2.807

AIRRBFRL 0.098 0.120 0.140 0.159 0.181 0.140 0.024
125

Table 8-3 shows how variations about the baseline value for b (i.e., b = 0.6) affect each

economic measure. The parameter, b, was selected for evaluation because it specifies the

rate of change for the diffusion model. The effect of b is as follows: higher values of b

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 Ph(t), whenever t is small), whereas lower values of b produce a lower rate of adoption

(i.e., lower values of Ph(t), whenever t is small). Reference to Table 8-3 reveals that b

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 $1.3 billion in 1997 dollars. Although

the present value of savings nationwide is strongly affected by changes in the value of b,

the measures of BFRL’s influence are affected to a lesser degree than for changes in a.

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 b

reduce these savings and higher values of b increase these savings over the value

calculated in the baseline analysis. However, these differences are less than those

associated with a, because a affects the thickness of the lower tail of Ph(t), whereas b

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 more than half the highest value, and PVNSBFRL varies by a factor of

slightly less than two. Note that PVCBFRL is unaffected by changes in the value of b.

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., $16.8 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 1,404.935 1,834.252 2,041.845 2,251.657 2,723.587 2,041.902 291.497

PVSBFRL 115.920 137.473 148.898 161.257 192.840 149.699 16.773

PVCBFRL 30.096 30.096 30.096 30.096 30.096 30.096 0.0

PVNSBFRL 85.824 107.376 118.801 131.161 162.743 119.602 16.773

SIRBFRL 3.852 4.568 4.947 5.358 6.407 4.974 0.557

AIRRBFRL 0.129 0.137 0.141 0.144 0.153 0.141 0.005

Table 8-4 shows how variations about the baseline value for h (i.e., h = 0.5) affect each

economic measure. The parameter, h, was selected for evaluation because it specifies the

level at which the market for CONSiAT products and services saturates. The effect of h

is as follows: higher values of h 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 Ph(t), for all values of t, especially whenever t is large), whereas lower

values of h produce a lower level of adoption (i.e., lower values of Ph(t)). Reference to

Table 8-4 reveals that h 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)
126

exceeds $1.7 billion in 1997 dollars. Although the present value of savings nationwide is

strongly affected by changes in the value of h, the measures of BFRL’s influence are

affected to a lesser degree than for changes in a. 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 h reduce these savings and higher

values of h increase these savings over the value calculated in the baseline analysis.

However, these differences are less than those associated with a, because a affects the

thickness of the lower tail of Ph(t), whereas h affects the level at which the market

saturates. Thus, the influence of h on the years immediately following the introduction

of CONSiAT products and services is quite modest. Consequently, the range of values

for the measures of BFRL’s influence due to variations about the baseline value for h

tend to be wider than for b, but narrower than for a.

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 1,219.790 1,770.095 2,026.181 2,310.484 2,959.476 2,038.923 367.166

PVSBFRL 92.291 130.599 147.841 166.585 207.939 148.342 24.437

PVCBFRL 30.096 30.096 30.096 30.096 30.096 30.096 0.0

PVNSBFRL 62.195 100.502 117.744 136.488 177.843 118.245 24.437

SIRBFRL 3.067 4.339 4.912 5.535 6.909 4.929 0.812

AIRRBFRL 0.119 0.135 0.140 0.146 0.156 0.140 0.008

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 $3.1

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.
127

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 1,321.505 1,887.285 2,230.423 2,759.449 4,508.559 2,367.167 636.863

PVSBFRL 114.148 141.953 157.118 178.703 239.911 161.466 25.884

PVCBFRL 27.136 29.507 30.775 32.553 37.447 31.114 2.133

PVNSBFRL 87.012 112.446 126.343 146.150 202.464 130.352 23.752

SIRBFRL 4.207 4.811 5.105 5.490 6.407 5.158 0.465

AIRRBFRL 0.099 0.125 0.136 0.145 0.164 0.135 0.014

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 $1.7

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. For example, the value

calculated in the baseline analysis for the SIRBFRL is 4.95. In Table 8-6, the minimum

value for the SIRBFRL is 3.542, and the maximum value is 8.219. 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 1,482.988 1,988.166 2,235.039 2,582.936 3,343.625 2,290.636 410.975

PVSBFRL 106.593 144.810 163.487 189.806 247.353 167.693 31.091

PVCBFRL 30.096 30.096 30.096 30.096 30.096 30.096 0.0

PVNSBFRL 76.496 114.714 133.390 159.709 217.257 137.596 31.091

SIRBFRL 3.542 4.812 5.432 6.307 8.219 5.572 1.033

AIRRBFRL 0.126 0.139 0.145 0.152 0.164 0.145 0.008

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 only minimal

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 $1,937.927 million, and

the maximum is $2,156.953 million, a range of slightly less than $220 million (measured

in 1997 dollars). Whereas, the minimum value for the PVSBFRL is $148.431 million, and

the maximum is $149.568 million, a range of only $1.1 million (measured in 1997

dollars). The value of PVCBFRL is not affected by the input variable maintenance and

repair cost savings.
128

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 1,937.927 2,009.539 2,041.028 2,074.552 2,156.953 2,042.315 46.381

PVSBFRL 148.431 148.802 148.966 149.140 149.568 148.973 0.241

PVCBFRL 30.096 30.096 30.096 30.096 30.096 30.096 0.0

PVNSBFRL 118.334 118.706 118.870 119.044 119.472 118.876 0.241

SIRBFRL 4.932 4.944 4.950 4.955 4.970 4.950 0.008

AIRRBFRL 0.141 0.141 0.141 0.141 0.141 0.141 0.0

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 8-6 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 $1.7 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 4.95. In Table 8-8, the

minimum value for the SIRBFRL is 2.452, and the maximum value is 7.433, a relative

difference of a factor of 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 1,179.578 1,770.534 2,016.121 2,271.635 2,901.429 2,021.755 353.400

PVSBFRL 73.783 125.234 146.616 168.862 223.694 147.106 30.768

PVCBFRL 30.096 30.096 30.096 30.096 30.096 30.096 0.0

PVNSBFRL 43.687 95.137 116.519 138.765 193.597 117.010 30.768

SIRBFRL 2.452 4.161 4.872 5.611 7.433 4.888 1.022

AIRRBFRL 0.109 0.133 0.140 0.146 0.159 0.139 0.010

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 moderate to 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 higher contractor

net income. Reference to Table 8-9 reveals that the range of values for PVSALL (i.e.,

Maximum – Minimum) exceeds $1.2 billion in 1997 dollars. Note that the ranges of

values and the standard deviations for the five economic measures for which variations
129

do occur for this input variable are less than those for first cost savings and 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 1,543.990 1,913.755 2,092.345 2,313.189 2,761.761 2,107.822 266.124

PVSBFRL 111.208 139.181 152.692 169.399 203.334 153.862 20.133

PVCBFRL 30.096 30.096 30.096 30.096 30.096 30.096 0.0

PVNSBFRL 81.111 109.085 122.595 139.303 173.238 123.766 20.133

SIRBFRL 3.695 4.625 5.073 5.629 6.756 5.112 0.669

AIRRBFRL 0.127 0.138 0.142 0.147 0.155 0.142 0.006

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 moderate 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 $400 million 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 4.95. In Table 8-10, the minimum value for the

SIRBFRL is 4.446, and the maximum value is 5.454 (i.e., the baseline value of the SIRBFRL

± 0.5). 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 1,842.763 1,980.217 2,039.650 2,099.005 2,243.845 2,038.547 82.690

PVSBFRL 133.810 144.209 148.705 153.195 164.153 148.622 6.256

PVCBFRL 30.096 30.096 30.096 30.096 30.096 30.096 0.0

PVNSBFRL 103.714 114.113 118.609 123.099 134.057 118.525 6.256

SIRBFRL 4.446 4.792 4.941 5.090 5.454 4.938 0.208

AIRRBFRL 0.136 0.139 0.141 0.142 0.145 0.141 0.002
130

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 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 1,228.759 1,600.363 2,043.250 2,043.250 2,565.989 1,895.803 377.778

PVSBFRL 39.878 81.620 148.978 148.978 254.392 131.538 60.316

PVCBFRL 27.724 29.590 30.096 30.096 37.152 30.520 2.565

PVNSBFRL 12.154 52.030 118.881 118.881 217.240 101.019 58.042

SIRBFRL 1.438 2.758 4.950 4.950 6.847 4.204 1.613

AIRRBFRL 0.086 0.114 0.141 0.141 0.156 0.129 0.021

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 the

minimum value for three of the five measures of BFRL’s influence—PVNSBFRL,

SIRBFRL, and AIRRBFRL—is lower than the corresponding minimum value for each of
131

these variables examined previously. Although the minimum values for these three

measures of BFRL’s influence are smaller for this input variable than for 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 1,228.759 1,600.363 2,043.250 2,043.250 2,043.250 1,809.484 304.240

PVSBFRL 39.878 81.620 148.977 148.977 148.977 115.552 42.329

PVCBFRL 30.096 30.096 30.096 30.096 30.096 30.096 0.0

PVNSBFRL 9.782 51.523 118.881 118.881 118.881 85.456 42.329

SIRBFRL 1.325 2.712 4.950 4.950 4.950 3.839 1.406

AIRRBFRL 0.082 0.114 0.141 0.141 0.141 0.125 0.021

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 any of the

previous tables in two important ways. First, the present value of cost savings

nationwide, PVSALL, ranges from just over $650 million to nearly $2.6 billion in 1997

dollars. Thus, the combined effects of the targeted time of first use and the length of the

delay are a key driver in estimating PVSALL. Second, the minimum values of four of the

key measures of BFRL’s influence (i.e., PVSBFRL, PVNSBFRL, SIRBFRL, and AIRRBFRL

101)

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

 

101 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.
132

reveals that a delay of one year for the 2007 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 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 2,565.989 2,043.250 1,600.362 1,228.758

PVSBFRL 254.392 148.977 81.620 39.878

PVCBFRL 37.152 30.096 29.590 27.724

PVNSBFRL 217.240 118.881 52.030 12.154

SIRBFRL 6.847 4.950 2.758 1.438

AIRRBFRL 0.156 0.141 0.114 0.086

Part B: One-Year Delay

Year of First Commercial Use Economic

Measure 2004 2005 2006 2007

PVSALL 2,043.250 1,600.362 1,228.758 920.073

PVSBFRL 148.977 81.620 39.878 14.717

PVCBFRL 37.152 30.096 29.590 27.724

PVNSBFRL 111.825 51.523 10.288 -13.007

SIRBFRL 4.010 2.712 1.348 0.531

AIRRBFRL 0.131 0.114 0.083 0.043

Part C: Two-Year Delay

Year of First Commercial Use Economic

Measure 2004 2005 2006 2007

PVSALL 1,600.362 1,228.758 920.073 667.724

PVSBFRL 81.620 39.878 14.717 0.0

PVCBFRL 37.152 30.096 29.590 27.724

PVNSBFRL 44.467 9.782 -14.872 -27.724

SIRBFRL 2.197 1.325 0.497 0.0

AIRRBFRL 0.104 0.082 0.041 n.a.
133

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 $975 million in

1997 dollars, and SIRBFRL reaches 26.1.

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 $175 million, whereas the

maximum is over $9.0 billion. Second, the computed value of the mean equals or

exceeds the computed value of the median for each 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 value of the median for five of the six economic measures is 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 175.344 1,281.868 2,021.767 2,880.348 9,458.448 2,241.291 1,264.779

PVSBFRL 0.0 49.370 102.290 197.981 965.197 144.963 137.569

PVCBFRL 25.332 29.082 30.563 32.969 43.914 31.519 3.573

PVNSBFRL -31.500 19.083 71.917 166.170 921.690 113.444 136.092

SIRBFRL 0.0 1.634 3.275 6.151 22.185 4.465 3.963

AIRRBFRL n.a. 0.083 0.115 0.144 0.224 0.115 0.043
134

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. 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.102

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 $2,043.2

million. As was seen in Table 8-14, the median value of the 1,000 observations was

$2,021.8 million, more than $20 million less 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 $175.3 million. However, the 1st percentile for PVSALL is

$434.9 million. Thus, only 10 observations out of 1,000 are clustered between $175.3

and the 1st percentile ($434.9 million). Second, the CDF increases at a steady, almost

linear rate between the 5th percentile ($722.5 million) and the 65th percentile ($2,482.9

million). Third, above the 65th 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

large. Finally, the maximum value of PVSALL is more than $9.0 billion. However, the

99th percentile is $6,159.9 million. Thus, only 10 observations out of 1,000 account for

 

102 Ott, Lyman. 1984. An Introduction to Statistical Methods and Data Analysis. Boston, MA: Duxbury

Press.
135

approximately $3.3 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 $149.0

million. As was seen in Table 8-14, the median value of the 1,000 observations was

more than $45 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 $0.0. Reference to Table 8-15 reveals that

the 1st percentile for PVSBFRL is still $0.0. However, by the 2nd percentile, the computed

value of PVSBFRL becomes positive ($6.8 million). Thus, less than 20 observations out of

1,000 result in no savings attributable to BFRL. Stated another way, there is at least a 98

percent 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 5th percentile ($14.0 million) and the

55th percentile ($114.2 million). Third, above the 55th 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

$1.0 billion. However, the 99th percentile is $643.6 million. Thus, only 10 observations

out of 1,000 account for more than $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 ($28.388 million) and the

55th percentile ($30.927 million). Below the 15th percentile and above the 55th percentile

the traces exhibit similar patterns of non-linearity.
136

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

 Variables

PVSALL PVSBFRL PVCBFRL PVNSBFRL SIRBFRL AIRRBFRL

1

ST 434.878 0.000 26.036 -26.850 0.000 n.a.

2

ND 535.190 6.845 26.591 -21.885 0.247 0.004

3

RD 611.309 9.062 26.905 -20.003 0.322 0.018

4

TH 659.043 12.307 27.200 -17.723 0.392 0.023

5

TH 722.468 14.047 27.337 -15.570 0.478 0.029

6

TH 756.238 16.313 27.501 -12.919 0.541 0.035

7

TH 780.614 19.329 27.604 -9.873 0.670 0.039

8

TH 808.963 20.901 27.715 -8.470 0.725 0.043

9

TH 851.209 22.900 27.819 -6.664 0.781 0.047

10TH 881.443 24.145 27.926 -5.273 0.806 0.051

11TH 922.071 25.263 28.044 -3.841 0.872 0.053

12TH 943.764 27.136 28.128 -2.985 0.905 0.056

13TH 972.305 28.713 28.227 -1.651 0.947 0.059

14TH 988.239 30.432 28.315 -0.610 0.983 0.063

15TH 1,025.116 32.704 28.388 1.039 1.035 0.065

16TH 1,060.482 34.788 28.464 3.175 1.114 0.067

17TH 1,091.651 37.324 28.569 6.427 1.212 0.069

18TH 1,119.391 38.655 28.644 7.784 1.247 0.070

19TH 1,151.653 40.161 28.747 9.374 1.293 0.072

20TH 1,182.443 41.665 28.798 10.834 1.343 0.074

21ST 1,213.660 42.734 28.834 12.097 1.402 0.076

22ND 1,228.623 44.129 28.895 13.797 1.479 0.079

23RD 1,248.936 45.506 28.947 15.758 1.542 0.081

24TH 1,271.603 47.711 28.992 17.948 1.588 0.082

25TH 1,281.868 49.370 29.082 19.083 1.634 0.083

26TH 1,307.276 50.509 29.107 20.613 1.672 0.086

27TH 1,333.089 52.804 29.193 22.568 1.708 0.087

28TH 1,356.584 54.515 29.236 23.617 1.763 0.088

29TH 1,398.697 56.196 29.288 25.335 1.822 0.090

30TH 1,421.671 57.908 29.348 26.796 1.875 0.091

31ST 1,443.509 59.648 29.425 29.352 1.970 0.092

32ND 1,472.499 61.347 29.494 31.135 2.009 0.093

33RD 1,485.861 63.396 29.538 32.216 2.051 0.095

Economic Measure

Percentile
137

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

 Variables (continued)

PVSALL PVSBFRL PVCBFRL PVNSBFRL SIRBFRL AIRRBFRL

34TH 1,513.295 64.937 29.597 33.512 2.082 0.097

35TH 1,544.215 68.040 29.675 36.785 2.151 0.098

36TH 1,563.046 71.734 29.726 39.019 2.247 0.099

37TH 1,626.350 73.356 29.773 41.736 2.320 0.100

38TH 1,646.932 74.381 29.825 43.514 2.410 0.102

39TH 1,676.115 76.058 29.878 45.381 2.478 0.103

40TH 1,723.824 78.632 29.934 47.709 2.557 0.104

41ST 1,756.417 80.998 30.005 50.170 2.623 0.105

42ND 1,790.737 82.459 30.093 52.795 2.721 0.107

43RD 1,821.645 85.515 30.144 54.932 2.800 0.108

44TH 1,840.118 87.200 30.184 56.528 2.848 0.109

45TH 1,887.268 89.643 30.261 59.088 2.919 0.110

46TH 1,906.372 92.488 30.329 61.974 3.002 0.111

47TH 1,922.767 95.384 30.401 63.911 3.070 0.112

48TH 1,950.238 97.624 30.463 66.466 3.104 0.113

49TH 1,991.902 100.021 30.507 68.780 3.196 0.114

50TH 2,021.767 102.289 30.563 71.917 3.275 0.115

51ST 2,040.600 104.153 30.613 72.883 3.345 0.116

52ND 2,061.042 107.092 30.708 75.335 3.466 0.118

53RD 2,094.349 109.727 30.817 78.448 3.557 0.120

54TH 2,118.994 111.740 30.862 80.298 3.630 0.122

55TH 2,147.880 114.233 30.927 83.571 3.722 0.123

56TH 2,175.044 116.571 31.017 85.307 3.771 0.124

57TH 2,204.782 118.358 31.111 87.573 3.820 0.125

58TH 2,238.217 120.712 31.220 90.327 3.947 0.125

59TH 2,274.449 124.728 31.278 92.708 4.034 0.127

60TH 2,291.294 129.318 31.353 97.929 4.131 0.128

61ST 2,318.604 132.214 31.473 101.426 4.224 0.129

62ND 2,355.183 137.925 31.528 105.731 4.337 0.130

63RD 2,369.167 143.833 31.654 111.489 4.484 0.131

64TH 2,439.813 148.742 31.749 116.733 4.576 0.133

65TH 2,482.908 151.108 31.831 119.040 4.684 0.133

66TH 2,524.340 155.513 31.946 122.814 4.765 0.134

Economic Measure

Percentile
138

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

 Variables (continued)

PVSALL PVSBFRL PVCBFRL PVNSBFRL SIRBFRL AIRRBFRL

67TH 2,569.283 159.333 32.018 126.568 4.909 0.135

68TH 2,600.039 160.924 32.099 130.421 5.036 0.136

69TH 2,641.754 166.157 32.181 133.533 5.161 0.138

70TH 2,684.697 173.168 32.258 139.907 5.324 0.139

71ST 2,715.175 175.196 32.427 143.015 5.461 0.140

72ND 2,742.537 180.309 32.554 148.406 5.604 0.142

73RD 2,784.116 186.752 32.718 152.843 5.760 0.143

74TH 2,840.822 191.332 32.822 160.434 5.982 0.144

75TH 2,880.348 197.981 32.969 166.170 6.151 0.144

76TH 2,911.200 203.773 33.166 170.681 6.283 0.145

77TH 2,969.542 207.453 33.382 173.426 6.417 0.147

78TH 3,041.057 213.672 33.563 179.975 6.722 0.148

79TH 3,091.839 220.699 33.698 186.540 6.795 0.150

80TH 3,142.960 225.004 34.006 191.383 6.885 0.151

81ST 3,191.522 231.086 34.182 199.983 7.031 0.153

82ND 3,284.231 239.803 34.372 205.397 7.246 0.155

83RD 3,322.662 246.877 34.607 215.811 7.414 0.156

84TH 3,406.120 255.861 34.854 221.688 7.760 0.158

85TH 3,464.851 262.125 35.057 232.176 8.118 0.160

86TH 3,574.719 277.741 35.437 244.341 8.555 0.161

87TH 3,631.953 284.927 35.836 251.437 8.917 0.163

88TH 3,748.802 293.996 36.150 262.652 9.099 0.165

89TH 3,872.849 303.034 36.414 270.953 9.339 0.168

90TH 4,004.900 323.771 36.728 292.232 9.897 0.170

91ST 4,066.758 336.964 37.161 305.919 10.315 0.172

92ND 4,165.854 354.956 37.752 321.525 11.013 0.173

93RD 4,279.951 370.381 38.231 332.924 11.563 0.176

94TH 4,497.911 388.787 38.949 352.795 12.399 0.180

95TH 4,689.204 418.273 39.510 387.700 12.803 0.181

96TH 4,903.826 472.659 40.079 442.733 13.458 0.184

97TH 5,260.735 522.145 40.946 481.645 14.610 0.189

98TH 5,501.136 556.376 41.625 521.035 15.959 0.197

99TH 6,159.887 643.572 42.021 611.532 18.018 0.205

Economic Measure

Percentile
139

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

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0

500

1,0001,5002,0002,5003,0003,5004,0004,5005,0005,5006,0006,5007,0007,5008,0008,5009,0009,500

10,000

Present Value of Cost Savings Nationwide in Millions of 1997 Dollars

Cumulative Probability
140

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

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0 100 200 300 400 500 600 700 800 900 1,000 1,100

Present Value of Cost Savings Attributable to BFRL in Millions of 1997 Dollars

Cumulative Probability
141

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

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

24.0 26.0 28.0 30.0 32.0 34.0 36.0 38.0 40.0 42.0 44.0

Present Value of BFRL's Investment Costs in Millions of 1997 Dollars

Cumulative Probability
142

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 $118.9

million. As was seen in Table 8-14, the median value of the 1,000 observations was

nearly $50 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

1

st percentile for PVNSBFRL is still negative (-$26.8 million). However, by the 15th

percentile, the computed value of PVNSBFRL becomes positive ($1.0 million). Thus, less

than 150 observations out of 1,000 are negative. Stated another way, there is at least an

85 percent probability that BFRL’s CONSiAT-related investments are cost effective.

Second, the CDF increases at a steady, almost linear rate between the 5th percentile

(-$15.6 million) and the 55th percentile ($83.6 million). Third, above the 55th 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 $1.0 billion. However, the 99th percentile is $611.5 million. Thus,

only 10 observations out of 1,000 account for more than $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, only two had no

delay, whereas 35 had a two-year delay. Four other factors had a strong influence: (1)

reductions in delivery time, if its value is less than the 10th percentile of its parent CDF;
143

(2) reductions in first cost, if its value is less than the 10th percentile of its parent CDF;

(3) h, the level at which the market for FIAPP products and services saturates, if its value

is less than the 10th percentile of its parent CDF; and (4) a, the location parameter in the

diffusion model, 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, 48 of the 50 highest values had no delay and a “targeted” time of

first use of 2004 or 2005. Five 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 net

income, if its value is greater than the 90th percentile of its parent CDF; (3) a, if its value

is less than the 10th percentile of its parent CDF; (4) h, the level at which the market for

FIAPP products and services saturates, if its value is greater than the 90th percentile of its

parent CDF; and (5) the discount rate, 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 4.95. As was seen in Table 8-14, the median value of the

1,000 observations was 3.275. 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 0.0. Reference to Table 8-15 reveals that the 1st

percentile for SIRBFRL is still zero. However, by the 15th percentile, the computed value

of SIRBFRL exceeds 1.0 (1.035). Thus, less than 150 observations out of 1,000 are less

than 1.0. Stated another way, based on the calculated value of SIRBFRL, there is at least

an 85 % probability that BFRL’s CONSiAT-related investments are cost effective.

Second, the CDF increases at a steady, almost linear rate between the 10th percentile

(0.806) and the 50th percentile (3.275). 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 SIRBFRL gets large. Finally, the maximum value of SIRBFRL is nearly

22.2. However, the 99th percentile is 18.018. Thus, only 10 observations out of 1,000

account for approximately 4 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 0.141. As was seen in Table 8-14, both the mean and

median values of the 1,000 observations were 0.115. Figure 8-6 exhibits a pattern
144

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 for AIRRBFRL is n.a. This

entry is reflected by the “step-up” in the CDF at 0.0. By the 15th 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 0.0644. Thus, less than 150 observations out of 1,000

produce a value for the AIRRBFRL less than the average value of the discount rate.103

Stated another way, based on the calculated value of AIRRBFRL, there is at least an 85

percent probability that BFRL’s CONSiAT-related investments are cost effective.

Second, the CDF increases at a steady, almost linear rate between the 15th percentile

(0.065) and the 85th percentile (0.160). 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 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 15th percentile of its CDF. This point is noteworthy, since each measure

provides a different perspective, but produces the same end result in terms of identifying

the break-even point.

 

103 Note that the 18th percentile had a value of 0.07 (i.e., a value that equals the baseline value for the

discount rate).
145

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

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

-100 0 100 200 300 400 500 600 700 800 900 1000

Present Value of Net Savings Attributable to BFRL in Millions of 1997 Dollars

Cumulative Probability
146

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

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28

Savings to Investment Ratio on BFRL's Research and Development Investment

Cumulative Probability
147

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

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 0.22 0.24

Adjusted Internal Rate of Return on BFRL's Research and Development Investment

Cumulative Probability
148
149

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

fourth in a series of impact studies prepared by BFRL.104 It focuses on BFRL’s construction

systems integration and automation technologies (CONSiAT) major product. The CONSiAT

major product 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.

BFRL’s CONSiAT major product 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

 

104The 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).
150

CONSiAT major product 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 industrial facility 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 in excess of $2.0 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 industrial

facility 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 $150 million. These

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

costs of approximately $30.1 million. Stated in present value terms, every public dollar invested

in BFRL’s CONSiAT-related research, development, and deployment effort is expected to

generate $4.95 in cost savings to the public (i.e., an SIR of 4.95). The annual percentage yield

(AIRR) from BFRL’s CONSiAT-related investments over the study period is 14.1 %.

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
151

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 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 nonzero probability that BFRL’s CONSiAT-related investments are not cost effective. However, on

the opposite extreme, PVNSBFRL may reach nearly $1.0 billion in 1997 dollars.

The range of values for an economic measure is so wide that it prompted an in-depth

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 CONSiATrelated 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 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 150 observations out of 1,000 were

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

85 % probability that BFRL’s CONSiAT-related investments are cost effective. 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
152

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 1992.105 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.106 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.

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

 

105Civil Engineering Research Foundation. 1993. A Nationwide Survey of Civil Engineering-Related R&D. Report

no. 93-5006. Washington, DC: Civil Engineering Research Foundation.

106Although 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.
153

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 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 process operations due to facility-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 the design and creation of a
154

database for compiling information on CONSiAT-related impacts. This database is currently

under development by BFRL’s Office of Applied Economics (OAE). Once the database is in

place, OAE will 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 non-monetary benefits and costs is multiattribute decision analysis.107

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 been accepted by ASTM as a standard practice for investments

related to buildings and building systems.108

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.

 

107For 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.

108American 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

Conshohoken, PA: American Society for Testing and Materials.
155

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