Economic Analysis of Technology Infrastructure 

Needs for Advanced Manufacturing

Smart Manufacturing

August 2016

NIST GCR 16-007

Prepared for—

Economic Analysis Office

National Institute of Standards and 

Technology

100 Bureau Drive

Gaithersburg, MD 20899

This publication is available free of charge from:

http://dx.doi.org/10.6028/NIST.GCR.16-007

Prepared by—

Michael P. Gallaher

Zachary T. Oliver

Kirsten T. Rieth

Alan C. O’Connor

RTI International

3040 E. Cornwallis Road

Research Triangle Park, NC 27709
Acknowledgements

Many contributed to the preparation of this report, and we wish to acknowledge in particular the 

private companies, industry associations, research institutes, universities, and government 

agencies whose experts contributed data and insights underlying the analysis herein. 

In addition to this study’s lead authors, significant contributors included Luca Alessandro 

Remotti of the Joint Institute for Innovation Policy (formerly of Intrasoft S.A.), Raimund 

Broechler of Intrasoft S.A., and Jeff Cope of RTI’s Innovation Advisors. 

We would also like to acknowledge the advice and subject matter expertise contributed by NIST 

economic and scientific staff. Specifically, we are especially grateful for the contributions of 

 Gary Anderson of NIST’s Economic Analysis Office, 

 Timothy Burns of NIST’s Applied and Computational Mathematics Division,

 Stephen Campbell of NIST’s Economic Analysis Office,

 Heather Evan of NIST’s Program Coordination Office,

 Simon Frechette of NIST’s Systems Integration Division, 

 Albert Jones of NIST’s Systems Integration Division,

 James Liddle of NIST’s Center for Nanoscale Science and Technology,

 Eric Lin of NIST’s Materials Science and Engineering Division, 

 Richard Ricker of NIST’s Materials Measurement Science Division,

 Christopher Soles of NIST’s Materials Science and Engineering Division,

 Albert Wavering of NIST’s Intelligent Systems Division, and

 Paul Zielinski of NIST’s Technology Partnerships Office.

Disclaimer

This publication was produced as part of contract GS10F0097L with the National Institute of 

Standards and Technology. The contents of this publication do not necessarily reflect the views 

or policies of the National Institute of Standards and Technology or the US Government.

About RTI

RTI International is one of the world’s leading research institutes, dedicated to improving the 

human condition by turning knowledge into practice. Our staff of more than 4,150 provides 

research and technical services to governments and businesses in more than 75 countries in the 

areas of health and pharmaceuticals, education and training, surveys and statistics, advanced 

technology, international development, economic and social policy, energy and the environment, 

and laboratory testing and chemical analysis. For more information, visit www.rti.org.

RTI International is a registered trademark and a trade name of Research Triangle Institute.
NIST GCR 16-007

Economic Analysis of Technology 

Infrastructure Needs for Advanced 

Manufacturing: Smart Manufacturing

Prepared for 

U.S. Department of Commerce

Economic Analysis Office

Office of the Director

National Institute of Standards and Technology

Gaithersburg, MD 20899

By

Michael P. Gallaher

Zachary T. Oliver

Kirsten T. Reith

Alan C. O’Connor

RTI International

This publication is available free of charge from:

http://dx.doi.org/10.6028/NIST.GCR.16-007

August 2016

U.S. Department of Commerce 

Penny Pritzker, Secretary

National Institute of Standards and Technology 

Willie May, Under Secretary of Commerce for Standards and Technology and Director 
iii

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

Contents

Section Page

Executive Summary ES-1

Introduction1-1

1.1 Definition of Technology Infrastructure.......................1-3

1.1.1 Infratechnologies...........................................1-3

1.1.2 Technology Platforms .....................................1-4

1.1.3 Proprietary Technologies.................................1-4

1.1.4 Summary of Key Definitions............................1-5

1.2 Definition and Bounds of Smart Manufacturing for 

This Study ..............................................................1-6

1.3 Scope of Industries and Supply Chain Included in 

the Analysis ............................................................1-7

1.4 Market Failures and Barriers to Development and 

Adoption.................................................................1-8

1.5 Benefits from an Improved Technology 

infrastructure ..........................................................1-9

1.6 Report Overview....................................................1-10

Analysis Methods and Primary Data Collection2-1

2.1 Overview of Analysis Methods ...................................2-1

2.2 Conservative Approach.............................................2-2

2.3 Interview Process ....................................................2-3

2.4 Interview Topics ......................................................2-4

2.4.1 Economic Analysis Methodology.......................2-7

2.4.2 Firm-Level End-User Impact from the 

Sample.........................................................2-8

2.4.3 Firm-Level Developer Impacts .......................2-10
iv

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

2.4.4 National-Level End-User Impacts by KLEM ......2-11

2.4.5 National-Level End-User Impacts by 

Capability Gap Area .....................................2-12

2.4.6 National-Level Developer Impacts..................2-12

Smart Manufacturing Trends and Technology Gaps3-1

3.1 Key Components of Smart Manufacturing Processes.....3-1

3.1.1 Managing Digital Data Streams through 

Models .........................................................3-3

3.1.2 Sensing and Monitoring ..................................3-6

3.1.3 Seamless Transmission of Digital Data .............3-8

3.1.4 Advanced Data and Trend Analysis ................3-10

3.1.5 Communicating Information to Decision 

Makers .......................................................3-11

3.1.6 Determining Required Action and 

Implementing Action....................................3-12

3.2 Stakeholders.........................................................3-14

3.2.1 Smart Manufacturing Product Developers 

and Service Providers...................................3-14

3.2.2 Smart Manufacturing End Users.....................3-15

3.2.3 Smart Manufacturing Observers ....................3-16

3.3 Trends that Will Influence Smart Manufacturing.........3-18

3.3.1 Connected Factories.....................................3-18

3.3.2 Big Data Storage and Analytics......................3-18

3.3.3 Demand-Driven Supply Chain Optimization.....3-19

3.3.4 Increasing Awareness of and Interest in 

Smart Manufacturing....................................3-20

3.3.5 Other Disruptive Manufacturing 

Technologies ...............................................3-20

3.4 Need for Additional Development of Technology 

Infrastructure to Support Smart Manufacturing .........3-20

3.4.1 Relative Importance of Capabilities ................3-20

3.4.2 Barriers to the Adoption of Smart 

Manufacturing Technology ............................3-22

3.4.3 Need for Additional Technology 

Infrastructure Development ..........................3-25

Economic Impacts4-1

4.1 Firm-Level End-User Impacts ....................................4-1

4.1.1 Estimates of Average Impact by Factors of 

Production (KLEM) .........................................4-2

4.1.2 Distribution of Impacts across Respondents ......4-3
v

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

4.1.3 Aggregate Firm-Level Impacts.........................4-6

4.2 National-Level End-User Impacts ...............................4-7

4.2.1 National Expenditures on Factors of 

Production (KLEM) .........................................4-7

4.2.2 National Impact Estimates by KLEM .................4-8

4.2.3 National Impact Estimates by Capability Gap 

Analysis......................................................4-12

4.3 National-Level Developer Sales Impacts ...................4-13

4.3.1 Market Size.................................................4-13

4.3.2 Total Industry-Level Impacts for Developers ...4-14

Managing Digital Data Streams through Models5-1

5.1 Barriers to Adoption and Associated Market Failures.....5-2

5.2 Technology Infrastructure Needs ...............................5-3

5.2.1 Identified Needs ............................................5-3

Enhanced Sensing and Monitoring6-1

6.1 Barriers to Adoption and Associated Market Failures.....6-1

6.2 Technology Infrastructure Needs and Roles for 

NIST ......................................................................6-4

6.2.1 Sensors ........................................................6-4

6.2.2 Standards.....................................................6-4

6.2.3 Other Needs..................................................6-5

Seamless Transmission of Digital Information7-1

7.1 Barriers to Adoption and Associated Market Failures.....7-1

7.2 Technology Infrastructure Needs and Roles for 

NIST ......................................................................7-4

7.2.1 Data Formats, Standards, and Protocols ...........7-5

7.2.2 Interoperability of Systems .............................7-6

7.2.3 Machines ......................................................7-7

7.2.4 Data Security Protocols ..................................7-8

7.2.5 Bridging Information Technology with 

Operations Technology ...................................7-8

7.2.6 Other Needs..................................................7-9

Advances in Analyzing Data and Trends8-1

8.1 Barriers to Adoption and Associated Market Failures.....8-1

8.2 Technology Infrastructure Needs and Roles for 

NIST ......................................................................8-3
vi

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

8.2.1 Best Practices and Techniques for Data 

Analysis........................................................8-3

8.2.2 Virtual Factories and Simulation ......................8-4

8.2.3 Other ...........................................................8-5

Efficiently Communicating Information to Decision 

Makers 9-1

9.1 Barriers to Adoption and Associated Market Failures.....9-2

9.2 Technology Infrastructure Needs and Roles for 

NIST ......................................................................9-2

Determining and Implementing Required Action 10-1

10.1 Barriers to Adoption and Associated Market Failures...10-2

10.2 Technology Infrastructure Needs .............................10-3

Conclusions and Summary Needs 11-1

11.1 Capability Needs and Barriers to Adoption.................11-2

11.1.1 Capability Needs..........................................11-2

11.1.2 Barriers to Adoption .....................................11-2

11.2 Economic Impacts..................................................11-3

11.3 Technology Infrastructure Neeeds............................11-4

11.3.1 Communication Standards and Conformance 

Testing.......................................................11-5

11.4 Conclusion ............................................................11-7

References R-1

Appendixes

A: NIST Advanced Manufacturing Strategic Planning 

Study.....................................................................A-1

B: NIST Advanced Manufacturing Strategic Planning 

Study.....................................................................B-1

C: Example Vignettes ...................................................C-1
vii

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

Figures

Number Page

ES-1. Level of Additional Technology Infrastructure 

Development Needed for Smart Manufacturing 

Capabilities...................................................................ES-4

ES-2. Cost Savings by Factors of Production (millions of USD) 

for Process and Discrete Industries................................ ES-11

ES-3. Total Cost Impact, by Capability (Millions of 2013 USD) ... ES-12

3-1. Overview of Smart Manufacturing Processes and Data 

Flow ..............................................................................3-2

3-2. Average Importance of Each Smart Manufacturing 

Capability, by Type of Respondent...................................3-21

3-3. Level of Additional Technology Infrastructure 

Development Needed for Smart Manufacturing 

Capabilities...................................................................3-25

4-1. Average Percent Change in Factor Inputs: Discrete 

versus Process Industries .................................................4-3

4-2. Range of Cost Impacts on Shop Floor Expenditures 

Associated with Enhanced Infratechnology, by Factor 

Input.............................................................................4-4

4-3. Range of Total Composite Cost Impact on Shop Floor 

Expenditures Associated with Enhanced Infratechnologya.....4-6

4-4. KLEM Share of Cost Basis for Process and Discrete 

Industries.......................................................................4-8

4-5. Cost Savings by Factors of Production (millions of USD) 

for Process and Discrete Industries..................................4-11

4-6. KLEM Shares of Composite Cost Impact for Process and 

Discrete Industries ........................................................4-11

4-7. Total Cost Impact, by Capability (Millions of 2013 US$) .....4-12

5-1. Smart Manufacturing Data Feedback Loop: Model ...............5-1

6-1. Smart Manufacturing Data Feedback Loop: Sense ...............6-1

7-1. Smart Manufacturing Data Feedback Loop: Transmit ...........7-1

8-1. Smart Manufacturing Data Feedback Loop: Analyze.............8-1

9-1. Smart Manufacturing Data Feedback Loop: 

Communicate .................................................................9-1

10-1. Smart Manufacturing Data Feedback Loop: Action.............10-1

11-1. Percentage of Interviewees Indicating the Need for 

Additional Reach in Smart Manufacturing Capability 

Areas...........................................................................11-5
viii

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Tables

Number Page

ES-1. Smart Manufacturing Capabilities and Technology 

Infrastructure Needs......................................................ES-5

1-1. Definitions of Key Concepts ..............................................1-5

1-2. Major Industries Included in the Study...............................1-7

1-3. Sources of Market Failure: Barriers to Developing and 

Adopting New Technology ................................................1-9

2-1. Industry Sectors Covered in the Interviewsa.......................2-5

2-2. Economic Analysis Methodology ........................................2-7

3-1. Smart Manufacturing Capabilities and Technology 

Infrastructure Needs........................................................3-4

3-2. Percent of Sample Citing Each Barrier to Adoption, by 

Type of Respondent.......................................................3-23

3-3. Percentage High Level of Additional Development 

Needed, Developers versus End Users .............................3-26

4-1. Average Percent Change in Factor Inputs ...........................4-2

4-2. Industry Applicability Factor .............................................4-7

4-3. Stylized Example of Scaling Sample Impacts to the 

National Level, End User ..................................................4-9

4-4. Economic Impact Summary, Process versus Discrete .........4-10

4-5. Market Reports on Global Smart Manufacturing 

Revenues .....................................................................4-14

5-1. Identified Needs Related to Managing Digital Data 

Streams through Models ..................................................5-3

6-1. Identified Needs Related to Enhanced Sensing and 

Monitoring......................................................................6-5

7-1. Identified Needs Related to Seamless Transmission of 

Digital Information ..........................................................7-5

8-1. Identified Needs Related to Advances in Analyzing Data 

and Trends .....................................................................8-4

9-1. Identified Needs Related to Efficiently Communicating 

Information to Decision Makers.........................................9-3

10-1. Identified Needs Related to Determining Required Action 

and Implementing Action ...............................................10-3

11-1. Average Percent Change in Factor Inputs .........................11-3

11-2a. Economic Impact Summary, Process versus Discrete .........11-4

11-2b. Economic Impacts by Capability Area...............................11-4
ES

-1

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

Executive Summary

Smart manufacturing involves the integration of cyber and 

physical systems, which can enable innovative production 

processes and new product systems. However, industry faces 

barriers to the adoption of all but the simplest of smart 

manufacturing technologies. Enhancements in the technology 

infrastructure supporting smart manufacturing are needed to 

develop next-generation smart manufacturing technologies. 

Technology infrastructure like standards and technology 

platforms provides the necessary underpinning for advances in 

smart manufacturing. Meeting critical needs in technology 

infrastructure can drive product and process improvements that 

are only in the conceptual stages today. Such improvements 

will lower the cost and increase the benefits from adopting 

existing and future smart technologies. 

Economic impacts associated with meeting technology 

infrastructure needs for smart manufacturing could approach 

$60 billion per year. This represents, on average, a 3% 

reduction in the shop floor cost of production. The estimate is 

likely to be conservative because it does not capture several 

hard-to-measure benefits, such as improved product quality 

and accelerated market transformation.

ES.1 SCOPE OF THE ANALYSIS

This study is a collaborative effort of multiple units within the 

National Institute of Standards and Technology (NIST) to 

determine infrastructure needs to support advanced 

manufacturing. The purpose of the study is to provide NIST 

with information on industry’s technology infrastructure needs 

and help inform NIST’s strategic planning and current/future 

investments. The report investigates the barriers to adoption of 
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

ES-2

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

smart manufacturing technologies and processes, with a focus 

on the underlying technology infrastructure. 

Smart manufacturing incorporates not only generating and 

capturing data, but also understanding its implications and 

taking action as a result of that knowledge. Specifically for this 

study, we defined smart manufacturing as

the creation, communication and use of electronic 

information, as well as the interface of these 

information systems with the human element, for 

data-driven decision making and performance 

optimization. This includes how data and 

information generated during the production 

process are communicated and used during 

design, engineering, and production phases of 

the product cycle.

We present findings of an economic analysis of the technology 

infrastructure, which includes standards, measurement, and 

general-purpose technology, and the role of this infrastructure 

in the efficient development and adoption of smart

manufacturing in the United States. The objectives of this 

strategic planning study were to:

 identify current and emerging trends related to smart 

manufacturing;

 identify technology infrastructure needs to support the 

development and adoption of smart manufacturing 

technology;

 document the challenges and barriers that inhibit the 

development of technology infrastructure;

 estimate the economic impact of meeting these 

technology infrastructure needs; and

 assess potential roles for NIST in meeting technology 

infrastructure needs and realizing economic benefits.

ES.2 ANALYSIS APPROACH

The methodology includes both the collection and analysis of 

qualitative and quantitative data. We conducted more than 80 

interviews with a wide variety of smart manufacturing product 

and service providers, smart manufacturing end-user 

companies, and industry observers. We also had informal 

conversations with individuals at conferences and industry 

events, which contributed to the findings in this report.
Executive Summary

ES-3

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

The interviews investigated current trends, barriers to adoption, 

benefits that could be achieved from an improved technical 

infrastructure, and potential new roles for NIST. Quantitative 

information was collected on the potential benefits in terms of 

increased productivity, decreased production cost, and 

improved product quality. These quantitative impacts were 

used to calculate national economic impacts associated with an 

improved technical infrastructure to support smart 

manufacturing.

Quantitative responses were summarized at the firm level and 

applied to all or part of the industries represented by the 

interview respondents. Firm-level impacts were estimated for 

each firm that provided quantitative impact data during the 

interview. We made the effort to estimate impacts only for the 

relevant portion of the firm as reported by the respondent. 

Average impacts per expenditures for a given NAICS code were 

then scaled by national NAICS-level expenditures to obtain 

national impact estimates.

We grouped smart manufacturing activities and associated 

technology infrastructure into six capability areas, which are 

used throughout the report to facilitate discussion and 

presentation of results:  Managing digital data streams through models  Sensing and monitoring  Seamless transmission of digital data  Advanced data and trend analysis  Communicating information to decision makers  Determining required action and implementing action

ES.3 ANALYSIS OF TECHNOLOGY 

INFRASTRUCTURE NEEDS

To identify potential areas where NIST could effectively 

collaborate with and complement industry, interviewees were 

asked to characterize the level of additional development 

needed for each of the six smart manufacturing capability areas 

and corresponding infrastructure technologies. Interviewees 

were asked to classify each capability according to a scale of 

“low,” “medium,” or “high” level of additional development 
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

ES-4

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

needed. Figure ES-1 summarizes the responses to the level of 

additional development needed, by capability.

As shown in the figure, advances in analyzing data and trends 

was characterized by 49% of interviewees as needing a high

level of additional development. Next, 37% of interviewees 

rated seamless transmission of digital information as needing a 

high level of additional development. Relative to the other 

needs, the least important need for further development was 

communicating information to decision makers—only 16% of 

interviewees gave it a high rating.

Figure ES-1. Level of Additional Technology Infrastructure Development Needed for Smart 

Manufacturing Capabilities

Note: All columns sum to 100%.

Table ES-1 provides a summary of the technology 

infrastructure needed to support smart manufacturing 

capabilities along with the associated potential benefits and 

impacts resulting from an enhanced technology infrastructure. 

These benefits are the under pinning of the economic impacts 

estimated as part of the study. The following is a discussion 

highlighting the needs and barriers of each smart 

manufacturing capability area.
Executive Summary

ES-5

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Table ES-1. Smart Manufacturing Capabilities and Technology Infrastructure Needs

Smart Manufacturing 

Capabilities

Technology Infrastructure 

Needed to Support Capabilities

Potential Benefits and Impacts 

of Enhanced Technology 

Infrastructure

Managing digital data streams 

through models:

• CAD models including material 

characteristics,

• simulation models of part 

creation and plant layout, and

• rapid automated costing 

functions

High-fidelity process models, 

physical model representation for 

flexible objects, simplified modular 

applications of CAM software for 

less sophisticated uses, data 

standardization, standard and 

simpler equipment interfaces to 

facilitate consistent data entry for 

less-skilled workers, standard 

terminology for automated part 

costing

• Ability to capture feature-based 

information in design models

• Streamlined simulation of part 

creation

• Enhanced ability to design for 

manufacturing

• Reduced product-developmentto-production cycle

Enhanced sensing and monitoring:

• “state estimation” of critical 

manufacturing machines (for 

example: vibration, acoustics, 

temperature, tolerances, and 

pressure), and

• real-time monitoring of product 

attributes as they move 

through various stages of the 

production process

In-process measuring and 

monitoring for physical processes, 

self-powered sensors, robust 

sensors to withstand harsh 

manufacturing processes, data 

standardization, methods for 

calibrating sensing and monitoring 

systems

• More efficient measurement of 

equipment usage such as 

overall equipment effectiveness

• Reduced scrap

• Enhanced in-process product 

quality

• Ability to predict machine 

issues and intervene with 

preventive maintenance

• Reduced unplanned downtime

• Increased sensor reliability

• Greater visibility and 

transparency into 

manufacturing operations

Seamless transmission of digital 

information:

• wireless transmission of digital 

information without 

interference from other data 

channels, 

• seamless integration of smart 

sensors, 

• interoperability between 

different platforms such as 

CAD/CAM, and

• secure data transmission 

(wired and wireless)

Secure data transmission; secure 

cloud computing and data sharing; 

standard communication 

protocols; retrofitable, plug-andplay data communications 

systems; data interoperability of 

3D model parameters and product 

manufacturing information

• Reduced costs in factors of 

production such as capital, 

labor, energy, and materials 

through the increased ability to 

holistically analyze data, which 

leads to more efficient, 

optimized production.

• Energy reductions, active 

energy management

• Reduced downtime, increased 

uptime

• Improved security, reduced risk 

of IP or safety issues

• Reduced integration time 

(commissioning, debugging, 

etc.)

• Freeing up staff time that is 

currently spent on connectivity 

and collating data

(continued)
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

ES-6

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Table ES-1. Smart Manufacturing Capabilities and Technology Infrastructure Needs

(continued)

Smart Manufacturing 

Capabilities

Technology Infrastructure 

Needed to Support Capabilities

Potential Benefits and 

Impacts of Enhanced 

Technology Infrastructure

• Advances in analyzing data and 

trends:

• interpretation and aggregation 

of data from sensing and 

monitoring networks,

• “Big Data” techniques for 

manufacturing,

• predictive maintenance, 

• Reduction of false positives, 

and

• cloud computing and fee-forservice cloud based algorithms 

for product design, simulation, 

and manufacturing design

Algorithms to interpret data from 

disparate sensors and systems; 

definition of important, relevant, 

and meaningful data to collect for 

predictive maintenance

• Improved uptime

• Enhanced monitoring of inprocess quality

• Reduction of false positives

• Increased overall equipment 

effectiveness

• Better utilization of existing 

data sources 

Efficiently communicating 

information to decision makers:

• comprehensive information 

interfaces human-computer 

interaction-based design, and 

• easy-to-interpret interfaces 

accessible from any location

Common taxonomy across 

platforms and disciplines 

Standards in interface design for 

manufacturing equipment

• Accelerated development of 

interfaces by the private 

sector

• More timely, evidencebased decision making

Determining required action and 

implementing action:

• real-time feedback of enhanced 

sensing and monitoring data 

into factory decision making,

• automated optimization-based 

decision making that functions 

independent of human 

interaction, 

• machine-learning decisionmaking algorithms for 

manufacturing, and

• reconfigurability of 

manufacturing systems

Tested and validated decision 

models 

• Optimized manufacturing 

processes

• Greater visibility and 

transparency in real-time 

manufacturing operations

ES.3.1 Managing Digital Data Streams through Models

Managing digital data streams through models is key for 

product development and, ideally, would facilitate 

interoperability across CAD/CAM systems and include material 

characteristics as part of the digital information stored and 

shared. These models could then be used to streamline 

simulation for parts creation. Plant layout simulation and rapid 

automated costing functions are two additional types of data 

modeling that were commonly mentioned in the interviews.

The length of time and difficulty in developing high-fidelity 

process models can be a barrier to the use of process 
Executive Summary

ES-7

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

simulation. Creating realistic high-fidelity process models can 

take up to a year, and can be so complex that they require 

PhD-level employees to create them and high-performance 

computing systems to run them; moreover, in many instances, 

the end product still fails to provide accurate modeling.

Industry also needs more open-source platform development to 

allow large and small companies alike to rapidly innovate, 

express needs, and develop common platforms. Most large 

software companies want to sell end-to-end automation 

packages and pursue this approach as a competitive strategy. 

However, this limits flexibility. A marketplace is needed to 

encourage startups in this space and related Big Data 

applications.

ES.3.2 Enhanced Sensing and Monitoring Sensors are being integrated into most new machines produced 

today. Although a broad range of sensor capabilities is 

available, in many instances manufacturers are not fully taking 

advantage of these technologies. All study respondents said 

that state estimation of critical machines is an area that

industry is continually pursuing. Manufacturers believe that 

existing sensors are adequate for many applications, and that 

the most crucial need lies in improvement of data analysis 

capabilities. Nevertheless, we found that the industry still lacks 

many sensing and monitoring capabilities, and that the existing 

technologies need significant improvement. In addition, 

integrating sensing capabilities into older legacy systems is 

costly, and is often limited by technical constraints.

Manufacturers said thatsolutions are needed that are lower 

risk, economically feasible, and have fewer calibration and 

false-positive issues. Standards for reliability would significantly 

reduce the risk. Manufacturers feel that, currently, standards 

are loose and should be more stringent. For example, if a 

sensor has too much fluctuation, the time period in which the 

sensor is accurate needs to be extended. Tolerances of sensors 

needs to be improved. If standards were produced (and 

enforced), then developers would be forced to comply and 

publish the tolerance rates; users would then know what they 

are buying at various cost points. Companies individually 

conduct testing in their labs, but this is very costly and not 

efficient from a social perspective.
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

ES-8

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

ES.3.3 Seamless Transmission of Digital Data 

Interoperability between platforms is not a new issue, but one 

that study respondents still cited frequently as a significant 

source of ongoing inefficiency. Standards developments, such 

as the Standard for the Exchange of Product (STEP), have 

notably improved interoperability in several areas, but more 

advances are needed.

The most commonly cited technology-specific barriers included 

networking/communications between machines and equipment 

from different vendors and different eras. Significant time is 

spent setting up machines and equipment to talk to a network. 

Even equipment with an “Ethernet-enabled” label is a source of 

frustration because companies do not all use a standard 

communications protocol.

Respondents indicated that it has been a struggle to get 

manufacturers to adopt solutions such as MTConnect. Original 

equipment manufacturers (OEMs) and user communities have

concerns about using this technology, and smaller companies 

are not familiar with the standard. More OEMs need to build the 

capabilities into their equipment, but small OEMs do not have 

the staff capabilities to develop the software to integrate the 

standards into their equipment’s sensing capabilities.

The technology of transmitting data via wireless technology was 

also cited as an issue by some, but not all, respondents. 

Concerns centered on interference between transmissions of 

several data channels from multiple pieces of equipment. 

Neither quality nor cost of bandwidth were mentioned as 

issues. However, security was often cited as a concern—both 

data security and physical plant security when hazardous or 

explosive materials were involved.

Cost and uncertainty of benefits associated with connectivity 

continue to be barriers, and markets have not been able to 

adequately address these barriers. The total investment costs 

of connecting assets are high, and it is often difficult to 

guarantee or demonstrate the benefits/return. Differences

across plants and older, legacy equipment make plug-and-play 

solutions difficult to develop. In addition, entrenched vendors 

continue to promote proprietary software and solutions that

inevitably increase costs and limit adoption.
Executive Summary

ES-9

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

ES.3.4 Advanced Data and Trend Analysis

The analysis of big data is a rapid-growth sector. However, 

most manufacturers in our study thought that the 

manufacturing sector was not receiving sufficient attention from 

data analysis service providers. Most of the data analysis firms 

are better positioned to serve the financial service sector or 

marketing departments. They lack the engineering or chemicals 

expertise to support much of the manufacturing sector.

The cost associated with computing power and analysis 

software can be significant, and represents a barrier to 

adoption of smart manufacturing, especially to small and 

medium enterprises(SMEs). In general, we found significant 

disagreement with respect to future use of cloud computing. 

For some, the concept did not resonate at all due to concerns 

about the security of proprietary data and protection from 

hacking. However, for others, the benefits of the cloud’s low-

cost computing power and storage outweigh the risks. Typical 

comments were that a company “does not have to put the most 

sensitive information on the cloud. It is good for what it’s 

designed for.”

Best practices and techniques for data analysis emerged as the 

top technology infrastructure need in this area. Prediction, 

particularly predictive maintenance, was a central theme. In 

addition, advances in algorithm testing and development were 

needed to provide guidance on interpreting data from different 

sensors and determining which data are most meaningful to 

collect for predictive maintenance. To the extent that it is 

possible to develop standard algorithms that can be customized 

by end users, this approach would be ideal.

ES.3.5 Communicating Information to Decision Makers

Respondents interviewed were most familiar with activities 

related to physical components, data exchange, and related 

standards. However, the “softer” side of smart manufacturing, 

which includes efficiently communicating information to 

decision makers, was also cited as an area needing further 

research. For example, the value of smart manufacturing data 

and activities would be increased through research to develop 

simpler, user-friendly communication interfaces that would be 

customized for smart manufacturing and integrate statistical 

methods for establishing thresholds to trigger decisions.
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

ES-10

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Many industries encounter issues related to efficient and 

concise information interfaces. “Ease of use paradigms” are 

needed. Plant staff members need to be able to access and 

understand software and information in a manageable way. 

Mobile access with tablets would be a key selling factor; 

however, the wireless aspect of tablets may introduce security 

concerns.

Efficiently communicating information to decision makers is 

about the metrics used to communicate with decision makers 

rather than the design of interfaces. Overall equipment 

effectiveness (OEE) is one example of a high-level metric that 

has gained some traction in industry but is not consistently 

defined.

ES.3.6 Determining Required Action and Implementing Action

The ability to determine and implement required action was 

referred to as the “... ultimate capability. Without it, the other 

capabilities are of no use.” Most manufacturers expressed the 

need for enhanced decision support tools that would help make 

decisions much faster, including information and analysis tools 

targeted at both manufacturing operators and upper 

management. Using artificial intelligence and learning systems 

to identify and direct the most appropriate actions based on 

data analysis would advance the goal of building an automated 

and more flexible factory.

Improvements in decision systems will require continued 

research on statistical methods for analyzing Big Data (pattern 

recognition, trends, and correlations) and the intelligence to 

make critical decisions based on risk assessment and 

optimization modeling. This is a multidisciplinary research area 

in which coordination must be improved across the different 

sectors pursuing these enhanced capabilities.

Respondents thought that substantial opportunities to enhance 

the technology infrastructure in this area were needed and are 

possible, but could not offer specific recommendations. 

Manufacturers commented that it would be helpful to have 

closed-loop systems, but again offered little specific guidance 

on what technology infrastructure was needed to develop these 

systems.
Executive Summary

ES-11

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

ES.4 SUMMARY ECONOMIC IMPACTS

Total economic impacts associated with an enhanced 

technology infrastructure to support smart manufacturing are 

estimated to be approximately $57.4 billion per year and would 

accrue over multiple years. Discrete parts manufacturing 

accounts for $30.8 billion and process manufacturing $26.6 

billion. 

Figure ES-2 shows cost savings for each factor of production, 

by discrete parts and process manufacturers. The largest 

impact was associated with labor savings in discrete 

manufacturing sectors. In the process industries, the greatest 

savings was in materials (feedstock), followed by energy and 

labor.

Figure ES-2. Annual Cost Savings by Factors of Production (millions of USD) for Process and 

Discrete Industries

Figure ES-3 shows the distribution of total impacts apportioned 

to each of the six capabilities. The distribution of impacts is 

based on the relative importance scores provided by the

manufacturers interviewed. Enhanced sensing and monitoring, 

seamless transmission of digital information, and advances in 

analyzing data and trends all have potential annual impacts 
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

ES-12

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Figure ES-3. Total Cost Impact, by Capability (Millions of 2013 US$ per year)

greater than $10 billion. All of the identified capability areas 

were estimated to have annual benefits of over $7 billion, 

reflecting the generally overall importance of all the categories

and their interdependency. 

The relative magnitude across all the categories is evidence 

that there are strong complementarities or “interaction effects” 

among the capabilities. For example, it was stated that 

enhanced sensing and monitoring was only as important as the 

ability to transmit, analyze, and communicate the information.

Note that the economic impact estimates presented are 

considered to be conservative in that they focus on reductions 

in manufacturers’ production costs that would result from 

meeting the identified technology infrastructure needs. Not 

included in the economic impact calculations is the economic 

value associated with reduced R&D costs, improved product 

attributes, increased sales, or accelerating the introduction of 

new products to market.
1

-1

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

Introduction

The growth of smart manufacturing has been touted as a 

driving force for reinvigorating the manufacturing sector in the 

United States. U.S industry has long accepted that it will need 

to focus on production efficiency and labor productivity to 

maintain its competitive position in global markets. With the 

increasing complexity inherent within the manufacturing 

system, smart manufacturing has the potential not only to 

enhance the generation and capture of data, but also to build 

an understanding of the data’s meaning and to take action as a 

result. The implementation of smart manufacturing is key to 

advancing the U.S. manufacturing sector.

There is no shortage of studies investigating the potential 

impacts of smart manufacturing, claiming potential productivity 

and efficiency gains ranging from 5% to 30%. Two major U.S. 

consortia—the Smart Manufacturing Leadership Coalition 

(SMLC) and the Digital Manufacturing and Design Institute 

(DMDI)—both state that a 10% gain in overall efficiency is 

possible. Given that the technology focus of the SMLC and 

DMDI are overlapping but different, the sum total of their 

projected impacts is likely higher than 10%. Acatech—the 

German National Academy of Science and Engineering—

estimated that German firms could boost productivity by 30% 

with Industry 4.0, European smart manufacturing initiative. A 

second study of German firms prepared by the Boston 

Consulting Group was more conservative, estimating that 

productivity gains of implementing the Industry 4.0 smart 

manufacturing initiative would range between 5% and 8%.

Whereas almost all studies agree that smart manufacturing has 

significant potential, there is less agreement on why it is not 

being adopted at a faster rate in key manufacturing sectors of 

the U.S. economy; and, if barriers to adoption do exist, is there 

a role for government in mitigating these barriers and 
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

1-2

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

accelerating the penetration of smart manufacturing

technologies and processes?

This report investigates the barriers to adoption of smart 

manufacturing technologies and processes, with a focus the 

underlying technology infrastructure. We present findings of an

economic analysis of the technology infrastructure, which 

includes standards, measurement, and general-purpose 

technology, and the role of this infrastructure in the efficient 

development and adoption of smart manufacturing in the 

United States.

The objectives of this strategic planning study were to

 identify current and emerging trends related to smart 

manufacturing;

 identify technology infrastructure needs to support the 

development and adoption of smart manufacturing 

technology;

 document the challenges and barriers that inhibit the 

development of technology infrastructure;

 estimate the economic impact of meeting these 

technology infrastructure needs; and

 assess potential roles for the National Institute of 

Standards and Technology (NIST) in meeting technology 

infrastructure needs and realizing economic benefits.

As part of the analysis, the study investigated the connections 

between identified technology infrastructure needs and the 

market barriers that are impeding their adoption. We discuss 

how specific barriers could best be addressed with public-sector 

participation. NIST, for example, has unique capabilities and 

expertise in applied research on the development and diffusion 

of new technology, such as

 promulgation and adoption of standards;

 coordination, facilitation, and performance of 

cooperative research and development (R&D);

 facilitation of technology transfer; and

 communication and dissemination of scientific 

knowledge.
Section 1 

— Introduction 1-3

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

1.1 DEFINITION OF TECHNOLOGY 

INFRASTRUCTURE

Technology infrastructure is the broad base of public and quasi-

public technologies

1 and technical knowledge that support the 

R&D and production efforts of firms, universities, and 

laboratories, as well the development and adoption of improved 

and entirely new products, processes, and services (e.g., higher 

quality, more effective, more efficient, more productive).

Technology infrastructure supports and accelerates advances in 

advanced manufacturing capabilities. Specifically for smart 

manufacturing, enhanced technology infrastructure has the 

potential to enable capabilities such as  modeling and data creation;  sensing and monitoring;  transmitting information across multiple platforms; and  analyzing data and trends.

Technology infrastructure includes infratechnologies and 

technology platforms, which are defined in subsequent 

subsections of this report. Government agencies fund the 

majority of technology infrastructure research due to their 

public-good content (Tassey, 2008). NIST is the government 

agency that serves this role in the United States. This 

quasipublic technology infrastructure, together with proprietary 

technologies, comprise the three major elements of industrial 

technology (Tassey, 2008).

1.1.1 Infratechnologies

Historically, NIST has focused resources on this aspect of 

technology infrastructure. Infratechnologies are a varied set of 

“technical tools” that include measurement and test methods, 

artifacts such as standard reference materials that allow these 

methods to be used efficiently, scientific and engineering 

databases, process models, and the technical basis for both 

physical and functional interfaces between components of 

systems technologies such as robotics and automation 

technologies. Tassey (2008) wrote that “[c]ollectively they 

constitute a diverse technical infrastructure, various types of 

which are applied at each stage of economic activity.” New 

 1 Technologies with varying degrees of public

-good content.
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

1-4

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

infratechnologies often replace less efficient forms of 

infratechnology that support current standards (Tassey, 2008).

Infratechnologies influence the development of technology 

platforms and proprietary technologies. They also support 

efficient R&D, production and market transactions such as 

complying with customer requirements and regulations.

Infratechnologies provide the technical basis for standards that 

are set using consensus standards-setting processes that are 

usually led by industry organizations. Their benefits include full 

disclosure of information, reduced uncertainty regarding 

product attributes, and an overall improved level of trust that 

helps to reduce market transaction costs.

The provision of infratechnologies requires a combination of 

industry and government investment, because 

infratechnologies have substantial public-good content 

(Antonelli & Link, 2014). Some industries depend on hundreds 

of distinct infratechnologies and associated standards. 

Furthermore, a particular infratechnology may have spillover 

benefits for many industries.

1.1.2 Technology Platforms

Technology platforms are precompetitive proofs of concept that 

demonstrate the potential commercial viability of a new or 

improved product, process, or service. These fundamental 

technical concepts originate from basic science research and 

can even be enabled by measurement infratechnologies (Link &

Scott, 2010).

A characteristic of a technology platform is that it is often the 

case that it will be foundational to multiple products and 

processes, the scope of which is typically broader than the 

business model of any one firm. Therefore, no firm is able to 

fully appropriate the benefits of investing in the development of 

a technology platform, so that achieving the socially optimal 

level of investment will generally require additional public 

investment.

1.1.3 Proprietary Technologies

Proprietary technologies are not included in the definition of 

technology infrastructure for this study. Proprietary 

technologies are commercialized products, processes, and 

services that may be derivatives of technology platforms and 
Section 1 — Introduction

1-5

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

have been influenced by infratechnologies. Generally, firm 

investments in proprietary technology fall under the category of 

R&D spending. Ubiquitous proprietary technologies may behave 

like quasi-public goods despite being funded and developed by 

private sector firms.

1.1.4 Summary of Key Definitions

Table 1-1 contains abbreviated definitions of the key concepts 

outlined above for ease of reference. The table also lists 

examples of each term to make these concepts more 

understandable. Technology infrastructure is the focus of this 

study because of its quasi-public-good characteristics.

Table 1-1. Definitions of Key Concepts

Term Definition Examples

Technology 

infrastructure

The broad base of quasipublic technologies and 

technical knowledge that support the R&D and 

production efforts of firms, universities, and

laboratories, as well the development and 

adoption of improved products, processes, and 

services.

• Infratechnologies

• Technology platforms

Infratechnologies A varied set of “technical tools” that include 

measurement and test methods, artifacts such 

as standard reference materials that allow these 

methods to be used efficiently, scientific and 

engineering databases, process models, and the 

technical basis for both physical and functional 

interfaces between components of systems 

technologies such as factory automation and 

communications.

• Standard reference 

materials

• Reference data sets

• Process models

• Techniques for process and 

quality control

• Calibration services

• Traceability of 

measurements and test 

methods

Technology 

platforms

Precompetitive proofs of concept that 

demonstrate the potential commercial viability 

of a new or improved product, process, or 

service. A characteristic of a technology 

platform is that it is often the case that it will be 

foundational to multiple products and 

processes, generally from multiple firms.

• Bell Labs’ transistor proofof-concept using solidstate physics principlesa

• Prototype networks such 

as ARPANET and NSFNET 

that led to the Interneta

• Proof-of-concept for 

vibration-powered devices 

that could power remote 

sensors

Proprietary 

technologies

Commercialized products, processes, and 

services that may be derivatives of technology 

platforms and have been influenced by 

infratechnologies. Proprietary technologies do 

not have quasi-public-good characteristics

• Industrial robots

• Collaborative robots

• Machine vision systems

a Tassey, 2008.
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

1-6

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

1.2 DEFINITION AND BOUNDS OF SMART 

MANUFACTURING FOR THIS STUDY

The term smart manufacturing spans a wide range of functions 

and technologies, and means different things to different 

stakeholder groups and industries. In general, smart 

manufacturing refers to production systems at the equipment, 

factory, and enterprise levels that integrate cyber and physical 

systems to enable innovative production, products, and 

systems of products (Kilmer, 2014).

Specifically for this study, we defined smart manufacturing as

the creation, communication and use of electronic 

information, as well as the interface of these 

information systems with the human element, for 

data-driven decision making and performance 

optimization. This includes how data and 

information generated during the production 

process are communicated and used during 

design, engineering, and production phases of 

the product cycle.

Although the specifics of smart manufacturing vary by industry, 

the flow and use of data almost always involve the following 

core components:

 modeling and data creation to support design, testing, 

and automation;

 sensing and monitoring to collect real-time information 

on processes;

 transmitting information across multiple platforms and 

levels of the supply chain;

 analyzing data and trends to support real-time process 

control and management;

 communicating information to decision makers to 

support efficient and/or automated analysis; and

 determining and implementing required action in a 

timely and/or closed-loop setting.

Currently, and for the foreseeable future, a human being will 

participate in these processes performing several different 

tasks. Human involvement will be necessary to achieve the full 

potential of smart manufacturing, as will optimized

manufacturing processes and advanced artificial intelligence. 

Additionally, improvements in analysis capabilities will be 

required. For example, advanced statistical methods for 
Section 1 — Introduction

1-7

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

analyzing big data (pattern recognition, trends, and 

correlations) are needed, as is intelligence to make critical 

decisions based on risk assessment. These advancements will 

require a multidisciplinary research effort that will depend on 

coordination across the different sectors pursuing these 

enhanced capabilities.

1.3 SCOPE OF INDUSTRIES AND SUPPLY CHAIN 

INCLUDED IN THE ANALYSIS

This study focused on manufacturing and product design

activities within the traditional North American Industry 

Classification System (NAICS) codes of 31 to 33. These 

industry sectors capture the majority of smart manufacturing 

opportunities and provide a bounds for the study scope. As 

shown in Table 1-2, the study included major discrete parts 

manufacturers and process manufacturers as well as their 

suppliers of parts, equipment, software, and process-related 

services.

Excluded from this study are the agricultural sector, extraction 

of natural resources, and logistical activities related to supply of 

input materials and delivery of final products. Although 

opportunities related to smart technologies and processes are 

applicable to these industry sectors, it was necessary to bound 

the study to focus on industries and processes with the greatest 

economic potential.

Discrete Parts and Assembly Batch and Continuous Process

Motor vehicles and parts Food

Aerospace products and parts Chemicals

Fabricated metals Pharmaceuticals and medicine

Plastics and rubber products Oil and gas

Wood products Electricity

Furniture and related products Paper

Electrical equipment and 

appliances

Primary metals

Table 1-2. Major 

Industries Included in 

the Study
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

1-8

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

1.4 MARKET FAILURES AND BARRIERS TO 

DEVELOPMENT AND ADOPTION

A motivating principle for this study is that private investments 

in innovation and diffusion of new technologies typically 

generate social value in excess of their private returns. As a 

result, some socially productive technology investments are not 

undertaken because private firms do not perceive the research 

as profitable.2

This failure of markets to provide firms with the incentive to 

make socially productive investments in technology 

infrastructure opens up a role for public sector intervention to 

improve the efficiency of technology markets. The possibility 

that public sector investment in technology infrastructure can 

lower the costs or increase the benefits of adopting smart 

manufacturing technology suggests a role for NIST that fits well 

within it mission statement.

As will be discussed in this report, many of the general market 

failures listed in Table 1-3 have been identified as barriers to 

the development of the technology infrastructure needed to 

support and promote the adoption of smart technologies.3

The sidebar lists needs/barriers, either policy or business 

related, that underpin the technology issues investigated in this 

study of smart manufacturing. For example, institutionalized 

risk aversion and unwillingness to enable standardization

are commonly cited barriers to development and adoption of 

smart manufacturing technologies and processes. Similarly, 

lack of a common ontology creates interoperability issues in 

the sharing of electronic data, and is a barrier cited by 

manufacturers and machine equipment producers. Participants 

in this study suggested that government could help coordinate 

with companies to characterize the critical key performance 

indicators to measure on machines and determine how best to 

access the measurements.

 2 The private rate of return is less than what is required (the private 

hurdle rate), even though the social rate of return exceeds that 

required by society (the social hurdle rate). 3 The taxonomy of barriers presented here draws insight from Link and 

Scott (2010) and Jaffe (2005).

Barriers to Adoption

 Institutionalized risk 

aversion

 Unwillingness to 

enable 

standardization

• Concerns about 

data 

security/liability and 

intellectual property 

(IP) protection

 Lack of access to 

technology by small 

and medium-sized 

enterprises

 Lacked of workforce 

with needed skills

 Lack of common 

ontologies
Section 1 — Introduction

1-9

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Table 1-3. Sources of Market Failure: Barriers to Developing and Adopting New Technology

Barriers

Impacts Platform 

Technologies

Impacts 

Infratechnologies

• Inability to appropriate all social benefits, 

such as positive network externalities

√ √

• Scope of commercial applications broader 

than the market strategy of any one firm

√

• Risk that R&D outcomes will be technically 

insufficient (technical risk)

√

• Commercial or market risk, that is, the risk 

that R&D outcomes, while technically 

sufficient, will not be well received by the 

market, thereby providing an unacceptable 

return on investment (ROI)

√

• Long and uncertain lag between R&D 

investments and returns

√

• Asymmetric information between 

developers and adopters of new technology

√

• Difficulties in bringing together component 

technologies from different industry 

segments

√ √

• Industry structure, such as network 

externalities, presenting market-entry 

barriers to new technology

√

As will be discussed in the following sections, it is important to 

distinguish between general R&D market failures and those that 

specifically affect technology infrastructure. Although 

technology platforms, which are a form of early stage, are 

subject to the more common R&D market failures, such as 

appropriability and institutional risk aversion, infratechnologies 

are more of a special case.

Infratechnology market failures affect both research and 

marketing/distribution throughout supply chains. For example, 

a lack of standardization, certification, and test beds are market 

failures that create inefficiencies and increase transaction costs. 

The provision of infratechnologies by government can help 

overcome these issues.

1.5 BENEFITS FROM AN IMPROVED 

TECHNOLOGY INFRASTRUCTURE

Technology infrastructure provides the underpinning for 

advances in smart manufacturing. It lowers the cost and 

increases the benefits from adopting existing smart 
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

1-10

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

manufacturing technologies. Removing or lowering market 

barriers to adoption will increase penetration and yield 

economic benefits; the efficiency gained will ultimately improve 

the competitive position of U.S. manufacturing. Enhancements 

in the technology infrastructure will also lead to the 

development of next-generation smart manufacturing 

technologies that could drive product and process 

improvements that are just in the conceptual stages today.

1.6 REPORT OVERVIEW

The remainder of this report presents an analysis of technology 

infrastructure needs to support smart manufacturing. The core 

findings are based on in-depth interviews conducted with 

stakeholders throughout the manufacturing sector and the 

supporting supply chains. This report is organized as follows:

 Section 2 presents the methodology used in the 

analysis, including data collection, survey instruments, 

data analysis, and estimating of economic impacts.

 Section 3 provides an overview of key concepts related 

to smart manufacturing, stakeholder groups included in 

the study, market trends, and barriers to adoption.

 Section 4 presents findings from the stakeholder 

interviews. It presents a discussion of barriers to the 

adoption of smart manufacturing technologies, and the 

potential economic benefits from meeting smart 

manufacturing technology infrastructure needs.

 Sections 6 through 10 focus individual capability areas 

and present the specific technology infrastructure needs 

for smart manufacturing and potential roles for NIST in 

developing solutions to fill those needs.

 Section 11 provides the study conclusions and 

overarching themes.
2

-1

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

Analysis Methods 

and Primary Data 

Collection

We conducted more than 80 interviews with a wide variety of 

smart manufacturing product and service providers, smart 

manufacturing end-user companies, and industry observers. 

We also had informal conversations with individuals at conferences and industry events, which contributed to the 

findings in this report.

The interviews investigated current trends, barriers to adoption, 

benefits that could be achieved from an improved technical 

infrastructure, and potential new roles for NIST. Quantitative 

information was collected on the potential benefits in terms of 

increased productivity, decreased production cost, and 

improved product quality. These quantitative impacts were 

used to calculate national economic impacts associated with an 

improved technical infrastructure to support smart 

manufacturing.

This section discusses the details on the data collection process 

and the methodology for quantifying the potential impacts.

2.1 OVERVIEW OF ANALYSIS METHODS

The methodology includes both the collection and analysis of 

qualitative and quantitative data. It is important to understand 

where and why existing technologies have not penetrated the 

market, as well as what new and improved capabilities/

technologies would be most beneficial. Industry stakeholders 

were interviewed to investigate their current use of smart 

manufacturing technologies and practices, and the barriers 

limiting expanded or more rapid adoption. These discussions 
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

2-2

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

began with a general overview of barriers, such as adoption 

costs, overarching technical issues, and risks and return. Each 

interview last about an hour.

Once overarching barriers were identified, specific technical 

issues were discussed. The six smart manufacturing capability 

areas presented in Section 2 were investigated along with their 

associated technology infrastructure needs. The technology 

infrastructure needs were ranked and prioritized in terms of 

importance for advancing smart manufacturing and the level of 

the additional research and development needed. Then, indepth discussions were held on specific needs and how 

manufacturing processes could be improved if these needs 

were met.

Interviewees quantified how meeting these technology 

infrastructure needs would improve their manufacturing 

processes in terms of reduced capital, labor, materials, and 

energy costs, and/or impact their productivity and throughput. 

This information was used to estimate the economic impact of 

an improved technology infrastructure supporting smart 

manufacturing. Firm-level responses were scaled to obtain 

national industry-level impacts. Interviewees were also asked 

what role NIST could play in helping meet these needs.

2.2 CONSERVATIVE APPROACH

The quantitative economic impact estimates calculated in this 

study are considered to be conservative in that they do not 

capture all the benefits that would result from an improved 

technology infrastructure. As discussed below, the focus of our 

analysis is on reductions in manufacturers’ production cost that 

would result from meeting the identified technology 

infrastructure needs. However, this analysis does not capture 

all of the potential economic benefits associated with an 

enhanced technology infrastructure.

For example, a streamlined infrastructure for creating, 

transmitting, analyzing and communicating design and 

production data would accelerate the development and 

commercialization of altogether new product markets. These 

new products would have increased economic value stemming

from enhanced attributes such as
Section 2 

— Analysis Methods and Primary Data Collection 2-3

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

greater functionality,

lower maintenance costs, and

increased life expectancy.

However, valuing new (yet to be defined) products or product 

attributes is difficult, has great uncertainty, and is beyond the 

scope of the study. An improved technology infrastructure will also lead to reduced 

R&D costs. However, interviewees were not able to quantify 

R&D savings, saying that the benefits would be a mix of 

improved/accelerated R&D and enhanced product quality. 

Hence, these categories of benefits are discussed qualitatively, 

but not included in the quantitative economic impact estimates.

In general, focusing on manufacturing cost savings implies that

the analysis captures primarily gains in producer surplus, and 

does not capture gains in consumer surplus associated with 

improved product quality. In addition, the analysis does not 

capture increases in social welfare from increased output

(sales), which result from lower cost and higher demand;nor 

does the analysis capture increased exports that would result 

from the enhanced competitive position of U.S. manufacturers.

For these reasons, the economic impacts presented are 

considered to be conservative, lower-bound estimates.

2.3 INTERVIEW PROCESS

Interviews were selected as the principal mode of primary data 

collection (rather than a closed-form survey) because of the 

complexity of the subject matter and the need to be flexible 

with respect to the respondents’ areas of expertise. This 

approach to data collection provided a richness of information that could not be obtained using survey methods.

We identified potential interview respondents by first identifying 

firms operating in advanced manufacturing spaces. The focus 

was on the manufacturing sector as defined by the major two-

digit NAICS codes 31–33. Contacts were then identified within 

firms operating in these manufacturing sectors. High-value 

respondents were identified and secured by attending industry 

conferences and trade shows, and reviewing publically available 

publications and presentations. As part of this process, a large 

number of informal discussions (not counted as formal 
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

2-4

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

interviews) were conducted. Ultimately, 81 interviews were 

conducted with 38 developers4 and 43 users. Table 2-1 shows 

the industry sectors covered by the interviews.

End-user interviewees were distributed relatively evenly among 

discrete parts manufacturers (automotive, aircraft, etc.) and 

process-oriented manufacturers (chemicals, petroleum, etc.).

Interview participants varied in seniority, from staff engineers 

to middle management to executives. Job titles of the 

interviewees included:

 Senior Engineering Manager

 Director, Advanced Manufacturing Technology

 Senior Automation Engineer

 Process Automation Engineer/Process Modeling & 

Optimization Engineer

 Chief Executive Officer/President

 Founder

 Chief Technical Officer/Chief Technical Advisor

 Global Lead for Manufacturing

2.4 INTERVIEW TOPICS

Interview guides were developed and shared with respondents 

prior to the interviews. Two separate interview guides were 

used to differentiate between different positions in the value 

chain—a guide for firms that develop and sell smart 

manufacturing technology or services and a guide for firms that 

use smart manufacturing technology and services. Some firms 

were hybrid developers/users, and in these cases we asked 

about both perspectives. To simplify the analysis, industry 

observers were interviewed from the developer or end user 

perspective based on their particular experiences and 

knowledge.

Technology developers provided quantitative responses 

regarding industry sales growth. They also provided critical 

qualitative feedback on the specific technologies and 

infrastructure technologies that NIST can help provide. Sales 

 4 Of the 38 interviewees interviewed from the developer perspective, 5 

were industry observers.
Section 2 — Analysis Methods and Primary Data Collection

2-5

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Table 2-1. Industry Sectors Covered in the Interviewsa

Industry NAICS Code

Food manufacturing 311

Fruit and vegetable preserving and specialty food manufacturing 3114

Textile mills 313

Wood product manufacturing 321

Paper manufacturing 322

Pulp, paper, and paperboard mills 3221

Converted paper product manufacturing 3222

Petroleum and coal products manufacturing 3241

Basic chemical manufacturing 3251

Resin, synthetic rubber, and artificial synthetic fibers and filaments manufacturing 3252

Pharmaceutical and medicine manufacturing 3254

Plastics product manufacturing 3261

Other fabricated metal product manufacturing 3329

Agriculture, construction, and mining machinery manufacturing 3331

Industrial machinery manufacturing 3332

Metalworking machinery manufacturing 3335

Engine, turbine, and power transmission equipment manufacturing 3336

Other general-purpose machinery manufacturing 3339

Navigational, measuring, electromedical, and control instruments manufacturing 3345

Household appliance manufacturing 3352

Motor vehicle body and trailer manufacturing 3362

Motor vehicle parts manufacturing 3363

Aerospace products and parts manufacturing 3364

a The industries represented by the respondents were identified by querying a variety of sources. Some 

respondents provided a NAICS code for their line of business. Other companies were classified through secondary 

search of the Hoovers database and Census NAICS Web site searches. NAICS identified at the 3- and 4-digit 

level were used when scaling individual responses to national impacts.

growth estimates helped identify the impact of public 

investments in infrastructure technologies on size of the overall 

market and the extent to which the technologies will be 

adopted by end users.

End users provided quantitative responses on how smart 

manufacturing technologies would change their costs in four 

factors of production—capital, labor, energy, and materials 

expenses—and ancillary measures such as total throughput, 

maintenance costs, and the costs of integrating new 

manufacturing technologies. They also provided critical 

qualitative feedback on barriers to adoption, key technical pain 

points, and how advanced manufacturing technologies could 

improve their products.
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

2-6

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

The following is an overview of the topics investigated during 

the interviews. The formal interview guides for developers and 

end users are in Appendices A and B.

Respondent’s background: Respondents were asked to 

describe their background as it relates to smart manufacturing, 

and what share of their company’s or industry’s activities/

sales/research they felt comfortable discussing. Respondents 

were also asked if they were familiar with NIST’s activities 

and/or if they participated in research organizations (standards, 

calibration and measurement, scientific or data 

exchange/analysis relevant to smart manufacturing).

Current and planned use of smart manufacturing: 

Respondents were asked to provide a brief description of their 

company’s current use of smart manufacturing technologies, 

and what additional areas of smart manufacturing (e.g., 

automation, sensing/monitoring, data feedback/integration) 

their company has considered, investigated, or researched for 

potential future adoption. For example, has the company 

conducted feasibility studies or developed preliminary 

cost/benefit models?

Barriers to adoption: Respondents were asked why they 

decided not to move forward (or are not moving as fast as they 

would like) with certain investments in smart manufacturing.

A better state of the world: Respondents were asked what 

capabilities/technologies are needed to promote greater 

adoption of smart manufacturing and how these enhanced 

capabilities/technologies would impact their manufacturing 

activities. Then respondents were asked to rank the importance 

of these capabilities along with the level of additional 

development needed.

Economic valuation: Given the enhanced capabilities/

technologies cited, respondents were asked about the impact of 

these technologies on their manufacturing processes, products, 

and services. They were asked to quantify these impacts in 

terms of percent reduction in costs and improvements in 

productivity.

Importance: Respondents were asked to rate the importance 

of different smart manufacturing capabilities and technologies.
Section 2 — Analysis Methods and Primary Data Collection

2-7

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Technology infrastructure needs: Respondents were asked 

about which areas needed the most research in terms of 

technology infrastructure, and to identify specific research 

activities that should be pursued to further enhance smart 

manufacturing capabilities and functionality.

NIST’s potential role: Throughout the interviews, 

respondents were asked to consider the role NIST might play in 

supporting the development of an enhanced technology 

infrastructure. At the end of the interviews, respondents were 

asked to summarize their thoughts on NIST’s role and what 

activities NIST should prioritize.

2.4.1 Economic Analysis Methodology

Quantitative responses were summarized at the firm level and 

applied to part or all of the industries represented by the 

interview respondents. The following sections outline how the 

quantitative responses are summarized at firm level and 

industry level given our industry classification scheme 

previously described. Table 2-2 outlines and briefly describes 

four analysis components we conducted using information 

gathered from our interviews.

Firm-level impacts were estimated for each firm that provided 

quantitative impact data during the interview. We made the 

effort to estimate impacts only for the relevant portion of the 

firm as reported by the respondent. Average impacts per 

Table 2-2. Economic Analysis Methodology

Stakeholder 

Group

Scope of Impact 

Estimate Description Section

End users Firm Estimate of the impact that enhanced technology 

infrastructure would have on the individual end 

users in our sample

2.4.2

Developers Firm Estimate of the impact that enhanced technology 

infrastructure would have on the sales of the 

individual developers in our sample

2.4.3

End users U.S. National Estimate of the impact that enhanced technology 

infrastructure would have on the U.S. industries 

that currently use, or will use, smart manufacturing

2.4.4

Developers U.S. National Estimate of the impact that enhanced technology 

infrastructure would have on the sales of U.S. 

developers of smart manufacturing technologies

2.4.5
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

2-8

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

expenditures for a given NAICS code were then scaled by 

national NAICS-level expenditures to obtain national impact 

estimates.

2.4.2 Firm-Level End-User Impact from the Sample

Respondents provided percent changes to capital, labor, 

energy, and materials (KLEM) costs (as well as overall 

production throughput).5 Dollar impacts were derived by first 

estimating the firm-level domestic sales to which the impact 

estimates applied. We estimated firm-level costs using 

industry-level cost-to-sales ratios from secondary sources such 

as the Annual Survey of Manufacturers (ASM) and the Bureau 

of Labor Statistics (BLS) IO data. Percent change impacts were 

then applied to these firm-level cost estimates.

Firm-level sales were estimated through a variety of sources 

including public filings, annual reports, and Hoover’s database. 

Some respondents provided sales estimates that were verified 

against secondary sources. For larger firms with multiple lines 

of business where it was clear that the impact estimates only 

applied to a certain division, division-level sales were estimated 

using information from annual reports.

To estimate cost-to-sales ratios, we used industry data from 

national accounts provided by the BLS for industry-level energy 

and materials cost estimates. Energy costs included the 

manufacturing industry’s purchases of oil and gas extraction 

(NAICS 211), coal (NAICS 2121), electricity (NAICS 2211), 

natural gas (NAICS 2212), and refined petroleum (NAICS 324). 

Materials costs included purchases from other manufacturing 

industries in the NAICS range 3210–3330, excluding 324 

(refined petroleum and coal).

The data provided by BLS give highly aggregated accounts for 

capital and labor. To better identify capital and labor costs 

 5 Changes in production throughput was used as a proxy for capacity 

utilization when respondents were not able to provide percent 

changes for KLEM. However, increased industry growth or sales are 

not included in the impact estimates; thus, the impact estimates 

are conservative.
Section 2 

— Analysis Methods and Primary Data Collection 2-9

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

associated with “shop floor” activities, we relied on the 2013 

ASM.

6

Firm-level KLEM cost estimates then equal the estimate of firm 

or division sales times the relevant industry’s cost-to-sales ratio 

based on the BLS (energy and materials) or ASM (labor and 

capital) data. For example, we estimate that production capital 

is 5.4% of sales in the iron and steel mills industry (NAICS 

3311). A respondent at a firm with $500 million in sales within 

this industry who reported a 5% reduction in firmwide capital 

costs would yield a $1.4 million ($500 x 0.054 x 0.05) capital 

impact estimate for smart manufacturing technology adoption.

Response Assumptions

Some respondents provided a combination of production 

throughput and capital and labor cost impact estimates. If the 

respondent provided estimates for capital and labor cost 

impacts, we used those numbers assuming that the respondent 

accounted for improvements in product throughput. If the 

respondent provided no estimates for capital and labor cost 

impacts, we applied the production throughput improvement as 

an equivalent percentage improvement in the costs of capital 

and labor.

Not all respondents were willing to provide quantitative 

estimates. Some respondents provided a rank order of the 

impacts or a direction on the impacts, but did not quantify 

them. We did not include these responses in the quantitative 

assessment. For respondents who provided an estimate for at 

least one KLEM category, we assigned zero impact to the 

categories for which they did not respond.

Impact Applicability

When scaling to national levels, we were careful in determining 

the appropriate quantity of KLEM expenditures. Applicability 

factors were calculated to capture both applicable expenditures 

within a given firm, and the share of firms within the 

 6 Capital costs used in the analysis include capital expenditures on 

machinery and equipment (CEXMCH, RPMCH), computer and 

peripheral equipment (CEXMCHC, PCHCMPQ), and other machinery 

and equipment (CEXMCHO, RPMCH). Labor costs include production 

workers’ annual wages (PAYANPW) grossed up to include nonwage 

benefits such as health insurance (BENHEA), retirement (BENPEC, 

BENPEB), and other fringe benefits (BENOTH). 
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

2-10

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

manufacturing sector for which smart manufacturing impacts 

are applicable.

These applicability factors (and the percent change impacts) 

were then applied to national accounts that reflected only a 

firm’s manufacturing activities. For example, only production 

labor and capital equipment expenditures were used as the 

basis for scaling impacts to the national level.

Since some respondents provided information specifically about 

their division’s operations, their cost and throughput estimates 

would not necessarily apply to the firm as a whole. This was 

particularly true for respondents working for larger, multiindustry or conglomerated firms.

Total sales were estimated based on our identification of the 

share of firm sales associated with the operation for which the 

responses were given. For example, we spoke with a 

respondent from a multinational chemical company who was 

comfortable answering for a single division of the company that 

accounted for 20% of the company’s sales. Furthermore, 

approximately 40% of the company’s sales took place in the 

United States. If the firm’s revenues were $100 million, the 

applicable sales base would be $8 million ($100 x 0.20 x 0.40).

2.4.3 Firm-Level Developer Impacts

End-user demand for smart manufacturing technologies 

requires commensurate supply from the industries that develop 

the technologies. Some end users estimated capital cost 

increases to reflect the purchase of these technologies, but 

most end users indicated that the bulk of their existing capital 

would be better optimized through smart manufacturing 

technologies and enhanced technical infrastructure; thus, on 

net, most end users cited decreases in capital costs.

Developers were asked what impact an enhanced technology 

infrastructure would have on their sales of existing and 

potential new products and services. However, it should be 

noted that developers’ increased sales and end user cost 

reductions are not additive when calculating economic impact. 

Sales of supporting smart manufacturing products and services 

are considered a transfer in the economic accounts. Users 

accounted for (netted out) additional purchases of these 

products and services when estimating their benefits (cost 

reductions).
Section 2 

— Analysis Methods and Primary Data Collection 2-11

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

2.4.4 National-Level End-User Impacts by KLEM

Respondents were given the option to provide a percentage 

estimate for each quantitative response or a range of impacts. 

In cases where a respondent provided a range, we took the 

midpoint of the range when calculating point impact estimate.

We excluded outlying impact estimates that were either 

unrealistic or likely to heavily bias industry-level impact 

estimates upward.

7 We considered several possible ways to 

summarize the remaining responses within each NAICS code, 

including:  Average: A simple average of the impact response for all 

respondents within a given NAICS code category

 Sales-weighted: A weighted average of impact 

responses based on the size of the respondent’s firm

We used the simple average method because it was less 

susceptible to being overly influenced by a single response from 

a very large firm.

Using the industry data on cost-to-sales ratios, we estimated 

the KLEM cost basis for each 4-digit industry that is 

represented by responses. We discounted the average cost 

impacts for each 4-digit industry by the average industry 

applicability factor. Then we applied the applicability-weighted 

cost impacts (%) to the KLEM costs for each industry. The total 

cost impact across industries is the sum of the industry-level 

impacts.

We received quantitative responses for industries representing 

nineteen 4-digit level NAICS and four 3-digit level NAICS, which 

account for 64% of total manufacturing sector sales. Because 

not all manufacturing industries are likely to benefit from 

incorporating advanced manufacturing technologies, we 

recognize that some industries were not captured in the 

interview process. Thus, national-level impact estimates are 

likely to be conservative in that some industries with potential 

benefits are omitted.

 7 For example, one respondent provided an estimate for all 

manufacturing sectors that had a significant impact on our average 

impacts. This response was dropped because the respondent could 

not be more specific about the industry or sector for which the 

response was applicable.
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

2-12

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Respondents were also asked to provide industry applicability 

factors for their estimates because not all impact categories are 

relevant for all companies within a sector. However, for the 

vast majority of respondents, these factors were 100% (i.e., 

respondents expected their impact estimates to hold for all 

firms in the industry). Responses less than 100% were often 

stated as a fraction of the industry or for a particular type of 

firm or set of firms within the industry, which required 

secondary estimates such as market shares from sources like 

IBISWorld Reports. Applicability factors were then applied by 

industry to further adjust the industry scaling size.

2.4.5 National-Level End-User Impacts by Capability Gap Area

We apportioned the total cost impact to each of the six 

capabilities in Table 3-1 by using the average share of 

importance points awarded to each capability. We used only the 

importance scores provided by end users (see Section 3.3 on 

importance scores).

For example, if an interviewee awarded a total of 20 points 

across the six capabilities, and they provided the highest 

importance score of 5 to safe HRI and a 2 for objective, lowcost performance characterization, then the shares would be 

25% and 10%, respectively. We then averaged these shares 

across individuals and applied them to the total cost impact.

2.4.6 National-Level Developer Impacts

To estimate the size of the market for developers’ advanced 

manufacturing technologies, we combined estimates of global 

market revenue from market reports. We then made an 

assumption about the U.S. market share, using a sample of the 

largest players in the market for smart manufacturing 

technologies8 as well as an industry report.9 These estimates 

and the U.S. share of the global smart manufacturing 

technology market are used to scale the sales growth that 

developers said would result from enhanced technical 

infrastructure and end-use capabilities. The total value of 

advanced manufacturing technology purchases (sales) should 

not exceed its benefit to end users.

 8 Using a sample of 10 firms, we found that the portion of global 

industrial automation and control as well as optimization software 

revenues attributable to the United States was about 35%. 9 Cisco, IoE Report. IoE value to the United States was $4.6 billion, 

compared with $14.4 billion globally.
3

-1

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

Smart 

Manufacturing 

Trends and 

Technology Gaps

This section provides an overview of the key components of 

smart manufacturing processes, and discusses current 

shortcomings and future technology needs. These issues 

formed the focal point of the stakeholder interviews and set the 

stage for the quantitative and qualitative findings presented in 

the following section.

3.1 KEY COMPONENTS OF SMART 

MANUFACTURING PROCESSES

Smart manufacturing incorporates not only generating and 

capturing data, but also understanding its implications and 

taking action as a result of that knowledge. Figure2-1 captures 

the processes included within the “smart manufacturing” area 

and depicts the complexity inherent within the manufacturing 

system.

In Figure3-1, three levels of systems that embody smart 

manufacturing are represented: the enterprise management 

system, the operations system, and the product creation 

system. Each of these systems have a data feedback loop that 

models, senses, transmits, analyzes, communicates, and takes 

action on data. Smart manufacturing processes are intertwined 

within the overall manufacturing system, spanning multiple 

levels of function from product creation to process management 

to enterprise and supply chain interaction.
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

3-2

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Figure 3-1. Overview of Smart Manufacturing Processes and Data Flow

The potential benefits of smart manufacturing and the 

associated technology needs described by stakeholders depend 

on each stakeholder’s unique perspective, access to resources, 

and position in the supply chain relative to the industries they 

serve. The security aspect of smart manufacturing is all 

encompassing and represents a significant challenge to 

adoption. The figure shows a circle where data security is 

employed, and another circle where cyber-physical security is 

critical to prevent hostile takeover of equipment.

In its simplest form, smart manufacturing turns data from the 

manufacturing process into actionable knowledge. Data are 

generated and input manually into a system through design 

and simulation, or are captured automatically through sensing 

environmental measures and process and equipment 

performance indicators. Data are extracted and communicated 

in a form that can be analyzed. Software algorithms and 

models analyze the data and produce actionable intelligence. 
Section 3 

— Smart Manufacturing Trends and Technology Gaps 3-3

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

The intelligence is communicated to decision makers, either 

human or machine, and action is taken based on that

intelligence.

We grouped smart manufacturing activities and associated 

technology infrastructure into six categories, which are used 

throughout the report to facilitate discussion and presentation 

of results:

 Managing digital data streams through models  Sensing and monitoring  Seamless transmission of digital data  Advanced data and trend analysis  Communicating information to decision makers  Determining required action and implementing action

Table3-1provides an overview of the smart manufacturing 

capabilities and the technology infrastructure needed to support 

these capabilities.

3.1.1 Managing Digital Data Streams through Models

Data are generated in numerous ways within the product 

manufacturing environment. One way is through the creation of 

digital models, which are virtual representations of the real-

world products and manufacturing systems. Digital models of 

products entering the build cycle are created using computer-

aided design (CAD) tools. Material characteristics and behaviors 

are modeled to enable simulations of product performance 

through environmental conditions. The simulations provide 

models of physical environments, such as temperature and 

stress, and generate predicted performance data.

Factory floor layouts and process simulations are also digital 

models. High-fidelity process models are particularly data-

intensive and require high-performance computing systems to 

manage the data calculations. Predictive models, using key 

performance indicators from equipment and processes, 

generate and manage data. Models are created for business 

data, such as operational cost and part lead time, as they affect 

the operation of the factory.
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

3-4

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Table 3-1. Smart Manufacturing Capabilities and Technology Infrastructure Needs

Smart Manufacturing 

Capabilities

Technology Infrastructure 

Needed to Support Capabilities

Potential Benefits and Impacts 

of Enhanced Technology 

Infrastructure

Managing digital data streams 

through models:

• CAD models including material 

characteristics,

• simulation models of part 

creation and plant layout, and

• rapid automated costing 

functions

High-fidelity process models, 

physical model representation for 

flexible objects, simplified modular 

applications of CAM software for 

less sophisticated uses, data 

standardization, standard and 

simpler equipment interfaces to 

facilitate consistent data entry for 

less-skilled workers, standard 

terminology for automated part 

costing

• Ability to capture featurebased information in design 

models

• Streamlined simulation of part 

creation

• Enhanced ability to design for 

manufacturing

• Reduced productdevelopment-to-production 

cycle

Enhanced sensing and monitoring:

• “state estimation” of critical 

manufacturing machines (e.g., 

vibration, acoustics, 

temperature, tolerances, and 

pressure), and

• real-time monitoring of product 

attributes as they move through 

various stages of the production 

process

In-process measuring and 

monitoring for physical processes, 

self-powered sensors, robust 

sensors to withstand harsh 

manufacturing processes, data 

standardization, methods for 

calibrating sensing and monitoring 

systems

• More efficient measurement of 

equipment usage such as 

overall equipment 

effectiveness

• Reduced scrap

• Enhanced in-process product 

quality

• Ability to predict machine 

issues and intervene with 

preventive maintenance

• Reduced unplanned downtime

• Increased sensor reliability

• Greater visibility and 

transparency in manufacturing 

operations

Seamless transmission of digital 

information:

• wireless transmission of digital 

information without interference 

from other data channels, 

• seamless integration of smart 

sensors, 

• interoperability between 

different platforms such as 

CAD/CAM, and

• secure data transmission (wired 

and wireless)

Secure data transmission; secure 

cloud computing and data sharing; 

standard communication protocols; 

retrofitable, plug-and-play data 

communications systems; data 

interoperability of 3D model 

parameters and product 

manufacturing information

• Reduced costs in factors of 

production such as capital, 

labor, energy, and materials 

through the increased ability 

to holistically analyze data, 

which results in more efficient, 

optimized production

• Energy reductions, active 

energy management

• Reduced downtime, increased 

uptime

• Improved security, reduced 

risk of IP or safety issues

• Reduced integration time 

(commissioning, debugging, 

etc.)

• Freeing up staff time that is 

currently spent on connectivity 

and collating data 

(continued)
Section 3 — Smart Manufacturing Trends and Technology Gaps

3-5

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Table 3-1. Smart Manufacturing Capabilities and Technology Infrastructure Needs 

(continued)

Smart Manufacturing 

Capabilities

Technology Infrastructure 

Needed to Support Capabilities

Potential Benefits and Impacts 

of Enhanced Technology 

Infrastructure

Advances in analyzing data and 

trends:

• interpretation and aggregation 

of data from sensing and 

monitoring networks,

• “Big Data” techniques for 

manufacturing,

• predictive maintenance, 

• reduction of false positives, and

• cloud computing and fee-forservice cloud-based algorithms 

for product design, simulation, 

and manufacturing design

Algorithms to interpret data from 

disparate sensors and systems; 

definition of important, relevant, 

and meaningful data to collect for 

predictive maintenance

• Improved uptime

• Enhanced monitoring of inprocess quality 

• Reduction of false positives

• Increased overall equipment 

effectiveness

• Better utilization of existing 

data sources 

Efficiently communicating 

information to decision makers:

• comprehensive information 

interfaces, human-computer 

interaction-based design, and 

• easy-to-interpret interfaces 

accessible from any location

Common taxonomy across 

platforms and disciplines 

Standards in interface design for 

manufacturing equipment 

• Accelerated development of 

interfaces by the private 

sector

• More timely, evidence-based 

decision making

Determining required action and 

implementing action:

• real-time feedback of enhanced 

sensing and monitoring data 

into factory decision making,

• automated optimization-based 

decision making that functions 

independently of human 

interaction, 

• machine-learning decisionmaking algorithms for 

manufacturing, and

• reconfigurability of 

manufacturing systems

Tested and validated decision 

models 

• Optimized manufacturing 

processes

• Greater visibility and 

transparency in real-time 

manufacturing operations

Capabilities Needed, Importance and Benefits

Managing digital data streams through models is key for 

product development and, ideally, would facilitate 

interoperability across CAD/CAM models and include material 

characteristics as part of the digital information stored and 

shared. These models could then be used to streamline 

simulation for parts creation. Plant layout simulation and rapid 

automated costing functions are two additional types of data 

modeling that were mentioned in the interviews.
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

3-6

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Of value would be high-fidelity process models, physical model 

representation for flexible objects, and simplified modular 

applications of CAM software for less sophisticated uses. 

Simpler equipment interfaces to facilitate consistent data entry 

would also enhance the process and enable less skilled workers.

Simulation models of the manufacturing process and how it 

behaves is very important from a plant optimization standpoint. 

Moving forward, simulation models that use real-time data 

could yield significant benefits. Static or preliminary simulation 

of factory floor and operations is not always sufficient. There is 

a need for dynamic simulation as more real-time data become 

available.

In addition to product and manufacturing information, several 

respondents discussed the need for capturing more detailed 

information on material characteristics in design models. 

Similarly, feature-based designs have databases for features 

rather than parts.

3.1.2 Sensing and Monitoring

Data are generated not only through models, but also through 

the sensing and monitoring of physical elements. Temperature, 

pressure, and humidity of the factory environment are captured 

to monitor effects on production quality. Measurement of usage 

(power on/off, idle time, etc.) from machines and equipment 

captures data for overall equipment effectiveness (OEE). Inprocess measurement within and during equipment operation 

provides data that can monitor and control the outcome of the 

product being produced. For example, measuring temperature 

within a metal forging operation provides feedback such that 

the hammer strike can be controlled to deform a part with the 

correct material properties.

Beyond sensing and measurement of environmental elements, 

capture of communications data provides another data source 

within the factory. Human–machine interfaces capture data 

from operators on the factory floor—data such as the 

equipment functions they are performing, feedback on the 

result of those actions, and information on equipment 

performance. In addition, metadata associated with part build 

history can be captured for actionable intelligence.

One of the challenges 

of smart manufacturing 

is the limited 

development of 

modeling systems, 

which leads to the need 

to set up pilot plants. 

These small plants are 

necessary just to make 

sure that the 

production process is 

safe. With the 

improvement of 

modeling systems, the 

very expensive pilot 

plant implementation 

should disappear.
Section 3 

— Smart Manufacturing Trends and Technology Gaps 3-7

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

Capabilities Needed, Importance and Benefits

Enhanced sensing and monitoring includes in-process 

measuring and monitoring for physical processes, self-powered 

sensors, and robust sensors to withstand harsh manufacturing 

processes. Overarching examples include:  “state estimation” of critical manufacturing machines 

(e.g., vibration, acoustics, temperature, tolerances, and 

pressure), and

 real-time monitoring of product attributes as they move 

through various stages of the production process.

Predictive maintenance was commonly cited as an area where 

improved sensing (and associated data analysis) would provide 

significant benefits. Downtime is costly for all manufacturing 

processes, and minimizing downtime is a key objective. 

Proactive management and maintenance of machines can help 

to isolate where the problems are being created and minimize 

planned downtime. One commonly mentioned topic was related 

to vibration. As sensors for vibration grow more affordable, 

companies are more interested in using them. The real 

challenge is how to interpret data: Operators do not have the 

historical data needed to generate traditional predictive models 

and companies do not have the in-house expertise to develop 

theoretic predictive models.

Another growing area is related to tolerances and inspection. 

Respondents said that more work is needed in this area and 

that the potential benefits here could be significant. New 

technologies are needed to support nondestructive tests and 

measurements.

One specific need cited was related to monitoring humidity. 

Humidity has a large impact on the efficiency of machines. 

Currently, there is no good way of understanding humidity 

effects in real time. One plant, for example, had a poorly 

performing cooling tower. Had they been able to monitor 

humidity they would have recognized the problem much earlier 

and saved on costly repairs. Other plants have machines, 

pumps, and compressors where their efficiencies are sensitive 

to humidity. If the humidity were monitored, the company 

could adjust this equipment’s performance in real-time, thus 

maintaining the ultimate level of efficient operations.
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

3-8

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

In process plants, product quality can be greatly improved with 

enhanced sensing. On process engineer noted that “If you have 

better control systems, measurement systems, sensors, and 

better ways to close the loop, you will have more consistent 

operation and as a result will have a more consistent quality 

product.” This leads to fewer bad batches and higher 

throughput.

Currently, many sensors indirectly measure parameters like 

conductivity, pH, temperature, and flow rates of materials 

through pipes, and these indirect measures are used to make 

inferences about the chemical species. What is needed are new 

sensors that can make direct measurements of the chemical 

species that are present in a substance while in process (real 

time). For example, when changing grades in a paper plant, the 

process is not stopped, but slowed during the changeover, 

which can last 8 to 10 hours. This paper is junk grade and the 

fiber is recycled back into the process, which is costly. Better 

sensors in the plant and greater integration could shorten 

changeover times and reduce recycling costs.

A commonly cited problem was sensor failure. A sensor may 

show that something is out of spec—but is it an accurate 

reading, or did the sensor just fail? In a case such as this, a 

high level of human intervention is needed, as a senior 

engineer may be needed to make decisions about process 

modifications. The value of sensors in a case such as this is 

diminished. Another concern is that the life cycle of machine 

tools can be 15 years or more, and the life cycles of process 

product lines are even greater, but a sensor life cycle is 

typically much shorter. The durability of sensors needs to be 

improved to increase their cost effectiveness and reliability.

3.1.3 Seamless Transmission of Digital Data

After the data are captured, they need to be communicated to 

the appropriate system that can apply computing to generate 

actionable intelligence. In this step, communications and data 

protocols are important to ensure that data are transmitted in a 

seamless manner between software and systems. 

Interoperability among CAD systems and between CAD and 

manufacturing systems ensures that technical data packages 

from CAD models are communicated appropriately. 

Communication protocols, such as MTConnect and Ethernet,

ensure that machinery and equipment from different 
Section 3 

— Smart Manufacturing Trends and Technology Gaps 3-9

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

manufacturers can talk to and understand each other’s data. 

Both wired and wireless communications occur within the 

factory, and must account for sending and receiving the right 

data packets in the right formats in synchronized and fast 

transmissions. Wireless communications must have technology 

in place to reduce interference. Data security is also a 

transmission issue. Encryption and other technologies are used 

to protect data in flight as they travel across the factory.

Location of data storage is another key aspect of the 

transmission process. Storage of and access to data in the 

cloud provides cost savings and flexibility, yet has security 

implications in the storage of, access to, and external 

transmission of data. External storage can get hacked, access 

to sites can be denied, and data can be captured in 

transmission. Security technologies are an important aspect of 

cloud computing for manufacturing.

Capabilities Needed, Importance and Benefits

Seamless transmission of digital information includes secure 

data transmission; secure cloud computing and data sharing; 

retrofitable, plug-and-play data communications systems; data 

interoperability of 3D model parameters and product 

manufacturing information; open systems; “information 

models”; next generation of STEP and MTConnect. Examples 

cited during the interviews include:  seamless, real-time integration of large volumes of data 

from devices such as general-purpose machines and 

specialized machines, machine tools, smart sensors, and 

other instrumentation;

 interoperability between different platforms/systems/ 

information models/software applications such as 

CAD/CAM, ERP, MES, and PLM that are currently “islands 

of information”;

 machine-to-machine communication that includes more 

classes of machines and can be applied to older 

machines;

 secure data transmission throughout the facility network 

(wired and wireless), across the firm, and across the 

supply chain, including secure cloud storage and cloud 

computing;

 the convergence of information technology (IT) and 

operations technology (OT) or control networks; and
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

3-10

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

 wireless transmission of digital information without 

interference from other data channels.

3.1.4 Advanced Data and Trend Analysis

Once data are captured, there is a need to process and 

interpret the data so that the information becomes actionable. 

To fully realize the potential of smart manufacturing, analysis 

capabilities are needed to extract and express insights from 

large amounts of data. These insights can then be used to spur 

immediate action, such as addressing operational downtime 

issues. They can also be used for future action, such as 

improving operational efficiency based on causal information, or 

scheduling maintenance on equipment that is predicted to fail 

at a specified time in the future.

Condition-based maintenance is a technology that monitors and 

evaluates many different parameters of machinery that affect 

performance and maintenance. In a simple system, few 

parameters need to be analyzed; however, for multiple 

parameters affecting wear and tear on machines, models are 

created using pattern-recognition software that predicts 

success and failure by evaluating parameters such as amperage 

fluctuation, vibrations, reset frequency, and 

temperature/humidity.

To meet the intelligence needs of factory managers, research 

and analysis algorithms determine which performance 

indicators of equipment are needed to determine failure modes 

and which operator and process measures indicate efficiencies 

and issues. Models are created and applied to the appropriate 

data, using predictive analytics and machine learning 

algorithms to analyze and understand the data in a context of 

importance to the decision makers. Similarly, parts designers 

can learn and adapt their designs based on trends and 

functions determined by production data captured along the 

part build process.

Capabilities Needed, Importance and Benefits

Advances in analyzing data and trends includes the ability to 

interpret data from disparate sensors and systems for uses. 

This capability would support:

 enhanced interpretation and aggregation of data from 

sensing and monitoring networks;
Section 3 

— Smart Manufacturing Trends and Technology Gaps 3-11

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

 a better understanding of which data points are the 

most important and which can be disregarded, given the 

proliferation of data;

 “big data” mining techniques and algorithms for 

manufacturing, which includes more data scientists and 

mathematicians interested in manufacturing;

 enhanced ability to correlate upstream factors, such as 

material properties, with product quality;

 enhanced monitoring of in-process quality;

 reduction of false positives;  rapid cloud/parallelized computing for complex discrete 

event or process models/simulations and cloud-based 

fee-for-service algorithms for product design, 

simulation, and manufacturing design; and

 new forms of computing such as edge/distributed 

computing.

Improved predictive maintenance was cited as the most 

important benefit associated with advances in data analysis. 

Equipment is always going to fail and when it does, it is costly. 

Being able to detect or forecast failure events would be 

extremely advantageous. In addition, monitoring a process and 

being able to detect when something is going off spec, offgrade, or generally in the wrong direction would allow 

equipment operators to make timely decisions about equipment 

use, but this requires real-time analysis capabilities.

From a more generic perspective, dynamic contextualization is 

needed—that is, a system capability to adjust the algorithm 

depending on the question asked. For example, “What is the 

best way to quickly look at the output of 100,000 variables in 

terms of what is important?”

3.1.5 Communicating Information to Decision Makers

Interfaces allow individuals to access and understand the 

information being collected efficiently in real time. Data are 

only useful if they are readily accessible and can be interpreted 

easily. Visualization of data such that meaningful information is 

conveyed to plant managers and operators alike helps to 

optimize efficiency and solve problems quickly within the 

factory.

Virtualized information devices are used for training and 

maintenance on the factory floor. Mobile devices that provide 
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

3-12

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

repair instructions along with visual depictions of a model 

shorten the learning curve for maintenance personnel and 

mitigate errors in the repair procedure.

Capabilities Needed, Importance and Benefits

Efficiently communicating information to decision makers would 

involve:

 comprehensive information interfaces with human–

computer interaction-based design,

 consistent, easy-to-interpret, real-time interfaces 

accessible from any location,

 interactive information interfaces with various levels of 

aggregation suitable to different users, and

 interfaces that can efficiently filter and sift through large 

amounts of information to support decision making.

Companies do not always have the ability to bring domainspecific problems occurring in a plant quickly to the attention of 

domain experts. Benefits from more traditional human–machine 

interaction (HMI) could be enhanced by making the interaction 

more intuitive.

Simpler and more consistent interface structures for operators 

would generate value. There is a need for consistency across 

the industry and within companies’ interfaces to accommodate 

workforce transformation around smart manufacturing, the 

increased level of automation, and the increasingly varied level 

of employee capabilities. One respondent suggested leveraging 

research from pilot cockpit and air traffic control 

systems/studies.

Another area where improvement is needed is in 

communication between manufacturing operators and the 

design community. Feedback from the manufacturing process 

through the supply chain should be much greater. 

Manufacturers’ ability to influence the design specifications of 

new products would reduce the “guesswork” and the 

uncertainty associated with new product development and 

certification.

3.1.6 Determining Required Action and Implementing Action

Data are generated and gathered, analyzed, and communicated 

to decision makers who can take action. Manufacturing 

execution systems (MES), enterprise resource planning (ERP), 
Section 3 

— Smart Manufacturing Trends and Technology Gaps 3-13

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

product lifecycle management (PLM), and supervisory control 

and data acquisition systems (SCADA) all provide intelligence 

and enable decision makers to act. However, too much 

information can overwhelm systems operators; information 

overload can be mitigated by using specially adapted machine 

learning algorithms that detect responses to information and 

provide guidance on only the most important information.

Machine learning also enables automated actions, taking the 

human out of the loop. For example, a learning system on a 

welder using sensors to detect mistakes can self-adjust to 

prevent future errors. This self-optimization system determines 

the required action from data detected from sensors and 

analyzed to provide intelligence, then implements the action. 

To truly achieve the full potential of smart manufacturing 

techniques in optimizing manufacturing processes, 

improvements in artificial intelligence are needed.

Capabilities Needed, Importance and Benefits

Determining required action and implementing action would 

enable real-time feedback of enhanced sensing and monitoring 

data into factory decision making through advanced control. 

Desired capabilities cited include:  automated optimization-based decision making that 

functions independent of human interaction, including 

machine-to-machine communication and decision 

making,

 machine learning decision-making algorithms for 

manufacturing such as adaptive tool planning,

 reconfigurability and flexibility of manufacturing 

systems, and

 computational horsepower to deal with large amounts of 

data and complex systems.

Supporting tools would include physics-based models, virtual 

models, and discrete event models for the plant floor that will 

help make decisions faster. One end user stated that “In many 

instances, decisions could be made in half the time of our 

factory, which would lead to significant cost reductions.” 

Currently, some discrete event models take days to produce 

and analyze results. Cloud computing has the potential to 

shorten analysis time, but large companies are not comfortable 

exposing their data outside internal firewalls. 
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

3-14

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Modifying/segmenting the data structure is a potential solution 

for protecting IP in a secure environment.

Another challenge facing manufacturers is the limited 

development of modeling systems, which leads to the need to 

set up pilot plants. These small plants are necessary just to 

make sure that the production process is safe. With the 

improvement of modeling systems, the need for the very 

expensive pilot plant implementation should disappear.

Decisions about replacement could be easier in a smart 

manufacturing environment. Maintenance and machine 

throughput will be more visible, and the decision on ROI will be 

much easier when evaluating continued operation versus 

reinvestment. Companies need to be aware of all the costs 

associated with continued operation of older machines.

3.2 STAKEHOLDERS

Stakeholders included in the study were grouped into three 

major categories: smart manufacturing product developers and 

service providers, end users, and observers, described in detail 

below.

3.2.1 Smart Manufacturing Product Developers and Service 

Providers

Product developers and service providers (referred to as 

developers) are those companies that make products to enable 

smart manufacturing processes and provide the services for 

installation/integration. Developers encounter barriers to 

implementation of their technologies on a regular basis as they 

seek to sell their products to manufacturers. Developers include 

the following categories of vendors and providers.

 Consultants and Service Providers: These companies 

provide services such as engineering design; design for 

manufacturing; process engineering; modeling; 

simulation; and analysis, prototyping, and machine tool 

and robotic programming. These service providers are 

hired by both large and small companies to fill capability 

gaps in these organizations. They are hired to 

implement new technology in factory settings, and in 

doing so they are exposed to the issues faced by their 

customers.

 Design, process, and automation software 

suppliers: These suppliers support product design and 
Section 3 

— Smart Manufacturing Trends and Technology Gaps 3-15

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

simulation and offer traditional CAD/CAM systems. This 

group also includes companies that focus on the 

integration of product design and product simulation 

systems and process modeling systems.

 Network infrastructure and information systems 

providers: This category includes companies that 

provide the systems-level software to support the 

management and operations of the factory and supply 

chain. This software includes infrastructure involved in 

cloud-based computing, network support, sensor 

communication, and software interfaces.

 Equipment and hardware manufacturers: This 

group includes suppliers that make the machine tools 

and equipment that support the build of the product. 

CNC machines, punch presses, material handling 

equipment, coordinate measurement machines, and 

programmable logic controllers are a few examples of 

the hardware made by these manufacturers.

3.2.2 Smart Manufacturing End Users

End users are companies that are the customers of the 

developers’ products and services. They have much to say 

about why they adopted specific technologies, the ongoing 

issues they have with those technologies, and the factors

preventing them from implementing other technologies. In 

addition to seeking the perspectives of large manufacturers, 

which generally have both the financial and human resources to 

implement new technology, we sought the perspective of small 

manufacturers, which are often unaware of new technologies or 

do not have the in-house capabilities to implement them. End 

users are described below.  Large manufacturers

: This groupincludes both large 

product and process manufacturers typically focusing on 

high-value, customized products as well as continuous 

process operations, where network systems and data 

can lead to large efficiency gains in areas such as 

electricity and heat use.

 Small and medium enterprises (SMEs): These 

companies primarily form the multiple-tiered supply 

chain for the industries, but also include a growing 

number of companies generating final products with new 

advanced manufacturing techniques.

Within the end-user group, manufacturers are segmented into 

two broad groups: (1) discrete parts and assembly 

manufacturers and (2) batch and continuous process 
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

3-16

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

manufacturers. Table 1-1 (see Section 1.3) provides a list of 

major industry sectors included in each group.

 Discrete parts and assembly manufacturers include 

traditional assembly sectors such as the automotive and 

aerospace industries along with their supply chain of 

fabricated metal, electronic, plastic, and component 

parts. Discrete parts manufacturers also include 

appliances, furniture, and other factory-floor industries. 

Technology infrastructure needs for discrete 

manufacturers focus on the life cycle of products or 

parts, which includes product design, prototype 

development, testing, scale-up, and production. The 

exchange of electronic data throughout the value chain 

is key to increasing efficiency in both product design and 

manufacturing projects.

 Batch and continuous process manufacturers focus 

on the life cycle of the process. They work toward

optimizing the life cycle to increase energy efficiency 

and minimize product waste/process failures (“bad 

batches”). Historically, many process manufacturers 

have been more vertically integrated than discrete parts 

manufacturers, which would have multiple-tiered supply 

chains. As a result, for process manufacturers, 

technology needs focus less on supply chain 

interoperability issues. However, as globalization 

increases, some aspects of the process sector are 

becoming increasingly specialized, leading to more 

subcontracting and increasing the need for better 

communication across companies.

3.2.3 Smart Manufacturing Observers

Observers represent those working in the field but that have a 

broader perspective beyond the day-to-day operations of a 

specific company. Organizations in the observer category are 

people working in consortia, universities, and federal 

laboratories who are focused on solving problems and 

developing new smart manufacturing technologies. Observers 

interface with many people in the industry and are often 

working to solve the industry’s problems. They have a more 

global view and see the landscape of issues and opportunities.

A few of the most relevant consortia for smart manufacturing 

are described below.

 Smart Manufacturing Leadership Coalition (SMLC),

located at the University of California at Los Angeles. 

With 37 university, federal, and industry members, the 
Section 3 

— Smart Manufacturing Trends and Technology Gaps 3-17

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

SMLC is “committed to overcome barriers to the 

development and deployment of Smart Manufacturing 

(SM) Systems through an implementation agenda for 

building a scaled, shared infrastructure called the Smart 

Manufacturing Platform” (Smart Manufacturing 

Leadership Coalition, 2014).

 Digital Manufacturing and Design Institute 

(DMDI), located in Chicago, Illinois. The DMDI will 

focus on the life cycle of digital data interchanged 

among design, engineering, manufacturing, and 

maintenance systems and across supply chain networks. 

Its members comprise leading players in the field, with 

41 companies, 23 universities, and 9 supporting 

organizations.

 Industrial Internet Consortium (IIC), located in 

Needham, Massachusetts. The IIC was founded in 2014 

by AT&T, Cisco, General Electric, IBM, and Intel. It was 

formed to accelerate the development and adoption of 

interconnected machines and devices. The IIC is now an 

open-membership organization that coordinates the 

priorities and enabling technologies of the Industrial 

Internet.

 Internet of Things Consortium (IoTC), located 

headquartered in San Francisco, California, with 

business development offices in New York City. IoTC 

members include hardware, software, and analytics 

companies across a variety of sectors including 

automation and virtual reality. The IoTC educates 

stakeholders about the value of IoT and supports the 

growth of the IoT marketplace.

Manufacturing Enterprise Solutions Association 

(MESA) International, located in Chandler, Arizona. 

MESA International is a global organization of 

manufacturers, IT hardware and software suppliers, 

system integrators, and consultants. Member companies 

cover the range of operations from discrete to process. 

MESA strives to help manufacturers achieve real-time 

visibility into the production process and through the 

optimized implementation of IT and other management 

best practices.

 Control System Integrators Association (CSIA).

CSIA’s mission is to advance the industry of control 

system integration so that industries everywhere have 

access to safe and successful applications of automation 

technology.
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

3-18

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

3.3 TRENDS THAT WILL INFLUENCE SMART 

MANUFACTURING

Several megatrends are shaping the future of smart 

manufacturing.

3.3.1 Connected Factories

Connected factories provide decision makers greater visibility 

into real-time information about production, inventories, and 

workers which allows for greater optimization of existing 

resources and better planning for the future.

GE, Cisco, and other multinational companies are developing 

and deploying industrial internet technologies. Industry experts

have envisioned a factory operating platform and an online 

marketplace for industrial apps.

As more and more devices and machines are built and

retrofitted with internet connectivity, it will become increasingly

economically advantageous for factories to invest in more 

robust communications networks. These machines and devices 

will not only capture and stream data that can be used in 

higher level analyses, but they will also communicate directly 

with other machines and respond autonomously to incoming 

data streams.

3.3.2 Big Data Storage and Analytics

As factories become more connected and sensors more 

prevalent, manufacturers will need to efficiently collate large 

amounts of digital information and to apply more complex data

analysis techniques such as machine learning and data mining 

in addition to more established analysis techniques.

Sensors are moving from discrete output to continuous output, 

which ultimately provides more value but in the short term is a 

challenge for manufacturers as they try to determine the 

appropriate frequency with which to store and analyze sensor 

data. Additionally, there will be other data streams feeding in 

from the supply chain, machines, and workers that will need to 

be managed and used to optimize production systems.

Cloud computing is an enabling technology that could make big 

data storage and analytics more accessible for SMEs. To take 

advantage of big data analytics, manufacturers will not 

necessarily have to purchase and maintain their own data 

storage and data processing equipment. Instead, 
Section 3 

— Smart Manufacturing Trends and Technology Gaps 3-19

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

manufacturers can rent it on an as needed basis from cloud 

service providers. Although there are potentially large economic 

benefits associated with cloud computing, perceived risks about 

data security and IP protection may inhibit its use.

3.3.3 Demand-Driven Supply Chain Optimization

Enabled by improvements cloud-based processing speeds and 

the digitization of supply chain information, demand driven 

supply chain optimization is becoming more of a possibility. 

Using real-time information about downstream changes in the 

supply chain, supply chain partners can better adapt to 

unexpected changes in demand. This can yield benefits such as 

reducing inventory, improving forecasting, reducing 

transportation costs, and improving customer satisfaction.10

While supply chain optimization is beyond the scope of our

study, it is nonetheless an important overarching trend that will 

shape smart manufacturing within the factory walls.

Distributed manufacturing, perhaps a more futuristic trend, 

related to demand driven supply chain optimization. Distributed 

manufacturing, also referred to as the localization of 

production, describes a manufacturing paradigm in which raw 

materials and production processes are decentralized and final 

products are manufactured very close to the end customer.11

Distributed manufacturing will require large amounts of 

computing power and advanced optimization techniques that do 

not currently exist today.

Additive manufacturing could be a potential enabler of 

distributed manufacturing, although only certain types of 

products can currently be manufactured using additive 

methods.12 Proponents of distributed manufacturing approaches 

believe that it may yield less waste compared with more 

centralized production, however raw materials like plastics and 

metals will still need to be transported across the world in order 

for distributing manufacturing to take place. Furthermore, the 

economics of distributed manufacturing may only work for 

certain kinds of products.

 10 http://www.bcg.com/documents/file106861.pdf 11 http://www.weforum.org/agenda/2015/03/emerging

-tech-2015-

distributed-manufacturing/ 12 See the NIST companion study on additive manufacturing for 

additional discussion.
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

3-20

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

3.3.4 Increasing Awareness of and Interest in Smart 

Manufacturing

The prevalence of industry consortia that are starting to 

coalesce around smart manufacturing issues indicates that 

there is increasing private sector awareness of and interest in 

smart manufacturing around the world. Government-funded 

initiatives such as the SMLC and DMDI in the United States and 

Industry 4.0 initiatives in Europe show that the public sector is 

also playing an active role in supporting the development and 

adoption of smart manufacturing technologies.

3.3.5 Other Disruptive Manufacturing Technologies

Advanced robotics technology and additive manufacturing 

technology are expected to be disruptive for manufacturing.13

As these technologies gain widespread adoption, manufacturers 

will need to consider how to more effectively leverage these 

technologies within a smart manufacturing approach.

3.4 NEED FOR ADDITIONAL DEVELOPMENT OF 

TECHNOLOGY INFRASTRUCTURE TO 

SUPPORT SMART MANUFACTURING

This section discusses the relative importance of smart 

manufacturing capabilities and identifies the areas where 

additional development is needed in the six smart 

manufacturing capability areas outlined in Section 2. Section 5 

through Section 10 focus individually on the six capability 

areas, providing greater detail on specific technical needs, 

market barriers to their development, and roles for NIST. 

Appendix C includes two vignettes that provide examples of 

specific needs and opportunities.

3.4.1 Relative Importance of Capabilities

Respondents were asked a series of questions to elicit their 

opinion on the most important capabilities related to the use of 

smart manufacturing technologies and processes. They ranked 

the capabilities groupings on a scale of importance of 1 to 5, 

with 5 being most important. Figure 3-2 shows the scoring for 

all respondents and for developers and users separately.

 13 See the NIST companion study on robotics for additional discussion.
Section 3 — Smart Manufacturing Trends and Technology Gaps

3-21

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Figure 3-2. Average Importance of Each Smart Manufacturing Capability, by Type of 

Respondent

3.3 4.0 3.9 3.9 3.4 3.1

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Managing digital

data streams

through models

Enhanced

sensing and

monitoring

Seamless

transmission of

digital

information

Advances in

analyzing data

and trends

Efficiently

communicating

information to

decision makers

Determining

required action

and

implementing

action

Share of responses

Developers

3s and below 4s and 5s Mean score

3.4 3.9 3.9 3.8 3.0 3.6

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Managing

digital data

streams through

models

Enhanced

sensing and

monitoring

Seamless

transmission of

digital

information

Advances in

analyzing data

and trends

Efficiently

communicating

information to

decision makers

Determining

required action

and

implementing

action

Share of responses

Users

3s and below 4s and 5s Mean score
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

3-22

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Respondents scored all of the categories as important (as 

judged by an average above 3.0), with the highest scores given 

to sensing and monitoring and seamless transmission of digital 

information. The next highest scored was the need for 

advances in capabilities to analyze data and trends.

Responses were relatively similar across developers and users—

with the exception that developers focused more on the 

importance of communicating information to decision makers, 

and users focused more on how to use the information to 

generate implementable actions.

The relatively high scores for importance across all of the 

capability areas supporting smart manufacturing highlight how 

interdependent the different functionalities are across the 

spectrum of smart manufacturing activities. Interviewees said 

that enhanced sensing capabilities are needed but will only add 

value if they are accompanied by cost-effective and secure 

transmission of the information. Similarly, the growth and 

availability of real-time digital information on manufacturing 

activities is only as valuable as the ability to analyze the 

information. Thus, in many ways the value of smart 

manufacturing systems is a function of the weakest link in the 

chain.

3.4.2 Barriers to the Adoption of Smart Manufacturing 

Technology

Respondents were asked to identify the barriers to adopting 

smart manufacturing and realizing the full benefits of the 

capabilities described in this section. The barriers named were 

associated with adopting existing smart manufacturing 

technologies as well as implementing the necessary 

enhancements in the technology infrastructure to make smart 

manufacturing technologies more viable, functional, and cost 

effective in the future.

Table 3-2 provides the categories of barriers most frequently 

identified during the interviews. Frequency of response is 

provided for all interviews as a single group, as well as 

separately for developers and users. Users are further broken 

out into discrete and process manufacturers.

Financial issues were the most commonly cited barrier to 

adoption of smart manufacturing technologies and processes. 

One end user stated that financial returns are paramount 

Interviewees said that 

enhanced sensing 

capabilities are needed 

but will only add value 

if they are 

accompanied by costeffective and secure

transmission of the 

information. Similarly, 

the growth and 

availability of real-time 

digital information on 

manufacturing 

activities is only as 

valuable as the ability 

to analyze the 

information. Thus, in 

many ways the value 

of smart manufacturing 

systems is a function of 

the weakest link in the 

chain.
Section 3 — Smart Manufacturing Trends and Technology Gaps

3-23

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

unless his firm is forced by regulations or customer 

specifications to adopt new technologies. In general, users cited 

low ROI—in part driven by cost issues associated with legacy 

systems, vendor lock in, and age of plants—as the greatest 

barrier to adoption.

A direct challenge to ROI expressed by one chemical process 

manufacturer was that “benefits have to be continuously 

greater than maintenance costs, and [smart manufacturing] 

instruments consume resources for supporting and 

maintaining.” In other cases, while adequate ROI may be 

feasible, users mentioned that uncertainty about new 

technologies makes it difficult to understand and justify ROI, 

and this aversion to uncertainty acts as a barrier to adoption. A 

small machine tool company suggested that other SMEs may 

find it hard to justify investment in smart manufacturing 

technologies unless their companies are confident that they will 

be manufacturing a significant amount of repeat products in the 

future.

Table 3-2. Percent of Sample Citing Each Barrier to Adoption, by Type of Respondent

Barrier

Percent

All Developers Users

Users –

Discrete

Users –

Process

ROI, lack of financial resources 

(low margins)

46 30 59 60 60

Legacy systems, vendor lock in, 

age of plants

33 17 45 40 53

Lack of technical resources and 

knowledge

31 22 38 40 33

Small business, size of plant 27 30 24 13 33

Cultural and organizational 

barriers

27 43 14 13 20

Uncertainty, risk 19 17 21 27 13

Technical issues 15 9 21 33 7

Data security (malicious and IP 

issues)

15 13 17 20 13

Long machine-plant lifetimes, cost 

of retrofit

13 9 17 7 27

Logistical, downtime, “hassle” 

factors

12 4 17 20 13
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

3-24

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Standards issues 8 9 7 7 7

Regulation 8 13 3 0 7

Market structure, market issues 6 0 10 20 0

Safety 4 4 3 0 7

Future growth industries and 

applications

4 9 0 0 0

Lack of government support 2 4 0 0 0

Product mix 2 0 3 7 0

Concerns about financial issues were followed by a lack of 

technical resources and knowledgeable staff needed for 

implementation. One process manufacturer noted, “I spend so 

much of my time in upkeep, migrating and supporting systems. 

There is really no allotment of my time to how can we look for 

technology that might be a step-change improvement.” Other 

users pointed out that personnel with specialized knowledge are 

scarce because of the aging/retiring workforce and the lack of

multidisciplinary engineering programs. For smaller 

manufacturers in particular, the lack of technical resources and 

knowledgeable staff appears to be a major barrier.

Top barriers cited were relatively consistent across discrete 

parts and process manufacturers, with legacy systems and age 

of plants being more of a barrier to adoption in the process 

industries.

In contrast, developers did not see financial and legacy issues 

as significant barriers; they had a more optimistic view of their 

products’ cost effectiveness and ease of adoption. Developers 

commonly cited cultural and organizational barriers to adopting 

new technologies and processes—more specifically, internal 

company politics, inertia of older workers and their resistance 

to change, and a hesitancy to invest in technology that may 

supplant some types of jobs.

Specific examples of barriers limiting adoption, as well as the 

underlying market failures, are provided in the following 

sections discussing the individual capability areas.
Section 3 — Smart Manufacturing Trends and Technology Gaps

3-25

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

3.4.3 Need for Additional Technology Infrastructure 

Development

To identify potential areas where NIST could effectively 

collaborate with and complement industry, interviewees were 

asked to characterize the level of additional development 

needed for each of the six smart manufacturing capability areas 

and corresponding infrastructure technologies. Interviewees 

were asked to classify each capability according to a scale of 

“low,” “medium,” or “high” level of additional development 

needed. Figure 3-3 summarizes the responses to the level of 

additional development needed, by capability.

As shown in the figure, advances in analyzing data and trends 

was characterized by 49% of interviewees as needing a high

level of additional development. Next, 37% of interviewees 

rated seamless transmission of digital information as needing a 

high level of additional development. Relative to the other 

needs, the least important need for further development was 

communicating information to decision makers—only 16% of 

interviewees gave it a high rating.

Figure 3-3. Level of Additional Technology Infrastructure Development Needed for Smart 

Manufacturing Capabilities

Note: All columns sum to 100%.
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

3-26

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Another way to consider these responses is by adding together 

the medium and high levels of additional development, thus 

excluding any low ratings for additional development needed. 

When considering the data in this manner, the majority of 

interviewees stated that each capability required a medium or 

high level of additional development.

The perceived level of additional development varies based on 

position in the value chain. Table 3-3 focuses on the percentage 

of interviewees who characterized each capability as needing a 

high level of additional development, broken out by technology 

developer perspective and end-user perspective. End-user

responses are further segmented into process manufacturers 

versus discrete parts manufacturers.

Developers placed the highest need for additional development 

on seamless transmission of digital information, whereas end 

users placed the highest need on advances in analyzing data 

and trends. In general, developers struggle to get their 

equipment to talk with other developers’ equipment, and this 

consumes much of their focus. Developers’ customers, the end 

users, also want seamless communications, but are more 

concerned with better data analytics to obtain greater value 

from their smart manufacturing investment.

Process manufacturers and discrete manufacturers tended to 

agree in general; however, discrete manufacturers cited a

Table 3-3. Percentage High Level of Additional Development Needed, Developers versus End 

Users 

Interviewee 

Type

Percent, %

Managing 

Digital Data 

Streams 

through 

Models

Enhanced 

Sensing 

and 

Monitoring

Seamless 

Transmission 

of Digital 

Information

Advances in 

Analyzing 

Data and 

Trends

Efficiently 

Communicating 

Information to 

Decision 

Makers

Determining 

Required 

Action and 

Implementing 

Action

Developers 6 25 44 38 13 25

End users 26 30 33 56 19 30

Process 8 38 31 62 15 23

Discrete 40 27 33 53 20 33

Overall 19 28 37 49 16 28

Developers placed the 

highest need for 

additional development 

on seamless 

transmission of digital 

information, whereas 

end users placed the 

highest need on 

advances in analyzing 

data and trends.
Section 3 

— Smart Manufacturing Trends and Technology Gaps 3-27

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

much higher level of development needed for managing digital 

data streams through models. This reflects the fact that process 

manufacturers do not operationalize product information in the 

same way that discrete parts manufacturers do. Specifically, 

the 3D modeling and parts focus of this capability was less 

relevant for process industries. Simulation models of plant 

layouts seemed to be more relevant to process manufacturers. 

One industrial gas process manufacturer simply stated that 

“CAD models are not resonating with me.” However, a building 

materials process manufacturer expanded, “This is not as 

relevant as it would be to a discrete industry... this is less 

important for process industries. However, simulation models of 

the manufacturing process and how it behaves is very 

important from a plant optimization standpoint.”
4

-1

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

Economic Impacts

Total economic impacts associated with meeting technology 

infrastructure needs for smart manufacturing are estimated to 

be $57.4 billion. This represents, on average, a 3.2% reduction 

in the shop floor cost of production. Note that benefits of 

adopting smart manufacturing processes, in general and not 

just for those tied to technology infrastructure gaps, would 

provide even more economic value for the economy.

Section 4 describes how impacts were calculated. It also 

includes a discussion of the relative importance of smart 

manufacturing capability, barriers to adoption, and areas where 

additional technology infrastructure development is needed. 

Later sections explore key areas of need in greater detail.

4.1 FIRM-LEVEL END-USER IMPACTS

During the interviews, respondents were asked to estimate the 

percent change in inputs (capital, labor, energy, and materials,

referred to as KLEM) and the effects on outcomes (throughput, 

utilization, and product quality) resulting from the potential 

adoption of improved smart manufacturing technologies and 

processes.

These impacts were said to be a result of greater ability to 

holistically analyze data, which leads to more efficient, 

optimized production. Examples of benefits included:  freeing up staff time that is currently spent on 

connectivity and collating data together;

 reduction in energy use through active energy 

management;

 reduced downtime and increased uptime, resulting in 

increased throughput and productivity;

 improved security, reduced risk of IP or safety issues; 

and
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

4-2

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

 reduced integration time (commissioning, debugging, 

etc.).

4.1.1 Estimates of Average Impact by Factors of Production 

(KLEM)

Table 4-1 shows the average percent change in factor inputs 

provided by respondents. All users indicated that the greatest 

potential for cost savings would be in labor (at 12%) and 

energy (at 13%). Capital and materials potential savings were 

estimated at up to 5% each. Note that the percent change in 

capital costs reflects net capital expenditures, which include 

both additional capital expenditures to implement smart 

manufacturing and capital savings due increased capital 

utilization and life expectancy.

Figure 4-1 shows percent changes in factor inputs for discrete 

and process manufacturers. The results indicate that, compared 

with process industries, discrete industries have the potential 

for greater percent change impacts, likely because process 

industries tend to be more mature, with older facilities and 

longer equipment life expectancies. In addition, process 

industries typically have less flexibility in making modifications 

because of the rigid nature of their systems. Note that the 

average change in capital expenditures was shown to increase 

slightly for process industries, reflecting legacy issues from 

adopting smart manufacturing technologies.

Table 4-1. Average Percent Change in Factor Inputs

Factor Input Mean Impact

K: Capital −5%

L: Labor −12%

E: Energy −13%

M: Materials −5%
Section 4 —Economic Impacts

4-3

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Figure 4-1. Average Percent Change in Factor Inputs: Discrete versus Process Industries

4.1.2 Distribution of Impacts across Respondents

Figure 4-2 shows the responses from the interviews broken out 

by each factor of production. Furthermore, each bar is colorcoded based on whether the response came from a discrete 

manufacturer (blue) or a process manufacturer (orange). 

Whereas the y-axis varies across the panel of graphs, one can 

quickly glean these points: although labor, energy, and 

materials are unambiguously unchanged or reduced due to 

enhanced smart manufacturing technology infrastructure, the 

impact on capital is more varied, with some users—both 

process and discrete—citing net capital cost increases due to 

increased adoption of smart manufacturing technology.
Smart Manufacturing

Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

4-4

7 00 - GCR.16 http://dx.doi.org/10.6028/NIST. This publication is available free of charge from: 

Figure 4-2. Range of Cost Impacts on Shop Floor Expenditures Associated with Enhanced Infratechnology, by Factor Input

Capital (K)

Mean for discrete industries = -9%

Mean for process industries = +1%

Labor (L)

Mean for discrete industries = -15%

Mean for process industries = -9%

(continued)
Section 4 

— Economic Impacts

4-5

7 00 - GCR.16 This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.

Figure 4-2. Range of Cost Impacts on Shop Floor Expenditures Associated with Enhanced Infratechnology, by Factor Input 

(continued)

Energy (E)

Mean for discrete industries = -16%

Mean for process industries = -10%

Materials (M)

Mean for discrete industries = -5%

Mean for process industries = -4%
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

4-6

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

4.1.3 Aggregate Firm-Level Impacts

For each respondent, the percent change in factor inputs was 

then applied to actual expenditures on the inputs to obtain a 

combined aggregate reduction in production costs. This was 

conducted for each user interviewed, obtaining/calculating 

applicable firm-level factor expenditures as described in 

Section 3. Aggregating the firm-level factor impacts yields a 

single (weighted average) percent change in production costs. 

Figure 4-3 shows the aggregate firm-level impacts for each 

respondent.

As shown in Figure 4-3, firm-level impacts vary. A small group 

of firms indicated savings close to 20% of production costs; 

after those four companies, the estimated impact on production 

costs is higher for discrete parts manufacturers than for process 

manufacturers.

Figure 4-3. Range of Total Composite Cost Impact on Shop Floor Expenditures Associated 

with Enhanced Infratechnologya

a

Note: Composite cost impacts for each user are a weighted average of the impacts provided for each factor input. 

Weights are determined using the industry average factor input cost-to-sales ratios based on the NAICS code of 

each respondent.
Section 4 — Economic Impacts

4-7

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

4.2 NATIONAL-LEVEL END-USER IMPACTS

Average percent change impacts for discrete parts and process 

manufacturers were then applied to national factor 

expenditures (KLEM) to calculate potential national-level 

impacts.

Respondents were asked about the share of their industry to 

which the smart manufacturing benefits they described are 

applicable. As shown in Table 4-2, respondents view smart 

manufacturing as applicable for the majority of firms in their 

industry.

4.2.1 National Expenditures on Factors of Production (KLEM)

Figure 4-4 shows the distribution of national factor 

expenditures by factor of production for process and discrete 

industries. Materials make up the majority of national factor 

expenditures for process and discrete industries, with 76% and 

63% of total expenditures, respectively. Compared with 

discrete industries, process industries require more than three 

times the energy as a percentage of total factor expenditures 

costs. On the other hand, at 26%, labor is also a substantial 

cost component. Capital costs are important to both process 

and discrete industries; however, they will make up a much 

smaller percentage of the total costs. Using the discrete parts 

industries as an example, 1% cost savings for material will 

have a much larger absolute dollar impact than a 1% cost 

savings for capital or labor.

Table 4-2. Industry Applicability Factor

Discrete Parts 

Manufacturers

Process 

Manufacturers All Users

Measure Mean Impact Mean Impact Mean Impact

Industry Applicability Factor 93% 95% 94%
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

4-8

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Figure 4-4. KLEM Share of Cost Basis for Process and Discrete Industries

Note: Only national accounts related to manufacturing activities were are used in the analysis.

4.2.2 National Impact Estimates by KLEM

National impact estimates are calculated at the four-digit NAICS 

level. Industries included are listed in Table 2-1 Industry-level 

percent changes are applied to KLEM expenditures for each 

industry to calculate national impact estimates.

Following the procedures outlined in Section 3, we scaled out 

firm-level impacts to the national-level using a series of 

informed assumptions about how the responses in our sample 

mapped to U.S. industries. Table 4-3 illustrates our calculations 

with a stylized example for a particular NAICS code. These 

same calculations were carried out for the NAICS codes in our 

sample. The estimates for each NAICS code derived in Step #5 

in Table 4-3 were then summed together to obtain estimates of 

national economic impacts.

Table 4-4 provides impact estimates aggregated to process 

industries and discrete parts industries.
Section 4 — Economic Impacts

4-9

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Table 4-3. Stylized Example of Scaling Sample Impacts to the National Level, End User

Step Description Figures and Calculation(s)

Step #1—Begin with 

KLEM percentage 

impact responses.

Percentage changes in KLEM 

factor inputs are recorded during 

the interview.

K: +25%

L: -10%

E: +10%

M: -5%

Step #2—Calculate 

applicability-weighted 

cost impacts.

Multiply the industry applicability 

factor from the interview with 

the percentage changes in KLEM 

factor inputs to derive the 

applicability-weighted cost 

impacts.

Applicability factor = 50%

K: +25% x 50% = +12.5%

L: -10% x 50% = -5%

E: +10% x 50% = +5%

M: -5% x 50% = -2.5%

Step #3—Assign an 

industry code and 

average responses 

from the sample.

Assign a 3- or 4-digit NAICS 

code based on the interviewee’s 

company and average the 

applicability-weighted cost 

impacts from Step #2 with other 

responses from the sample that 

are categorized with the same 

NAICS code, if applicable.

The NAICS code that best describes this 

interviewee’s company is NAICS 3329 

Other fabricated metal product 

manufacturing.

No other responses in the sample have 

the same NAICS code, so the average 

responses are simply the figures from 

Step #2.

K: +12.5%

L: -5%

E: +5%

M: -2.5%

Step #4—Estimate 

KLEM expenditures for 

the industry.

Using ASM and BLS data on 

KLEM-to-sales ratios, estimate 

the KLEM expenditures for the 

3- or 4-digit NAICS code. Sum 

the individual KLEM 

expenditures to estimate the 

total national factor expenditure 

for the NAICS code.

2013 Industry Sales from ASM 

= $75.4 billion

K: 2.8% x $75.4 billion = $2.1 billon

L: 13.3% x $75.4 billion = $10.0 billon

E: 1.7% x $75.4 billion = $1.3 billon

M: 31.1% x $75.4 billion = $23.5 billon

Total KLEM national factor expenditure 

= $36.9 billion

Step #5—Calculate 

KLEM expenditure 

impacts and composite 

cost impact on 

national factor 

expenditures.

Calculate KLEM expenditure 

impacts using average 

percentage impacts from Step 

#3 and KLEM expenditures from 

Step #4. Sum together each 

KLEM expenditure impact from 

Step #5 to estimate the 

composite cost impact.

K: +12.5% x $2.1 billon = +$265 million

L: -5% x $10.0 billon = -$502 million

E: +5% x $1.3 billon = +$63 million

M: -2.5% x $23.5 billon = -$586 million

Composite cost impact = -$760 million

Step #6—Calculate 

composite cost impact 

in percentage terms.

Divide the composite cost 

impact from Step #5 by the

total national factor expenditure 

from Step #4.

Percentage composite cost impact

= -$760 million/$36.9 billion = -2.1%
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

4-10

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Table 4-4. Annual Economic Impact Summary, Process versus Discrete

Industry Data, 2013 Cost Impacts, $ Billions

Type of 

Industry

Sales,

$ Billions

KLEM 

National 

Factor 

Expenditure,

$ Billions K L E M Total

Percentage 

Savings in 

Shop Floor 

Production 

Costs

Process 2,423.3 1,281.1 −1.1 −4.9 −3.9 −16.7 −26.6 −2.1%

Discrete 1,320.5 537.0 −5.1 −17.2 −1.8 −6.7 −30.8 −5.7%

Total 3,743.9 1,818.1 −6.2 −22.1 −5.7 −23.5 −57.4 −3.2%

Note that KLEM expenditures used in the analysis represent 

approximately one-third of the industry sales, reflecting that 

smart manufacturing technologies and processes are applicable 

for only a portion of the manufacturing sector activities.

Total Annual economic impacts associated with smart 

manufacturing are estimated to be approximately $57.4 

billion, and will accrue for many years. This represents, 

on average, approximately 3.2% reduction in the shop

floor cost of production. Discrete parts manufacturing 

accounts for $30.8 billion and process manufacturing $26.6 

billion. Discrete parts industries had larger potential impacts, 

although process industries represented a larger share of the 

manufacturing sector.14

Figure 4-5 shows cost savings for each factor of production, by 

discrete parts and process manufacturers, and Figure 4-6 the 

relative distributions. The largest impact was associated with 

labor savings in discrete manufacturing sectors. In the process 

industries, the greatest savings was in materials (feedstock), 

followed by energy and labor.

 14 End users were also asked about the impact that smart 

manufacturing capabilities and associated technology infrastructure 

would have on their sales. Most interviewees were not able to draw 

an inference between the capabilities and increases in their sales so 

we did not attempt to quantify a sales impact.
Section 4 — Economic Impacts

4-11

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Figure 4-5. Annual Cost Savings by Factors of Production (millions of USD) for Process and 

Discrete Industries

Figure 4-6. KLEM Shares of Composite Cost Impact for Process and Discrete Industries
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

4-12

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

4.2.3 National Impact Estimates by Capability Gap Analysis

Figure 4-7 shows the distribution of total KLEM impacts 

apportioned to each of the six capabilities. The distribution of 

impacts reflects the average importance scores provided by end 

users. Enhanced sensing and monitoring, seamless 

transmission of digital information, and advances in analyzing 

data and trends all have potential annual impacts greater than 

$10 billion. All of the identified capability areas were estimated 

to have annual benefits of over $7 billion, reflecting the 

generally overall importance score of all the categories.

The relative magnitude across all the categories is evidence 

that there are strong complementarities or “interaction effects” 

among the capabilities. For example, it was stated that 

enhanced sensing and monitoring was only as important as the 

ability to transmit, analyze, and communicate the information.

Figure 4-7. Total Annual Cost Impact, by Capability (Millions of 2013 USD)
Section 4 

— Economic Impacts 4-13

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

4.3 NATIONAL-LEVEL DEVELOPER SALES 

IMPACTS

Developers were asked about the impact on their sales and the 

value of the products and services they provide in a future 

scenario with an enhanced technology infrastructure for smart 

manufacturing. Most developers responded that sizable 

increases in sales were not likely, in large part because 

manufacturing companies (users) did not have the budgets to 

boost spending on smart manufacturing.

However, developers did say that their products and services 

would be better and would provide added value for their 

customers, which, ultimately, could translate into additional 

revenue and profits.

This section quantifies the market size for developers of smart 

manufacturing products and services in the United States. Our 

findings on average percent changes in sales were applied to 

the market sales figure to obtain an estimate of potential 

market growth.

4.3.1 Market Size

We estimated the size of the U.S. smart manufacturing 

technology market using a series of market reports on the 

smart factory market, industrial control and automation, and 

the industrial internet (see Table 4-5). Our interviews revealed 

that these markets are highly interrelated and overlap 

substantially. Therefore, we interpreted each of these market 

reports as within the scope of smart manufacturing technology. 

One of these reports was from 2012, but we included this one 

to be conservative since there has likely been some growth in 

the market since that 2012 report was published. Furthermore, 

we took an average of these reports to be conservative about 

the size of the market for smart manufacturing technology.

Given that the market size estimates are global in scope, we 

used two distinct data points for determining the share of the 

global market that can be attributed to the United States. The 

first data point came from a Cisco report on how value from the 

IoE will be distributed across countries. The U.S. value at stake 

was 32.4% of the global value at stake. This is a rough proxy 

for how smart manufacturing technologies are distributed 

globally. A more direct method, which yielded a remarkably
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

4-14

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Table 4-5. Market Reports on Global Smart Manufacturing Revenues

Market

Global Market 

Size, $/billion Year Source

Smart factory market 50.1 2015 MarketsandMarkets

Industrial control and factory automation market 146.2 2015 MarketsandMarkets

Global factory automation and industrial control 

solutions 

156.9 2015 Mordor Intelligence

Spend on industrial internet technology 20.0 2012 Wikibon

similar percentage, involved gathering sales, product, and 

geographic information for a dozen of the largest smart

manufacturing players in the world. This allowed us to estimate 

that approximately 35% of global smart manufacturing sales 

occur in the United States. We averaged these two data points 

together to obtain 33.7%, which is the number we applied. This 

yielded a U.S. smart manufacturing market of $31.5 billion.15

4.3.2 Total Industry-Level Impacts for Developers

Of the 20 developers that we spoke with, 16 provided 

quantitative estimates of the impact that enhanced technology 

infrastructure would have on sales. Of the four individuals who 

did not provide quantitative estimates, three said that sales

would be directly impacted, but that it was too difficult to 

quantify. Therefore, our estimate is likely conservative because 

we excluded these respondents.

Of the 16 quantitative answers, the distribution was skewed 

toward zero impact on sales, as 13 developers did not believe 

the enhanced technology infrastructure would lead to an 

increase their sales of smart manufacturing products and 

services. However, several developers said that although their 

sales might not increase significantly, an enhanced technology 

infrastructure would enable them to provide greater valueadded activities. They would be able to move beyond basic 

 15 We also explored a more direct approach for determining the smart 

manufacturing market size. Using the sales, product, and 

geographic information gathered for a dozen of the largest players 

in the market, we calculated a lower bound estimate for the market 

for U.S. smart manufacturing technologies of $27.6 billion.

Therefore, we are confident that our estimate of $31.5 billion for 

the market size is conservative, because it is not much larger than 

this lower bound.
Section 4 

— Economic Impacts 4-15

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

interoperability/communications issues and focus on enhanced 

functionality, which if increasingly valued by used, could lead to 

new products and markets. However, most developers were 

reluctant to speculate/quantify what the impact might be on 

sales.

Three respondents provided quantitative estimates. However, 

one response was a severe outlier, so we removed it from our 

analysis. Of the final 15 quantitative answers, the average 

increase was 16% and the median was 0%.

Using the market value of $31.5 billion calculated in Section 

4.3.1 as a baseline for the total value of smart manufacturing 

technology in the United States, we multiplied by the average 

percentage increase of 16% to yield a $5.0 billion increase in 

sales for the U.S. smart manufacturing technology market that 

is associated with enhanced technology infrastructure.

Assuming that the purchases of smart manufacturing 

technology are proportional to estimated benefits, we expect 

that the discrete parts industries will buy $3.3 billion of the new 

technology and the process industries will buy the remaining 

$1.7 billion. We also investigated if benefits varied based on the 

size of industries or the size of vendors. However, we found no 

evidence of any such trends, in part due to the limited sample 

size.
5-1

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Managing Digital 

Data Streams 

through Models

As discussed in Section 2, the core of smart manufacturing is 

the data feedback loop that runs through each level of the 

factory systems—product creation, operational planning, and 

enterprise management (see Figure 3.1). Sections 5 through 10 

individually discuss industry needs expressed for each of these 

steps in the feedback loop. This section examines the modeling 

phase of the data feedback loop (Figure 5-1).

Data models are used to represent material behavior, product 

design, and process flow digitally to simulate and test different 

scenarios before actually building a product or arranging a 

factory floor. Data models hold key information that is 

transmitted electronically so that products can be 

manufactured. Machinery and corresponding maintenance 

guides are “virtualized” and placed on mobile devices, allowing 

quick and easy access to maintenance instructions. Predictive 

models, using key performance indicators from equipment and 

processes, generate and manage data.

Figure 5-1. Smart 

Manufacturing Data 

Feedback Loop: Model
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

5-2

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Industry characterized data model creation as the second 

lowest capability in terms of importance score. Needs varied by 

industry and by segment of the supply chain. Electronic data 

exchange for CAD/CAM has been the focus of industry (and 

NIST) for the past decade, and developers indicated that they 

were working on products that accommodated multiple data 

formats. However, respondents still identified this area as a 

continued source of frustration. In general, there is tension 

between competitive positioning with proprietary systems or 

formats versus the move toward open-source system, which 

support competition and ease of entry into the modeling 

market.

5.1 BARRIERS TO ADOPTION AND ASSOCIATED 

MARKET FAILURES

The length of time and difficulty in developing high-fidelity 

process models can be a barrier to the use of process 

simulation. Creating realistic high-fidelity process models can 

take up to a year, and can be so complex that they require 

PhD-level employees to create them and high-performance 

computing systems to run them; moreover, in many instances, 

the end product still fails to provide accurate modeling.

End users indicated that models need not be “too realistic,” and 

in many instances simpler models are sufficient. For example, it 

was suggested that if a model could capture 90% of behavior, it 

would be preferred over more complex models that capture 

95%. In some instances, higher-fidelity process models may 

not be quickly adopted because they are more difficult to 

manage. It is easier for someone to pass on if it is simpler. 

There is a need for a metric for model efficiency. For example, 

how well does the model reproduce the data relative to how 

complex the model is or the amount of time it takes for 

adoption? In addition, minimizing the number of parameters 

would be preferable. Models with “parameters without limit 

becomes a thing that no one can really understand.”

Industry also needs more open-source platform development to 

allow large and small companies alike to rapidly innovate, 

express needs, and develop common platforms. Most large 

software companies want to sell end-to-end automation 

packages and pursue this approach as a competitive strategy. 

However, this limits flexibility. A marketplace is needed to 
Section 5 — Managing Digital Data Streams through Models

5-3

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

encourage startups in this space and related big data 

application. SMLC has the vision to create an open source cloud 

infrastructure to allow people to create code and solutions that 

are plug and play. Enabling multiple businesses to create 

modeling or other manufacturing software on an open source 

platform provides buyer choice, which drives competition and 

lowers product costs.

5.2 TECHNOLOGY INFRASTRUCTURE NEEDS

Interviewees were asked to comment on the areas that needed 

additional development. We summarize interviewee comments 

in tables that describe the percentage of interviewees that 

identified a need (Table 5-1). These tables summarize openended conversations about the technology infrastructure needs, 

and therefore should be interpreted as a guide to the critical 

issues rather than a strict prioritization. The most commonly 

cited category are discussed below.

5.2.1 Identified Needs

Standards

Respondents indicated that there was a need for a broker, 

coordinating with organizations such as ISO and intervening in 

standards groups when multiple camps start to form. The 

rationale is that everyone benefits from technology agnostic 

standards but that companies often try to shape standards in a 

way that maximizes their own welfare, which may or may not 

be aligned with the public interest. In some cases, such actions 

can ultimately slow down the adoption of standards. A 

respondent from the aerospace industry thought that managing 

digital data streams through models was not currently a major 

Identified Needs

Standards

Quality/metrology

New features of CAD/CAM models

Costing capabilities

Metrics

Interoperable models

Table 5-1. Identified

Needs Related to

Managing Digital Data 

Streams through Models
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

5-4

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

limitation, but noted that a common standard for 2D and 3D 

model representations could be helpful.

Quality/Metrology

It was suggested that there was a need for developing 

techniques for measuring the quality of as-built products and 

parts in terms of their variance from underlying digital models. 

Quantifying variance in this context would require the 

systematic comparison of 3D models with their physical 

offspring/reproductions. This concept was mentioned in the 

context of CAD/CAM models for discrete parts manufacturing. 

As the end user stated, “It would be interesting to generate a 

mismatch of the machining process and digital expectation. 

...How close is the actual product to the digital model?”

In contrast, it was also noted that tolerance and inspection 

were important issues, but that industry would likely take a 

lead in solving any challenges in these areas, suggesting only a 

supporting role for NIST in helping industry address these 

issues.

New Features of CAD/CAM Models

Several ideas emerged for how CAD/CAM technology could be 

even more powerful than it is currently. A startup company 

noted a need for better methods for generating 3D 

representations of an entire manufacturing environment. 

Currently, such representations are typically in point-cloud 

format, but something more akin to solid models would be 

more powerful because of the metadata that are paired with 

geometric data.

A developer of machine tools and machine tool software 

suggested a need for a method of tracking materials properties 

that can sync up with tool settings in a smart way.

Other Capability Needs

Several other comments centered on automated costing as a 

helpful tool, but did not offer specific details in this area. 

Integrating automated or rapid costing functions in the design 

process could greatly lower the materials and manufacturing 

costs. However, to enable automated costing requires a 

standardized and accepted vocabulary linking physical 

attributes and manufacturing techniques to materials and 

production costs.

“It would be interesting 

to generate a 

mismatch of the 

machining process and 

digital expectation. 

...How close is the 

actual product to the 

digital model?”
6-1

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Enhanced Sensing 

and Monitoring

Sensors are being integrated into most new machines produced 

today. Although a broad range of sensor capabilities is 

available, in many instances manufacturers are not fully taking 

advantage of these technologies. All study respondents said 

that state estimation of critical machines is an area industry is 

continually pursuing. Manufacturers believe that existing 

sensors are adequate for many applications, and that the most 

crucial need lies in improvement of data analysis capabilities.

Nevertheless, we found that the industry still lacks many 

sensing and monitoring capabilities, and that the existing 

technologies need significant improvement. In addition, 

integrating sensing capabilities into older legacy systems is 

costly, and is often limited by technical constraints. This section 

examines these issues in more detail, and Figure 6-1 illustrates 

the position of sensing in the data feedback loop.

6.1 BARRIERS TO ADOPTION AND ASSOCIATED 

MARKET FAILURES

For many industries, the availability of simple, low-cost sensors 

is not an issue. Many of the laser, acoustic, and vibration 

Figure 6-1. Smart 

Manufacturing Data 

Feedback Loop: Sense
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

6-2

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

sensors needed are commercially 

available. The challenge is 

associated with the 

communications standards, 

interference, security (wireless 

being a security issue), and data 

processing (analysis of big data). 

The areas of need associated with 

the advanced sensors focus on 

operation in extreme environments 

(extreme heat or pressure). For 

these environments, the 

capabilities of specialized sensors 

could be improved.

Self-powered sensors that can 

harvest energy from temperature 

gradients or vibrations are needed. 

They would enable truly wireless 

applications (eliminating both 

communications and power supply 

hard-wiring), lowering the cost of 

implementation and increasing the 

potential for factory flexibility.

For existing process facilities, 

access and cost of installation are 

issues. Installation of new sensors 

as part of routine maintenance 

cycles is possible, but must be well 

coordinated. Often, access to the 

appropriate location for installation 

can be difficult, and retrofits are 

always more expensive than 

installing sensors at the time of 

original construction. It was cited 

that emerging countries like China, which are building large 

numbers of new plants, have an advantage because they are 

able to build in extensive monitoring capabilities at low cost, 

enabling their plants to operate more efficiently. This places the 

United States at a competitive disadvantage—where it is almost 

impossible to site a new refinery or petrochemical plant—hence, 

creating cost and feasibility issues associated with legacy 

The Need for Open-Source 

Communications Platforms 

In the electric utility industry, the 

price of a generation turbine 

ranges from $20 million to $80 

million; thus, predictive capabilities 

to help avoid catastrophic failure 

could have a significant economic 

benefit. There is no shortage of 

companies producing the required 

sensors, analysis software, and 

reporting packages. 

However, the open-source 

communications protocols to link 

individual products do not exist. As 

a result, turbine manufacturers 

have been able to dominate the 

market by providing dedicated 

vertical systems that integrate the 

sensing, communication, and 

analysis. However, these custom 

systems tend to be expensive 

(about $250,000 each), which has 

greatly limited their adoption. If 

open-source communications 

platforms were available, flexible 

systems could be developed at a 

much lower cost (potentially as low 

as $10,000) using readily available 

components from specialized 

developers. See Appendix C for a 

more detailed discussion.
Section 6 

— Enhanced Sensing and Monitoring 6-3

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

systems. Research, information, and/or case studies on how to 

most efficiently upgrade existing plants would be helpful.

Manufacturers said that solutions are needed which are lower 

risk, economically feasible, and have fewer of calibration and 

false-positive issues. If there were standards for reliability, it 

would significantly reduce the risk. Manufacturers feel that, 

currently, standards are loose and should be more stringent. 

For example, if a sensor has too much fluctuation, the time 

period in which the sensor is accurate needs to be extended. 

Tolerances of sensors needs to be improved. If standards were 

produced (and enforced), then developers would be forced to 

comply and publish the tolerance rates; users would then know 

what they are buying at various cost points. Users individually 

conduct testing in their labs, which is very costly and inefficient 

from a social perspective.

In general, sensor developers and vendors are well positioned 

to design, produce, and market new advanced sensors and 

monitoring equipment. However, with manufacturers not fully 

aware or convinced of the benefits, there can be significant 

technical risk for developers in investing in the required R&D. 

In addition, in some instances the developers do not have the 

fundamental technical expertise needed for the enhanced 

sensing capability and/or would not be able to fully appropriate 

the social benefits due to the infrastructure nature of the 

technology development. Similar manufacturers, in many instances, do not have the in

-

house capabilities to fully realize the benefits from enhanced 

monitoring and sensing, even though they have in-depth 

knowledge of their manufacturing processes. For example, one 

company respondent said that:

Wireless sensing is extremely interesting, but we 

have to think about how do we put the 

information in the right context? Where does its 

implementation belong within our organization? 

Where does it make sense to store the data? How 

do you want to store the data? What is the ideal 

sensing frequency? Why should we do this—

because we can? We have to learn what the 

value of that information is and how it can 

benefit us. At the moment that is all blurry.
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

6-4

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

6.2 TECHNOLOGY INFRASTRUCTURE NEEDS 

AND ROLES FOR NIST

A common theme from the interviews was that individual 

sensors and sensing devices are relatively advanced, but at the 

systems level, common typology, architecture, protocols, 

communication, and data structures are “all over the map.”

Almost one-third (28%) of all interviewees indicated a high

need for additional development of sensing and monitoring 

capabilities. Developers and end users appeared to give roughly 

equal weight to this capability, although process manufacturers 

tended to think there was greater room for development than 

discrete parts manufacturers. Table 6-1 lists specific needs 

identified by respondents in general order of importance.

6.2.1 Sensors

Interviewees cited the need for technology infrastructure to 

support development of novel sensors. A pharmaceutical expert 

thought that the industry has a major need for noncontact, 

nondestructive sensors that can measure attributes such as 

density, cohesiveness, uniformity, potency, strength, and 

compressibility. A machine tool developer said that his industry 

needs sensors that can measure coolant concentrations on 

machine tools. And, there was common agreement on the need 

for increased accuracy of sensors—if not self-calibrating, then

they should at least have the capability to detect when 

calibration is off and recalibration is needed. NIST has engaged 

in this type of work in the past when developing sensors for 

monitoring pipelines (NIST, 2008).

As the costs of sensors start to fall and they become more 

ubiquitous, there will be significantly more data for 

manufacturers to process. Hence, there will be a need for 

manufacturers to manage and analyze sensor data in a way 

that maximizes the value of rapidly expanding sensor networks. 

This highlights the interrelated technology infrastructure needs 

across many of the capability areas.

6.2.2 Standards

The need for standards supporting sensing and monitoring was 

mentioned by several companies. Companies pointed out the 

need for sensor interoperability. Sensors from any 

manufacturer should be compatible with the full range of data 

transmission and analysis systems.
Section 6 — Enhanced Sensing and Monitoring

6-5

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Identified Needs

Sensors

Standards

Best practices

Data on fixtures

Predictive maintenance

Security

Wireless

Developers considered security a barrier for enhanced sensing 

and monitoring, noting that there was a need for generating 

better data standards that account for the level of security 

needed in different contexts. One developer stated, “Maybe 

there should be standards for how secure each type of data has 

to be. Sensing data may not need to be as secure because it is 

a different network.”

Enhanced sensing and monitoring will not only make companies 

more efficient, but as a developer posited, more invention will 

happen in the private sector if standards for sensing and 

perception were put in place.

6.2.3 Other Needs

Best practices need to be established for setting up wireless 

sensing and tools/models to enable predictive maintenance. 

Companies suggested that there was a need for algorithms to 

enable more widespread implementation of predictive 

maintenance in the manufacturing sector.16

In addition, there is a need for miniaturized, noncontact, 

nondestructive sensors that measure more diverse parameters. 

For example, in the pharmaceutical industry, for producing 

powders the processes are monitoring temperature and speed 

of mixing, but are really trying to control density, cohesiveness, 

and content uniformity of the powder. These are the attributes 

that impact product quality for the customer. The industry is 

 16 Predictive maintenance (or “state estimation”) was considered by 

some interviewees as belonging in enhanced sensing and 

monitoring and by others interviewees as belonging in advances in 

analyzing data and trends. This example illustrates the fact that the 

six technical areas have a significant degree of overlap.

Table 6-1. Identified

Needs Related to 

Enhanced Sensing and 

Monitoring
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

6-6

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

interested in specialty sensor devices that can perform realtime monitoring of strength, physical structure, and 

compressibility of powders, to determine whether the 

compound will meet the required quality attributes. Better 

optimization of formulation would reduce the time to market 

and minimize the production of throw-away batches.
7-1

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Seamless 

Transmission of 

Digital Information

Interoperability between platforms is not a new issue, but one 

that study respondents still cited frequently as a significant 

source of ongoing inefficiency. Standards developments, such 

as the Standard for the Exchange of Product (STEP), have 

definitely improved interoperability in several areas, but more 

advances are needed. Figure 7-1 illustrates the position of the 

transmit phase of the data feedback loop.

7.1 BARRIERS TO ADOPTION AND ASSOCIATED 

MARKET FAILURES

The most commonly cited technology-specific barriers cited 

during the interviews included networking/communications 

between machines and equipment from different vendors and 

different eras. Significant time is spent setting up machines and 

equipment to talk to a network. Even equipment with an 

“Ethernet-enabled” label is a source of frustration because 

companies do not use a standard communications protocol. 

Figure 7-1. Smart 

Manufacturing Data 

Feedback Loop: 

Transmit
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

7-2

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Specific to CAD/CAM systems, the ability to transfer not just 

geometric data but also the associated metadata would be very 

useful. The metadata design history would be particularly useful 

for assembling systems digitally when a large number of 

individual parts need to be integrated.

Although MTConnect was mentioned as a potential standard 

that could receive widespread adoption, the cost of 

implementing MTConnect could run from $10,000 to $30,000 

per machine—a substantial cost barrier for large manufacturers 

with a lot of equipment. Also, one company commented that 

MTConnect was not able to replace data from operations down 

at the PLC level. One respondent commented, “Everyone has a 

different way of implementing MTConnect, and it is remarkably 

inconsistent. Each machine has a different way to get the 

program. Any standards here would be great. A lot of people 

are still using USB sticks because they can’t figure out another 

way to do it.”

Moreover, use of MTConnect is limited to certain classes of 

machines, and several users suggested that it would be helpful 

if its use could be extended to additional classes of machines. 

Illustrating the lack of interconnectivity between machines and 

monitoring equipment is the common practice of moving files 

between machines via the “sneakernet” (removing electronic 

data and manually walking it to its destination), simply because 

it is easier than doing it electronically. In many instances, the 

cost of achieving interconnectivity is still too high.

Respondents indicated that it has been a struggle to get 

manufacturers to adopt solutions such as MTConnect. Original 

equipment manufacturers (OEMs) and user communities have 

concerns about using this technology, and smaller companies 

are not familiar with the standard. More OEMs need to build the 

capabilities into their equipment, but small OEMs do not have 

the staff capabilities to develop the software to integrate the 

standards into their equipment’s sensing capabilities. In 

addition, some application developers are concerned that this 

will impact/eliminate a large part of their service market. Their 

market is to write and install software to solve communication 

problems. In response, proponents of solutions such as 

MTConnect indicate that the software will not solve all the 

problems; there will still be issues to address, but these 

solutions can help streamline the basic communication issues, 
Section 7 

— Seamless Transmission of Digital Information 7-3

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

enabling service providers to promote and sell more advanced 

applications.

The technology of transmitting data via wireless technology was 

cited as an issue by some, but not all, respondents. Concerns 

centered on interference between transmissions of several data 

channels from multiple pieces of equipment. Neither quality nor 

cost of bandwidth were mentioned as issues. However, security 

was often cited as a concern. Hard (copper) wiring sensors are 

costly and cumbersome. To maximize the use and benefits of 

sensors, wireless communication is optimal. However, sending 

sensitive information wirelessly opens up a wide range of 

security issues that, as mentioned previously, include both 

physical security (sabotage, random disturbances) and 

intellectual property (IP) security. IP security includes 

protecting internal IP as well as the IP of customers.

For example, an interviewee from a petrochemical plant 

compared their production facility to a finely controlled “potential bomb,” stating that if someone could gain control of 

the monitoring system they could influence operation in a way 

that could lead to catastrophic failure and/or explosion. One 

industry observer thought that the high security issue was 

acting as an artificial barrier to the increased adoption of smart 

manufacturing technology, masking more basic issues of “status quo mentality,” risk aversion, and lack of 

skilled/motivated staff. This person stated, “Is high security 

really worth the opportunity costs that SM optimization 

provides?” and went on to describe ways that truly sensitive 

data could be decoupled from potentially less secure flows of 

information.

The most common concerns associated with wireless 

communication across all industries were loss of IP and 

competitiveness. Chemical companies said that the inability to 

stop communications from being transmitted beyond the 

factory gates was a serious concern, and that it influenced how 

they implement their sensing and monitoring systems. This 

concern also applied to how they view future potential use of 

cloud computing.

Cloud computing, data management, and software sharing 

were frequently mentioned as sources of cost reduction and 

increased availability of smart manufacturing solutions, but 

security was the main barrier to adoption. Different industries 
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

7-4

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

and companies have different views on security concerns 

related to the cloud, ranging from loss of IP to the legal 

implications of disclosure of customers’ personal financial and 

health data. Improved security protocols are needed, as is 

certification of existing security products and services.

However, several respondents indicated that data security 

concerns were overstated and, in some instances, were being 

used as an excuse for not adopting wireless communications. 

The typical comment was, for example, “Yes all data is at risk 

and anything can be hacked. But what is really the cost of the 

lost data—who is targeting it? What will they do with it?” They 

questioned whether high security outweighed the opportunity 

costs that smart manufacturing optimization provides, and 

stated that it is simple to disconnect truly sensitive data (IP) 

from logistics or basic operating sensing and monitoring. These 

respondents were proponents of using the cloud—but not for 

everything. It was thought that 80% of storage and large 

computing needs could be served by the cloud without any 

security concerns.

Cost and uncertainty of benefits associated with connectivity 

continue to be barriers, and markets have not been able to 

adequately address these barriers. The total investment costs 

of connecting assets are high, and it is often difficult to 

guarantee or demonstrate the benefits/return. Differences 

across plants and older, legacy equipment make plug-and-play 

solutions difficult to develop. In addition, entrenched vendors 

continue to promote proprietary software and solutions that 

inevitably increase costs and limit adoption.

7.2 TECHNOLOGY INFRASTRUCTURE NEEDS 

AND ROLES FOR NIST

More than one-third of the interviewees stated that seamless 

data transmission required a high level of additional 

development. There were many substantive comments on how 

NIST could play an active role in this area.

Table 7-1 lists particular areas of research that need to be 

advanced to accelerate the development and realization of truly 

seamless transmission of digital information. Data formats, 
Section 7 — Seamless Transmission of Digital Information

7-5

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Identified Needs

Data formats, standards, and protocols

Interoperability of systems

Machines

Data security protocols

Bridging information technology with operations technology

Typology, architectures, standards, structures

Facilitate consortia

Sensors

Closed-loop systems

Test beds

Programming tools

standards, and protocols, along with related interoperability 

issues, were the most commonly cited areas in which NIST 

could play a role.

7.2.1 Data Formats, Standards, and Protocols

The most critical need in this area involves the structure of data 

streams coming from distributed and disparate sources. 

Currently, it takes substantial amounts of skilled labor to collate 

data from systems, machines, and devices that are physically 

spread across factory floors. Currently, no single organization 

has the private sector has the capability or incentive to provide 

critical technical inputs and guidance on the standardization of 

data formats, protocols, and configurations. One end user in 

the heavy equipment sector pointed out the difficulty in 

bringing together different forms of data: “In the past we had a 

lot of point data. Now, we’re collecting 2D data from radars, 

and we have 3D data from reverse-engineering tools and 

computed tomography (CT)17 data. It is more than just 

seamless transmission. Data structure is the key.”

 17 Computed tomography, or CT, is just one of several 3D scanning 

technologies that can be used in the reverse-engineering process. 

For more information, refer to https://www.asme.org/engineeringtopics/articles/modeling-computational-methods/the-rise-ofreverse-engineering.

Table 7-1. Identified

Needs Related to 

Seamless Transmission 

of Digital Information

“In the past we had a 

lot of point data. Now, 

we’re collecting 2D 

data from radars, and 

we have 3D data from 

reverse-engineering 

tools and computed 

tomography (CT) data. 

It is more than just 

seamless transmission. 

Data structure is the 

key.”
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

7-6

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Several interviewees pointed out the early success of the 

MTConnect standard. Although, thus far, adoption has been 

limited to the machine tool industry, MTConnect has potential 

for expansion to other arenas.

Respondents commented that individual firms cannot solve the 

data standards challenge on their own. Firms do not want to 

invest in developing these systems. “We have all sorts of 

different systems all around the world. So, it is important to get 

more commonality. He who figures out those key relationships, 

causal relationships, that identification and recognition will be 

pivotal.” Another developer described itself as a willing 

collaborator. One of the challenges faced by standards-setting 

bodies is that the market for smart manufacturing technologies 

is highly fragmented and proprietary, which would require 

cooperation from a large number of companies, although there 

is a more limited number of large players that could potentially 

move the market.

Several interviewees compared the current manufacturing 

environment to the early days of the Internet when there were 

dozens of different networking protocols before TCIP became 

the widely accepted internet protocol. One developer put it 

aptly: “What needs to happen is an equivalent collapse of these 

things into a consistent, ubiquitous protocol.”

7.2.2 Interoperability of Systems

Another critical area is the interoperability of systems, which is 

related to the data format issue but with some differences. 

Several individuals described islands of information that exist in 

different factory systems that perform different functions. 

These systems cannot easily be integrated because they do not 

always talk to each other very well. Even different vintages of 

the same system can create version issues. One SME pointed 

out that “the interoperability of platforms can be improved. 

When a CAD version changes, then everyone else has to catch 

up and update their stuff.” Manufacturing environments can 

have 10 to 20 different systems, ranging from supply systems 

to production systems. Standards are needed at key connection 

points between systems so that islands of information can be 

tied together. An industry observer stated that standards are 

needed to “support the full chain all the way through ERP 

systems.”

Several interviewees 

pointed out the early 

success of the 

MTConnect standard. 

Although, thus far, 

adoption has been 

limited to the machine 

tool industry, 

MTConnect has 

potential for expansion 

to other arenas.

Several interviewees 

compared the current 

manufacturing 

environment to the 

early days of the 

Internet when there 

were dozens of 

different networking 

protocols before TCIP 

became the widely 

accepted internet 

protocol. One 

developer put it aptly: 

“What needs to happen 

is an equivalent 

collapse of these things 

into a consistent, 

ubiquitous protocol.”
Section 7 — Seamless Transmission of Digital Information

7-7

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

STEP is currently being used to translate dimensions and 

tolerances. However, more manufacturing attribute definitions 

need to be added. These definitions would include 

manufacturing features such as concentricity, smoothness, 

hardness of material, and stress relief requirements. The 

problem is that every CAD vendor characterizes these 

attributes differently, so common definitions would improve 

interoperability. A developer pointed out that there should be 

standards for simulation-readiness of STEP files. As much as 

30% to 40% of the data in a STEP file can be represented by a 

3D company logo, which adds no value at all to the simulation 

process and, in fact, can slow down processing. The comments 

we gathered about STEP standards show the interrelationship 

between managing data streams through models and advances 

in analyzing data and trends. One approach for improving 

interoperability is for platforms to have built-in capabilities to 

communicate with multiple commercial software products. For 

example, Windchill is a PLM tool with built-in capabilities to 

work with multiple CAD options, including Autodesk, Microsoft, 

Adobe, I-Deas, Unigraphics, and Dassault CATIA. Expanded 

development of such platforms could lower the cost of 

accessing specialized applications/functions across a wide range 

of products.

There is a real opportunity cost associated with these 

interoperability challenges. Currently, interoperability issues 

can require substantial investments of staff time that could 

otherwise be spent on other value-added tasks. As one 

developer explained, “A lot of our time is spent implementing 

solutions to get connectivity [which is] wasted time that could 

be spend on optimization.” The good news is that systems 

appear to be opening up, albeit at a slow pace.

A consortium was recommended as a way to solve 

interoperability issues. Industry partners could share particular 

pain points and find common ground, with companies willing to 

collaborate on these issues at low risk to losing competitive 

advantage.

7.2.3 Machines

Two main themes emerged related to machines: (1) there is a 

need to continue to build on MTConnect to enhance machineto-machine communications and (2) guidance is needed on 

Currently, 

interoperability issues 

can require substantial 

investments of staff 

time that could 

otherwise be spent on 

other value-added 

tasks.
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

7-8

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

best practices for retrofitting machines to enable smarter use of 

factory assets.

The ability to integrate machine tools, robotics, and sensors is 

an example that was raised. Another respondent noted the 

need for a common protocol, and that currently there are too 

many options. Another developer suggested that industry 

should build on the success of MTConnect by extending it to 

additional classes of machine such as CNC machines, 

autoclaves, and ply-cutters as well as older machines that can 

be retrofitted.

7.2.4 Data Security Protocols

Another area where industry believed that the technical 

infrastructure needs to be enhanced is data security. One 

potential solution to cyberphysical security concerns is to 

develop carefully defined monitoring communication protocols 

that are decoupled from control communication protocols. It 

was mentioned that for predictive maintenance of turbines, 

wireless sensors transmit real-time vibration data in a one-way 

fashion to the control room, but in a way that is completely 

separate from control communications that are wired.

Without effective and trusted security protocols, factories have 

limited capability for seamless and integrated data analysis. As 

the importance of cloud computing grows to handle massive 

datasets, companies will face an increasing need to transmit 

data offsite for processing. However, some manufacturers are 

hesitant to let data out because of potential IP risks.18

Furthermore, inadvertently providing access to control systems 

can expose factories to malicious security threats from third 

parties.

7.2.5 Bridging Information Technology with Operations 

Technology

The information technology–operations technology (IT–OT) gap 

is essentially the challenge of getting IT systems and staff to 

efficiently interoperate with OT systems and staff. The IT–OT 

gap is not unique to the manufacturing sector, but it is 

intensified in manufacturing because many factories have 

operations technologies that were developed decades before IT 

protocols and practices were implemented.

 18 One interviewee even mentioned their company’s hesitancy to hire 

outside consultants because of IP concerns.
Section 7 — Seamless Transmission of Digital Information

7-9

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

A process optimization software developer spoke of the value of 

sensor data and some of the technical challenges associated 

with the IT–OT gap: “There is a lot of value in getting the 

information [from sensors] that doesn’t put the control 

networks and operations at risk. Traditionally, sensors were out 

there were for control loops. A lot of sensing going on now is 

for higher order monitoring and analytics. The challenge with a 

lot of our customers as low-cost sensors go in is that 

information is still incredibly valuable back in the control 

network and operations space.” The developer went on to 

describe that customers are worried about feeding the sensor 

data from the IT network into operations systems because that 

may introduce cyber security vulnerabilities.

The IT–OT gap is as much an organizational and cultural 

challenge as it is a technical challenge. A food manufacturer 

spoke about the organizational challenge: “The issue that we’ve 

had is traditionally there are two separate streams: 

(1) manufacturing, then (2) enterprise/corporate IT structure. 

IT people aren’t familiar at all with process computers, and vice 

versa. There are some security concerns. For example, 

concerns about transmitting to a vendor to help troubleshoot 

equipment which can involve special private VPNs, not standard 

protocols.” One end user suggested that degree programs in 

IT–OT or advanced certification programs could supply industry 

with appropriately skilled people to bridge the gap. Cisco 

Systems has launched the Industrial Networking Specialist 

certification specifically toward that end (Cisco.Com, n.d.).

7.2.6 Other Needs

Other needs mentioned related to interoperability include 

developing new sensors (discussed in the previous section), 

programming tools, typologies, and architectures; providing 

test beds; and acting as a facilitator through consortia or other 

mechanisms.

The IT–OT gap is as 

much an organizational 

and cultural challenge 

as it is a technical 

challenge.
8-1

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Advances in 

Analyzing Data and 

Trends

The analysis of big data is a rapid-growth sector. However, 

most manufacturers in our study thought that the 

manufacturing sector was not receiving sufficient attention from 

data analysis service providers. Most of the data analysis firms 

are better positioned to serve the financial service sector or 

marketing departments. They lack the engineering or chemicals 

expertise to support much of the manufacturing sector. It was 

suggested that consortia and long-term partnerships need to be 

fostered to bring the multidisciplinary teams together to solve 

the analysis problems that would advance smart 

manufacturing. Figure 8-1 illustrates the position of the analysis 

phase in the data feedback loop.

8.1 BARRIERS TO ADOPTION AND ASSOCIATED 

MARKET FAILURES

The cost associated with computing power and analysis 

software can be significant, and represents a barrier to 

Figure 8-1. Smart 

Manufacturing Data 

Feedback Loop: Analyze
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

8-2

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

adoption of smart manufacturing, 

especially to SMEs. One 

interviewee said that up to 70% of 

their factory’s network 

infrastructure was “nonvalue,”

meaning that it was associated 

with the infrastructure need—not

with the customized/task-specific 

software that was generating the 

benefit. Some people noted that, 

from this perspective, the cloud 

has the potential to greatly reduce 

the overhead cost associated with 

smart manufacturing capabilities 

by making shared resources 

available. One company described 

cloud computing as a “tidal wave”

for the industry, but that it had 

experienced particular challenges 

implementing cloud computing 

because of security and concerns 

about complying with export 

controls.

In general, we found significant 

disagreement with respect to 

future use of cloud computing. For 

some, the concept did not 

resonate at all due to security 

concerns related to proprietary 

data and protection from hacking. 

However, for others the benefits 

of the cloud’s low-cost computing 

power and storage outweigh the 

risks. Typical comments were that 

a company “does not have to put 

the most sensitive information on 

the cloud. It is good for what it’s 

designed for.”

Companies talked about not only 

shared computing power but also the need for a new model of 

shared software resources for analyzing big data or accessing 

customized, complex programs. If a company is going to use a 

Reducing Costs for SMEs

Virtual simulation of 

manufacturing production 

systems can increase innovation 

and help more efficiently manage 

the entire product life cycle. 

However, the current market is 

primarily limited to large 

manufacturers that are able to 

justify the high fixed cost of a 

new software package and the 

required computational power 

based on economies of scale/ROI. 

It is difficult for most SMEs to 

justify the high fixed costs of 

licensing these types of software 

in the traditional manner or 

purchasing the computers needed 

to run the simulations. 

Decreasing the cost of software 

plus the cost of implementation

would increase market 

penetration and adoption of these 

technologies among SMEs, which 

could yield substantial economic 

benefits. A more centralized 

approach where software tools 

and computing capacity were 

hosted in the cloud and rent 

access available would decrease 

the direct software costs incurred 

by any single manufacturer and 

potentially decrease the indirect 

implementation costs. See 

Appendix C for a more detailed 

discussion. 
Section 8 — Advances in Analyzing Data and Trends

8-3

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

customized design or simulation software only a few times a 

year, it is very expensive to purchase and maintain software for 

such a limited need. However, if licensing mechanisms can be 

developed to allow access for a fee, a cloud-based service 

sector could greatly lower the cost of many of the digital 

advances in product design, simulation, and manufacturing 

design. Sharing resources is not a technical research topic per 

se, but it is a structural issue that professional associations 

might address.

Also associated with the need for improvement of analysis 

capabilities is the reduction of false positives. False positives 

can be very costly if they lead to unnecessarily shutting down a 

line or delaying restart.

Following costs, the second most common barrier mentioned 

related to data analysis is that staff do not have graduate 

degrees in engineering. Senior staff are more likely to be longterm employees with on-the-job experience and expertise. 

Thus, skill level is an issue in utilizing analysis systems that are 

very complex. Several companies said they have been 

advocating for bringing more computer scientists and 

mathematicians into manufacturing. In general, the concern is 

that data can be misleading as they are currently aggregated 

and reported, and taking disparate data and aggregating them 

across the different data formats and structures leads to errors.

8.2 TECHNOLOGY INFRASTRUCTURE NEEDS 

AND ROLES FOR NIST

Most interviewees stated that data analysis required at least a 

medium level of additional development. Table 8-1 shows the 

major needs identified by interviewees for advancing the 

analysis of manufacturing data and trends.

8.2.1 Best Practices and Techniques for Data Analysis

Best practices and techniques for data analysis emerged as the 

top technology infrastructure need in this area. Prediction, 

particularly predictive maintenance, was a big theme. In 

addition, advances in algorithm testing and development were 

needed to provide guidance on how to interpret data from 

different sensors and determine which data are most important, 

relevant, and meaningful to collect for predictive maintenance. 

To the extent that it is possible to develop standard algorithms 

“There is so much data 

to pull out of machines, 

which are the leading 

indicators of predictive 

failure, and how will 

you know that? What 

are those models, how 

should they be 

structured?”
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

8-4

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

that can be customized by end users, this approach would be 

ideal. Another term for predictive maintenance is analysis of 

failure modes, which is a big gap, according to some. One 

developer noted, “There is so much data to pull out of 

machines, which are the leading indicators of predictive failure, 

and how will you know that? What are those models, how 

should they be structured?”

Identified Needs

Best practices and techniques for data analysis

Virtual factories and simulation

Metrology

Standard and test beds

New architectures for information technology–operational 

technology (IT–OT)

Other technical challenges include time calibration of data 

streams and what information should be used when pulling 

from particular types of sensors. For example, for vibration 

sensors, guidance on the optimal time domain and frequency 

domain is needed.

8.2.2 Virtual Factories and Simulation

Currently, industry appears to lack sufficient technology

infrastructure to enable virtual factories and simulation to 

support the analysis of data and trends. Several interviewees 

commented that simulation standards have not been as widely 

adopted as hoped because they are not “complete enough.” For 

example, if STEP standards were more comprehensive, OEMs 

would expand use for communicating to suppliers (especially 

build to print).

A chemical manufacturer suggested that more model 

complexity is not always necessary. This concept is analogous 

to model selection techniques in statistics that quantify the 

incremental predictive power of adding another variable into a 

statistical model.

Another compelling idea called for the need for a common way 

to quantify model usefulness versus complexity. A chemical 

manufacturer suggested that more model complexity is not 

Table 8-1. Identified 

Needs Related to 

Advances in Analyzing 

Data and Trends
Section 8 

— Advances in Analyzing Data and Trends 8-5

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

always necessary. This concept is analogous to model selection 

techniques in statistics that quantify the incremental predictive 

power of adding another variable into a statistical model.19 The 

need for model selection in manufacturing environments is 

driven by an information asymmetry issue between developers 

and end users. According to the same chemical manufacturer, “Service providers are always trying to sell their latest and 

greatest complex model to establish a niche and follow-on 

services. Similar academics are looking to push the frontier and 

hence are not interested in developing simple/usable, less 

accurate models.”

Finally, modular applications and expanded data libraries were 

mentioned as infrastructure technologies that could lower 

development costs and improve the accuracy of simulation 

models. Increased information for process flows, material 

characteristics, and simulation platforms could improve 

efficiency and lower the required skill level for modeling tasks. 

Providing these building blocks could increase the use of 

simulation modeling, especially for SMEs.

A fundamental platform such as a standard factory operating 

system that ties together modular applications and data 

libraries with existing systems in the manufacturing 

environment could be transformative. Other groups, such as 

the SMLC, are leading efforts to develop a real-time 

manufacturing platform, so close coordination with the SMLC 

would be vital to determine the support needed for technical 

inputs.

8.2.3 Other

Additional roles for NIST include informing and shaping 

standards, creating test beds, and providing leadership in 

metrology to support advanced data analysis. Developers noted that the information technology

–operational 

technology (IT

–OT) gap (discussed at length in Section 7) is a 

barrier to data analysis because it prevents the fluid flow of 

information from the shop floor to engineering groups that can 

provide a different perspective.

 19 For example, the Akaike information criterion (AIC) and Bayesian 

information criterion (BIC) are two criteria that statisticians 

consider when a model.
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

8-6

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Centrally hosted cloud-based applications could make analyzing 

big datasets more accessible to SMEs. Big data tools such as 

Hadoop tend to be cost prohibitive for SMEs, but cloud 

computing can make applications and computing power much 

more accessible for them. A discrete parts manufacturer stated 

that the cloud could offer a scalable solution to this problem; a 

large OEM or another group could host services in the cloud, 

which could reduce the cost of the individual licenses for 

smaller shops.

Despite the fact that there were many ideas shared for how 

NIST can play a role, several interviewees stated that they did 

not see a role for NIST in advanced analysis of data and trends, 

viewing it as an industry-driven technology. However, there 

seemed to be confusion over the distinction between NIST 

developing the technology infrastructure and making it 

available to the public sector to support the development of 

commercial products, and NIST developing open-source 

software that would be made available to the public and 

potentially crowd out certain products and services currently 

being offered by product and service providers. Several 

companies seemed threatened by NIST involvement due to 

concerns about competition, and hence were not enthusiastic.
9-1

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Efficiently 

Communicating 

Information to 

Decision Makers

Respondents interviewed were most familiar with activities

related to physical components, data exchange, and related 

standards. However, the “softer” side of smart manufacturing, 

which includes efficiently communicating information to 

decision makers, was also cited as an area needing further 

research. For example, the value of smart manufacturing data 

and activities would be increased through research to develop 

simpler, user-friendly communication interfaces that would be 

customized for smart manufacturing and integrate statistical 

methods for establishing thresholds to trigger decisions.

Figure 9-1 shows the position of the communication phase in 

the data feedback loop.

Figure 9-1. Smart 

Manufacturing Data 

Feedback Loop: 

Communicate
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

9-2

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

9.1 BARRIERS TO ADOPTION AND ASSOCIATED 

MARKET FAILURES

Many industries encounter issues related to efficient and 

concise information interfaces. “Ease of use paradigms” are 

needed. Plant staff members need to be able to access and 

understand software and information in a manageable way. 

Mobile access with tablets would be a key selling factor; 

however, the wireless aspect of tablets introduces security 

issues.

A key concern is that everyone wants information differently 

and thinks about issues differently. It is difficult to develop one 

interface that fits. Examples such as OPC GE and OMAC exist, 

where it was shown that developing common interfaces is very 

difficult.

In many instances, users get too involved with a particular 

piece of machine or family of machines. What is needed is a 

higher level approach to more generally think about the science 

of communication. This is closer to basic research (one 

respondent referred to it as “textbook” method) and reflects 

investments that would have a broad impact on the 

manufacturing sector. The distinction is that not all investments 

should be machine specific. Higher level, public-good research 

is needed in this area.

A related overarching barrier to adoption discussed by 

respondents is that existing, older staff members are frequently 

uncomfortable with electronic information interfaces—and, in 

contrast, younger staff who are comfortable with electronic 

information interfaces do not have the level of technical 

background/experience to quickly interpret the results and 

identify solutions.

9.2 TECHNOLOGY INFRASTRUCTURE NEEDS 

AND ROLES FOR NIST

Two-thirds of interviewees stated that this capability required at 

least a medium level of additional development. Table 9-1 lists 

the areas identified. Interviewees provided very few specific

technical areas, possibly due to the elusiveness of the concept 

or its broad nature. The few comments that were provided were 

evenly spread across a range of ideas about enhancements 

needed.
Section 9 — Efficiently Communicating Information to Decision Makers

9-3

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Identified Needs

Common taxonomy

How and when to use a directed closed-loop system

Information representation

Metrics

One particularly poignant comment from a developer described

how industry could approach the topic of communicating 

information to decision makers: “Another way to think about 

human–computer interaction is how you integrate closed-loop 

systems with a human in the loop. What are the best ways to 

design integration between automated systems and people who 

implement advice? Direct close-loop versus having humans in 

the loop. When is each best?” One end user offered a different 

perspective by pointing out that efficiently communicating 

information to decision makers is about the metrics used to 

communicate with decision makers rather than the design of 

interfaces. Overall equipment effectiveness (OEE) is one 

example of a high-level metric that has gained some traction in 

industry but is not consistently defined.

Another developer stated that, although it would be difficult to 

determine how to standardize the representation of information 

for decision makers, he would be eager to see some level of 

standardization because it would, in theory, enable rapid 

development.

Table 9-1. Identified 

Needs Related to 

Efficiently 

Communicating 

Information to Decision 

Makers

“Another way to think 

about human–

computer interaction is 

how you integrate 

closed-loop systems 

with a human in the 

loop. What are the best 

ways to design 

integration between 

automated systems 

and people who 

implement advice? 

Direct close-loop 

versus having humans 

in the loop. When is 

each best?”
10-1

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Determining and 

Implementing 

Required Action

The ability to determine and implement required action was 

referred to as the “... ultimate capability. Without it, the other 

of the capabilities are of no use.” Most manufacturers 

expressed the need for enhanced decision support tools that 

would help make decisions much faster, including information 

and analysis tools targeted at both manufacturing operators 

and upper management. Using artificial intelligence and

learning systems to identify and direct the most appropriate 

actions based on data analysis would help toward building an 

automated and more flexible factory. A challenge commonly 

cited for determining and implementing required action is

communicating meaningful information to management, where 

many of the investment and high-level operating decisions are 

made. This challenge demonstrates that some of the 

capabilities are interrelated. Figure 10-1 illustrates the position 

of the action phase in the data feedback loop.

Figure 10-1. Smart 

Manufacturing Data 

Feedback Loop: Action
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

10-2

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

10.1 BARRIERS TO ADOPTION AND ASSOCIATED 

MARKET FAILURES

Improvements in decision systems will require continued 

research on statistical methods for analyzing big data (pattern 

recognition, trends, and correlations) and the intelligence to 

make critical decisions based on risk assessment and 

optimization modeling. This is a multidisciplinary research area 

in which coordination must be improved across the different 

sectors pursuing these enhanced capabilities.

The ability to easily reconfigure manufacturing systems was 

discussed as a need in several interviews, but very few specific 

technologies that would facilitate in-process flexibility and 

reconfiguration were mentioned. One product on the market is 

Lynx, a mobile conveyer belt that can be deployed to any 

location in the factory and can work safely alongside humans, 

avoiding collisions by using real-time path planning. The 

benefits in terms of cost and speed of implementation hinged 

on developing analysis systems that could function independent 

of human interaction.

The lack of a specialized workforce is one of the critical 

components limiting the development of decision support tools. 

Educators are working to catch up their offerings in this area, 

and organizations such as the National Science Foundation 

(NSF) have conducted workshops to highlight the need. In 

general, smart manufacturing intelligence systems require a 

highly multidisciplinary workforce. Europe, which is more 

advanced in this area, encourages courses specifically designed 

to supply qualified students trained to “understand data, how to 

capture it, how to model it, and how to use it.” Employees who 

work in the manufacturing factory of the future will have to be 

highly trained in understanding digital data, how to work with 

the machine, and achieve better integration of the human 

resources with the digital resources.

An example was provided from the paper industry. Within the 

paper industry, the workforce is generational. On average, staff 

are not highly educated and typically work their way up, 

gaining a wealth of practical experience. Control rooms are not 

very sophisticated. Controllers have been working with the 

same processes for years, leveraging tacit knowledge about 

what the output should be saying to make product that meets 

specification. This operating model does not lend itself well to 
Section 10 — Determining and Implementing Required Action

10-3

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

the introduction a smart manufacturing technology, and 

represents a barrier to adoption.

10.2 TECHNOLOGY INFRASTRUCTURE NEEDS

Again, two-thirds of interviewees stated that this capability

required at least a medium level of additional development. 

Similar to efficiently communicating information to decision 

makers, there were few substantive recommendations for areas

in which to implement required action. Table 10-1 summarizes 

those needs.

Large process manufacturers commented that it would be 

helpful to have closed-loop systems, but offered no specific 

guidance on what technology infrastructure was needed to 

develop these systems. A major developer of industrial 

automation and control systems suggested that industry could 

better deal with uncertainty and risk when determining and 

implementing actions. The same respondent suggested that 

there are standardization needs for control models. Finally, a 

respondent from a manufacturing simulation software company 

stated the need for a standard virtual factory operating system.

Overall, respondents thought that substantial opportunities to 

enhance the technology infrastructure in this area were needed 

and are possible, but respondents did not offer specific 

recommendations.

Identified Needs

Standards

Closed-loop systems

Dealing with uncertainty

Table 10-1. Identified

Needs Related to 

Determining Required 

Action and 

Implementing Action
11

-1

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

Conclusions and 

Summary Needs

With the increasing complexity of manufacturing processes, 

smart manufacturing has the potential to increase efficiency 

and productivity. Through the generation and capture of data, 

the understanding of its implications, and the ability to take 

timely action as a result of that knowledge, smart 

manufacturing can increase the U.S. manufacturing sectors’

competitive position globally.

Although the term smart manufacturing means different things 

to different industries, there is a growing consensus that 

significant cost savings can be realized through optimizing 

product design and production systems at the equipment, 

factory, and enterprise levels. At the core of this technology is 

the integration of cyber and physical systems, which can enable 

innovative production processes and new product systems.

However, barriers exist to the adoption of all but the simplest of 

smart manufacturing technologies. Enhancements in the 

technology infrastructure are needed to develop the next-

generation smart manufacturing technologies. Advances in the 

technology infrastructure, which provides the underpinning for 

advances in smart manufacturing, can drive product and 

process improvements that are just in the conceptual stages 

today. Improvements in the technology infrastructure will lower 

the cost and increase the benefits from adopting existing and 

future smart manufacturing technologies.

The following insights and recommendations are based on more 

than 80 interviews and many additional informal conversations 

with individuals representing a variety of smart manufacturing 

product and service providers, smart manufacturing end-user 

companies, and industry observers.
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

11-2

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

11.1 CAPABILITY NEEDS AND BARRIERS TO 

ADOPTION

11.1.1 Capability Needs

Respondents indicated the importance and need for research on 

a broad range of capability areas supporting smart 

manufacturing. These areas ranged from product design to data 

collection and transmission, and from data analysis to decision 

support tools. This finding illustrates how interdependent the 

different functionalities are across the spectrum of smart 

manufacturing activities. For example, interviewees said that 

enhanced sensing capabilities are needed, but will only add 

value if they are accompanied by cost-effective and secure 

transmission of the information. Similarly, the growth and 

availability of real-time digital information on manufacturing 

activities is only as valuable as the ability to analyze the 

information. Thus, in many ways the value of smart 

manufacturing systems are a function of the weakest link in the 

chain.

11.1.2 Barriers to Adoption

Financial issues were the most commonly cited barriers to 

adoption of smart manufacturing technologies and processes—

however, this is likely a symptom of underlying issues and 

market failures. In general, users cited low ROI—in part driven 

by cost issues associated with legacy systems, vendor lock in, 

and age of plants—as the greatest barrier to adoption. In other 

cases, while adequate ROI may be feasible, users mentioned 

that uncertainty about new technologies makes it difficult to 

understand and justify ROI, and this aversion to uncertainty 

acts as a barrier to adoption.

Concerns about financial issues were followed by a lack of 

technical resources and knowledgeable staff needed for 

implementation. End users pointed out that personnel with 

specialized knowledge are scarce because of the aging/retiring 

workforce and the lack of multidisciplinary engineering 

education programs. For smaller manufacturers in particular, 

the lack of technical resources and knowledgeable staff appears 

to be a major barrier.
Section 11 — Conclusions and Summary Recommendations

11-3

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

11.2 ECONOMIC IMPACTS

Interviewees were asked what impact enhanced smart 

manufacturing capabilities could have on their manufacturing 

processes if technology infrastructure needs were met. 

Table 11-1 shows the average percent change in factor inputs 

provided from the interviews. Manufacturers indicated that 

labor costs could be reduced by an average of 12%, and energy 

savings of 13% could be achieved. Capital and materials 

potential savings were estimated to be, on average, 5% each.

Changes in factor inputs were then scaled to national levels to 

estimate the economic impacts associated with an enhanced 

technical infrastructure for smart manufacturing. Table 11-2a

shows that economic impacts are estimated to be 

approximately $57.4 billion. This represents, on average, an 

approximately 3.2% reduction in the shop floor cost of 

production, which is on the lower end of the spectrum 

compared to other studies evaluating the potential benefits of

smart manufacturing. Discrete parts manufacturing accounts 

for $30.8 billion, and process manufacturing $26.6 billion. 

Discrete parts industries had larger potential impacts, but 

process industries represented a larger share of the 

manufacturing sector. Materials accounts for the largest share 

of savings, accounting for approximately two-thirds of 

manufacturing production costs.

Economic impacts were also estimated by capability area based 

on the level of importance provided by respondents. As shown 

in Table 11-2b, enhanced sensing and monitoring, seamless 

transmission of digital information, and advances in analyzing 

data and trends all have potential annual impacts greater than 

$10 billion. All of the identified capability areas were estimated 

to have annual benefits of over $7 billion, reflecting the overall

interdependency across all the capability areas.

Factor Input Mean Impact

K: Capital −5%

L: Labor −12%

E: Energy −13%

M: Materials −5%

Table 11-1. Average 

Percent Change in 

Factor Inputs
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

11-4

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Table 11-2a. Economic Impact Summary, Process versus Discrete

Type of 

Industry

Cost Impacts, $ Billion KLEM National 

Factor 

Expenditure, 

$ Billions

Percentage 

Savings in Shop 

Floor Production 

K L E M Total Costs

Process −1.1 −4.9 −3.9 −16.7 −26.6 1,281.1 −2.1%

Discrete −5.1 −17.2 −1.8 −6.7 −30.8 537.0 −5.7%

Total −6.2 −22.1 −5.7 −23.5 −57.4 1,818.1 −3.2%

Table 11-2b. Economic Impacts by Capability Area

Capability Area Economic Impact

($ Billion)

Managing digital data streams through models 8.2

Enhanced sensing and monitoring 10.9

Seamless transmission of digital information 10.3

Advances in analyzing data and trends 10.1

Efficient communication of information to decision makers 7.7

Determining required actions and implementing action 9.4

Total 57.4

Note, that as discussed in Section 2.2, the economic impact 

estimates presented are considered to be conservative in that 

they focus on reductions in manufacturer’s production cost that 

would result from meeting the identified technology 

infrastructure needs. Not included in the economic impact 

calculations is the economic value associated with reduced R&D 

costs, improved product attributes, increased sales, or 

accelerating the introduction of new products to market.

11.3 TECHNOLOGY INFRASTRUCTURE NEEDS

As shown in Figure 11-1, respondents indicated that there is a 

need for technology infrastructure development across all of the 

capability areas supporting smart manufacturing. Although 

advances have been made over the past decade, seamless

transmission of digital information was ranked as the area in 

which the most research was needed. This was followed by 

decision support tools to determine and implement action, and 

advances in analyzing data and trends. The implication is that 

NIST may have a potential role in contributing to the 

technology infrastructure for most of these capabilities.
Section 11 — Conclusions and Summary Recommendations

11-5

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Figure 11-1. Percentage of Interviewees Indicating the Need for Additional Reach in Smart 

Manufacturing Capability Areas

11.3.1 Communication Standards and Conformance Testing

All interviewees said that government should continue to 

support standards and communications protocols that span the 

capabilities and functionality of smart manufacturing; a crucial 

need was cited for research on the structure of data streams 

coming from distributed and disparate sources. Currently, 

substantial amounts of skilled labor are needed to collate data 

from systems, machines, and devices that are physically 

distributed across entire factories. Research is needed to better 

understand the critical technical inputs on the standardization 

of data formats, protocols, and configurations.

Other technical challenges include the area of calibration. Selfcalibrating machines, or ones that could detect when calibration 

was off, would be very helpful. In addition, methods for time 

calibration of data streams and best practices regarding what 

information should be used from particular types of sensors are 

needed. For example, for a vibration sensor, guidance is 

needed on the optimal time domain and frequency domain.
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

11-6

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Security Issues

Security concerns related to wireless communications, data 

storage, and access to computational power were consistently 

cited as issues limiting the adoption of smart manufacturing 

technology. Security concerns included both protection of 

sensitive IP as well as physical plant security and worker 

safety. Data encryption and security protocols tailored for the 

manufacturing sector are needed, as well as approaches for 

decoupling sensitive IP data from routine process monitoring 

data. However, several interviewees thought the security 

concerns were overstated, and that cloud computing and data 

storage were applicable (safe) for over 80% of the smart 

manufacturing needs.

Data Analysis and Intelligent Systems

Best practices and techniques for data analysis emerged as the 

top technology infrastructure need. Prediction, particularly 

predictive maintenance, was cited as an area with large 

potential benefits. For example, it was stated that government 

organizations, with their expertise in algorithm testing and 

development, could provide guidance on how to interpret data 

from different sensors and determine which data are most 

important, relevant, and meaningful to collect for predictive 

maintenance. An area of focus, suggested by respondents, was 

the development of standard algorithms that can be customized 

by manufacturers; in this category, analysis of failure modes 

was cited a major gap.

Coordinating with Other Organizations and Industries

Interviews clearly pointed to a demand for, and enthusiasm 

about, partnering with government organizations, such as 

NIST, to address some of the challenges discussed in this work. 

Companies feel strongly that the public sector should help 

coordinate activities with private organizations that are involved 

in the smart manufacturing space, so that all stakeholders 

clearly articulate the value of smart manufacturing. Many 

interviewees believe that NIST involvement would add muchneeded credibility to initiatives to support smart manufacturing.

In general, it is difficult to engage industry in an open 

discussion and to expect them to share information. However, 

this is essential for identifying the pain points and developing 

solutions that will be acceptable to a broad cross section of 

manufacturers. There are network externalities to be leveraged 
Section 11 — Conclusions and Summary Recommendations

11-7

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

through coordinated efforts if resources can be pooled and 

focused.

Building Human Capital through Education and Training

A common theme that emerged from the interviews was the 

importance of human capital in the successful application of 

smart manufacturing technologies. Whether directly (through 

funding) or indirectly (through endorsements) contributing to 

degree and/or certification programs or simply documenting 

best practices, government has a role to play in ensuring that 

skills keep up with the pace of technological advancement. With 

the attrition of the aging U.S. manufacturing workforce, the 

manufacturing sector will be looking for skilled and talented 

people to fill the need for new workers.

Supporting SMEs

Several interviewees pointed out that NIST’s Manufacturing

Extension Partnership (MEP) program could be an excellent 

mechanism for supporting smart manufacturing. They said that 

MEPs engage SMEs in an array of training, innovation, and 

process improvement activities, so there are existing avenues 

through which smart manufacturing could be reinforced. 

Manufacturers suggested that the MEP program could also help 

connect users with developers of smart manufacturing 

technologies by creating platforms that allow regional offices to 

direct SMEs to new marketplaces.

11.4 CONCLUSION

Enhanced technology infrastructure for smart manufacturing 

can help U.S. manufacturers gain cost efficiencies on the order 

of billions of dollars (compared with the current baseline 

trajectory of smart manufacturing technology). The substantial 

economic benefits quantified as part of this study are likely to 

be conservative because we focus primarily on efficiency gains 

and production cost savings, rather than quantifying the value 

of increased product quality, increased sales, and exports. 

These benefits, combined with the market failures and barriers 

to adoption, suggest opportunities for public policy to 

encourage and support industry in addressing these barriers 

and, ultimately, increasing total social benefits.

Our interviews with industry identified specific areas where the 

technology infrastructure needs to be improved and advanced. 

A common theme that 

emerged from the 

interviews was the 

importance of human 

capital in the successful 

application of smart 

manufacturing 

technologies.
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

11-8

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Interview findings demonstrate that companies in the 

manufacturing sector appreciate and welcome NIST’s traditional 

role as a standards coordinator and leader in metrology; 

however, additional important areas where the public sector

can help include data analysis, cyber security, human capital, 

and interoperability of systems, machines, and other data 

streams. Our findings clearly point to a broader role for the

public sector in making the U.S. manufacturing sector more 

innovative, efficient, and competitive.
R

-1

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

References

Antonelli, C., & Link, A. N. (2014). Handbook of the Economics 

of Knowledge. London: Routledge.

Cisco.com (n.d.). Cisco Industrial Networking Specialist. Get 

certified today. Accessed 2015.

http://www.cisco.com/c/en/us/training-events/training-

certifications/certifications/specialist/internet-of-

things/iot-networking.html

Jaffe, A. B. (2005). A tale of two market failures: Technology 

and environmental policy. Ecological Economics, 54, 

164–174.

Kilmer, R. D. (2014). Supporting Manufacturing to Promote 

U.S. Innovation and Industrial Competitiveness. 

Retrieved from 

http://www.nist.gov/director/upload/Kilmer-HBCU-

November-3-2014-2.pdf

Link, A. N., & Scott, J. T. (2010). Public goods, public gains: 

Calculating the social benefits of public R&D. New York: 

Oxford University Press.

National Institute of Standards and Technology (NIST), U.S. 

Department of Commerce (2008, October 1). Materials 

Measurement Library: NIST/CSM Sensor Could Help 

Avert Pipeline Failures. Retrieved from 

http://www.nist.gov/mml/acmd/hydrogen_100108.cfm

Tassey, G. (2008). Modeling and measuring the economic roles 

of technology infrastructure. Economics of Innovation 

and New Technology, 17(7), 617–631.
This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Appendix A:

NIST Advanced 

Manufacturing 

Strategic Planning 

Study
A

-1

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

A.1 INTERVIEW GUIDE: PERSPECTIVES OF 

USERS OF SMART MANUFACTURING 

PROCESSES

The National Institute of Standards and Technology (NIST) in 

the U.S. Department of Commerce has contracted with RTI 

International to conduct an economic analysis of standards, 

measurement, and general purpose technology needs that 

inhibit efficient development and adoption of advanced 

manufacturing in the United States.

The objectives of this critical strategic planning study are to  identify current and emerging needs related to 

standards and measurement,

 estimate the economic impact of meeting these needs, 

and

 review public policy and investment options.

The study has a particular focus on 4 aspects of advanced 

manufacturing: (1) robotics and automation, (2) smart 

manufacturing processes, (3) 3D Printing (additive 

manufacturing), and (4) roll-to-roll manufacturing. The focus of 

our conversation is smart manufacturing.

Your perspective will help guide NIST’s planning and investment 

process. Participation in this analysis is confidential; only 

aggregated information will be included in any deliverables or 

communications. Your name and your company’s name will not 

be disclosed. We do not wish to discuss specific products, 

strategies, or technologies; but rather your thoughts about how 

investments in standards and measurement technologies would 

affect your company and companies like yours.

Our research products will be an economic analysis, final 

report, and presentation materials. All deliverables will be 

publicly available in late 2015 and these will be shared with you 

as soon as they are released.

If you have questions, please contact:  Mike Gallaher, Case Study Lead, RTI, 919

-541-5935 or 

mpg@rti.org

 Alan O’Connor, Principal Investigator, RTI, 919-541-

8841 or oconnor@rti.org
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

A-2

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

 Gary Anderson, NIST Project Officer, NIST, 301-975-

5238 or gary.anderson@nist.gov

This collection of information contains Paperwork Reduction Act 

(PRA) requirements approved by the Office of Management and 

Budget (OMB). Notwithstanding any other provisions of the law, 

no person is required to respond to, nor shall any person be 

subject to a penalty for failure to comply with, a collection of 

information subject to the requirements of the PRA unless that 

collection of information displays a currently valid OMB control 

number. Public reporting burden for this collection is estimated 

to be 35 minutes per response, including the time for reviewing 

instructions, searching existing data sources, gathering and 

maintaining the data needed and completing and reviewing the 

collection of information. Send comments regarding this burden 

estimate or any aspect of this collection of information, 

including suggestions for reducing this burden, to the National 

Institute of Standards and Technology, Attn., Gary Anderson, 

gary.anderson@nist.gov, (301) 975-5238. The OMB Control 

Number is 0693-0033, with an expiration date of 03/31/2016.

Respondent Contact Information

Name

Title

Division

Company

Industry

Phone

Email

Location

For our conversation today we are defining smart 

manufacturing to include

The creation, communication and use of 

electronic information, as well as the interface of 

these information systems with the human 

element, for data-driven decision support. This 

includes how data and information generated 

during the production process is communicated 

and used to improve the design, engineering, and 

production phases of the product cycle.

1. Does this definition cover the relevant areas you typically 

include under “smart manufacturing?” If not, what else do 

you include in “smart manufacturing”?
Appendix A 

— NIST Advanced Manufacturing Strategic Planning Study A-3

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

2. Please describe your background as it relates to smart 

manufacturing. How did you come to be in your current 

position?

3. Are you familiar with NIST’s activities? If so, please give a 

brief description of any ways that NIST currently impacts 

your work (through, e.g., any standards, calibration and 

measurement, scientific and engineering data relevant to 

your work).

Company Background and Level of R&D

4. How would you describe your company’s primary line of 

business (e.g., industry classification)? What kinds of 

products/services does your firm produce/provide and which 

are currently – or in the future could – benefit from smart 

manufacturing technology/techniques?

a. Approximately what percentage of your company’s sales 

revenue is associated with your division, or the division 

for which you are responsible? A range is fine.

5. Is the amount of R&D your company spends as a 

percentage of revenue similar to that of other companies in 

in your industry? If there are important differences, can you 

describe why your company may be more/less R&D-

intensive than others? As a proportion of revenue in a typical year, how much does your company invest in R&D?

6. Does your company conduct R&D related to the 

use/enhancement/implementation of smart manufacturing 

technology or techniques? If so, what are the broad 

objectives of that R&D (e.g., cost reduction, quality 

improvement, other)?

If Yes to question 6 -

7. Roughly what proportion of your company’s total R&D is 

related to the use of smart manufacturing? Would you 

expect other companies in the same lines of business to say 

roughly the same?
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

A-4

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

8. As far as you are aware, is your division or company 

engaged with any industry consortia, standards 

organizations, or governing bodies specifically for smart 

manufacturing? If so, in which bodies do you participate and 

what are the underlying drivers for participation?

Current Use of Smart Manufacturing and Barriers to 

Adoption

9. Please provide a brief description of your company’s use of 

smart manufacturing technologies in your manufacturing 

processes.

10. What additional areas of smart manufacturing (automation, 

sensing/monitoring, data feedback/integration) has your 

company considered, investigated or researched?

a. Have you conducted feasibility studies? Please describe.

b. Have you developed preliminary cost/benefit models? 

Please describe.

11. Why did you decide not to move forward (or are not moving 

as fast as you would like) with certain investments in smart 

manufacturing?

a. Financial benefits were not large enough

b. Technology not mature enough

c. Risk to product quality or delivery schedule

d. Limited resources such as investment capital and/or

knowledgeable staff

e. Limited technical resources such as guidelines, tools, 

software or test cases

f. Legacy systems and entrenched staff and management 

practices

g. Cost of software

h. Logistical, down time, or hassle factors

Potential Future State of Smart Manufacturing

Table A-1 describes a better state of the world in which a 

bundle of capabilities and technology infrastructure is available 

throughout the manufacturing industry. Industry needs are in 

the left hand column, and NIST-provided technology 

infrastructure that is needed to develop and diffuse the 

capabilities is in the right hand column.
Appendix A — NIST Advanced Manufacturing Strategic Planning Study

A-5

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Table A-1. Better State of the World: Capabilities and Technology Infrastructure

Available

Smart Manufacturing Capabilities

Infrastructure Technology to Help 

Meet Needs

Managing digital data streams through models:

• CAD models including material characteristics,

• Simulation models of part creation and plant 

layout, and

• Rapid automated costing functions.

High-fidelity process models, physical 

model representation for flexible objects, 

simplified modular applications of CAM 

software for less sophisticated uses, data 

standardization, standard and simpler 

equipment interfaces to facilitate consistent 

data entry for less-skilled workers, 

standard terminology for automated part 

costing.

Enhanced sensing and monitoring:

• “State estimation” of critical manufacturing 

machines (for example: vibration, acoustics, 

temperature, tolerances, and pressure), and

• Real-time monitoring of product attributes as they 

move through various stages of the production 

process

In-process measuring and monitoring for 

physical processes, self-powered sensors, 

robust sensors to withstand harsh 

manufacturing processes, data 

standardization, methods for calibrating 

sensing and monitoring systems.

Seamless transmission of digital information:

• Wireless transmission of digital information 

without interference from other data channels,

• Seamless integration of smart sensors,

• Interoperability between different platforms such 

as CAD/CAM, and

• Secure data transmission (wired and wireless).

Secure data transmission; secure cloud 

computing and data sharing; standard 

communication protocols; retrofitable, 

plug-and-play data communications 

systems; data interoperability of 3D model 

parameters and product manufacturing 

information

Advances in analyzing data and trends:

• Interpretation and aggregation of data from 

sensing and monitoring networks,

• “Big data” techniques for manufacturing,

• predictive maintenance,

• Reduction of false positives, and

• Cloud computing and fee-for-service cloud based 

algorithms for product design, simulation, and 

manufacturing design.

Algorithms to interpret data from disparate 

sensors and systems; definition of 

important, relevant, and meaningful data to 

collect for predictive maintenance

Efficiently communicating information to decision 

makers:

• Comprehensive information interfaces humancomputer interaction-based design, and

• Easy-to-interpret interfaces accessible from any 

location.

Common taxonomy across platforms and 

disciplines

Standards in interface design for 

manufacturing equipment 

Determining required action and implementing action:

• Real-time feedback of enhanced sensing and 

monitoring data into factory decision making,

• Automated optimization-based decision making 

that functions independent of human interaction,

• Machine learning decision making algorithms for 

manufacturing, and

• Reconfigurability of manufacturing systems.

Tested and validated decision models 
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

A-6

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

12.Based on your role as a user of smart manufacturing 

processes, are there any additional capabilities or 

technology infrastructure needs that you feel are not 

captured in the table above?

13. Using the below scale, please indicate (a) the importance of 

each of the capabilities listed in the table and (b) the level 

of additional development needed.

(a) Importance

(b) Level of additional 

development needed

Capabilities 1 2 3 4 5 Low Medium High

Managing digital data streams 

through models

Enhanced sensing and 

monitoring

Seamless transmission of 

digital information

Advances in analyzing data 

and trends

Efficiently communicating 

information to decision 

makers

Determining required action 

and implementing action

1 being low; 5 being high

Potential Benefits of Enhanced Smart Manufacturing 

Capabilities

The following questions ask about the impact to your 

company’s product design and manufacturing processes if these 

enhanced manufacturing capabilities were available:

14. To enable us to combine your responses with the responses 

of others and provide NIST with a sense of potential impacts 

at the industry level, could you quantify the impacts we 

have discussed in terms of the following metrics? A range is 

fine.

Production Costs +/- % Change

a Cost of materials %

b Cost of energy/electricity %

c Cost of labor %

d Cost of capital equipment %

Costs of maintenance %
Appendix A 

— NIST Advanced Manufacturing Strategic Planning Study A-7

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

Production Costs +/- % Change

Costs related to integrating 

manufacturing technologies

%

e Production throughout%

f Overall cost of production%

15. Would you say that your answer to the question above is 

representative of your industry (of companies in similar 

lines of business), or of only a subset? Please explain briefly 

how, if at all, the anticipated impacts for your company may 

be different from the industry as a whole, or how different 

industry segments may be affected differently.

16.Switching from thinking about costs to thinking about 

product quality or new product offerings, could you describe 

what changes could be expected if these capabilities were 

all met today? A range is fine.

a. Changes in the performance of existing products?

b. Changes in the amount of customization within existing 

product lines?

c. Introduction of new products or product lines?

d. Other

17. To enable us to combine your responses with the responses 

of others and provide NIST with a sense of potential impacts 

at the industry level, could you quantify these impacts on 

product quality or new product offerings in terms of a 

relative change in your company’s sales? A range is fine.

Impact on Sales%

18. Would you say that your answer to the product quality 

question above is representative of your industry (of 

companies in similar lines of business), or of only a subset? 

Please explain briefly how, if at all, the anticipated impacts 

for your company may be different from the industry as a 

whole, or how different industry segments may be affected 

differently.

19. Would you expect any changes in your company’s 

investment patterns or risk tolerance, if the types of 

technologies discussed above were made available? If so, 

what types of changes?

Potential Roles for NIST

20.Reflecting back on the barriers to adoption for smart 

manufacturing technologies discussed earlier, which would 

be the most important infrastructure technologies NIST 

could support to promote adoption?
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

A-8

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

21.Are there near term activities NIST could engage in that 

would help promote the adoption of existing (or close to 

market) smart manufacturing technologies and techniques 

(activities having impact over next 3–5 years)?

22.Are there longer terms research agendas NIST could 

undertake/coordinate/fund that would increase the benefit 

of smart manufacturing in the future (activities having 

impact over the next 5–10 years)?

23. Is there anything else that we should have asked you or 

that you would like to mention?

24.Could you recommend any developers or end-users that we 

could reach out to for this study?

We Greatly Appreciate Your Time and Input
This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Appendix B:

NIST Advanced 

Manufacturing 

Strategic Planning 

Study
B

-1

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

B.1 INTERVIEW GUIDE: PERSPECTIVES OF 

USERS OF SMART MANUFACTURING 

PROCESSES

The National Institute of Standards and Technology (NIST) in 

the U.S. Department of Commerce has contracted with RTI 

International to conduct an economic analysis of standards, 

measurement, and general purpose technology needs that 

inhibit efficient development and adoption of advanced 

manufacturing in the United States.

The objectives of this critical strategic planning study are to  identify current and emerging needs related to 

standards and measurement,

 estimate the economic impact of meeting these needs, 

and

 review public policy and investment options.

The study has a particular focus on 4 aspects of advanced 

manufacturing: (1) robotics and automation, (2) smart 

manufacturing processes, (3) 3D Printing (additive 

manufacturing), and (4) roll-to-roll manufacturing. The focus of 

our conversation is smart manufacturing.

Your perspective will help guide NIST’s planning and investment 

process. Participation in this analysis is confidential; only 

aggregated information will be included in any deliverables or 

communications. Your name and your company’s name will not 

be disclosed. We do not wish to discuss specific products, 

strategies, or technologies; but rather your thoughts about how 

investments in standards and measurement technologies would 

affect your company and companies like yours.

Our research products will be an economic analysis, final 

report, and presentation materials. All deliverables will be 

publicly available in late 2015 and these will be shared with you 

as soon as they are released.

If you have questions, please contact:  Mike Gallaher, Case Study Lead, RTI, 919

-541-5935 or 

mpg@rti.org

 Alan O’Connor, Principal Investigator, RTI, 919-541-

8841 or oconnor@rti.org
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

B-2

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

 Gary Anderson, NIST Project Officer, NIST, 301-975-

5238 or gary.anderson@nist.gov

This collection of information contains Paperwork Reduction Act 

(PRA) requirements approved by the Office of Management and 

Budget (OMB). Notwithstanding any other provisions of the law, 

no person is required to respond to, nor shall any person be 

subject to a penalty for failure to comply with, a collection of 

information subject to the requirements of the PRA unless that 

collection of information displays a currently valid OMB control 

number. Public reporting burden for this collection is estimated 

to be 35 minutes per response, including the time for reviewing 

instructions, searching existing data sources, gathering and 

maintaining the data needed and completing and reviewing the 

collection of information. Send comments regarding this burden 

estimate or any aspect of this collection of information, 

including suggestions for reducing this burden, to the National 

Institute of Standards and Technology, Attn., Gary Anderson, 

gary.anderson@nist.gov, (301) 975-5238. The OMB Control 

Number is 0693-0033, with an expiration date of 03/31/2016.

Respondent Contact Information

Name

Title

Division

Company

Industry

Phone

Email

Location

For our conversation today we are defining smart 

manufacturing to include

The creation, communication and use of 

electronic information, as well as the interface of 

these information systems with the human 

element, for data-driven decision support. This 

includes how data and information generated 

during the production process is communicated 

and used to improve the design, engineering, and 

production phases of the product cycle.
Appendix B 

— NIST Advanced Manufacturing Strategic Planning Study B-3

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

1. Does this definition cover the relevant areas you typically 

include under “smart manufacturing?” If not, what else do 

you include in “smart manufacturing”?

2. Please describe your background as it relates to smart 

manufacturing. How did you come to be in your current 

position?

3. Are you familiar with NIST’s activities? If so, please give a 

brief description of any ways that NIST currently impacts 

your work (through, e.g., any standards, calibration and 

measurement, scientific and engineering data relevant to 

your work).

Company Background and Level of R&D

4. How would you describe your company’s primary line of 

business (e.g., industry classification)? What kinds of 

products/services does your firm produce/provide and which 

are currently – or in the future could – benefit from smart 

manufacturing technology/techniques?

a. Approximately what percentage of your company’s sales 

revenue is associated with your division, or the division 

for which you are responsible? A range is fine.

5. Is the amount of R&D your company spends as a 

percentage of revenue similar to that of other companies in 

in your industry? If there are important differences, can you 

describe why your company may be more/less R&D-

intensive than others? As a proportion of revenue in a 

typical year, how much does your company invest in R&D?

6. Does your company conduct R&D related to the 

use/enhancement/implementation of smart manufacturing 

technology or techniques? If so, what are the broad 

objectives of that R&D (e.g., cost reduction, quality 

improvement, other)?

If Yes to question 6 -

7. Roughly what proportion of your company’s total R&D is 

related to the use of smart manufacturing? Would you 

expect other companies in the same lines of business to say 

roughly the same?

8. As far as you are aware, is your division or company 

engaged with any industry consortia, standards 

organizations, or governing bodies specifically for smart 

manufacturing? If so, in which bodies do you participate and 

what are the underlying drivers for participation?
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

B-4

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Current Use of Smart Manufacturing and Barriers to 

Adoption

9. Please provide a brief description of your company’s use of 

smart manufacturing technologies in your manufacturing 

processes.

10. What additional areas of smart manufacturing (automation, 

sensing/monitoring, data feedback/integration) has your 

company considered, investigated or researched?

a. Have you conducted feasibility studies? Please describe.

b. Have you developed preliminary cost/benefit models? 

Please describe.

11. Why did you decide not to move forward (or are not moving 

as fast as you would like) with certain investments in smart 

manufacturing?

a. Financial benefits were not large enough

b. Technology not mature enough

c. Risk to product quality or delivery schedule

d. Limited resources such as investment capital and/or 

knowledgeable staff

e. Limited technical resources such as guidelines, tools, 

software or test cases

f. Legacy systems and entrenched staff and management 

practices

g. Cost of software

h. Logistical, down time, or hassle factors

Potential Future State of Smart Manufacturing

The Table B-1 describes a better state of the world in which a 

bundle of capabilities and technology infrastructure is available 

throughout the manufacturing industry. Industry needs are in 

the left hand column, and NIST-provided technology 

infrastructure that is needed to develop and diffuse the 

capabilities is in the right hand column.
Appendix B — NIST Advanced Manufacturing Strategic Planning Study

B-5

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Table B-1. Better State of the World: Capabilities and Technology Infrastructure

Available

Smart Manufacturing Capabilities

Infrastructure Technology to Help 

Meet Needs

Managing digital data streams through models:

• CAD models including material characteristics,

• Simulation models of part creation and plant 

layout, and

• Rapid automated costing functions.

High-fidelity process models, physical 

model representation for flexible objects, 

simplified modular applications of CAM 

software for less sophisticated uses, data 

standardization, standard and simpler 

equipment interfaces to facilitate 

consistent data entry for less-skilled 

workers, standard terminology for 

automated part costing.

Enhanced sensing and monitoring:

• “State estimation” of critical manufacturing 

machines (for example: vibration, acoustics, 

temperature, tolerances, and pressure), and

• Real-time monitoring of product attributes as they 

move through various stages of the production 

process

In-process measuring and monitoring for 

physical processes, self-powered 

sensors, robust sensors to withstand 

harsh manufacturing processes, data 

standardization, methods for calibrating 

sensing and monitoring systems.

Seamless transmission of digital information:

• Wireless transmission of digital information 

without interference from other data channels,

• Seamless integration of smart sensors,

• Interoperability between different platforms such 

as CAD/CAM, and

• Secure data transmission (wired and wireless).

Secure data transmission; secure cloud 

computing and data sharing; standard 

communication protocols; retrofitable, 

plug-and-play data communications 

systems; data interoperability of 3D 

model parameters and product 

manufacturing information

Advances in analyzing data and trends:

• Interpretation and aggregation of data from 

sensing and monitoring networks,

• “Big data” techniques for manufacturing,

• predictive maintenance,

• Reduction of false positives, and

• Cloud computing and fee-for-service cloud based 

algorithms for product design, simulation, and 

manufacturing design.

Algorithms to interpret data from 

disparate sensors and systems; definition 

of important, relevant, and meaningful 

data to collect for predictive maintenance

Efficiently communicating information to decision 

makers:

• Comprehensive information interfaces humancomputer interaction-based design, and

• Easy-to-interpret interfaces accessible from any 

location.

Common taxonomy across platforms and 

disciplines

Standards in interface design for 

manufacturing equipment 

Determining required action and implementing action:

• Real-time feedback of enhanced sensing and 

monitoring data into factory decision making,

• Automated optimization-based decision making 

that functions independent of human interaction,

• Machine learning decision making algorithms for 

manufacturing, and

• Reconfigurability of manufacturing systems.

Tested and validated decision models 
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

B-6

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

12.Based on your role as a user of smart manufacturing 

processes, are there any additional capabilities or 

technology infrastructure needs that you feel are not 

captured in the table above?

13. Using the below scale, please indicate (a) the importance of 

each of the capabilities listed in the table and (b) the level 

of additional development needed.

(a) Importance

(b) Level of additional 

development needed

Capabilities 1 2 3 4 5 Low Medium High

Managing digital data streams 

through models

Enhanced sensing and 

monitoring

Seamless transmission of 

digital information

Advances in analyzing data 

and trends

Efficiently communicating 

information to decision 

makers

Determining required action 

and implementing action

1 being low; 5 being high

Potential Benefits of Enhanced Smart Manufacturing 

Capabilities

The following questions ask about the impact to your 

company’s product design and manufacturing processes if these 

enhanced manufacturing capabilities were available:

14. To enable us to combine your responses with the responses 

of others and provide NIST with a sense of potential impacts 

at the industry level, could you quantify the impacts we 

have discussed in terms of the following metrics? A range is 

fine.

Production Costs +/- % Change

a Cost of materials %

b Cost of energy/electricity %

c Cost of labor %

d Cost of capital equipment %

Costs of maintenance %
Appendix B 

— NIST Advanced Manufacturing Strategic Planning Study B-7

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

Production Costs +/- % Change

Costs related to integrating 

manufacturing technologies

%

e Production throughout%

f Overall cost of production%

15. Would you say that your answer to the question above is 

representative of your industry (of companies in similar 

lines of business), or of only a subset? Please explain briefly 

how, if at all, the anticipated impacts for your company may 

be different from the industry as a whole, or how different 

industry segments may be affected differently.

16.Switching from thinking about costs to thinking about 

product quality or new product offerings, could you describe 

what changes could be expected if these capabilities were 

all met today? A range is fine.

a. Changes in the performance of existing products?

b. Changes in the amount of customization within existing 

product lines?

c. Introduction of new products or product lines?

d. Other

17. To enable us to combine your responses with the responses 

of others and provide NIST with a sense of potential impacts 

at the industry level, could you quantify these impacts on 

product quality or new product offerings in terms of a 

relative change in your company’s sales? A range is fine. 

Impact on Sales%

18. Would you say that your answer to the product quality 

question above is representative of your industry (of 

companies in similar lines of business), or of only a subset? 

Please explain briefly how, if at all, the anticipated impacts 

for your company may be different from the industry as a 

whole, or how different industry segments may be affected 

differently.

19. Would you expect any changes in your company’s 

investment patterns or risk tolerance, if the types of 

technologies discussed above were made available? If so, 

what types of changes?
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

B-8

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Potential Roles for NIST

20.Reflecting back on the barriers to adoption for smart 

manufacturing technologies discussed earlier, which would 

be the most important infrastructure technologies NIST 

could support to promote adoption?

21.Are there near term activities NIST could engage in that 

would help promote the adoption of existing (or close to 

market) smart manufacturing technologies and techniques 

(activities having impact over next 3–5 years)?

22.Are there longer terms research agendas NIST could 

undertake/coordinate/fund that would increase the benefit 

of smart manufacturing in the future (activities having 

impact over the next 5–10 years)?

23. Is there anything else that we should have asked you or 

that you would like to mention?

24.Could you recommend any developers or end-users that we 

could reach out to for this study?

We Greatly Appreciate Your Time and Input
This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

Appendix C:

Example Vignettes
C

-1

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

The following are two examples of technology infrastructure

needs identified during stakeholder interviews and the approach 

to estimating the economic benefits.

C.1 CLOUD-BASED SOFTWARE AND COMPUTING 

FOR SMALL AND MEDIUM-SIZED 

MANUFACTURERS

RTI spoke with several software companies that are currently 

looking to license digital manufacturing software packages to 

companies in a variety of sectors, including industrial 

equipment, automotive, aerospace, and shipbuilding. In the 

terminology described above, these software companies can be 

considered technology developers supporting users in the 

discrete manufacturing sectors.

The software enables SMEs and OEMs to more efficiently 

manage the entire life cycles for their products. Among the 

many capabilities these software packages offer, one key 

capability is virtual simulation of manufacturing production 

systems. Being able to accurately and flexibly simulate the 

production of new products in a digital environment has the 

first-order effect of decreasing the costs of prototyping and 

testing, which has traditionally been done in the physical 

environment and can lead to substantial costs. The second-

order effect of virtual simulation is increased innovation, 

because the marginal costs associated with bringing new 

products to fruition can be orders of magnitude smaller.

The current market is primarily limited to large manufacturers 

that are able to justify the high fixed cost of a new software 

package and the required computational power based on 

economies of scale/ROI. It is difficult for most SMEs to justify 

the high fixed costs of licensing these types of software in the 

traditional manner or purchasing the computers needed to run 

the simulations. Given that the current market is mainly 

constrained to large firms, decreasing the cost (cost of software 

plus the cost of implementation) would increase market 

penetration and adoption of these technologies among SMEs, 

which could yield substantial economic benefits.

To estimate the economic benefits associated with the cloud-

based licensing of manufacturing software and computing

power, we would need information on both the cost reduction of 

existing R&D activities and the value creation of new 
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

C-2

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

products/processes enabled by enhanced simulation 

capabilities. From a market perspective, we are modeling the 

product or service the SME is selling, hence we need 

information on the following:

 Change in production costs (shift in the supply function)

– Cost reduction of the software ($/use) relative to the 

old market model

– Decreased cost of prototyping and testing new 

products (percentage change in the average 

cost/product)

 Change in produced value/quality (shift in the demand 

function)

– Value of increased levels of innovation and improved 

product attributes resulting from using the 

simulation (users’ increased willingness to pay)

With these shifts, adoption may increase as a result of the 

lower cost and increased demand. The quantity being modeled 

in this supply-demand framework can either be the number of 

SMEs using simulation capabilities or the number of products 

being designed using simulation capabilities. In the latter, the 

new simulation capabilities would streamline the process, 

leading to companies redesigning their product offerings more 

frequently, which would provide increased value to OEMs as 

products evolve and improve at a faster rate.

One software company that RTI spoke with envisions a model 

in which NIST’s MEPs would host their software tools in the 

cloud and rent access to the software to regional manufacturing 

companies. This more centralized approach to software 

ownership would decrease the direct software costs incurred by 

any single manufacturer and potentially decrease the indirect 

implementation costs. In addition to software ownership, MEPs 

could provide ongoing training and support for the cloud-based 

software. While NIST would want to consider such a model 

based on the merits of particular software packages, the idea 

could be applied more broadly to an open protocol 

manufacturing platform, and MEPs could purchase and license 

plug-in applications developed by the private sector. As 

mentioned in the Technology & Industry Report, a specific 

technology infrastructure need for smart manufacturing 

processes is high-fidelity process models. If successfully 
Appendix C 

— Example Vignettes C-3

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

developed, NIST could distribute these process models using 

the MEP network.

C.2 PREDICTIVE MAINTENANCE IN THE 

ELECTRIC UTILITY INDUSTRY

One large electric utility company is actively expanding the 

sensing/monitoring performance of its gas turbines and then 

centralizing the data analysis at its office headquarters. The 

price of an electricity generation turbine ranges from $20 

million to $80 million; thus, predictive capabilities to help avoid 

catastrophic failure could have a significant economic benefit. 

In addition, predictive analysis systems could help coordinate 

routine maintenance and minimize equipment downtime.

There is no shortage of companies producing the required 

sensor, analysis software, and reporting packages. However, 

the open-source communications protocols to link individual 

products do not exist. As a result, turbine manufacturers have 

been able to dominate the market by providing dedicated 

vertical systems that integrate the sensing, communication, 

analysis, and reporting on equipment vibration and other key 

parameters. However, these custom systems tend to be 

expensive (about $250,000 each), which has greatly limited 

their adoption (hence potential benefits).

If, however, open-source communications platforms were 

available, flexible systems could be developed at a much lower 

cost using readily available components from specialized 

developers (potentially as low as $10,000). The following 

outcomes might result:

1. The cost of delivering the sensing, analysis, and 

reporting capabilities needed for predictive maintenance 

might decrease. This change can be viewed as a 

downward shift in the supply curve that lowers the cost 

per system (keeping capabilities of the system constant 

for the time). This lower cost generates a producer 

surplus for the technology developers, lowering 

production cost and greatly increasing the number of 

units sold (adoption).

2. The electric utilities as the users of the system now are 

able to realize the benefits from predictive maintenance, 

which are represented as the area under the demand 

curve. Benefits could include:
Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: 

Smart Manufacturing

C-4

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-00

7

– extended life expectancy of turbines due to a 

reduction in catastrophic failures,

– reduced repair and routine maintenance costs, and

– higher overall capacity utilization rate (less need for 

redundant excess capacity).

In the above example, because the capabilities of the system 

did not change, the demand curve does not shift; users benefit 

from greater adoption and the realization of the benefits 

(because the cost of adoption has decreased). However, if 

additional capabilities are now available (e.g., in addition to 

monitoring vibration, the system is now capable of monitoring 

bearing wear and hot spots), this would shift the demand curve 

because the overall value of the system to the electric utility 

has increased (see Figure C-1).

Figure C-1. Market for Smart Sensing/Monitoring Systems

To estimate the economic benefits associated with the 

technology infrastructure (protocols) needed to support the 

open-source communications system, we would need the 

following information:

1. Cost reduction of the new systems ($$/system)

2. Potential increased adoption resulting from the cost 

reductions (number of additional systems)

3. Value of the benefits (listed above) to the electric utility

a. Holding capabilities the same and/or

b. with expanded/enhanced capabilities

System 

Value 

& 

Cost

Enhanced Functions 

 increased value to users

Open Source

 lowers costs for developers

Sales

Demand

Supply
Appendix C 

— Example Vignettes C-5

This publication is available free of charge from: http://dx.doi.org/10.6028/NIST.GCR.16-007

In this example, a single electric utility has launched an 

initiative to solve the open-source protocol barriers in 

conjunction with a team of sensor and software suppliers. This 

example illustrates the magnitude of the potential benefit to the 

utility. However, respondents indicated that what was more 

urgently needed was a larger initiative that would include 

additional utilities, suppliers, and standards organizations such 

as NIST.