NIST Economic Analysis Briefs 4 

The Economic Impact of Technology Infrastructure for 

Smart Manufacturing 

Gary Anderson (gary.anderson@nist.gov) 

Technology Partnerships Office, Innovation & Industry Services 

October 2016 Key Findings 

•	 Novel smart manufacturing technology infrastructure would save manufacturers $57.4 billion
 

annually.
 

•	 Barriers to innovation increase the cost of smart manufacturing R&D, weaken private investment 

incentives, and magnify the role of public institutions. 

•	 Overcoming critical technical barriers may require investments in public-private manufacturing
 

consortia.
 

•	 Small enterprises face significant barriers to adoption of smart manufacturing technology. 

Figure 1: Overview of Smart Manufacturing 

Introduction 

Smart manufacturing processes use digital information to optimize product, factory, and supply-chain 

operations. Advances in sensing, communicating, and analyzing digital information have introduced a 

vast array of new opportunities for increasing efficiency, lowering costs, and improving quality. 

However, a recent study1 commissioned by the National Institute of Standards and Technology (NIST) 

finds that while prior research contains little “agreement on why [smart manufacturing] is not being 

National Institute of Standards and Technology 

Willie May, Under Secretary of Commerce for Standards 1 

and Technology and Director 
 

 

 

adopted at a faster rate in key manufacturing sectors of the U.S. economy.” In fact “barriers exist to 

the adoption of all but the simplest of smart manufacturing technologies. Enhancements in the 

technology infrastructure are needed to develop next-generation smart manufacturing technologies.” 

Technology infrastructure2 includes a broad array of public and quasi-public technologies and 

technical knowledge. That infrastructure can support the research, development, production, and 

diffusion activities taking place at national laboratories, universities and firms alike. 

The unique contribution of this analysis is the identification of barriers to the adoption of smart 

manufacturing technology and estimation of the impact of eliminating these barriers. Based on over 

80 in-depth interviews and an extensive review of prior studies, the analysis identifies six critical gaps 

in smart manufacturing capabilities and the specific technology infrastructure needed to close those 

gaps. Table 1 identified these gaps. The analysis reflects the insights of stakeholders throughout the 

entire manufacturing supply chain including smart manufacturing equipment developers, smart 

manufacturing service providers, and manufacturers, as well as stakeholders in research organizations 

and industry associations. Table 1 details the critical gaps identified in this analysis and identifies the 

potential economic benefits of meeting these needs. Meeting these needs offers an array of 

opportunities to expand capabilities, improve product quality, reduce costs and cut the time to 

introduce innovative products, services and processes to the market. 

The analysis estimates that meeting these needs would save manufacturing companies $57.4 billion 

annually. Figure 2 shows the projected impacts of meeting each identified need. Enhanced sensing 

and monitoring, seamless transmission of digital information, and advances in analyzing data and 

trends each has the potential to save manufacturers in excess of $10 billion annually. There are 

slightly lower annual benefits associated with meeting needs in the following areas: determining and 

implementing required actions; managing digital data through models; and efficient communication 

of information to decision makers. 

Figure 2: Total Annual Impact, Apportioned by Technology Need (Millions of 2013 US$) 

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Table 1. Capabilities, Needs and Potential Impacts 

Potential Benefits and Impacts 

Smart Manufacturing Technology Infrastructure of Enhanced Technology 

Capabilities Needed to Support Capabilities Infrastructure 

Managing digital data streams 

through models: 

material characteristics, 

creation and plant layout 

functions 

• CAD models including 

• simulation models of part 

• rapid automated costing 

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 

part creation 

for manufacturing 

development-to-production 

cycle 

• Streamlined simulation of 

• Enhanced ability to design 

• Reduced productEnhanced sensing and 

monitoring: 

•	 “state estimation” of 

critical manufacturing 

machines (e.g., vibration, 

acoustics, temperature, 

tolerances, and pressure), 

•	 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 • Reduced costs in factors 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 

• 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 

production such as capital, 

labor, energy, and materials 

from data analysis and 

efficient production 

• Energy reductions, active 

energy management 

• Reduced downtime, 

increased uptime 

• Improved security, reduced 

risk of Intellectual Property 

or safety issues 

• Reduced integration time 

• Freeing up staff time on 

connectivity and collating 

data 

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Table 1(Cont.). Capabilities, Needs and Potential Impacts 

Potential Benefits and Impacts 

Smart Manufacturing Technology Infrastructure of Enhanced Technology 

Capabilities Needed to Support Capabilities Infrastructure 

• Improved uptime 

• Enhanced monitoring of inAlgorithms to interpret data from 

disparate sensors and systems; 

definition of important, relevant, process quality 

and meaningful data to collect for 

predictive maintenance 

equipment effectiveness 

• Reduction of false positives 

• Increased overall 

• Better utilization of existing 

data sources 

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, 

• cloud computing and feefor-service cloud-based 

algorithms for product 

design, simulation, and 

manufacturing design 

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 • Accelerated development 

platforms and disciplines of interfaces by the private 

sector Standards in interface design for 

manufacturing equipment •	 More timely, evidencebased decision making 

Tested and validated decision 

models processes 

• Optimized manufacturing 

• Greater visibility and 

transparency in real-time 

manufacturing operations 

Determining required action 

and implementing action: 

• real-time feedback of 

enhanced sensing and 

monitoring data into 

factory decision making, 

• automated optimizationbased decision making 

that functions 

independent of human 

interaction, 

• machine-learning 

decision-making 

algorithms for 

manufacturing, and 

• reconfigurability of 

manufacturing systems 

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Key Findings 

The analysis conservatively estimates that meeting the identified needs would save manufacturing 

companies $57.4 billion annually, an approximately 3.2% reduction in the shop floor cost of 

production. The estimate is conservative because the quantified benefits do not include impacts 

such as R&D cost savings, improved product quality, accelerated development and 

commercialization of entirely new products, long-term growth and competitiveness impacts and 

other societal benefits. Respondents indicates these impacts would result from improved smart 

manufacturing technology infrastructure but they were not able to rigorously quantify these 

impacts. Further, the benefits of providing the needed technical infrastructure would persist but 

estimate accounts for impact in a single year. Given this conservative approach, the estimate is 

particularly impressive. 

Second, the analysis finds that investments in public-private manufacturing research consortia and 

technology extension services may be required to develop and disseminate smart manufacturing 

technology infrastructure. The study identifies consortia as an import tool for developing critical 

technology platforms that meet industry specifications. Consortia can also address critical 

interoperability issues and “bring the multidisciplinary teams together to solve the analysis problems 

that would advance smart manufacturing.” Consortia and public private partnerships aid the 

implementation of new technology which can be “as much an organizational and cultural challenge 

as it is a technical challenge.” These partnerships and extension services “could also help connect 

users with developers of smart manufacturing technologies by creating platforms” to transfer and 

disseminate technology. Consequently, developing and disseminating the technology infrastructure 

needed to support those platforms will require investments in both consortia and technologyextension services. 

The study also demonstrates that uncertainty, risk, network externalities and other barriers to 

innovation, or market failures3, can increase the cost of smart manufacturing R&D, diminish private 

investment incentives and increase the importance of public institutions in overcoming these 

barriers. The barriers exist across a number of the identified needs, because “manufacturers [are] 

not fully aware or convinced of the benefits, there can be significant technical risk for developers in 

investing in the required R&D.” Respondents indicated that they “have to learn what the value of 

that information is and how it can benefit us. At the moment that is all blurry.” Given this 

uncertainty, potential adopters are unwilling to pay for novel features, which in turn diminishes 

incentive to invest in the technologies. Wireless communications and cloud-based smart 

manufacturing each present risks to physical and intellectual property. Only by meeting needs for 

trusted third-party standards and performance data will adopters “know what they are buying at 

various cost points,” be confident in their cyber-physical security and fully compensate developers 

for their research investments. 

Small companies are both key beneficiaries of novel, smart-manufacturing technology infrastructure 

and critical to realizing its full value. However, the study finds that currently “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).” Novel technology 

infrastructure that decreased “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.” Providing the technology infrastructure to enable cloud-based smart 

manufacturing, for example, “could make big data storage and analytics more accessible for SMEs.” 

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Further, “a marketplace is needed to encourage startups in this space and related big data 

application.” SMEs stand to benefit as both adopters of smart -manufacturing technology and as 

providers of smart-manufacturing services. The study identifies potential new business models and 

public-private partnerships as a potential route to overcome these barriers. 

Finally, interviewees indicated that there are critical complementarities across the identified gaps in 

the technical infrastructure. For example, enhanced sensing capabilities 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.” Consequently, unbalanced investment, closing select technical gaps 

while leaving other needs unmet, would likely fail to fully realize economic impact. 

References 

[1] Anderson G (2013) The Impact of Federally Performed R&D: Twenty Years of Economic Analysis at 

the National Institute of Standards and Technology. 2013 Technology Transfer Society 

Conference. 

[2] Gallaher MP, Oliver ZT, Reith KT and O’Connor AC (2016) Economic Analysis of Technology 

Infrastructure Needs for Advanced Manufacturing: Smart Manufacturing. (NIST, Gaithersburg, 

MD) NIST GCR 16-007. 

[3] Link A Scott J (2011) The theory and practice of public-sector R&D economic impact analysis. 

(NIST, Gaithersburg, MD) Planning Report 11-1. 

https://www.nist.gov/sites/default/files/documents/director/planning/report11-1.pdf. 

[4] Tassey G (2007) The Technology Imperative. (Edward Elgar, Cheltenham). 

1 See Gallaher et al. [2]. 2 Technology infrastructure includes infratechnologies and technology platforms. Infratechnologies are 

technical tools, such as measurement and test methods, reference materials, scientific and engineering 

databases, process models, and the technical basis for physical and functional interfaces between 

individual components of both cyber and physical systems technologies. Technology platforms are 

precompetitive proofs of concept that demonstrate the potential commercial viability of multiple new or 

improved products, processes, or services. Technology infrastructure shares many common feature with 

tangible infrastructure. Namely, it is difficult and even undesirable to exclude potential users 

implementing the technology and usage of the technology infrastructure by a particular organization does 

not does not preclude others from benefiting to much the same extent. See Anderson [1], Link and Scott 

[3] and Tassey [4] for a richer discussion of the public good nature of technology infrastructure. 3 A market failure is a situation where free markets do not allocate resources efficiently. In particular, the 

study finds evidence that market failures such as network externalities, high technical risk, uncertainty 

and asymmetric information, and economies of scope all impact research in smart manufacturing 

technology infrastructure. The result is that markets invest too few resources in R&D. 

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