Elizabeth Engler Modic Editor emodic@gie.net

Manufacturing represents 12.5% of the U.S. Gross Domestic Product (GDP), so any improvement to a manufacturing operation can have a significant impact on overall economic competitiveness. However, to remain competitive, advanced, smart manufacturing technologies must continue to be developed, and barriers to implementation need to be addressed.

Smart manufacturing encompasses more than a machine tool with its spindle turning and chips flying. Manufacturers need software, hardware, robots, conveyers, sensors, quality/inspection equipment, and data collection – all working in harmony for efficient production and to make clear when things aren’t going as planned.

However, a recent economic analysis brief from the National Institute of Standards and Technology (NIST) finds six gaps in smart manufacturing capabilities:

  • 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
  • Required action and implementing action

These barriers to innovation increase the cost of smart manufacturing research & development (R&D), weaken private investment incentives, and magnify the role of public institutions. In turn, if these gaps are addressed it could save manufacturing companies $57.4 billion annually.

The NIST brief goes on to note that, “investments in public-private manufacturing research consortia and technology extension services may be required to develop and disseminate smart manufacturing technology infrastructure.”

Federal research funding cuts will hinder advancements in U.S. manufacturing competitiveness, such as the $4 million, National Science Foundation-supported project led by University of Michigan (UofM) engineering researchers collaborating with researchers from the University of Illinois at Urbana-Champaign and Cornell University, (Ithaca, New York). The Software-Defined Control project looks at increasing factory productivity and competitiveness and how by making a computer model of a physical system, operators can better detect and address anomalies, adapting quickly to manufacturing changes with minimal disruption to operations or production. UofM researchers say the same algorithms can also be used to redefine the production routes when a new part is introduced, or the desired production volume is changed, to maximize the security and profitability of the manufacturing operation.

As the cost to implement technology decreases and is more accessible, small- and medium-sized enterprises (SMEs) will be better positioned to adopt advanced technologies to become smarter manufacturers. Their return on investment (ROI) will be increased productivity, less downtime, deeper operation insights, and improved product quality, delivering positive financial impact on companies, and in turn consumers.

So, while many manufacturers have been supportive of the administration’s efforts to ease regulations and lower taxes, proposed cuts shouldn’t be ignored.

How much support for R&D can be eliminated without it causing manufacturers to lose their competitiveness?