Options for a National Plan for Smart Manufacturing (2024) / Chapter Skim
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2 The Value and State of Smart Manufacturing in the United States
2 The Value and State of Smart Manufacturing in the United States
Pages 11-26
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From page 11... ...
Broadly accessible core information and operational modeling and simulation capabilities are developed through a facilitated community source and market approach for contribution and validation; sharing through standardized ap proaches; and low-barrier accessibility to small, medium, and large enterprises. Smart manufacturing can also be viewed beyond technology and operations as the strategic investment in people, technology, and practice that enables manu facturers to extract significantly increased value from their existing assets and 1 The original version appeared in a Time magazine informational cover in 2010 as an article by S ujeet Chand (Chief Technology Officer, Rockwell Automation)
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From page 12... ...
Original Artwork Copyright © 2010 Rockwell Automation, Inc.
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From page 13... ...
As a general statement, smart manufacturing defines the digital transformation of the U.S. manufacturing industry for proactive management, automation and autonomous operation of assets, line and factory operations, supply chains, and ecosystems.
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(b) A visualization of the changes that smart manufacturing can provide to the traditional manufacturing relationships.
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Sta, 2018, "Cloud Computing: Potential Risks and Security Approaches," Pp. 69–78 in Lecture Notes of the ICST Institute for Computer Sciences, Social Informatics and Telecommu nications Engineering, T.F.
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"Industrie 4.0" started from discrete parts manufacturing, which has remained a dominant continuing focus. Smart manufacturing evolved to emphasize energy consumption, environmental sustainability, and safety.
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• Consistently shared data, models, and tools curated and developed for shared application needs within industry segments while using industry wide infrastructure, tools, and practices within the United States, including a national transformative data infrastructure. • Distributed technology and user interface simplification, with complexity managed with data-centered modeling and workflow.
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From page 18... ...
One big challenge is that IIoT technology companies are under intense market pressure to focus on "first to market" over security, thereby passing on risk to final consumers. The aggregated vulnerability assumed by consumers has created a sig nificant national concern.11 The Cyberspace Solarium Commission's call for action urges companies to shift from a "first to market" mentality to a "secure to market" manufacturing ecosystem to enable a secure IT/OT convergence.12 Artificial Intelligence at Scale Smart manufacturing and AI have evolved together over 50 years such that today they are integrated concepts that continue to evolve and that are expected to be pervasive with dramatic impact.
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From page 19... ...
There are also the AI systems that support vari ous stages of the manufacturing process, including human–computer interfacing; validating and verifying data; selecting data and models; managing contracts; exchanging data within and between companies; and enhancing security, privacy, and protection of confidential information. Finally, AI is expected to offer key network capabilities to learn, categorize, and scale data accumulated throughout the industry from years of experience and provide opportunities to derive new insights for continuous improvement of factory operations and products.
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From page 20... ...
interfacing with interprocess, -factory, -company, and –supply chain operations; and the business structures and market drivers that have reinforced them. Compartmentalization -- as result of vertical optimization, software systems developed for individual function often with independent architectures, standards that isolate operational layers, business structures designed for individual opera tions, and business policies designed to isolate the use of data -- has promoted and continues to promote a large legacy of software applications in which data are trapped in a software function.
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With the understanding that there are many challenges yet to overcome (e.g., legal, intellectual property, security) , to be discussed in Chapter 3, one can anticipate equally valuable "data supply chains," cyber and IT "workflows," inter- and intrafactory data transformations, and opera tional orchestration of data and models throughout the industry ecosystem.
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According to a survey of manufacturing companies performed by the Clean Energy Smart Manufacturing Innovation Insti tute (CESMII) and SME18 the number one challenge encountered while pursuing a smart manufacturing strategy is a lack of skilled talent (59 percent of respondents)
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CURRENT STATE OF SMART MANUFACTURING IN THE UNITED STATES AND THE WORLD Global Race Fifty years of manufacturing digitization have made it far easier to conduct business in a distributed fashion. The digitization of the existing business structures is now giving way to the digital transformation of the manufacturing industry into a networked industry defined by smart manufacturing.
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Methodologies such as the Cybersecurity Maturity Model Certification programs are a beginning, but work is required to ensure robust implementation and understanding.21 More efforts around devising the future of robust, resilient, energy efficient, and decarbonized smart manufacturing systems, which extend beyond the cur rent world of insecure technologies to new systems of architectures that will exponentially increase the United States' ability to resist cyberattacks, are needed. Manufacturing innovation institutes such as the Cybersecurity Manufacturing Innovation Institute (CyManII)
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From page 25... ...
Industry Data- and Expertise-Sharing Strategies The potential for smart manufacturing and AI to increase U.S. manufactur ing productivity, precision, and performance accrues from actions that create a market-driven, networked industry willing to share and aggregate data; generate AI development with a broader base of data science and data engineering expertise; and collaborate on applications, all at an industry scale.22 There is a rich base of contextualized data for many kinds of operations across the collective base of small, medium, and large manufacturers in the United States, but currently the data reside in distributed and compartmentalized operational and business structures.
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From page 26... ...
Those benefits include new network-based business models that provide faster process development and increased productivity, quality, and environmental sustainability.24 Realizing this potential requires the adoption of new business structures and practices that enable the industry to become truly networked and interconnected. Even though the real-time collection and analysis of streaming data from the shop floors are key enablers for improving automated processes and monitoring the state of cyber-physical manufacturing plants, streaming data are character ized by high-volume, diverse, heterogeneous formats and protocols, and variable spatial-temporal resolution, which imposes multiple challenges when it comes to transforming data into actionable knowledge.
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