It is crucial that we recognize the role of the manufacturing worker has had in building manufacturing capabilities, a cornerstone of US competitiveness. Investment in innovative, smart manufacturing processing methods, industrial cyber-physical systems (ICPS), automation technologies, and information/operational technology (IT/OT) has advanced US manufacturing (across small, medium, and large manufacturing enterprises) connectivity, automation, and operational efficiency (Reischauer, 2018, Kim et al., 2018, Lasi et al., 2014). As an enabler of smart manufacturing, industrial cyber-physical systems (ICPS) are critical to the success of manufacturing (Ahmed et al., 2021). In ICPS, the cyber, physical, and hybrid technologies integrate billions of objects with unique addressability, with or without human involvement. The volume of information generated in smart and connected factories is overwhelming, and worker roles must change as traditionally separate IT and OT domains converge (Gartner, 2014). Information technology covers “…[t]he entire spectrum of technologies for information processing, including software, hardware, communications technologies, and related services. IT does not include embedded technologies that do not generate data for enterprise use.” Operating technology “[i]s hardware and software that detects or causes a change through the direct monitoring and/or control of physical devices, processes and events in the enterprise” (Gartner in Desai 2016).

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Most US manufacturers identify the primary cause of today’s worker shortage as a “shifting skill set due to the introduction of new advanced technology and automation.” (Giffi et al., 2018) While more than half of manufacturers surveyed in Accenture (2015) have adopted collaborative robots (cobots), artificial intelligence (AI), and other smart manufacturing technologies, many manufacturers simply do not have a capable IT/OT workforce for implementing ICPS, which limits their ability to realize the benefits (e.g., improved cost, quality, productivity, and safety). Even though smart manufacturing technologies and ICPS advance manufacturing worker productivity, these technologies can also be responsible for exacerbating the skill gaps (WEF, 2018). This is because ICPS shift worker skill sets, from low/medium to high-skill jobs (WEF, 2018). Further, professional skills have a reported “half-life” of five years, compelling workers to change jobs every 4.5 years (WEF, 2017). This threat is magnified due to the loss of worker knowledge and expertise through retirements of the baby-boom generation (Giffi et al., 2018). Manufacturing workers are experiencing a paradigm without precedent: a pace of skills obsolescence that requires continuous learning and career agility (WEF, 2019). To respond to the aforementioned challenges, innovations that incorporate the expertise, flexibility, and adaptability of manufacturing workers are being investigated in industry and academia in order to reach new levels of efficiency, productivity, and safety. Example innovations include collaborative robots, augmented reality, intelligent machine tools with embedded metrology, digital twinning, IoT sensors and sensor fusion, industrial AI/ML business intelligence, ultrafast 3D printing and hybrid manufacturing, smart projector interfaces, and voice directed actions (Kim et al., 2019, Wiedenmaier et al., 2003, Cahya & Giuliani, 2018, Mihelj et al., 2019, Gattullo et al., 2019, Chheda et al., 2013).

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Adapting manufacturing processes to better respond to the emerging and fundamentally different manufacturing environment requires rapid interventions and continuous worker training. Modern learning technologies, when appropriately applied, can embed learning processes intrinsic to production routines. The new training and education systems should be more worker specific and promote knowledge transfer across manufacturing workers while reducing the mental resources. The dynamic character of future manufacturing jobs requires continuous learning and new and expanded skillsets (e.g., analytics and cybersecurity) that can balance business understanding, innovative thinking, and personal interactions. The current training paradigm for manufacturing does not equip workers for future manufacturing jobs or supporting ICPS environments. Future training paradigms for ICPS environment that includes collaborative robots, augmented reality, intelligent machine tools with embedded metrology, digital twinning, IoT sensors and sensor fusion, industrial AI/ML business intelligence, ultrafast 3D printing and hybrid manufacturing, smart projector interfaces, and voice directed actions, should be designed with a human worker centric approach (i.e., a human-in-the-loop focus). However, creating these training paradigms requires a fundamental understanding of how a manufacturing human worker fits within smart manufacturing or ICPS technology loop.

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