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This paper introduces a Workforce Readiness Level (WRL) framework designed to bridge the skills gap in smart manufacturing by adapting the Technology Readiness Level scale into nine competency stages and a four-pillar rubric. The framework was applied in a university smart-manufacturing lab, analyzing 89 capstone projects to assess workforce readiness, revealing that industry-embedded experience is crucial for advancing to higher competency stages. Key findings indicate that while strong analytics profiles exist, significant gaps in cyber-physical systems and data-driven decision-making persist, highlighting the need for targeted educational improvements.
Industry-embedded experience, not just coursework, is the key to advancing workforce readiness in smart manufacturing.
The convergence of artificial intelligence (AI), Industrial Internet of Things, cyber-physical systems, and advanced robotics is reshaping manufacturing faster than engineering curricula can adapt, widening the gap between the competencies required on the shop floor and those delivered by traditional engineering and technology education. This paper proposes a Workforce Readiness Level (WRL) framework, which adapts the Technology Readiness Level scale into nine progressive competency stages and a four-pillar rubric, digital and AI literacy, cyber-physical systems fluency, human-machine collaboration, and data-driven decision making, aggregated through a composite stage score and a cohort-level workforce-readiness index under a ``no-thin-pillar''rule. The framework is instantiated at a university smart-manufacturing teaching laboratory and draws on 89 sponsored capstone projects delivered over four semesters, four of which are analyzed in depth. Four pillars jointly span the relevant ABET student outcomes. Across the highlighted cohorts the workforce-readiness index ranged from 5.2 to 6.4, and the no-thin-pillar rule was diagnostically informative in three of the four cases and the binding certification constraint in one, repeatedly surfacing cyber-physical and data-driven-decision gaps concealed behind strong analytics profiles; advancement to the highest stages was gated by industry-embedded experience rather than additional coursework. WRL offers educators, accreditation bodies, and regional workforce systems a common, evidence-based instrument for diagnosing and advancing workforce readiness; future work will calibrate pillar weights and test reliability and predictive validity.
