arXiv:2606.17269v1 Announce Type: new Abstract: In skill-constrained production-inventory systems, the qualified human capacity available tomorrow depends on training decisions made today: production requires certified workers, certifications decay unless maintained, and training consumes the same scarce worker hours that production needs now. We study a closed-loop skill-constrained model predictive controller that, at every shift, solves a finite-horizon mixed-integer program over production, inventory, backlog, and training, with binary predicted certification, hard production eligibility,
The increasing complexity and fragility of global supply chains, coupled with growing labor skill gaps, drives the need for more sophisticated and adaptive control systems.
This research outlines a method for managing complex human-machine interactions in manufacturing through AI, directly impacting operational efficiency, resilience, and strategic workforce planning.
The explicit incorporation of human skill constraints and decay into predictive control models shifts manufacturing optimization from purely material/machine factors to integrated human capital management.
- · Manufacturers adopting advanced control systems
- · Supply chain software providers
- · Skilled labor with adaptable training programs
- · Manufacturing firms resistant to automation and data-driven workforce management
- · Outdated supply chain management methodologies
Manufacturing plants can optimize production and training simultaneously, leading to more resilient and efficient operations.
This approach could lead to a re-evaluation of workforce development and training strategies within industrial sectors, emphasizing continuous upskilling tied to production needs.
Broader adoption may enable more localized and agile manufacturing, reducing reliance on long, vulnerable global supply chains and potentially altering geopolitical production landscapes.
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Read at arXiv cs.AI