Generative AI for Industrial and Manufacturing Decision Support: A Targeted Review and Illustrative Framework

Generative artificial intelligence (GenAI) may support industrial decisions, but deployment requires feasibility checks and fair comparisons with conventional optimization. This targeted narrative synthesis proposes a Sense–Generate–Verify–Decide–Act decision contract for two methodological tracks: language-model-based operations support and generative design and optimization. The contribution is an inspectable integration of established mechanisms rather than a new solver or a validated end-to-end GenAI system. Five published job-shop instances and twelve seeds per instance compare a neural–heuristic portfolio with stochastic heuristic portfolios. The hybrid improves mean makespan on one instance, worsens it on four, and yields no familywise-significant advantage. A supplementary independent graph audit reproduces 120 selected portfolio schedules; 300 artificial semantic and constraint violations are rejected, and a bounded-time MILP offers a conventional reference. Four additional published instances provide a small, separately reported generalization check. Host-specific timing comparisons do not establish compute-invariant superiority. A small facility-layout geometry example illustrates the verifier interface without evaluating a generative layout model. Historical literature-screening counts are unavailable; industrial policy parameters, end-to-end LLM latency, and operational safety have not been validated. The results are therefore a conditional software feasibility demonstration, but they do not provide evidence of industrial deployment benefits.