Integrated Application of Overall Equipment Effectiveness and Free Generative-AI Tools to Improve Productivity in a CNC Machining Cell

Overall equipment effectiveness (OEE) is a consolidated metric for manufacturing performance, but the exponential growth of data generated by manufacturing execution systems (MES) in Industry 4.0 environments renders manual analysis impractical. This study investigates whether three freely available generative artificial intelligence (AI) tools—ChatGPT, Gemini, and Copilot—can support the diagnosis of OEE losses and the formulation of improvement actions in a real CNC machining cell. A single-case, mixed-method study was conducted in a Brazilian metalworking company from January 2023 to July 2025, using 31 months of MES-derived OEE data from a two-lathe U-shaped cell. The three large language models were queried under an identical prompt protocol, and their outputs were benchmarked against operationalized criteria of consistency, depth, and sensitivity, then validated by a cross-functional team of operations, maintenance, quality, and tooling experts. All three tools converged in identifying availability as the main loss driver (with setup-time growth as a secondary bottleneck), while differing in diagnostic depth and sensitivity. The validated diagnostics were consolidated into a five-phase strategic roadmap structured by the 5W2H method, producing expected ranges, derived from AI-projected scenarios and cross-functional team consensus, of approximately +18 percentage points in availability and +22 percentage points in cell OEE over a 90-day horizon. These ranges represent projections to be empirically validated in a planned longitudinal follow-up study, not post-intervention measurements. The study contributes a replicable low-cost framework for integrating free generative AI into OEE management and explicitly documents the limitations of non-deterministic, uncurated LLMs in industrial decision support.