AI in manufacturing
AI in manufacturing: predictive maintenance, quality inspection (vision), production and supply optimization, and the OT/safety constraints.
AI is reshaping manufacturing across the value chain, from predictive maintenance and quality inspection to demand forecasting and generative design. For MBA-level professionals, fluency means knowing where AI creates measurable value on the plant floor and in supply chains, not just in theory. This block grounds core AI concepts in manufacturing contexts, then examines real use cases with honest ROI expectations, since many pilots fail to scale. It closes with governance: model risk in safety-critical environments, data quality from sensors and MES systems, and the checks needed before deploying AI in production. The goal is sector-fluent judgment, enabling you to evaluate vendor claims, prioritize use cases, and ask the right risk questions.
Ce que vous allez maîtriser
- Explain core AI and machine learning concepts using manufacturing-specific examples like predictive maintenance and computer vision inspection
- Identify and prioritize high-value AI use cases across the manufacturing value chain, from R&D to production to supply chain
- Evaluate AI vendor solutions and pilot proposals with realistic ROI expectations and adoption timelines
- Apply governance checklists and risk assessments before approving AI deployment in safety-critical or production environments