case-studies.cases.manufacturing-ai-pilot-to-production.subtitle
case-studies.cases.manufacturing-ai-pilot-to-production.client.description
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case-studies.cases.manufacturing-ai-pilot-to-production.challenge.summary
Six AI pilots running for 18 months with no path to production — €800K spent with nothing to show
No MLOps infrastructure: models were trained locally with no reproducibility, versioning, or deployment pipeline
Severe data quality issues across factory floor sensors, ERP systems, and quality control databases
No production architecture — pilots were built as Jupyter notebooks and Flask demos, not production systems
Each pilot built by a different vendor with no integration plan, incompatible tech stacks, and siloed data
New CTO needed to demonstrate AI ROI to the board within one quarter or risk losing the entire AI budget
case-studies.cases.manufacturing-ai-pilot-to-production.solution.summary
case-studies.cases.manufacturing-ai-pilot-to-production.solution.approach
Audited all 6 pilots against production-readiness criteria. Triaged into 'ship' (4 pilots with viable models and clear ROI), and 'kill' (2 pilots with fundamental data or business case flaws). Honest assessment saved months of wasted effort.
2 weeksBuilt shared MLOps pipeline with CI/CD for models, model registry, and automated retraining triggers. Implemented data quality layer across factory sensors and ERP systems to ensure clean, reliable inputs.
2 weeksShipped 4 systems to production in parallel: (1) Predictive maintenance for CNC machines, (2) Quality inspection with computer vision, (3) Demand forecasting integrated with ERP, (4) Energy optimization across the factory floor.
8 weekscase-studies.cases.manufacturing-ai-pilot-to-production.results.summary
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