Industrial AI Transformation: Manufacturing Pilot-to-Production
How a representative engagement would help a manufacturer diagnose pilot purgatory and define a 90-day production path
Λειτουργικό πλαίσιο
A manufacturing context with three stalled AI pilots, uneven production readiness and a time-bound France 2030 funding decision.
Μέγεθος: Indicative engagement: 5,000-20,000 employee manufacturers
Η Πρόκληση
Transform three stuck AI pilots into production systems within the France 2030 timeline, while building internal AI capability.
Three AI pilots had been running for 18 months with no path to production—classic 'pilot purgatory'
Quality inspection AI achieved 94% accuracy in lab but failed in factory conditions with variable lighting
Predictive maintenance model generated too many false positives, causing maintenance team to ignore alerts
Supply chain optimization AI couldn't integrate with legacy SAP systems and ERP infrastructure
Internal team lacked production ML engineering experience—strong data scientists but no MLOps capability
A public funding milestone sets a fixed date for showing production use
Η Λύση μας
A representative engagement would diagnose root causes, prioritise the production-viable pilots, and take working AI systems to production with full capability transfer.
In this modelled scenario, systematic diagnosis surfaces the same fundamental issue in all three pilots: demo-quality architecture. Lab conditions don't reflect production reality. The engagement would prioritise the quality inspection system (the clearest baseline and path to production in this scenario), redesign it for production robustness, and put in place the minimum model-operations capability the internal team can run.
Φάσεις Υλοποίησης
Diagnosis & Prioritization
Technical audit of all three pilots. In the modelled scenario, quality inspection shows the clearest baseline and path to production.
2 weeksProduction Architecture Redesign
Redesign the quality inspection AI for real factory conditions: lighting normalization, camera calibration, edge deployment for <100ms latency.
4 weeksMLOps Infrastructure
Put in place the minimum model-operations capability the internal team can run: versioned models, retraining and drift monitoring; tooling is chosen with the team.
3 weeksProduction Deployment & Capability Transfer
Roll out to the first production lines; wider rollout is decided after acceptance on those lines. Intensive training for the internal team on MLOps practices.
3 weeksΤεχνολογίες & Προσεγγίσεις
Σχεδιαζόμενο αποτέλεσμα
The intended outcome is a production decision for each pilot, one production-viable system hardened first, and an MLOps foundation the internal team can operate. Savings remain dependent on baseline, scope and implementation conditions.
Υπηρεσίες σε μια Αντιπροσωπευτική Συνεργασία
Αντιμετωπίζετε παρόμοια κατάσταση;
Ας συζητήσουμε πώς μπορούμε να σας βοηθήσουμε να αντιμετωπίσετε τις συγκεκριμένες προκλήσεις σας και να επιτύχετε μετρήσιμα επιχειρηματικά αποτελέσματα.