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Manufacturing
Predictive maintenance on F&B production lines
Client · International food group · 40 production sites
A multi-site predictive maintenance platform on critical equipment (bottling, ovens, packaging), with a standardized rollout to 14 sites in 24 months.
01
Challenge
- Unplanned downtime causing on average 7% OEE loss on critical lines.
- Equipment heterogeneity (10+ machine brands, 2 to 25 years old) making a uniform approach difficult.
- Mostly curative or systematic maintenance despite available sensors.
- Limited data skills in maintenance teams and low adoption of existing tools.
02
Solution
- Edge-to-cloud architecture: IoT sensors → edge gateway → centralized data lake (Azure).
- Library of anomaly-detection models (Isolation Forest, LSTM autoencoders) packaged by equipment type, deployable in days on a new site.
- Mobile field app for technicians with contextualized recommendations and feedback loop on alert relevance.
- Change-management program training 140 maintenance staff over 24 months.
03
Business impact
- +3.5 pt OEE on equipped lines.
- −22% maintenance cost via fewer curative interventions and optimized parts inventory.
- Average ROI per site: 14 months.
- 'Pilot site → replication' is now the group standard for industrial data initiatives.
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