Manufacturing
Enterprise Client (Anonymized) — Industrial Manufacturing
Cutting unplanned downtime by a third across a three-plant manufacturing network
31% reduction in unplanned downtime
Deployment · 16 weeks
The situation
A manufacturer running three plants was losing an estimated $18,000 per hour to unplanned downtime, on a maintenance model that was almost entirely reactive: a machine failed, a technician was dispatched, production stopped until it was fixed. The plants already had SCADA and sensor infrastructure in place — the data existed. Nobody was using it to predict anything.
An earlier internal data science initiative had built a model in a notebook that never made it past a demo to plant management, because nobody had built the path from prediction to a work order a maintenance technician would actually act on.
The baseline
- Unplanned downtime cost: ~$18,000/hour
- Mean time to detect an anomaly: 6.4 hours
- Maintenance model: reactive, triggered by failure or fixed-interval inspection
- Maintenance cost per unit produced: baseline indexed at 100
The deployment
Two engineers embedded across the three plants for sixteen weeks, working alongside plant reliability engineers rather than a central data science team. We built a pipeline that streams existing sensor data — vibration, temperature, pressure — into an anomaly-detection model tuned per asset class, and wired its output directly into the plants' existing maintenance work-order system.
The deliberate design choice: the model never takes a machine offline on its own. It generates a prioritized, explained work order that a technician reviews and acts on, because the plants were not going to trust a system that could halt a production line unsupervised — and we agreed with them.
The outcome
- Unplanned downtime: down 31% across the three plants
- Mean time to detect an anomaly: 6.4 hours → 42 minutes
- Maintenance cost per unit: down 19%
- Estimated downtime cost avoided over the two quarters following go-live: $3.4M
What the client owns now
- The anomaly-detection models, retrained on an ongoing basis by the client's own reliability team
- The sensor data pipeline, running in the client's existing infrastructure
- The work-order integration and prioritization logic
- A runbook for extending detection to new asset classes as equipment is added
Client identity, sector specifics, and select figures in this case study have been altered or composited to protect client confidentiality, standard practice for published engagement work. The deployment, methodology, and results described reflect a real CopperPin engagement.