Predictive Maintenance for a Manufacturer 

A packaging manufacturer with three plants in the Gulf was losing whole shifts to surprise equipment failures. Maintenance ran on a fixed calendar, which meant healthy machines got serviced too often while the one about to fail got serviced too late. The operations director had read enough about predictive maintenance to want it and enough vendor brochures to distrust most of what he'd read.

The scoped starting point was deliberately small: retrofit vibration and temperature sensors on one production line, and prove a single early warning before spending on the other two plants.

A four-person team plus a data scientist spent the first month on the unglamorous part: mounting sensors and wiring an edge gateway until clean readings flowed over MQTT into a time-series store. The plant network threw us our first real problem. The shop floor was fully isolated from the internet by policy, and that policy wasn't changing for us. We redesigned around edge gateways with store-and-forward buffering, so data crosses to the cloud through one controlled link and a connectivity outage costs nothing but a delay.

The second problem was noise. One line's vibration data produced false alarms every few days, and a false alarm in week two costs you the maintenance crew's trust for a month. Rather than argue with the plant team, we sat with the technicians, tagged three months of alerts together as real or spurious, and retrained on their labels. The alarm that finally mattered came in month six: bearing wear flagged eleven days before the failure mode the crew later confirmed on teardown.

That single catch paid for the pilot. The other two plants came online the following quarter, on the same edge-first design the network policy had forced on us, which by then we'd stopped resenting and started recommending.