Predictive Maintenance in Manufacturing
The Problem
A Midlands-based manufacturing firm faced costly downtime due to unplanned equipment failures. Reactive maintenance disrupted production schedules, leading to missed deadlines and frustrated clients.
Leadership knew predictive maintenance could reduce downtime but didn’t know where to start. They lacked real-time monitoring tools, their data was fragmented, and they worried about the upfront cost of deploying sensors and analytics.
The Solution
While building the AI Roadmap, we evaluated existing data from production lines and identified key equipment for early monitoring. We developed a phased roadmap that combined sensor deployment with AI-powered predictive models trained on historic maintenance records.
We worked closely with the operations team to ensure the system flagged issues early, integrating alerts into existing workflows rather than adding new layers of complexity. Staff training focused on interpreting predictions and prioritising interventions.
The Success
Unplanned downtime dropped by 35% in the first six months, saving the company significant production costs and restoring client confidence in delivery schedules.
The project also unlocked insights into long-term equipment performance, enabling smarter capital investment decisions. With a scalable framework in place, the firm can now roll out predictive maintenance across all production lines without major disruption.
