>£12M saving in downtime costs
Background
Challenge
- Many root causes of downtime were impossible to detect through visual checks, making fault identification highly challenging
- Investigating these hidden issues required full and often time-consuming disassembly of the packing lines
- Recurring non-normal events, such as leaks and mechanical faults, were causing significant production stoppages
- Lack of precise fault detection led to reactive, labour-intensive maintenance instead of targeted or predictive interventions
Solution
If a link passed by with a leak, it emitted a high-pitched whistle which the microphone flagged on a dashboard. High-speed cameras reached inaccessible areas, capturing failure events for further analysis. PurpleSector also developed an ML-based performance monitoring model trained to provide predictive analytics: a human-machine interface that alerts operators to potential issues.
Results
This has saved the client more than £12 million a year. The predictive maintenance solution enables operators to get ahead of potential issues and fix them before problems occur. It is on track to increase the current ROI from 10:1 to 30:1.
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We deploy F1™ - derived tools, technologies and techniques to help organisations operate like an F1™ team. Our goal is to build the in-house capability for clients to solve challenges, uncover opportunities and deliver performance gains at race-pace.
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