Features and metrics to model against mean-time-to-repair KPI

One of the biggest bugbears in industrial plants, factories, mines or wherever people still use PPE is the time it takes to fix critical equipment when they break down. Think of load shedding or waiting for your car to be fixed while stuck in transit in Ga-Moloi.

Obviously, avoiding breakdowns altogether would be ideal, but these things happen like thunderstorms in Durban.

To get a feeling, maintenance professionals use a metric called mean-time-to-repair (#MTTR) to measure average repair times during these rather stressful downtimes. Many organizations collect lots of #data, but don't invest in data literacy skills, so they struggle to bring everything together to make sense of what the real issues are. Instead, decision-makers rely mostly on anecdotes from what's coming from the floor.

(Finance is probably the only exception that doesn't like anecdotes: they openly call your position for reimbursing expenses as "claims" until receipts (data) is shown. As in "he 'claims' to have spend his own money...". How is this level of hostile skepticism is justified while the very operational activities that generate income are left to the floor?)

Sometimes well-meaning managers send workers for training to reduce repair times, or production managers try to show urgency by shouting and showing chest-hair. But the point remains that there is too much guesswork involved. Sometimes there aren't even resources to sift through the 1000s of seemingly unrelated data points to draw a clear picture for decision-makers.

This problem isn't limited to just maintenance settings but is present in organizations that don't use data intentionally. Having KPIs and data science clevernauts is fantastic, but having process-owners who keep tabs on metrics that affect their KPIs is where the magic begins to happens. An organization with data-literate employees makes the process of understanding the data much faster. Process owners know what influences the KPIs as they get the operational context, and they may even be the ones who are closest to the operation and identify useful data to collect.

The value of an intentional approach to collecting, analyzing, and applying data while empowering employees to hold their own with data cannot be overemphasized. Ideally organizations need proper data strategies, however, nothing stops one from pioneering from a departmental level to at least remove the guessing.