Predictive Maintenance Without a Data Science Team: What's Actually Working on Indian Shop Floors
Full predictive maintenance platforms assume a data infrastructure most SME shops don't have. Here's what a scaled-down version looks like in practice.
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Priyanka Rao
Contributing Editor
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Your ad could be here — advertise with usPredictive maintenance in trade press usually comes with a picture of a fully instrumented, cloud-connected factory feeding a machine-learning model. For a 20-machine job shop in an Indian industrial estate, that picture is both unaffordable and, in most cases, unnecessary as a first step.
What's actually moving the needle for smaller shops isn't a full predictive maintenance platform — it's a handful of narrowly scoped, low-cost interventions on a small number of critical machines.
What's working
- Vibration and temperature sensors retrofit on bottleneck machines only, not the whole shop floor at once
- Simple threshold-based alerting rather than a full ML model, as a first step before investing in anything more sophisticated
- Basic spindle-hour and tool-change logging to catch patterns a maintenance team can act on without a data scientist
What to skip, for now
Full ERP-to-machine integration and shop-wide predictive maintenance rollouts are common places SME budgets get spent without a clear payback. Start on the one or two machines where unplanned downtime already hurts the most, and expand only once that pays for itself.