F&B company cut unplanned downtime by 30%

About the Client

An Indian-based mid-sized grain processing and flour manufacturing company, operating three high-capacity milling lines running round-the-clock to meet seasonal demand. Their operations are deeply tied to harvest cycles—which means unplanned stoppages cost more than money.

Challenges

Minor drifts in moisture that go unnoticed: A minor drop in moisture content: 0.08% off in September and 0.48% by November 2024. Numbers too small for sensors to read, dashboards to pick up or maintenance professionals to notice. But the moisture content decides grain hardness.

Harder grain, faster wear: When moisture content drops, roller mills need to compensate and mill the dry grain harder. The 0.48% moisture drop wore down corrugated roll surfaces. The roll gap widened. Output particle size coarsened. The rotary sifter downstream took on 28% more recirculation load. Machine didn’t stop running, but motor current climbed and screen cloth perforated ahead of schedule.

From small drifts to major crisis: By June 2025, roller mill M-101's vibration had crossed the ISO 10816 Zone C threshold. By August, it crossed Zone D — the unacceptable range. The machine had 94 major and minor breakdowns in a single month, reactive maintenance hitting 97%. That too in the peak harvest season where the production capacity was over 118%.

The datakulture Solution

Faults never happen at one place. It’s often reactive, happens as a cascade of events where one blunder leads to another. Our data analytics and data science team deployed PredictIQ and built a custom CUSUM detection model - An algorithm specifically designed to catch small, sustained deviations that standard threshold-based alarms miss entirely.

Here's what it does differently:

• Monitors every sensor signal continuously, flagging slow drifts in moisture, vibration, temperature, and motor current

• Traces how upstream changes (like moisture deviation) will propagate to downstream equipment — the mill, the sifter, the classifier — before that cascade becomes visible

• Calculates a Remaining Useful Life (RUL) estimate for each machine with an 80% confidence interval, updated in real time

• Generates a daily prioritised work order queue, ranked by MDP score (measure of the expected value of acting now vs deferring)

• Tells maintenance teams in plain language: this sensor needs calibrating today, this roll pair needs redressing in seven days, decisions more than alerts.

• In the case of M-101, the platform had flagged the root cause — a moisture sensor calibration drift — ten months before the August crisis, reducing the maintenance costs to almost negligible.

Impact

Three work orders. 11.5 hours of scheduled labour. Spread across two weekend maintenance windows. Zero impact on production schedules.

30% reduction in unplanned downtime: predictive alerts gave the maintenance team enough lead time to act before failures occurred, replacing emergency shutdowns with planned interventions.

25% reduction in maintenance costs: scheduled fixes during planned windows cost a fraction of what reactive breakdown repairs do, both in parts and in lost production hours.

10% improvement in Overall Equipment Effectiveness (OEE): with machines running closer to their designed parameters, output quality and throughput stabilised across the milling line.

15% reduction in equipment faults: catching upstream deviations early meant downstream machines stopped absorbing the consequences of problems that weren't theirs to begin with.

Conclusion

We built this predictive maintenance use case specifically for industries like grain processing, where the failure that matters isn't always a machine breakdown, but a slow upstream shift in material quality that quietly accelerates wear across every machine downstream. That's a physics the platform understands. And it's a story most conventional maintenance systems never even start reading. Want to customize this to suit your manufacturing plant? Send us your requirements.