Predictive Maintenance Dashboard: From Reactive to Proactive in 90 Days

The Business Case for Predictive Maintenance

Reactive maintenance (fix when broken) costs 3–5x more than predictive maintenance. An unplanned stoppage on a bottleneck machine typically costs ₹50,000–5,00,000 per hour in lost production. Predictive maintenance reduces unplanned downtime by 30–50% in the first year of implementation.

What Data to Monitor

Start with the signals already available from your PLCs: vibration (if sensors exist), temperature, current draw, cycle time (rising cycle time = wearing tooling or mechanical resistance), pressure. You do not need expensive vibration sensors on every machine to start — PLC signals alone reveal a surprising amount of degradation.

Establishing Baselines

Before you can detect anomalies, you need to know what "normal" looks like. Collect 30 days of clean data from each machine during normal operations. Calculate mean and standard deviation for each monitored parameter per machine type, per shift, per product. An anomaly is when a parameter exceeds 2 standard deviations from the baseline.

Alert Design

Alert Level 1 (Yellow): parameter 10–15% above baseline — notify maintenance team to inspect at next planned stop. Alert Level 2 (Orange): parameter 15–25% above baseline — schedule inspection within 48 hours. Alert Level 3 (Red): parameter >25% above baseline — inspect today, prepare for possible replacement.