Anomaly Detection in Operations: Catching Problems Early

The Firefighting Problem

Operations teams spend too much time reacting to problems that could have been caught earlier. That £4,000 stock-out? The warning signs were in the data a week ago. The production delay? Yield rates had been drifting for days.

Anomaly detection is about catching these signals before they become crises.

How Anomaly Detection Works

At its core, anomaly detection learns what "normal" looks like, then flags when something is significantly different.

• Statistical methods: Flag values outside expected ranges (e.g., 3 standard deviations)
• Trend detection: Identify when patterns change direction
• Seasonal adjustment: Account for expected variations (weekends, holidays, seasons)
• Machine learning: Learn complex patterns across multiple variables

Practical Applications

Inventory & Supply Chain

• Unusual demand spikes or drops
• Supplier delivery time changes
• Stock level drift from expected patterns

Production & Manufacturing

• Yield rate changes
• Quality metric drift
• Equipment performance degradation

Financial Operations

• Unusual transaction patterns
• Payment timing changes
• Cost category anomalies

The Alert Balance

Too few alerts: problems slip through. Too many: alert fatigue, everything gets ignored.

Good anomaly detection systems let you tune sensitivity and prioritise alerts by business impact. Not every anomaly matters equally.

Getting Started

1. Pick one metric: Start with something important that changes often
2. Define "normal": Establish baseline behaviour with historical data
3. Set thresholds: How unusual is unusual enough to flag?
4. Create response playbook: What happens when an alert fires?
5. Iterate: Adjust sensitivity based on false positive/negative rates