Collecting food waste data is step one. Extracting actionable insights from it is where value is created. Good analytics turns raw weights into decisions.
From Data to Insight
Raw waste data: "425kg food waste this week" With context: "15% of purchases, up 2% from last week" With analysis: "Prep waste increased 25% on Tuesday; potato trim from new supplier" To decision: "Quality check supplier delivery; consider alternative"
Analytics adds the context and analysis that enable decisions.
Key Analytics Approaches
Trend analysis: Are things getting better or worse over time? What's the trajectory?
Benchmarking: How does this site/shift/menu compare to others?
Pattern recognition: What conditions correlate with waste? Day of week, weather, events, staffing?
Composition analysis: What exactly are we wasting? Which items, which categories?
Cost attribution: What does waste cost us in financial terms?
Environmental translation: What's the carbon/water footprint of our waste?
Actionable Insights
Good analytics answers questions like:
- Which menu items generate most waste?
- What time of day is waste highest?
- Which stations/areas have problems?
- How does staffing affect waste?
- What's driving spoilage?
- Where should we focus improvement efforts?
Building Analytics Capability
Start simple: Basic trends and comparisons before sophisticated analysis.
Connect data sources: Waste data combined with POS, inventory, labour data reveals more.
Ask specific questions: Analytics works best when seeking specific answers, not just exploring.
Act on findings: Analysis not acted upon is wasted effort.
Iterate: Each analysis cycle suggests new questions.
Technology Enablers
Modern waste analytics platforms provide:
- Automated data visualisation
- Customisable dashboards
- Alert configurations
- Report generation
- Data export for custom analysis
Explore our analytics dashboard and how it transforms waste data into insights.