Predictive shelf-life forecasting
Sell products at the right time before expiry.
Predictive shelf-life forecasting combines batch data, storage conditions and sell-through rates to detect spoilage risk early, so distribution can be prioritised and promotions targeted.
- Cost reduction
- Higher margins
- Better planning
food waste
revenue through dynamic pricing
payback
forecast accuracy
Calculated from historical spoilage rates, warehouse turnaround times and improved sell-through control.
- 01
Data integration
Consolidate data from ERP, MES and further sources for Predictive shelf-life forecasting.
- 02
Data quality
Clean, harmonise and validate data for plausibility.
- 03
Model development
Train and validate the Predictive shelf-life forecasting model on historical data.
- 04
Pilot
Pilot Predictive shelf-life forecasting in one area and collect feedback.
- 05
Rollout
Scale the solution and integrate it into operational processes.
Steps
Data sources
Stakeholders
From first data access to production – every step delivers a tangible interim result.
ERP data
Master and transaction data from ERP.
Inventory data
Stock levels, movements and locations.
Sales data
Orders, revenue, channels and customer feedback.
Quality data
Inspection results, lab values and complaints.
Supplier data
Lead times, certificates, prices and ratings.
Weather data
Outside temperature, humidity and weather alerts.
Quality management
Secures compliance with standards and regulations.
Sales
Uses data-driven pricing and sales steering.
Logistics
Optimises transport, warehousing and deliveries.
Controlling
Quantifies effects and supports budgeting.
Cost reduction
Higher margins
Better planning
Higher customer satisfaction
Full traceability
With a solid data foundation this use case gets faster, cheaper and far more stable.
Central data platform
Real-time data integration
Scalable analytics pipelines
Reusable data products
Dynamic pricing
Demand and shelf-life data steer prices.
Traceability
Data lineage remains fully intact.
Supplier scoring
Quality and delivery data feed scoring.