Case Study #7: Process Analytics for Utilization of Warehouse Space
Warehouses often struggle to use their space well, and traditional methods leave valuable square footage untapped while orders lag and storage costs climb. For this manufacturing client, warehouse utilisation had become a major pain point. We applied process analytics and predictive modelling to optimise stock across production-to-order and production-to-inventory scenarios, and reduced the warehouse space required by 20%, lowering investment needs and improving production-line flexibility and loading efficiency.
At a glance
Client: manufacturer with production-to-order and production-to-inventory flows · Industry: Supply chain, manufacturing · Scope: process analytics and predictive stock modelling
20%
Less warehouse space
required after stock optimisation
2
Production scenarios
production-to-order and production-to-inventory
Business challenges
- Warehouse space was not fully utilised, driving inefficiency and wasted storage cost.
- Order fulfilment lagged as stock was not placed or sized for the production flow.
- Traditional planning methods could not see which stock was really needed and when.
Project objectives
- Optimise warehouse stock levels with process analytics and predictive modelling.
- Cover both production-to-order and production-to-inventory scenarios.
- Reduce required space and investment while keeping production flexible.
Our approach
1. Process analytics
We mapped the actual material and order flows through the warehouse to understand where stock accumulated and why.
2. Predictive stock modelling
Predictive models sized stock for production-to-order and production-to-inventory scenarios, so the warehouse holds what production needs and no more.
3. Optimised utilisation
The resulting stock policy and placement freed space, improved production-line flexibility and made loading more efficient.
Results
- 20% reduction in required warehouse space.
- Decreased investment in storage.
- Improved production-line flexibility.
- Enhanced loading efficiency.
Business impact
A 20% reduction in required warehouse space translated into lower investment, a more flexible production line and more efficient loading, with faster order fulfilment and lower storage costs.
Conclusion
Turning process data into a stock policy gave the client a more competitive warehouse without adding a square metre.
Warehouse overflowing, orders lagging?
We optimise stock and space with process analytics. See our Supply Chain Optimization solution, or talk to us about your warehouse.
Tools used
