Retail: Predictive Demand and Merchandising Planning
The Challenge
Client operated over 100 stores and managed thousands of SKUs across suppliers, categories, and perishables. All core planning ran through large XLS files, creating challenges across:
- Store-level planning
- Supplier ordering
- MOQ and batch constraints
- Perishables and wastage
- Demand estimation
Processes were slow, fragile, and highly manual. Use of XLS was not sustainable and could not scale.
Off-the-shelf retail tools could not model the Client's operating logic, and did not support custom supplier constraints, perishables workflows, or internal planning structures.
The Solution
An Intelligent Retail Planning System was deployed using AI Cloud Foundry. The solution included:
- Centralised store, POS, and supply-chain data
- Embedded custom logic around MOQs, batches, and perishables
- Structured demand planning and replenishment workflows
- A persistent operational layer for planning and execution
The solution replaced manual processes and failing XLS-based reporting.
The Results
The following benefits are what such transformations are modelled to deliver. These are illustrative estimates and not measured against the Client's actuals.
- Inventory reduction: >10%, by replacing per-buyer judgement with calculated batch and MOQ constraints, and right-sized safety stock in place of blanket buffers
- Waste reduction: >20%, from the same constraint logic applied to perishables
- Forecast accuracy: >15%, by removing the lag and version-conflicts inherent to store-by-store spreadsheets
- Planning cycle time: >40% faster, by eliminating manual XLS consolidation and reconciliation across stores
- Stock-outs: >20% fewer, by replacing "round up / round down" manual ordering with calculated replenishment