One of the leading British denim wear brands with 750+ retail outlets, selling on its own website, and 6 major marketplaces, was experiencing excess stock in its warehouses and stores due to inaccurate inventory distribution. This led to a loss in sales opportunities at some locations and a blockage of working capital at others.
The brand was looking for a solution to help them analyze local demand and smartly distribute/ re-distribute inventory across their network of warehouses and stores, to facilitate faster order fulfilment at a lower logistics cost.
- Optimally distribute inventory across a multi-store and multi-warehouse network to avoid inventory understocking and overstocking
- Automate inventory allocation process to eliminate human decision-making errors
- Maximize revenue by identifying core styles for each store location & ensure accurate stock availability at all times.
Increff Distributed Warehouse Inventory Optimization Module was implemented to build supply chain resilience and improve stock distribution with demand-wise allocation. Using in-built algorithms, the tool was able to analyze true customer demand, map at the Pin code level, and suggest intelligent inventory split across the brand’s network of managed warehouses and stores. Placing the right stock in the required quantity, at the right location, closer to the customers enabled faster fulfilment. It also facilitated fresh season allocation and mid-season replenishments/replacements with inventory redistribution through inter-store transfers, to increase revenue, reduce inventory holding and optimize sales.
Some notable features of Increff Distributed Warehouse Inventory Optimization Module include:
- No clustering of stores, considering each store has its unique DNA, predicts the store-wise demand based on historical performance
- ROS (Rate of Sale) based Dynamic Distribution at store and SKU-level by correcting unavailability, liquidation, brokenness & over/under-stocking scenarios
- Allocating inventory considering essential demand influencing parameters like recency, seasonality & festivities and store stock, open orders & GIT (Goods in Transit)
- New Style distribution based on attribute group performance of similar sister styles to gauge demand
- Allocating inventory based on 2D Store Style ranking such that every piece is optimized to its maximum potential
- Considering business constraints like store capacity, store revenue targets, and days of stock cover it can hold, when devising an ideal strategy
- Creating intelligent inputs by consuming only raw data and uniquely tailoring size ratios for every store-product group. Stock cover that needs to be maintained for every SKU is system created input, rather than manual user input norms
Implementing Increff Distributed Warehouse Inventory Optimization Module resulted in:
- 18% reduction in working capital by reducing inventory holdings
- 12% reduction in stock brokenness to improve inventory health leading to higher conversions
- 22% increase in revenue by eliminating sales opportunity loss and stocking the right core styles for each location
- 80% saving in working hours spent on stock allocation with end-to-end automation