IBM Uses Advanced Analytics to Transform Inventory Management for Retail Industry

IBM Uses Advanced Analytics to Transform Inventory Management for Retail Industry

IBM today unveiled a new solution that uses advanced data analytics to help retailers achieve the delicate balance between demand and inventory levels. The new IBM Dynamic Inventory Optimization Solution mines data for customer order patterns and inventory levels and then applies patented optimizing technology — enabling clients to potentially cut their inventory levels up to 40 percent in some cases while maintaining or even increasing overall service levels. With the retail industry being driven by empowered shoppers — who have higher expectations, more information than ever and broader awareness of their choices — retailers are moving to find inventory management solutions which can help them manage in the midst of consumers’ almost daily shifting buying patterns.

The IBM Dynamic Inventory Optimization Solution for Retail leverages a number of data sources once thought to be too detailed to quickly and properly parse efficiently. In the retail industry for example, forecasts using point of sale (POS) data are traditionally prepared weekly. However, through the use of advanced algorithms, patented by IBM, the new solution analyzes daily POS and other vital data to project stock overages and shortages. It then evaluates retailer and vendor sourcing rules, and suggests orders and replenishments to help maintain the optimal balance of stock and service at the store level.

IBM recently deployed the Dynamic Inventory Optimization Solution for Retail at Germany-based do-it-yourself retailer Max Bahr. Previously, Max Bahr had relied on local planners from each of its 90 stores to manually forecast inventory needs for over 70,000 items. Working with Max Bahr’s business leaders, IBM Global Business Services implemented a system which automatically generates their order proposals and provides improved forecasts for their network of retail outlets.

Each evening the solution takes the roughly 15 to 20 million POS transactions from all 90 stores and analyzes them against a two-year history of each product Max Bahr has sold. Overnight the solution calculates approximately 340 million replenishment targets and automatically turns 90 percent of them into orders, allowing planners to focus on managing the exceptions.

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