IBM's 30 Percent Solution
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IBM today introduced Dynamic Inventory Optimization Solution, a tool that promises to help retailers reduce inventory levels by 30 percent while maintaining or improving out-of-stock levels.
The tool was designed to help retailers walk the tightrope between having enough items on their shelves when customers want to buy them, and carrying too much inventory.
The problem is especially challenging for chain stores doing business over wide geographic areas and carrying large numbers of stock-keeping units (SKUs).
The timing couldn't be better for IBM, according to a report authored by Beth Enslow of Aberdeen Group, a consulting firm based in Boston.
"Inventory optimization and replenishment management technology is the top-rated area companies wish to improve to drive supply chain innovation," Enslow wrote.
Enslow reported that in 13 out of 14 deployments of this solution, retailers were able to realize inventory savings of over 30 percent while holding or improving projected customer service levels.
Gary Cross, global supply chain optimization leader at IBM, told internetnews.com that one client, German home improvement retailer Max Bahr, was able to produce 90 percent of the replenishment orders for over 70,000 SKUs without human oversight.
Cross explained that the solution was most well suited to large retailers because it reacts to stock levels in real time, across a full geographic spectrum, and down to the individual SKU.
"The more dynamic your business, the more value you can get out of a solution like this," he said.
The solution runs daily, allowing for more frequent analysis and more timely replenishment orders than most retail solutions, which only run detailed reports on a weekly basis.
The software also uses advanced data analytics to mine up to two years' worth of data for customer order patterns and inventory levels, and then applies optimizing technology patented by IBM to suggest replenishment orders.
The tool takes a variety of factors into account, including regional and seasonal patterns. The product also gauges supplier constraints, such as lead times, pallet sizes, and batch requirements, as well as the retailer's own policies on service levels and safety stock.
It then calculates optimal replenishment policies for each class of SKU in order to achieve full service levels at the lowest possible cost.
Managers are then presented with suggested replenishment orders for approval.