Manhattan upgrades inventory planning and replenishment tool
Software platform lets retailers position goods closer to customers in complex omnichannel environments, firm says.
By Ben Ames
Supply chain software developer Manhattan Associates Inc. said today it upgraded its inventory management platform to help retailers better manage the demanding omnichannel fulfillment environment by making it easier and more efficient to locate goods sitting in stores and warehouses.
The updates to Manhattan's "Inventory Demand Forecasting and Replenishment" platform contain tools that help retailers position inventory wherever it is needed to meet demand, the Atlanta-based firm said. The software replaces the traditional strategy used by retailers of balancing their inventory across a pyramid-shaped network of a few national DCs, a handful of regional DCs, and a large collection of brick and mortar stores, Manhattan President and CEO Eddie Capel said at its annual user conference in Hollywood, Fla. Instead, users can follow a "multi-channel, multi-echelon optimization" approach to filling online orders, Capel said.
The system gives retailers control over their distribution by using its omni-inventory optimization (OIO) feature to constantly monitor and automatically adjust inventory levels to balance cost, sales, and customer satisfaction, the firm said.
"[OIO] provides the retail industry with full insight into the impact of omni-fulfillment experiences on the network and the ability to learn and automatically adapt as those experiences mature," Scott Fenwick, Manhattan's director of product strategy, said in an email. "[OIO] will ensure your supply chain is poised to fulfill your customer expectations at the least cost of goods."
At the conference, Manhattan unveiled a data visualization tool called "Buyer Workbench" that provides wholesalers and distributors with a broad view of how inventory, demand, and service levels impact their businesses. It also enhanced its "Manhattan Active Inventory" to allow retailers simplify the process of launching promotional campaigns.
The latter feature uses machine learning-based algorithms to measure the net impact of each individual promotion when multiple promotions are running simultaneously, according to Manhattan.
About the Author
Ben Ames has spent 20 years as a journalist since starting out as a daily newspaper reporter in Pennsylvania in 1995. From 1999 forward, he has focused on business and technology reporting for a number of trade journals, beginning when he joined Design News and Modern Materials Handling magazines. Ames is author of the trail guide "Hiking Massachusetts" and is a graduate of the Columbia School of Journalism.
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