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Better Management

4/13/2011

It’s no secret: When the Great Recession hit in the fall of 2008, retailers sustained a serious blow in the form of sharply declining consumer demand. Privately held apparel retailer Bob’s Stores was no exception.



“For us, the slowdown had a significant impact on our inventory movement,” said Victor D’Amato, VP planning and analysis, Bob’s Stores, Meriden, Conn., which operates 34 stores in the Northeast.



Prior to the economic downturn, Bob’s had started to embark on an inventory reduction strategy to improve its gross margin. The recession intensified the importance of the project. 



Sales projections for 2009 and beyond were dismal. Plus, the chain was facing a host of uncontrollable factors, including the rising cost of cotton and wholesale partners that were pulling back on orders or canceling them altogether. It became clear that if the chain wanted to continue serving its shoppers, Bob’s needed to transition away from costly advanced orders to more targeted weekly and just-in-time orders — a process that also required the company to rethink the way it was stockpiling inventory.



“A high level of holding stock was costing us working capital and reducing turns,” D’Amato recalled. “To be successful in this new sales environment, we knew we needed to lower on-hand merchandise, and better manage our inventory to match consumer demand. To do so, we needed help from better solutions.”



The retailer was supporting an outdated legacy system that had limited functionality when presenting users with forecasting data. It was not uncommon for the chain to create inventory orders without knowledge of on-hand stock.



“We used Excel spreadsheets to manually calculate our available safety stock, then added our calculations into the system to improve the next order decisions,” D’Amato said.



Bob’s wanted a solution that could account for promotions, seasonality and other events that impact demand. It also had to take into account a large volume of SKUs and store planning combinations.



“The ideal system needed to analyze the product hierarchy and aggregate a sales forecast based on consumer demand and merchandise performance,” D’Amato said. “It also needed to be affordable and provide a return on investment.”



That solution was a cloud-based demand forecasting management system from JustEnough Software, Newport Beach, Calif. Bob’s launched it in 2009, with an initial goal to eliminate the burdens caused by its safety stock. 



As users launch the software-as-a-service application via desktop or laptop, the solution uses pre-established inventory policies, such as existing safety stock levels, service levels and demand, and then generates an inventory forecast around these details. 



Since adding the solution, Bob’s has slashed its holding stock by 16% and reduced its average in-store stock levels by 9%. “Even with the reduced stock, we did not sacrifice sales, turns or gross margin,” D’Amato said.



Based on the solution’s cost-effective configuration, Bob’s expects to see a return on its investment within a year. Since the solution is a secure, Web-based system accessed via a Web browser, the company didn’t need to purchase, install and run software in-house. A SaaS-based solution also eliminates the burden and expense of software maintenance or running a proprietary server infrastructure.



At presstime, the chain was completing the second phase of the technology deployment, which included completely transitioning off of its legacy system and managing its enterprise-wide forecasting and replenishment processes through JustEnough. 



Once it is supporting the entire enterprise, Bob’s plans to apply it to inventory held at the distribution center and remove safety stock from the back room of the store.



“The goal is to reduce our network inventory,” D’Amato said. “We believe carrying a limited supply of inventory will save us capital, and we will still be able to service our shoppers by having more accurate assortments, such as sizes and styles, based on their demand and purchase behavior.”


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