ATLANTA, US: Before implementing BI, Restoration Hardware was performing well in each of its channels, but had no way of viewing consumer demand and inventory across the enterprise as a whole.

"During a holiday season, our store managers told us they'd stock out of key items early on and didn't get enough new stock to meet demand. With QuantiSense, the process was completely different," said Rex Stratton, VP of Product Allocation, Restoration Hardware. "The store managers would think they were going to run out of something, then a truck would come in with exactly the right merchandise every time, because of QuantiSense's imminent stock-out analysis."
Rob Kellow, Director of Business Intelligence and Data Services with Restoration Hardware, added, "This is the first time that Restoration Hardware has been able to see all of its channels together in a single view, and that is a significant information systems win for us."

A press release shares that after picking a BI solution, with inventory analytics across all channels, the company was able to dramatically reduce its inventory investment by gaining the intelligence to know what it needs – and what it doesn't – to ensure customer satisfaction with the appropriate inventory in the right place at the right time.

Out-of-stock merchandise is a $93 billion problem for retailers and the number two source of frustration for shoppers, according to the 2008 RIS News/IHL Group Store Systems Study, adds the solution provider QuantiSense.
"For multichannel retailers like Restoration Hardware, the challenge is even more complex. Channels are often run by disparate systems, creating silos of data that disable visibility between channels. According to the RIS News/IHL study, the average retailer loses the equivalent of $3.19 on every customer transaction due to out-of-stocks. However, accurately calculating stock-outs can be one of retail's most challenging problems. Most retailers use a measure called 'weeks on-hand' or 'weeks of supply', but these calculations are fundamentally flawed since they do not calculate data at the level of individual stores and SKUs, stresses QuantiSense.