Listening to the Data
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Listening to the Data
That was the problem at The Bombay Company Inc., a national retail chain that specializes in classic furniture and accessories. Bombay had a nagging 35 percent error rate on its forecasting, including overstocking as well as lost sales due to understocking. Unfortunately, says Roger Tyler, vice president of merchandise planning at the Fort Worth, Texas-based company, a 35 percent error rate is typical for his industry.
Without consistent data on stocking levels for Bombay's more than 400 retail outlets, demand could never be projected accurately. The company was carrying more than $100 million in average inventory at costfar more than it wantedand had a 2.02 inventory turnover rate, effectively selling through its inventory only about twice a year. And the company's gross margin return on investment was only 2.61: For every dollar spent on a product stocked in stores, the company was getting $2.61 backnot exactly stellar figures. "The problem as I saw it was that we were using a lot of art in our forecasting, and a little bit of science," says Tyler. "I wanted to be able to use a lot more science, and very little art."
The company went live with a demand planning application from Nonstop Solutions Inc., called Score, in June 2001. Loaded with two years' worth of historical sales information, the system was used to crunch numbers on a wide range of factors, such as the most profitable products, items taking the most shelf space, and the best-selling items in each store. The software allowed Tyler and his team to set service levels for each of the products stocked in stores. Lamps, for example, which sell fast and are highly profitable, could be positioned in the "A" categorya 98 percent "service level"meaning the product would be made available 98 percent of the time. The software then tracks forecasting errors by flagging discrepancies in service levels and recognizing when an item falls below the ideal level. Meanwhile, administrators can always bump products to higher levels.
By January, the company had reduced its average inventory at cost to $79 million while improving its inventory turnover rate to 2.42. But perhaps the figure Tyler is most happy with is the company's gross margin return on investment, which by January had increased to $3.26a 25 percent improvement. And the company's overall forecasting error rate, which can now be tracked by the software, has been reduced from 35 percent to about 12 percent. Tyler thinks that figure will be further trimmed as the analytical engine has more sales data to crunch.