Toward More Sophisticated DRP
Will those companies that share data see success? To answer that question, I helped conduct a study to examine how accurately store-level collaborative data could predict manufacturing requirements. In a trial that lasted over a year, we surveyed 63 stores and six different SKUs for a Fortune 100 retailer, while its supplier used new technologies to analyze point-of-sale data provided by that retailer. Ultimately, 378 different forecasts (i.e., 63 x 6) were produced. The company later compared this information with what the retailer actually ordered over the course of the trial. (For more information, please visit http://bit.ly/cbQL3R, pp. 11-51.)Results showed forecast accuracy between 83 percent and 97 percent. In addition, the retailer divulged that it had ordered more from the manufacturer during certain months in anticipation of higher holiday sales volumes — meaning that forecasting accuracy would have been higher had the manufacturer been provided with additional data from past years.
The model we used was simply a more technologically driven, sophisticated enactment of DRP. So the key takeaway is that while new tools exist to help collaborative companies manage their forecasts, the process of putting those forecasts together has not changed, nor will it ever. Smart businesses will do well to remember their fundamentals and use advancing technologies to make the hard work easier.
About the Author: Darryl Landvater is co-founder of the RedPrairie Collaborative Flowcasting Group, a joint venture with RedPrairie, which delivers productivity solutions to help companies around the world with inventory, transportation and workforce management. Landvater can be reached at email@example.com. More information on RedPrairie at www.RedPrairie.com.