Improving Salesforce Forecasting

Describes how sales forecasting performance can be improved by acquiring forecasting intelligence ... companies who obtain forecasting input from their salesforce often do not take maximum advantage of potential insight they provide... input from the salesforce is particularly important for a company with bumpy demand patterns. Outstanding performance in sales forecasting is a difficult, yet worthy goal for any company. There are different tools available to companies to help them improve overall forecasting performance. Some tools are quantitative in nature, and these quantitative tools can identify patterns in historical demand data, then project those patterns into the future. However, because the past is not always a perfect predictor of the future, judgment-based tools are also needed to optimize forecasting performance. One important source of judgment about how the future might be different from the past is a company's salesforce. Over the past five years, the Sales Forecasting Research Team at the University of Tennessee has undertaken a data collection effort aimed at identifying factors that contribute to a company's ability to be successful in sales forecasting. This data collection took the form of indepth analysis of the sales forecasting management practices at 33 companies. The purpose of this paper is to present one of the key findings from this research. Specifically, the intent here is to discuss the important role that a company's salesforce can play in the critical business process of sales forecasting, and how a company can improve its overall forecasting performance by enhancing its ability to acquire and use the customer intelligence available from its salesforce. First, we will briefly describe the data collection effort that serves as the foundation for our ideas about salesforce forecasting. Second, based upon our findings from the 33 companies we have studied, we will discuss those conditions where salespeople can make a meaningful contribution to forecasts. Next, we will discuss approaches that management can take to maximize the value of salespeople's contribution to forecasting efforts. THE DATA COLLECTION EFFORT This research began with the selection of companies with histories as leading financial and/or market share performersthough not necessarily top performers in sales forecasting. In fact, to understand the variations in sales forecasting management performance in successful companies, the selection process was intended to include companies that have achieved varying degrees of success in forecasting sales. In addition, companies at different levels within the supply chain were included. This selection process resulted in site visits with the following companies: AlliedSignal, Anheuser-Busch, Avery Denison, Becton-Dickinson, Coca Cola, Colgate Palmolive, ConAgra, Corning, DuPont, Eastman Chemical, Ethicon, Exxon, Federal Express, Hershey Foods, Kimberly Clark, Lucent Technologies, Lykes Pasco, Michelin, Nabisco, J.C. Penney, Pillsbury, ProSource, Reckitt Colman, Red Lobster, RJR Tobacco, Sandoz, Schering Plough, Smith and Nephew, Sysco, Tropicana, Union Pacific Railroad, Warner Lambert, and Westwood Squibb. Company analysis began with a request for any documentation of the sales forecasting management process. This documentation included reports, documentation of systems and/or management procedures, and informal protocols. Once this information was analyzed, interviews were conducted with anyone in the company affiliated with sales forecasting, including developers and users of the sales forecasts. Before visiting the company to conduct the interviews, a detailed 8-page protocol was sent to each person to be interviewed. The interviews were conducted on-site by the research team, with two members of the research team in each interview to ensure inter-judge reliability. Interviews were tape recorded and the interview transcripts were analyzed for sales forecasting management content. …