Turning datamining into a management science tool: New algorithms and empirical results

• 成果类型: Article
• 署名作者: Cooper, LG; Giuffrida, G
• 署名单位: University of California System; University of California Los Angeles; University of California System; University of California Los Angeles
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.46.2.249.11932
• 发表日期: 2000
• 页码: 249-264
• 关键词: datamining; rule generators; residual analysis; promotion event forecasting

摘要

This article develops and illustrates a new knowledge discovery algorithm tailored to the action requirements of management science applications. The challenge is to develop tactical planning forecasts at the SKU level. We use a traditional market-response model to extract information from continuous variables and use datamining techniques on the residuals to extract information from the many-valued nominal variables, such as the manufacturer or merchandise category. This combination means that a more complete array of information can be used to develop tactical planning forecasts. The method is illustrated using records of the aggregate sales during promotion events conducted by a 95-store retail chain in a single trading area. In a longitudinal cross validation, the statistical forecast (PromoCast(TM)) predicted the exact number of cases of merchandise needed in 49% of the promotion events and was within +/- one case in 82% of the events. The dataminer developed rules from an independent sample of 1.6 million observations and applied these rules to almost 460,000 promotion events in the validation process. The dataminer had sufficient confidence to make recommendations on 46% of these forecasts. In 66% of those recommendations, the dataminer indicated that the forecast should not be changed. In 96% of those promotion events where no change was recommended, this was the correct action to take. Even including these no change recommendations, the dataminer decreased the case error by 9% across all promotion events in which rules applied.