Sales forecasting for computer wholesalers: A comparison of multivariate adaptive regression splines and artificial neural networks

a r t i c l e i n f o Keywords: Sales forecasting Computer wholesaler Multivariate adaptive regression splines Artificial neural networks IT industry Artificial neural networks (ANNs) have been found to be useful for sales/demand forecasting. However, one of the main shortcomings of ANNs is their inability to identify important forecasting variables. This study uses multivariate adaptive regression splines (MARS), a nonlinear and non-parametric regression methodology, to construct sales forecasting models for computer wholesalers. Through the outstanding variable screening ability of MARS, important sales forecasting variables for computer wholesalers can be obtained to enable them to make better sales management decisions. Two sets of real sales data collected from Taiwanese computer wholesalers are used to evaluate the performance of MARS. The experimental results show that the MARS model outperforms backpropagation neural networks, a support vector machine, a cerebellar model articulation controller neural network, an extreme learning machine, an ARIMA model, a multivariate linear regression model, and four two-stage forecasting schemes across various performance criteria. Moreover, the MARS forecasting results provide useful information about the relationships between the forecasting variables selected and sales amounts through the basis functions, important predictor variables, and the MARS prediction function obtained, and hence they have important implications for the implementation of appropriate sales decisions or strategies. In the consumer-centric environment of today's business world, enterprises seeking good sales performance often need to maintain a balance between meeting customer demand and controlling inventory costs. Carrying a larger inventory allows customer demand to be satisfied at all times, but can result in over-stocking, leading to problems such as tied up capital, inventory writedowns, and reduced profit margins. Lower inventory levels, in contrast, may reduce inventory costs, but can result in opportunity costs arising from missed sale opportunities , reduced customer satisfaction, and other problems. Sales forecasting can be used to determine the required inventory level and avoid the problem of under/over-stocking. In addition, sales forecasting can have implications for corporate financial planning, marketing, client management, and other areas of business. Improving the accuracy of sales forecasts has therefore become an important aspect of operating a business. There is an extensive body of literature on sales forecasting in such very few studies center on sales forecasting in the information technology (IT) industry, especially for computer wholesalers. Lu and Wang [34] employed a combination of independent component analysis, growing hierarchical self-organizing maps, and support vector regression analysis …