An AHP Based Scheme for Sales Forecasting in the Fashion Industry
In this paper, we propose a novel aggregation disaggregation scheme based on the analytic hierarchy process (AHP) to conduct sales forecasting in the fashion industry, where a stock keeping unit (SKU) is usually characterized by a short product life cycle and low sales volume because of various reasons such as the wide variety of products and the constant changes in fashion trends. To improve the accuracy of sales forecast, an AHP based scheme is proposed to aggregate historical sales data so that future sales can be predicted with higher certainty, and then disaggregate forecast quantities over SKUs, products, or other entities of interest. The proposed scheme is evaluated by using real data collected from the fashion industry, and the experimental results show that under this scheme, the performance of two popular and well established time series forecasting techniques, namely "moving average" and "exponential smoothing," can be improved to predict the sales of the products with short history and low sales volumes. To continue this work, future research directions are also discussed.