A Clusteringbased Sales Forecast Method for Big Promotion Days in O2O OnDemand Retailing O2O ondemand service is a fastgrowing business model which integrates traditional offline retailing and ecommerce. Big promotion is an effective way to attract new customers and promote sales in O2O service. However, high sales brought by big promotions can incur challenges for the operation management, especially for delivery capacity planning and replenishment scheduling. Using an accurate sales forecast is shown to be an effective way to alleviate such problems, though this technique still has its challenges. In this paper, we propose a clusteringbased random forest (RF) method to predict the sale levels in shops for big promotion days. We further apply a dataset from one of the largest O2O grocery platforms in China to validate our method. The results show that the forecast accuracy of our proposed algorithm is 13.43 % higher than nonclustering RF method, 22.85% higher than nonclustering ARIMA method.

The Application of FANP and BOCR in O2O Service Model for SportsProduct Retailers The competition of sportsproduct retailers is getting fierce because of the progress of electronic commerce. It poses a great challenge to make a good decision for sportsproduct retailers to incorporate O2O (online to offline offline to online) service model to their business model. In this study, FANP with BOCR are applied to construct an O2O service model for sportsproduct retailers. In this O2O service model, expertsu0027 opinions are collected and aggregated in evaluating O2O implementation alternatives. A case study of a sportsproduct retailer is used in evaluating the most suitable service model for the retailer when plan to introduce O2O service into their business model.