Innovative replenishment management for perishable items using logistic regression and grey analysis

In this paper, an innovative decision support system is proposed, by consolidating the newsboy model, logistic regression, and grey relation analysis, to develop an efficient replenishment policy, which maximises the total profit of perishable items in a convenience store. First, the basic order quantity of the overall meal-box is determined by the newsboy model. Next, we develop a wastage-free system by employing logistic regression to adjust the overall basic order quantity, which may deviate from the real demand due to the effect of uncertain factors such as the weather and the number of customers. Finally, grey relation analysis is conducted to allocate the order quantity of each kind of meal-box efficiently. Based on actual data from a convenience store of the President Chain Store Corporation in Taiwan, the superiority of the decision support system was evaluated. The experimental findings reveal that the proposed policy can outperform the traditional replenishment policy. Since customers' tastes can be precisely monitored through this system, daily needs can be estimated and controlled more accurately and the quantities of shortage and wastage can be reduced. This system is believed to raise customer satisfaction and increase the profit of the store.