Replenishment decision support system of perishable products by using logistic regression and grey analysis

• Main Authors: Chun-Hsien Cheng, 鄭群嫻
• Other Authors: Jia-Yen Huang
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/58385301582557749131

碩士 === 嶺東科技大學 === 行銷與流通管理研究所 === 99 === The key to the success of a convenience store is the ability to make decisions that not only follow consumer needs, but also reduces operational costs such as efficiently control their stock replenishment, especially for perishable items such as meal-boxes. To solve this problem, we proposed an innovative decision support system to determine the optimal amount of replenishment. In the first step, we obtained the basic order quantity of the overall meal-box by newsboy model. The basic order quantity may not actually match with the real demand due to the effect of uncertain factors such as the climate and promotion activity of substitute products. Therefore, in the second step, a novel warning system is established by employing the logistic regression model to modify the basic order quantity. In the third step, we employ GRA to allocate the optimal order quantity of each kind of meal-box. Using actual data from a convenience store which is a part of the President Chain Store Corporation in Taiwan, the prediction accuracy of the decision support system was evaluated. Through numerical experiments, that the proposed policy can accurately determine the optimal order quantity is confirmed.