The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Designing And Managing The Supply Chain
Designing And Managing The Supply Chain
Novel questionnaire-responded transaction approach with SVM for credit card fraud detection
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
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In the past decade, convenience stores have generally experienced low profit margins due to the intensive competition that exists in the industry. To reduce operating costs, these stores must be able to efficiently control their stock replenishment, especially for deteriorating items such as meal-boxes. To solve this problem, we employed a two-step model to determine the optimal amount of replenishment. In the first step, we obtained the basic reorder quantity by considering three inventory management methods involving the consideration of the probability forecast of demand, hypothesis testing and the newsboy method. In the second step of our model, a novel warning system is established by employing the support vector machine to modify the basic order quantity, which may be varied due to the effect of uncertain factors such as the weather, climate and economic prospects. Using actual data from a convenience store which was a part of the President Chain Store Corporation in Taiwan, the prediction accuracy of the two-step replenishment policies was evaluated. We also apply two methods to enhance accuracy and provide further insights into the model. The results show that the model is workable, and the results can be used as a valuable reference for future practical applications.