Reorder Decision System Based on the Concept of the Order Risk Using Neural Networks
Part of the
Lecture Notes in Computer Science
book series (LNCS, volume 3397)
Due to the development of the modern information technology, many companies share the real-time inventory information. Thus the reorder decision using the shared information becomes a major issue in the supply chain operation. However, traditional reorder decision policies do not utilize the shared information effectively, resulting in the poor performance in distribution supply chains. Moreover, typical assumption in the traditional reorder decision systems that the demand pattern follows a specific probabilistic distribution function limits practical application to real situations where such probabilistic distribution function is not easily defined. Thus, we develop a reorder decision system based on the concept of the order risk using neural networks. We train the neural networks to learn the optimal reorder pattern that can be found by analyzing the historical data based on the concept of the order risk. Simulation results show that the proposed system gives superior performance to the traditional reorder policies. Additionally, managerial implication is provided regarding the environmental characteristics where the performance of the proposed system is maximized.
KeywordsInventory Status Customer Demand Penalty Cost Reorder Point Supply Chain Operation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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