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Article

A Computational Model for Determining Levels of Factors in Inventory Management Using Response Surface Methodology

1
Department of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
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Department of Logistics and Supply Chain Management, Hong Bang International University, Ho Chi Minh 72320, Vietnam
3
School of Manufacturing Systems and Mechanical Engineering, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12121, Thailand
*
Authors to whom correspondence should be addressed.
Mathematics 2020, 8(8), 1210; https://doi.org/10.3390/math8081210
Received: 5 July 2020 / Revised: 17 July 2020 / Accepted: 20 July 2020 / Published: 22 July 2020
Inventory management plays a critical role in balancing supply availability with customer requirements and significantly contributes to the performance of the whole supply chain. It involves many different features, such as controlling and managing purchases from suppliers to consumers, keeping safety stock, examining the amount of product for sale, and order fulfillment. This paper involves the development of computational modeling for the inventory control problem in Thailand. The problem focuses on determining levels of factors, which are order quantity, reorder point, target stock, and inventory review policy, using a heuristic approach. The objective is to determine the best levels of factors that are significantly affected by their responses to optimize them using the response surface methodology. Values of the quantity of backlog and the average inventory amount, as well as their corresponding total costs, are simulated using the Arena software to gain statistical power. Then, the Minitab-response surface methodology is used to find the feasible solutions of the responses, which consist of test power and sample size, full factorial design, and Box–Behnken design. For a numerical example, the computational model is tested with real data to show the efficacy of the model. The result suggests that the effects from the reorder point, target stock, and inventory review policy are significant to the minimum total cost if their levels are set appropriately. The managerial implications of this model’s results not only suggest the best levels of factors for a case study of the leading air compressor manufacturers in Thailand, but also provide a guideline for decision-makers to satisfy customer demand at the minimum possible total inventory cost. Therefore, this paper can be a useful reference for warehouse supervisors, managers, and policymakers to determine the best levels of factors to improve warehouse performance. View Full-Text
Keywords: inventory; design of experiment; response surface methodology; full factorial design; Box–Behnken design; levels of factors; cost optimization inventory; design of experiment; response surface methodology; full factorial design; Box–Behnken design; levels of factors; cost optimization
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MDPI and ACS Style

Wang, C.-N.; Dang, T.-T.; Nguyen, N.-A.-T. A Computational Model for Determining Levels of Factors in Inventory Management Using Response Surface Methodology. Mathematics 2020, 8, 1210. https://doi.org/10.3390/math8081210

AMA Style

Wang C-N, Dang T-T, Nguyen N-A-T. A Computational Model for Determining Levels of Factors in Inventory Management Using Response Surface Methodology. Mathematics. 2020; 8(8):1210. https://doi.org/10.3390/math8081210

Chicago/Turabian Style

Wang, Chia-Nan, Thanh-Tuan Dang, and Ngoc-Ai-Thy Nguyen. 2020. "A Computational Model for Determining Levels of Factors in Inventory Management Using Response Surface Methodology" Mathematics 8, no. 8: 1210. https://doi.org/10.3390/math8081210

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