IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i8p1210-d387984.html
   My bibliography  Save this article

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

Author

Listed:
  • Chia-Nan Wang

    (Department of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan)

  • Thanh-Tuan Dang

    (Department of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
    Department of Logistics and Supply Chain Management, Hong Bang International University, Ho Chi Minh 72320, Vietnam)

  • Ngoc-Ai-Thy Nguyen

    (School of Manufacturing Systems and Mechanical Engineering, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12121, Thailand)

Abstract

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.

Suggested Citation

  • Chia-Nan Wang & Thanh-Tuan Dang & Ngoc-Ai-Thy Nguyen, 2020. "A Computational Model for Determining Levels of Factors in Inventory Management Using Response Surface Methodology," Mathematics, MDPI, vol. 8(8), pages 1-23, July.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:8:p:1210-:d:387984
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/8/1210/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/8/8/1210/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cardós, Manuel & Babiloni, Eugenia, 2011. "Exact and approximate calculation of the cycle service level in periodic review inventory policies," International Journal of Production Economics, Elsevier, vol. 131(1), pages 63-68, May.
    2. Silver, Edward A. & Bischak, Diane P., 2011. "The exact fill rate in a periodic review base stock system under normally distributed demand," Omega, Elsevier, vol. 39(3), pages 346-349, June.
    3. Rossi, Roberto & Tarim, S. Armagan & Hnich, Brahim & Prestwich, Steven, 2010. "Computing the non-stationary replenishment cycle inventory policy under stochastic supplier lead-times," International Journal of Production Economics, Elsevier, vol. 127(1), pages 180-189, September.
    4. Cardós, Manuel & Babiloni, Eugenia, 2011. "Exact and approximated calculation of the cycle service level in a continuous review policy," International Journal of Production Economics, Elsevier, vol. 133(1), pages 251-255, September.
    5. Wafik Hachicha & Ahmed Ammeri & Faouzi Masmoudi & Habib Chabchoub, 2010. "A multi-product lot size in make-to-order supply chain using discrete event simulation and response surface methodology," International Journal of Services, Economics and Management, Inderscience Enterprises Ltd, vol. 2(3/4), pages 246-266.
    6. Guijarro, Ester & Cardós, Manuel & Babiloni, Eugenia, 2012. "On the exact calculation of the fill rate in a periodic review inventory policy under discrete demand patterns," European Journal of Operational Research, Elsevier, vol. 218(2), pages 442-447.
    7. Shib Sana, 2015. "An EOQ model for stochastic demand for limited capacity of own warehouse," Annals of Operations Research, Springer, vol. 233(1), pages 383-399, October.
    8. Hariga, Moncer A., 2010. "A single-item continuous review inventory problem with space restriction," International Journal of Production Economics, Elsevier, vol. 128(1), pages 153-158, November.
    9. Wang, Pengfei & Wen, Yi & Xu, Zhiwei, 2014. "What inventories tell us about aggregate fluctuations—A tractable approach to (S,s) policies," Journal of Economic Dynamics and Control, Elsevier, vol. 44(C), pages 196-217.
    10. Teunter, Ruud & Sani, Babangida, 2009. "Calculating order-up-to levels for products with intermittent demand," International Journal of Production Economics, Elsevier, vol. 118(1), pages 82-86, March.
    11. Ahmed Abdel-Aleem & Mahmoud A. El-Sharief & Mohsen A. Hassan & Mohamed G. El-Sebaie, 2017. "A surface response optimization model for EPQ system with imperfect production process under rework and shortage," OPSEARCH, Springer;Operational Research Society of India, vol. 54(4), pages 735-751, December.
    12. Sphicas, Georghios P., 2014. "Generalized EOQ formula using a new parameter: Coefficient of backorder attractiveness," International Journal of Production Economics, Elsevier, vol. 155(C), pages 143-147.
    13. Qiu, Ruozhen & Sun, Minghe & Lim, Yun Fong, 2017. "Optimizing (s, S) policies for multi-period inventory models with demand distribution uncertainty: Robust dynamic programing approaches," European Journal of Operational Research, Elsevier, vol. 261(3), pages 880-892.
    14. Minner, Stefan, 2009. "A comparison of simple heuristics for multi-product dynamic demand lot-sizing with limited warehouse capacity," International Journal of Production Economics, Elsevier, vol. 118(1), pages 305-310, March.
    15. Sphicas, Georghios P., 2006. "EOQ and EPQ with linear and fixed backorder costs: Two cases identified and models analyzed without calculus," International Journal of Production Economics, Elsevier, vol. 100(1), pages 59-64, March.
    16. Teunter, R.H. & Syntetos, A.A. & Babai, M.Z., 2010. "Determining order-up-to levels under periodic review for compound binomial (intermittent) demand," European Journal of Operational Research, Elsevier, vol. 203(3), pages 619-624, June.
    17. Kanokwan Singha & Jirachai Buddhakulsomsiri & Parthana Parthanadee, 2019. "Computational experiment of methods to determine periodic ( R , Q ) inventory policy parameters: a case study of information decentralised distribution network," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 32(2), pages 212-242.
    18. A A Syntetos & M Z Babai & Y Dallery & R Teunter, 2009. "Periodic control of intermittent demand items: theory and empirical analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(5), pages 611-618, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rahal, Imen, 2023. "Analyse de la gestion des stocks de produits périssables en utilisant le modèle EOQ en Tunisie [Analysis of stock management for perishable products using EOQ model in Tunisia]," MPRA Paper 118592, University Library of Munich, Germany.
    2. Afonso Vaz de Oliveira & Carina M. Oliveira Pimentel & Radu Godina & João Carlos de Oliveira Matias & Susana M. Palavra Garrido, 2022. "Improvement of the Logistics Flows in the Receiving Process of a Warehouse," Logistics, MDPI, vol. 6(1), pages 1-23, March.
    3. Rahal, Imen, 2023. "Analyse de la gestion des stocks de produits périssables en utilisant le modèle EOQ en Tunisie Analysis of the management of stocks of perishable products using the EOQ model in Tunisia [Analysis o," MPRA Paper 118193, University Library of Munich, Germany.
    4. Mohammed Alnahhal & Bashir Salah & Rafiq Ahmad, 2022. "Increasing Throughput in Warehouses: The Effect of Storage Reallocation and the Location of Input/Output Station," Sustainability, MDPI, vol. 14(8), pages 1-16, April.
    5. Ziquan Xiang & Jiaqi Yang & Muhammad Hamza Naseem & Wenjie Ge, 2022. "Research on Dynamic Cooperative Replenishment Optimization of Shipbuilding Enterprise Inventory Control under Uncertainty," Sustainability, MDPI, vol. 14(4), pages 1-15, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Disney, Stephen M. & Gaalman, Gerard J.C. & Hedenstierna, Carl Philip T. & Hosoda, Takamichi, 2015. "Fill rate in a periodic review order-up-to policy under auto-correlated normally distributed, possibly negative, demand," International Journal of Production Economics, Elsevier, vol. 170(PB), pages 501-512.
    2. Altay, Nezih & Litteral, Lewis A. & Rudisill, Frank, 2012. "Effects of correlation on intermittent demand forecasting and stock control," International Journal of Production Economics, Elsevier, vol. 135(1), pages 275-283.
    3. Teunter, R.H. & Syntetos, A.A. & Babai, M.Z., 2017. "Stock keeping unit fill rate specification," European Journal of Operational Research, Elsevier, vol. 259(3), pages 917-925.
    4. Kamal Sanguri & Kampan Mukherjee, 2021. "Forecasting of intermittent demands under the risk of inventory obsolescence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1054-1069, September.
    5. Babai, M.Z. & Jemai, Z. & Dallery, Y., 2011. "Analysis of order-up-to-level inventory systems with compound Poisson demand," European Journal of Operational Research, Elsevier, vol. 210(3), pages 552-558, May.
    6. Prak, Derk & Teunter, Rudolf & Babai, M. Z. & Syntetos, A. A. & Boylan, D, 2018. "Forecasting and Inventory Control with Compound Poisson Demand Using Periodic Demand Data," Research Report 2018010, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    7. Shih-Hsien Tseng & Jia-Chen Yu, 2019. "Data-Driven Iron and Steel Inventory Control Policies," Mathematics, MDPI, vol. 7(8), pages 1-15, August.
    8. Guijarro, Ester & Cardós, Manuel & Babiloni, Eugenia, 2012. "On the exact calculation of the fill rate in a periodic review inventory policy under discrete demand patterns," European Journal of Operational Research, Elsevier, vol. 218(2), pages 442-447.
    9. Dreyfuss, Michael & Giat, Yahel, 2019. "Allocating spares to maximize the window fill rate in a periodic review inventory system," International Journal of Production Economics, Elsevier, vol. 214(C), pages 151-162.
    10. Rossi, Roberto & Kilic, Onur A. & Tarim, S. Armagan, 2015. "Piecewise linear approximations for the static–dynamic uncertainty strategy in stochastic lot-sizing," Omega, Elsevier, vol. 50(C), pages 126-140.
    11. Teunter, Ruud H. & Syntetos, Aris A. & Zied Babai, M., 2011. "Intermittent demand: Linking forecasting to inventory obsolescence," European Journal of Operational Research, Elsevier, vol. 214(3), pages 606-615, November.
    12. Vargas, Vicente & Metters, Richard, 2011. "A master production scheduling procedure for stochastic demand and rolling planning horizons," International Journal of Production Economics, Elsevier, vol. 132(2), pages 296-302, August.
    13. Kourentzes, Nikolaos, 2013. "Intermittent demand forecasts with neural networks," International Journal of Production Economics, Elsevier, vol. 143(1), pages 198-206.
    14. Kourentzes, Nikolaos, 2014. "On intermittent demand model optimisation and selection," International Journal of Production Economics, Elsevier, vol. 156(C), pages 180-190.
    15. Pennings, Clint L.P. & van Dalen, Jan & van der Laan, Erwin A., 2017. "Exploiting elapsed time for managing intermittent demand for spare parts," European Journal of Operational Research, Elsevier, vol. 258(3), pages 958-969.
    16. Leung, Kit-Nam Francis, 2010. "Some comments on "A simple method to compute economic order quantities"," European Journal of Operational Research, Elsevier, vol. 201(3), pages 960-961, March.
    17. Borodin, Valeria & Dolgui, Alexandre & Hnaien, Faicel & Labadie, Nacima, 2016. "Component replenishment planning for a single-level assembly system under random lead times: A chance constrained programming approach," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 79-86.
    18. Kourentzes, Nikolaos & Athanasopoulos, George, 2021. "Elucidate structure in intermittent demand series," European Journal of Operational Research, Elsevier, vol. 288(1), pages 141-152.
    19. Tamjidzad, Shahrzad & Mirmohammadi, S. Hamid, 2015. "An optimal (r, Q) policy in a stochastic inventory system with all-units quantity discount and limited sharable resource," European Journal of Operational Research, Elsevier, vol. 247(1), pages 93-100.
    20. Visentin, Andrea & Prestwich, Steven & Rossi, Roberto & Tarim, S. Armagan, 2021. "Computing optimal (R,s,S) policy parameters by a hybrid of branch-and-bound and stochastic dynamic programming," European Journal of Operational Research, Elsevier, vol. 294(1), pages 91-99.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:8:y:2020:i:8:p:1210-:d:387984. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.