IDEAS home Printed from https://ideas.repec.org/a/spr/opmare/v16y2023i1d10.1007_s12063-022-00308-1.html
   My bibliography  Save this article

Inventory systems with uncertain supplier capacity: an application to covid-19 testing

Author

Listed:
  • Mohammad Ebrahim Arbabian

    (University of Portland)

  • Hossein Rikhtehgar Berenji

    (Pacific University)

Abstract

The COVID-19 pandemic has forced governments to impose crippling restrictions on the day-to-day activities of citizens. To contain the virus and lift these restrictions safely, policymakers need to know quickly where the virus is spreading. This has been possible only through widespread testing. Not long after starting largescale testing in the early stages of the pandemic and more recently with a surge of new variants, countries hit a roadblock—the shortage of swabs used in the testing kits due to disruptions in the supply chain caused by COVID-19. This disruption translates to a variable production capacity of the swab suppliers. As a result, when countries order swabs from a swab supplier, their order might not be fully satisfied. Hence, adopting a proper swab inventory management model can help countries better manage COVID-19 testing and avoid widespread shortages of testing supplies. By considering two different swab demand patterns (i.e., stationary and stochastic) and two different production capacity scenarios for the swab supplier (i.e., ample and variable production capacity), we develop four analytical models, in which we consider all combinations of the above demand and capacity scenarios, to derive the optimal swab-procurement policy for a country. Given the rapid change of COVID-19 infection cases and the limited planning period, countries should aim for reactive scheduling. Through a comprehensive numerical study, we also provide guidelines on how countries should optimally react to these changes in the supply and demand of swabs. The research implications for managing inventory with stochastic supplier capacity and uncertain demand in a finite time horizon extend well beyond the application to COVID-19 testing.

Suggested Citation

  • Mohammad Ebrahim Arbabian & Hossein Rikhtehgar Berenji, 2023. "Inventory systems with uncertain supplier capacity: an application to covid-19 testing," Operations Management Research, Springer, vol. 16(1), pages 324-344, March.
  • Handle: RePEc:spr:opmare:v:16:y:2023:i:1:d:10.1007_s12063-022-00308-1
    DOI: 10.1007/s12063-022-00308-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12063-022-00308-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s12063-022-00308-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ford W. Harris, 1990. "How Many Parts to Make at Once," Operations Research, INFORMS, vol. 38(6), pages 947-950, December.
    2. Syed Abdul Rehman Khan & Muhammad Waqas & Xue Honggang & Naveed Ahmad & Zhang Yu, 2022. "Adoption of innovative strategies to mitigate supply chain disruption: COVID-19 pandemic," Operations Management Research, Springer, vol. 15(3), pages 1115-1133, December.
    3. Ilkyeong Moon & Byung-Hyun Ha & Jongchul Kim, 2012. "Inventory systems with variable capacity," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 6(1), pages 68-86.
    4. Iida, Tetsuo, 2002. "A non-stationary periodic review production-inventory model with uncertain production capacity and uncertain demand," European Journal of Operational Research, Elsevier, vol. 140(3), pages 670-683, August.
    5. Wang, Yunzeng & Gerchak, Yigal, 1996. "Continuous review inventory control when capacity is variable," International Journal of Production Economics, Elsevier, vol. 45(1-3), pages 381-388, August.
    6. Edward H. Kaplan, 2020. "OM Forum—COVID-19 Scratch Models to Support Local Decisions," Manufacturing & Service Operations Management, INFORMS, vol. 22(4), pages 645-655, July.
    7. Asli Sencer Erdem & Mehmet Murat Fadilog̃lu & Süleyman Özekici, 2006. "An EOQ model with multiple suppliers and random capacity," Naval Research Logistics (NRL), John Wiley & Sons, vol. 53(1), pages 101-114, February.
    8. Harvey M. Wagner & Thomson M. Whitin, 1958. "Dynamic Version of the Economic Lot Size Model," Management Science, INFORMS, vol. 5(1), pages 89-96, October.
    9. Hariga, Moncer & Haouari, Mohamed, 1999. "An EOQ lot sizing model with random supplier capacity," International Journal of Production Economics, Elsevier, vol. 58(1), pages 39-47, January.
    10. Nagurney, Anna, 2021. "Supply chain game theory network modeling under labor constraints: Applications to the Covid-19 pandemic," European Journal of Operational Research, Elsevier, vol. 293(3), pages 880-891.
    11. Marco Ardolino & Andrea Bacchetti & Dmitry Ivanov, 2022. "Analysis of the COVID-19 pandemic’s impacts on manufacturing: a systematic literature review and future research agenda," Operations Management Research, Springer, vol. 15(1), pages 551-566, June.
    12. Aref Gholami & Abolfazl Mirzazadeh, 2018. "An inventory model with controllable lead time and ordering cost, log-normal-distributed demand, and gamma-distributed available capacity," Cogent Business & Management, Taylor & Francis Journals, vol. 5(1), pages 1469182-146, January.
    13. Nikolopoulos, Konstantinos & Punia, Sushil & Schäfers, Andreas & Tsinopoulos, Christos & Vasilakis, Chrysovalantis, 2021. "Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions," European Journal of Operational Research, Elsevier, vol. 290(1), pages 99-115.
    14. A. Federgruen & P. Zipkin, 1986. "An Inventory Model with Limited Production Capacity and Uncertain Demands II. The Discounted-Cost Criterion," Mathematics of Operations Research, INFORMS, vol. 11(2), pages 208-215, May.
    15. Candace Arai Yano & Hau L. Lee, 1995. "Lot Sizing with Random Yields: A Review," Operations Research, INFORMS, vol. 43(2), pages 311-334, April.
    16. Matsuo, Hirofumi, 2015. "Implications of the Tohoku earthquake for Toyota׳s coordination mechanism: Supply chain disruption of automotive semiconductors," International Journal of Production Economics, Elsevier, vol. 161(C), pages 217-227.
    17. T. M. Whitin, 1954. "Inventory Control Research: A Survey," Management Science, INFORMS, vol. 1(1), pages 32-40, October.
    18. A. Federgruen & P. Zipkin, 1986. "An Inventory Model with Limited Production Capacity and Uncertain Demands I. The Average-Cost Criterion," Mathematics of Operations Research, INFORMS, vol. 11(2), pages 193-207, May.
    19. Frank W. Ciarallo & Ramakrishna Akella & Thomas E. Morton, 1994. "A Periodic Review, Production Planning Model with Uncertain Capacity and Uncertain Demand---Optimality of Extended Myopic Policies," Management Science, INFORMS, vol. 40(3), pages 320-332, March.
    20. Hong, Yoo Suk & Huh, Woonghee Tim & Kang, Changmuk, 2017. "Sourcing assemble-to-order inventories under supplier risk uncertainty," Omega, Elsevier, vol. 66(PA), pages 1-14.
    21. Gerchak, Yigal, 1992. "Order point/order quantity models with random yield," International Journal of Production Economics, Elsevier, vol. 26(1-3), pages 297-298, February.
    Full references (including those not matched with items on IDEAS)

    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. Kutzner, Sarah C. & Kiesmüller, Gudrun P., 2013. "Optimal control of an inventory-production system with state-dependent random yield," European Journal of Operational Research, Elsevier, vol. 227(3), pages 444-452.
    2. Y. Boulaksil & J. C. Fransoo & T. Tan, 2017. "Capacity reservation and utilization for a manufacturer with uncertain capacity and demand," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(3), pages 689-709, July.
    3. Gullu, Refik, 1998. "Base stock policies for production/inventory problems with uncertain capacity levels," European Journal of Operational Research, Elsevier, vol. 105(1), pages 43-51, February.
    4. Iida, Tetsuo, 2002. "A non-stationary periodic review production-inventory model with uncertain production capacity and uncertain demand," European Journal of Operational Research, Elsevier, vol. 140(3), pages 670-683, August.
    5. Arifoglu, Kenan & Özekici, Süleyman, 2010. "Optimal policies for inventory systems with finite capacity and partially observed Markov-modulated demand and supply processes," European Journal of Operational Research, Elsevier, vol. 204(3), pages 421-438, August.
    6. Wen Chen & Burcu Tan, 2022. "Dynamic procurement from multiple suppliers with random capacities," Annals of Operations Research, Springer, vol. 317(2), pages 509-536, October.
    7. Altug, Mehmet Sekip & Muharremoglu, Alp, 2011. "Inventory management with advance supply information," International Journal of Production Economics, Elsevier, vol. 129(2), pages 302-313, February.
    8. Jian Yang & Zhaoqiong Qin, 2007. "Capacitated Production Control with Virtual Lateral Transshipments," Operations Research, INFORMS, vol. 55(6), pages 1104-1119, December.
    9. Xinxin Hu & Izak Duenyas & Roman Kapuscinski, 2008. "Optimal Joint Inventory and Transshipment Control Under Uncertain Capacity," Operations Research, INFORMS, vol. 56(4), pages 881-897, August.
    10. Han Zhu, 2022. "A simple heuristic policy for stochastic inventory systems with both minimum and maximum order quantity requirements," Annals of Operations Research, Springer, vol. 309(1), pages 347-363, February.
    11. Qi Feng & J. George Shanthikumar, 2018. "Supply and Demand Functions in Inventory Models," Operations Research, INFORMS, vol. 66(1), pages 77-91, 1-2.
    12. Yang, Jian & Qi, Xiangtong & Xia, Yusen & Yu, Gang, 2006. "Inventory control with Markovian capacity and the option of order rejection," European Journal of Operational Research, Elsevier, vol. 174(1), pages 622-645, October.
    13. Rossi, Roberto & Chen, Zhen & Tarim, S. Armagan, 2024. "On the stochastic inventory problem under order capacity constraints," European Journal of Operational Research, Elsevier, vol. 312(2), pages 541-555.
    14. Weidong Chen & Cong Shi & Izak Duenyas, 2020. "Optimal Learning Algorithms for Stochastic Inventory Systems with Random Capacities," Production and Operations Management, Production and Operations Management Society, vol. 29(7), pages 1624-1649, July.
    15. Jain, Tarun & Hazra, Jishnu, 2017. "Sourcing strategies under agglomeration economies, capacity risks and retail competition," International Journal of Production Economics, Elsevier, vol. 191(C), pages 311-322.
    16. Chaolin Yang & Diyuan Huang & Chenyang Xu, 2022. "Multi-index base-stock policy for inventory systems with multiple capacitated suppliers," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(1), pages 155-177, March.
    17. Wen Chen & Ying He, 2022. "Dynamic pricing and inventory control with delivery flexibility," Annals of Operations Research, Springer, vol. 317(2), pages 481-508, October.
    18. Arifoglu, Kenan & Özekici, Süleyman, 2011. "Inventory management with random supply and imperfect information: A hidden Markov model," International Journal of Production Economics, Elsevier, vol. 134(1), pages 123-137, November.
    19. Sandun C. Perera & Suresh P. Sethi, 2023. "A survey of stochastic inventory models with fixed costs: Optimality of (s, S) and (s, S)‐type policies—Continuous‐time case," Production and Operations Management, Production and Operations Management Society, vol. 32(1), pages 154-169, January.
    20. Chao, Xiuli & Chen, Hong & Zheng, Shaohui, 2008. "Joint replenishment and pricing decisions in inventory systems with stochastically dependent supply capacity," European Journal of Operational Research, Elsevier, vol. 191(1), pages 142-155, November.

    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:spr:opmare:v:16:y:2023:i:1:d:10.1007_s12063-022-00308-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.