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A COVID-19 Supply Chain Management Strategy Based on Variable Production under Uncertain Environment Conditions

Author

Listed:
  • Mohammed Alkahtani

    (Industrial Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
    Raytheon Chair for Systems Engineering (RCSE), Advanced Manufacturing Institute, King Saud University, Riyadh 11421, Saudi Arabia)

  • Muhammad Omair

    (Department of Industrial Engineering, Jalozai Campus, University of Engineering and Technology, Peshawar 25000, Pakistan)

  • Qazi Salman Khalid

    (Department of Industrial Engineering, Jalozai Campus, University of Engineering and Technology, Peshawar 25000, Pakistan)

  • Ghulam Hussain

    (Faculty of Mechanical Engineering, GIK Institute of Engineering Sciences & Technology, Topi 23460, Pakistan)

  • Imran Ahmad

    (Department of Industrial Engineering, Jalozai Campus, University of Engineering and Technology, Peshawar 25000, Pakistan)

  • Catalin Pruncu

    (Department of Mechanical Engineering, Imperial College London, Exhibition Rd., London SW7 1AY, UK
    Design, Manufacturing & Engineering Management, University of Strathclyde, Glasgow G1 1XJ, UK)

Abstract

The management of a controllable production in the manufacturing system is essential to achieve viable advantages, particularly during emergency conditions. Disasters, either man-made or natural, affect production and supply chains negatively with perilous effects. On the other hand, flexibility and resilience to manage the perpetuated risks in a manufacturing system are vital for achieving a controllable production rate. Still, these performances are strongly dependent on the multi-criteria decision making in the working environment with the policies launched during the crisis. Undoubtedly, health stability in a society generates ripple effects in the supply chain due to high demand fluctuation, likewise due to the Coronavirus disease-2019 (COVID-19) pandemic. Incorporation of dependent demand factors to manage the risk from uncertainty during this pandemic has been a challenge to achieve a viable profit for the supply chain partners. A non-linear supply chain management model is developed with a controllable production rate to provide an economic benefit to the manufacturing firm in terms of the optimized total cost of production and to deal with the different situations under variable demand. The costs in the model are set as fuzzy to cope up with the uncertain conditions created by lasting pandemic. A numerical experiment is performed by utilizing the data set of the multi-stage manufacturing firm. The optimal results provide support for the industrial managers based on the proactive plan by the optimal utilization of the resources and controllable production rate to cope with the emergencies in a pandemic.

Suggested Citation

  • Mohammed Alkahtani & Muhammad Omair & Qazi Salman Khalid & Ghulam Hussain & Imran Ahmad & Catalin Pruncu, 2021. "A COVID-19 Supply Chain Management Strategy Based on Variable Production under Uncertain Environment Conditions," IJERPH, MDPI, vol. 18(4), pages 1-23, February.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:4:p:1662-:d:496521
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    References listed on IDEAS

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    1. Dmitry Ivanov & Ajay Das & Tsan-Ming Choi, 2018. "New flexibility drivers for manufacturing, supply chain and service operations," International Journal of Production Research, Taylor & Francis Journals, vol. 56(10), pages 3359-3368, May.
    2. R. E. Bellman & L. A. Zadeh, 1970. "Decision-Making in a Fuzzy Environment," Management Science, INFORMS, vol. 17(4), pages 141-164, December.
    3. Biswajit Sarkar & Kripasindhu Chaudhuri & Shib Sankar Sana, 2010. "A stock-dependent inventory model in an imperfect production process," International Journal of Procurement Management, Inderscience Enterprises Ltd, vol. 3(4), pages 361-378.
    4. Claver Diallo & Uday Venkatadri & Abdelhakim Khatab & Sriram Bhakthavatchalam, 2017. "State of the art review of quality, reliability and maintenance issues in closed-loop supply chains with remanufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 55(5), pages 1277-1296, March.
    5. Sana, Shib Sankar, 2010. "A production-inventory model in an imperfect production process," European Journal of Operational Research, Elsevier, vol. 200(2), pages 451-464, January.
    6. Warwick McKibbin & Roshen Fernando, 2021. "The Global Macroeconomic Impacts of COVID-19: Seven Scenarios," Asian Economic Papers, MIT Press, vol. 20(2), pages 1-30, Summer.
    7. SeyedEsfahani, Mir Mehdi & Biazaran, Maryam & Gharakhani, Mohsen, 2011. "A game theoretic approach to coordinate pricing and vertical co-op advertising in manufacturer-retailer supply chains," European Journal of Operational Research, Elsevier, vol. 211(2), pages 263-273, June.
    8. Klibi, Walid & Martel, Alain & Guitouni, Adel, 2010. "The design of robust value-creating supply chain networks: A critical review," European Journal of Operational Research, Elsevier, vol. 203(2), pages 283-293, June.
    9. Sana, Shib Sankar, 2010. "An economic production lot size model in an imperfect production system," European Journal of Operational Research, Elsevier, vol. 201(1), pages 158-170, February.
    10. Glock, C. H., 2011. "Batch sizing with controllable production rates in a multi-stage production system," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 57822, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    11. Bouslah, Bassem & Gharbi, Ali & Pellerin, Robert, 2013. "Joint optimal lot sizing and production control policy in an unreliable and imperfect manufacturing system," International Journal of Production Economics, Elsevier, vol. 144(1), pages 143-156.
    12. Mohammad Hossein Zavvar Sabegh & Mohammad Mohammadi & Bahman Naderi, 2017. "Multi-objective optimization considering quality concepts in a green healthcare supply chain for natural disaster response: neural network approaches," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1689-1703, November.
    13. Mitali Sarkar & Byung Do Chung, 2020. "Flexible work-in-process production system in supply chain management under quality improvement," International Journal of Production Research, Taylor & Francis Journals, vol. 58(13), pages 3821-3838, July.
    14. Ling Liu & Sen Liu, 2020. "Integrated Production and Distribution Problem of Perishable Products with a Minimum Total Order Weighted Delivery Time," Mathematics, MDPI, vol. 8(2), pages 1-18, January.
    15. Ramanathan, Usha & Gunasekaran, Angappa, 2014. "Supply chain collaboration: Impact of success in long-term partnerships," International Journal of Production Economics, Elsevier, vol. 147(PB), pages 252-259.
    16. Ju Myung Song & Weiwei Chen & Lei Lei, 2018. "Supply chain flexibility and operations optimisation under demand uncertainty: a case in disaster relief," International Journal of Production Research, Taylor & Francis Journals, vol. 56(10), pages 3699-3713, May.
    17. Vujosevic, Mirko & Petrovic, Dobrila & Petrovic, Radivoj, 1996. "EOQ formula when inventory cost is fuzzy," International Journal of Production Economics, Elsevier, vol. 45(1-3), pages 499-504, August.
    18. AlDurgam, Mohammad & Adegbola, Kehinde & Glock, Christoph H., 2017. "A single-vendor single-manufacturer integrated inventory model with stochastic demand and variable production rate," International Journal of Production Economics, Elsevier, vol. 191(C), pages 335-350.
    19. AlDurgham, M. & Adegbola, K. & Glock, C. H., 2017. "A single-vendor single-manufacturer integrated inventory model with stochastic demand and variable production rate," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 87594, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    20. Wong, Christina W.Y. & Lirn, Taih-Cherng & Yang, Ching-Chiao & Shang, Kuo-Chung, 2020. "Supply chain and external conditions under which supply chain resilience pays: An organizational information processing theorization," International Journal of Production Economics, Elsevier, vol. 226(C).
    21. Sarkar, Biswajit & Ganguly, Baishakhi & Sarkar, Mitali & Pareek, Sarla, 2016. "Effect of variable transportation and carbon emission in a three-echelon supply chain model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 91(C), pages 112-128.
    22. Giri, B. C. & Yun, W. Y. & Dohi, T., 2005. "Optimal design of unreliable production-inventory systems with variable production rate," European Journal of Operational Research, Elsevier, vol. 162(2), pages 372-386, April.
    23. Arunava Majumder & Rekha Guchhait & Biswajit Sarkar, 2017. "Manufacturing quality improvement and setup cost reduction in a vendor-buyer supply chain model," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 11(5), pages 588-612.
    24. Evan L. Porteus, 1986. "Investing in new parameter values in the discounted EOQ model," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 33(1), pages 39-48, February.
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