IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v231y2021ics0925527320302000.html
   My bibliography  Save this article

Multi-stage hybrid model for supplier selection and order allocation considering disruption risks and disruptive technologies

Author

Listed:
  • Kaur, Harpreet
  • Prakash Singh, Surya

Abstract

In recent times, Supply chains are required to undergo the structural changes in order to adapt to the positive events such as Industry 4.0 and negative events such as natural and man-made disasters. Both positive and negative events tend to cause disruptions and affect business operations continuity. Supplier selection, being the critical and foremost activity must ensure that selected suppliers are capable of supporting the organizations against disruptions caused by these events. Hence, supplier selection and order allocation must be restructured considering the dynamics of Industry 4.0 and disaster events to ensure undisrupted flow of materials across supply chain. The paper proposes a multi stage hybrid model for integrated supplier segmentation, selection and order allocation considering risks and disruptions. The suppliers are then evaluated based on set of criteria suitable in Industry 4.0 environment using Data Envelopment Analysis (DEA) and are further prioritized using Fuzzy Analytical Hierarchical Process and Technique for Order of Preference by Similarity to Ideal Solution (FAHP-TOPSIS). The risk associated with each supplier is computed. The paper also proposes a Mixed Integer Program (MIP) as to optimize multi-period, multi item order allocation to suppliers in such a way that overall cost and risk of disruption is simultaneously minimized. In event of any disruption either from supply or demand side, the multi-stage hybrid model tends to reduce its economic impact by allocating emergency orders, thus, ensuring business operations continuity. The proposed multi-stage hybrid model is illustrated using a case of an automobile company.

Suggested Citation

  • Kaur, Harpreet & Prakash Singh, Surya, 2021. "Multi-stage hybrid model for supplier selection and order allocation considering disruption risks and disruptive technologies," International Journal of Production Economics, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:proeco:v:231:y:2021:i:c:s0925527320302000
    DOI: 10.1016/j.ijpe.2020.107830
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527320302000
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2020.107830?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. Alexandre Dolgui & Dmitry Ivanov & Boris Sokolov, 2018. "Ripple effect in the supply chain: an analysis and recent literature," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 414-430, January.
    2. Seyed Mohammad Khalili & Fariborz Jolai & Seyed Ali Torabi, 2017. "Integrated production–distribution planning in two-echelon systems: a resilience view," International Journal of Production Research, Taylor & Francis Journals, vol. 55(4), pages 1040-1064, February.
    3. Hosseini, Seyedmohsen & Ivanov, Dmitry & Dolgui, Alexandre, 2019. "Review of quantitative methods for supply chain resilience analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 285-307.
    4. Lovell, C. A. Knox & Pastor, Jesus T., 1999. "Radial DEA models without inputs or without outputs," European Journal of Operational Research, Elsevier, vol. 118(1), pages 46-51, October.
    5. Sawik, Tadeusz, 2013. "Selection of resilient supply portfolio under disruption risks," Omega, Elsevier, vol. 41(2), pages 259-269.
    6. Torabi, S.A. & Mansouri, S.A., 2015. "Integrated business continuity and disaster recovery planning: Towards organizational resilienceAuthor-Name: Sahebjamnia, N," European Journal of Operational Research, Elsevier, vol. 242(1), pages 261-273.
    7. Varun Gupta & Bo He & Suresh P. Sethi, 2015. "Contingent sourcing under supply disruption and competition," International Journal of Production Research, Taylor & Francis Journals, vol. 53(10), pages 3006-3027, May.
    8. Mohr, Sebastian & Khan, Omera, 2015. "3D Printing and Supply Chains of the Future," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Innovations and Strategies for Logistics and Supply Chains: Technologies, Business Models and Risk Management. Proceedings of the Hamburg Internationa, volume 20, pages 147-174, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    9. Seyedmohsen Hosseini & Abdullah Al Khaled, 2019. "A hybrid ensemble and AHP approach for resilient supplier selection," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 207-228, January.
    10. Yu, Haisheng & Zeng, Amy Z. & Zhao, Lindu, 2009. "Single or dual sourcing: decision-making in the presence of supply chain disruption risks," Omega, Elsevier, vol. 37(4), pages 788-800, August.
    11. Brian Tomlin, 2006. "On the Value of Mitigation and Contingency Strategies for Managing Supply Chain Disruption Risks," Management Science, INFORMS, vol. 52(5), pages 639-657, May.
    12. Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov, 2019. "The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics," International Journal of Production Research, Taylor & Francis Journals, vol. 57(3), pages 829-846, February.
    13. Opricovic, Serafim & Tzeng, Gwo-Hshiung, 2004. "Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS," European Journal of Operational Research, Elsevier, vol. 156(2), pages 445-455, July.
    14. Hosseini, Seyedmohsen & Barker, Kash, 2016. "A Bayesian network model for resilience-based supplier selection," International Journal of Production Economics, Elsevier, vol. 180(C), pages 68-87.
    15. Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov & Frank Werner & Marina Ivanova, 2016. "A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0," International Journal of Production Research, Taylor & Francis Journals, vol. 54(2), pages 386-402, January.
    16. Dmitry Ivanov, 2018. "Structural Dynamics and Resilience in Supply Chain Risk Management," International Series in Operations Research and Management Science, Springer, number 978-3-319-69305-7, January.
    17. Zhu, Stuart X., 2013. "Dynamic replenishment, production, and pricing decisions, in the face of supply disruption and random price-sensitive demand," International Journal of Production Economics, Elsevier, vol. 146(2), pages 612-619.
    18. Hosseini, Seyedmohsen & Morshedlou, Nazanin & Ivanov, Dmitry & Sarder, M.D. & Barker, Kash & Khaled, Abdullah Al, 2019. "Resilient supplier selection and optimal order allocation under disruption risks," International Journal of Production Economics, Elsevier, vol. 213(C), pages 124-137.
    19. Masoud Kamalahmadi & Mahour Mellat-Parast, 2016. "Developing a resilient supply chain through supplier flexibility and reliability assessment," International Journal of Production Research, Taylor & Francis Journals, vol. 54(1), pages 302-321, January.
    20. Torabi, S.A. & Baghersad, M. & Mansouri, S.A., 2015. "Resilient supplier selection and order allocation under operational and disruption risks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 79(C), pages 22-48.
    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. Hosseini, Seyedmohsen & Ivanov, Dmitry & Dolgui, Alexandre, 2019. "Review of quantitative methods for supply chain resilience analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 285-307.
    2. Aldrighetti, Riccardo & Battini, Daria & Ivanov, Dmitry & Zennaro, Ilenia, 2021. "Costs of resilience and disruptions in supply chain network design models: A review and future research directions," International Journal of Production Economics, Elsevier, vol. 235(C).
    3. Dmitry Ivanov, 2022. "Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 319(1), pages 1411-1431, December.
    4. Antonio Zavala-Alcívar & María-José Verdecho & Juan-José Alfaro-Saiz, 2020. "A Conceptual Framework to Manage Resilience and Increase Sustainability in the Supply Chain," Sustainability, MDPI, vol. 12(16), pages 1-38, August.
    5. Dmitry Ivanov & Boris Sokolov, 2019. "Simultaneous structural–operational control of supply chain dynamics and resilience," Annals of Operations Research, Springer, vol. 283(1), pages 1191-1210, December.
    6. Hosseini, Seyedmohsen & Morshedlou, Nazanin & Ivanov, Dmitry & Sarder, M.D. & Barker, Kash & Khaled, Abdullah Al, 2019. "Resilient supplier selection and optimal order allocation under disruption risks," International Journal of Production Economics, Elsevier, vol. 213(C), pages 124-137.
    7. Ivanov, Dmitry, 2020. "Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    8. Aghajani, Mojtaba & Ali Torabi, S. & Altay, Nezih, 2023. "Resilient relief supply planning using an integrated procurement-warehousing model under supply disruption," Omega, Elsevier, vol. 118(C).
    9. Sawik, Tadeusz, 2022. "Stochastic optimization of supply chain resilience under ripple effect: A COVID-19 pandemic related study," Omega, Elsevier, vol. 109(C).
    10. Guo, Yan & Yu, Xinning & Zhou, Caifeng & Lyu, Gaoyan, 2021. "Government subsidies for preventing supply disruption when the supplier has an outside option under competition," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 147(C).
    11. Seyedmohsen Hosseini & Dmitry Ivanov, 2022. "A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach," Annals of Operations Research, Springer, vol. 319(1), pages 581-607, December.
    12. Sawik, Tadeusz, 2021. "On the risk-averse selection of resilient multi-tier supply portfolio," Omega, Elsevier, vol. 101(C).
    13. Shoufeng Ji & Pengyun Zhao & Tingting Ji, 2023. "A Hybrid Optimization Method for Sustainable and Flexible Design of Supply–Production–Distribution Network in the Physical Internet," Sustainability, MDPI, vol. 15(7), pages 1-34, April.
    14. Alexander Pavlov & Dmitry Ivanov & Frank Werner & Alexandre Dolgui & Boris Sokolov, 2022. "Integrated detection of disruption scenarios, the ripple effect dispersal and recovery paths in supply chains," Annals of Operations Research, Springer, vol. 319(1), pages 609-631, December.
    15. Iman Kazemian & S. Ali Torabi & Christopher W. Zobel & Yuhong Li & Milad Baghersad, 2022. "A multi-attribute supply chain network resilience assessment framework based on SNA-inspired indicators," Operational Research, Springer, vol. 22(3), pages 1853-1883, July.
    16. Luo, Sha & Ahiska, S. Sebnem & Fang, Shu-Cherng & King, Russell E. & Warsing, Donald P. & Wu, Shuohao, 2021. "An analysis of optimal ordering policies for a two-supplier system with disruption risk," Omega, Elsevier, vol. 105(C).
    17. Gupta, Varun & Ivanov, Dmitry & Choi, Tsan-Ming, 2021. "Competitive pricing of substitute products under supply disruption," Omega, Elsevier, vol. 101(C).
    18. Lohmer, Jacob & Bugert, Niels & Lasch, Rainer, 2020. "Analysis of resilience strategies and ripple effect in blockchain-coordinated supply chains: An agent-based simulation study," International Journal of Production Economics, Elsevier, vol. 228(C).
    19. Dixit, Vijaya & Verma, Priyanka & Tiwari, Manoj Kumar, 2020. "Assessment of pre and post-disaster supply chain resilience based on network structural parameters with CVaR as a risk measure," International Journal of Production Economics, Elsevier, vol. 227(C).
    20. Ivanov, Dmitry & Dolgui, Alexandre & Sokolov, Boris, 2022. "Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service”," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).

    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:eee:proeco:v:231:y:2021:i:c:s0925527320302000. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    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.