IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v192y2024ics1366554524003831.html

Measuring supply chain resilience along the automotive value chain — A comparative research on literature and industry

Author

Listed:
  • Raaymann, Sophia
  • Spinler, Stefan

Abstract

About three years after the start of the COVID-19 pandemic disrupting global supply chains, companies increasingly focus on creating supply chains that are resilient to the next disruption. While researchers have developed multiple frameworks and quantitative models for assessing risk in supply chains, the question of how to measure supply chain resilience (SCR) with key performance indicators (KPIs) remains unanswered. This research provides answers on how to measure SCR in the automotive industry. Researchers investigated literature’s perspective through text mining on 195 published papers on SCR and compared that to the industry’s perspective. The Analytical Hierarchy Process is applied to text mining results to find the most suitable combination of KPIs. For the industry data, a conjoint method analyzed via an ordinal regression is applied in interviews. The research reveals that the most important KPIs, according to literature, are lead time variation, OTIF (On time in full), and volume flexibility of suppliers. At the same time, the industry also assigns the greatest contribution to OTIF and volume flexibility, and to the stock level of high-risk parts. This study also investigates the different priorities of OEMs, Tier 1 and Tier 2 suppliers when measuring SCR. Perspectives on how to measure resilience vary within the industry as well as between industry and academia. This research reveals the need for a greater exchange between industry and academia as well as a more structural discussion of resilience KPIs and their application within the industry.

Suggested Citation

  • Raaymann, Sophia & Spinler, Stefan, 2024. "Measuring supply chain resilience along the automotive value chain — A comparative research on literature and industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:transe:v:192:y:2024:i:c:s1366554524003831
    DOI: 10.1016/j.tre.2024.103792
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554524003831
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2024.103792?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Lam, Jasmine Siu Lee & Bai, Xiwen, 2016. "A quality function deployment approach to improve maritime supply chain resilience," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 92(C), pages 16-27.
    2. Sandy Q. Qu & John Dumay, 2011. "The qualitative research interview," Qualitative Research in Accounting & Management, Emerald Group Publishing Limited, vol. 8(3), pages 238-264, August.
    3. Sandy Q. Qu & John Dumay, 2011. "The qualitative research interview," Qualitative Research in Accounting & Management, Emerald Group Publishing Limited, vol. 8(3), pages 238-264, August.
    4. 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.
    5. Suryawanshi, Pravin & Dutta, Pankaj, 2022. "Optimization models for supply chains under risk, uncertainty, and resilience: A state-of-the-art review and future research directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    6. Zhang, Ying & Snyder, Lawrence V. & Ralphs, Ted K. & Xue, Zhaojie, 2016. "The competitive facility location problem under disruption risks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 93(C), pages 453-473.
    7. Ramani, Vinay & Ghosh, Debabrata & Sodhi, ManMohan S., 2022. "Understanding systemic disruption from the Covid-19-induced semiconductor shortage for the auto industry," Omega, Elsevier, vol. 113(C).
    8. Can Saglam, Yesim & Yildiz Çankaya, Sibel & Golgeci, Ismail & Sezen, Bulent & Zaim, Selim, 2022. "The role of communication quality, relational commitment, and reciprocity in building supply chain resilience: A social exchange theory perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    9. Aqlan, Faisal & Lam, Sarah S., 2015. "A fuzzy-based integrated framework for supply chain risk assessment," International Journal of Production Economics, Elsevier, vol. 161(C), pages 54-63.
    10. Paul E. Green & Abba M. Krieger & Yoram Wind, 2001. "Thirty Years of Conjoint Analysis: Reflections and Prospects," Interfaces, INFORMS, vol. 31(3_supplem), pages 56-73, June.
    11. David Simchi-Levi & William Schmidt & Yehua Wei & Peter Yun Zhang & Keith Combs & Yao Ge & Oleg Gusikhin & Michael Sanders & Don Zhang, 2015. "Identifying Risks and Mitigating Disruptions in the Automotive Supply Chain," Interfaces, INFORMS, vol. 45(5), pages 375-390, October.
    12. Anni-Kaisa Kähkönen & Pietro Evangelista & Jukka Hallikas & Mika Immonen & Katrina Lintukangas, 2023. "COVID-19 as a trigger for dynamic capability development and supply chain resilience improvement," International Journal of Production Research, Taylor & Francis Journals, vol. 61(8), pages 2696-2715, April.
    13. Li, Guo & Xue, Jing & Li, Na & Ivanov, Dmitry, 2022. "Blockchain-supported business model design, supply chain resilience, and firm performance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    14. I. Nyoman Pujawan, 2004. "Assessing supply chain flexibility: a conceptual framework and case study," International Journal of Integrated Supply Management, Inderscience Enterprises Ltd, vol. 1(1), pages 79-97.
    15. Dun Li & Bangdong Zhi & Tobias Schoenherr & Xiaojun Wang, 2023. "Developing capabilities for supply chain resilience in a post-COVID world: A machine learning-based thematic analysis," IISE Transactions, Taylor & Francis Journals, vol. 55(12), pages 1256-1276, December.
    16. Faraway, Julian J. & Augustin, Nicole H., 2018. "When small data beats big data," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 142-145.
    17. Thi Huong Tran & Mario Dobrovnik & Sebastian Kummer, 2018. "Supply chain risk assessment: a content analysis-based literature review," International Journal of Logistics Systems and Management, Inderscience Enterprises Ltd, vol. 31(4), pages 562-591.
    18. Ali, Mohd Helmi & Chung, Leanne & Kumar, Ajay & Zailani, Suhaiza & Tan, Kim Hua, 2021. "A sustainable Blockchain framework for the halal food supply chain: Lessons from Malaysia," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    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. Hart Nibbrig, Maurice & Sharif Azadeh, Shadi & Maknoon, M.Y., 2025. "Adaptive resilience strategies for supply chain networks against disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 200(C).
    2. Cantini, Alessandra & Leoni, Leonardo & Ferraro, Saverio & Carlo, Filippo De, 2025. "Centralized and decentralized supply chains: Performance maps for comparing the cost-effectiveness of distribution network configurations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 204(C).

    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. Lu, Xingwei & Xu, Xianhao & Sun, Yi, 2025. "Enhancing resilience in supply chains through resource orchestration and AI assimilation: An empirical exploration," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 195(C).
    2. Dmitry Ivanov, 2026. "Collaborative emergency adaptation for ripple effect mitigation in intertwined supply networks," Annals of Operations Research, Springer, vol. 359(2), pages 1727-1743, April.
    3. Hart Nibbrig, Maurice & Sharif Azadeh, Shadi & Maknoon, M.Y., 2025. "Adaptive resilience strategies for supply chain networks against disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 200(C).
    4. Aldrighetti, Riccardo & Battini, Daria & Ivanov, Dmitry, 2023. "Efficient resilience portfolio design in the supply chain with consideration of preparedness and recovery investments," Omega, Elsevier, vol. 117(C).
    5. Ivanov, Dmitry, 2023. "Intelligent digital twin (iDT) for supply chain stress-testing, resilience, and viability," International Journal of Production Economics, Elsevier, vol. 263(C).
    6. Francesca Bartolacci & Roberto Del Gobbo & Michela Soverchia, 2025. "Understanding the adoption of sustainable development practices in family businesses: An exploratory case study," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2025(1 Suppl.), pages 147-164.
    7. Roelofse, Emmalinde, 2017. "M3 Strategic Decision-Making Under Uncertainty: Modes, Models, & Momentum," SocArXiv uafvr_v1, Center for Open Science.
    8. Nordin, Fredrik & Ravald, Annika, 2023. "The making of marketing decisions in modern marketing environments," Journal of Business Research, Elsevier, vol. 162(C).
    9. Xu, Yang & Peng, Peng & Claramunt, Christophe & Qian, Jiale & Lu, Feng, 2026. "Resilience of the global maritime LNG network: From static Bow-tie to dynamic SIR modeling," Reliability Engineering and System Safety, Elsevier, vol. 265(PB).
    10. Müllerklein, Daniel & Fontaine, Pirmin, 2025. "Resilient transportation network design with disruption uncertainty and lead times," European Journal of Operational Research, Elsevier, vol. 322(3), pages 827-840.
    11. Nishat Alam Choudhary & Shalabh Singh & Tobias Schoenherr & M. Ramkumar, 2023. "Risk assessment in supply chains: a state-of-the-art review of methodologies and their applications," Annals of Operations Research, Springer, vol. 322(2), pages 565-607, March.
    12. Roelofse, Emmalinde, 2017. "M3 Strategic Decision-Making Under Uncertainty: Modes, Models, and Momentum," Thesis Commons dwt3a_v1, Center for Open Science.
    13. Li, Guo & Xue, Jing & Li, Na & Ivanov, Dmitry, 2022. "Blockchain-supported business model design, supply chain resilience, and firm performance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    14. Yoo, Sihyun & Beumer, Ruben & van Wee, Bert & Mouter, Niek & Molin, Eric, 2025. "Perceived accessibility by air transportation: A focus group study of potential air travelers in The Netherlands," Transportation Research Part A: Policy and Practice, Elsevier, vol. 193(C).
    15. Ivanov, Dmitry, 2024. "Supply chain resilience: Conceptual and formal models drawing from immune system analogy," Omega, Elsevier, vol. 127(C).
    16. 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.
    17. Nawaz, Muhammad Zahid & Nawaz, Shahid & Guzmán, Francisco & Plotkina, Daria, 2023. "The aftermath of Covid-19: The rise of pandemic animosity among consumers and its scale development," Journal of Business Research, Elsevier, vol. 157(C).
    18. Máté Pusker & Blanka Berényi & Judit T. Kárász & Szabolcs Takács, 2025. "Detecting emotions during interview simulations," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(2), pages 1507-1551, April.
    19. Burgos, Diana & Ivanov, Dmitry, 2021. "Food retail supply chain resilience and the COVID-19 pandemic: A digital twin-based impact analysis and improvement directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    20. Li, Yuhong & Zobel, Christopher W., 2020. "Exploring supply chain network resilience in the presence of the ripple effect," International Journal of Production Economics, Elsevier, vol. 228(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:transe:v:192:y:2024:i:c:s1366554524003831. 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/wps/find/journaldescription.cws_home/600244/description#description .

    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.