IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v222y2020ics0925527319303317.html
   My bibliography  Save this article

Modeling transportation disruptions in the supply chain of automotive parts manufacturing company

Author

Listed:
  • Fartaj, Seyedamir-Reza
  • Kabir, Golam
  • Eghujovbo, Victor
  • Ali, Syed Mithun
  • Paul, Sanjoy Kumar

Abstract

The transportation network plays a vital role in the strategic imperative of automotive parts manufacturing companies. There is a lack of academic and practical studies, which focus solely on transportation disruption analysis in the supply chain of automotive parts manufacturing company. Moreover, very few studies have taken into account the cause and effect relationship between transportation disruption factors. The objective of this study is to analyze the critical transportation disruption factors of the supply chain of automotive parts manufacturing company and to represent the interrelationships using the best-worst (BWM) and rough strength-relation (RSR) analysis methods. The newly integrated BWM-RSR framework considers the vagueness and ambiguity in disruption factor analysis. The applicability and effectiveness of the newly developed BWM-RSR framework are demonstrated at an automotive parts manufacturing company in Oldcastle, Ontario, Canada. The results show that infrastructural bottlenecks/congestion and inadequate skilled labor are the most critical factors to the disruption of the transportation network in the automotive industry. The developed new framework can be used as an effective tool to analyze critical transportation disruption factors and examine the associated interrelationships.

Suggested Citation

  • Fartaj, Seyedamir-Reza & Kabir, Golam & Eghujovbo, Victor & Ali, Syed Mithun & Paul, Sanjoy Kumar, 2020. "Modeling transportation disruptions in the supply chain of automotive parts manufacturing company," International Journal of Production Economics, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:proeco:v:222:y:2020:i:c:s0925527319303317
    DOI: 10.1016/j.ijpe.2019.09.032
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527319303317
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2019.09.032?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. Garvey, Myles D. & Carnovale, Steven & Yeniyurt, Sengun, 2015. "An analytical framework for supply network risk propagation: A Bayesian network approach," European Journal of Operational Research, Elsevier, vol. 243(2), pages 618-627.
    2. Saaty, Thomas L., 1990. "How to make a decision: The analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 48(1), pages 9-26, September.
    3. Jharkharia, Sanjay & Shankar, Ravi, 2007. "Selection of logistics service provider: An analytic network process (ANP) approach," Omega, Elsevier, vol. 35(3), pages 274-289, June.
    4. Jinfen Zhang & Ângelo P Teixeira & C. Guedes Soares & Xinping Yan & Kezhong Liu, 2016. "Maritime Transportation Risk Assessment of Tianjin Port with Bayesian Belief Networks," Risk Analysis, John Wiley & Sons, vol. 36(6), pages 1171-1187, June.
    5. Thun, Jörn-Henrik & Hoenig, Daniel, 2011. "An empirical analysis of supply chain risk management in the German automotive industry," International Journal of Production Economics, Elsevier, vol. 131(1), pages 242-249, May.
    6. Ghavamifar, Ali & Makui, Ahmad & Taleizadeh, Ata Allah, 2018. "Designing a resilient competitive supply chain network under disruption risks: A real-world application," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 115(C), pages 87-109.
    7. 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.
    8. Fattahi, Mohammad & Govindan, Kannan & Keyvanshokooh, Esmaeil, 2017. "Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 101(C), pages 176-200.
    9. Rezaei, Jafar, 2015. "Best-worst multi-criteria decision-making method," Omega, Elsevier, vol. 53(C), pages 49-57.
    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. Matthias Klumpp & Dominic Loske, 2021. "Sustainability and Resilience Revisited: Impact of Information Technology Disruptions on Empirical Retail Logistics Efficiency," Sustainability, MDPI, vol. 13(10), pages 1-20, May.
    2. Ashish Dwivedi & Claudio Sassanelli & Dindayal Agrawal & Md. Abdul Moktadir & Idiano D'Adamo, 2023. "Drivers to mitigate climate change in context of manufacturing industry: An emerging economy study," Business Strategy and the Environment, Wiley Blackwell, vol. 32(7), pages 4467-4484, November.
    3. Md. Rayhan Sarker & Md. Abdul Moktadir & Ernesto D. R. Santibanez-Gonzalez, 2021. "Social Sustainability Challenges Towards Flexible Supply Chain Management: Post-COVID-19 Perspective," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 22(2), pages 199-218, December.
    4. João M. Lopes & Sofia Gomes & Lassana Mané, 2022. "Developing Knowledge of Supply Chain Resilience in Less-Developed Countries in the Pandemic Age," Logistics, MDPI, vol. 6(1), pages 1-19, January.
    5. Chowdhury, Priyabrata & Paul, Sanjoy Kumar & Kaisar, Shahriar & Moktadir, Md. Abdul, 2021. "COVID-19 pandemic related supply chain studies: A systematic review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 148(C).
    6. Ma, Haicheng & Lou, Gaoxiang & Fan, Tijun & Chan, Hing Kai & Chung, Sai Ho, 2021. "Conventional automotive supply chains under China's dual-credit policy: fuel economy, production and coordination," Energy Policy, Elsevier, vol. 151(C).
    7. Sanjoy Kumar Paul & Priyabrata Chowdhury, 2020. "Strategies for Managing the Impacts of Disruptions During COVID-19: an Example of Toilet Paper," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 21(3), pages 283-293, September.
    8. Vafadarnikjoo, Amin & Tavana, Madjid & Chalvatzis, Konstantinos & Botelho, Tiago, 2022. "A socio-economic and environmental vulnerability assessment model with causal relationships in electric power supply chains," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    9. Mohammed Al Awadh, 2022. "Utilizing Multi-Criteria Decision Making to Evaluate the Quality of Healthcare Services," Sustainability, MDPI, vol. 14(19), pages 1-21, October.

    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. 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.
    2. Kik, M.C. & Claassen, G.D.H. & Meuwissen, M.P.M. & Smit, A.B. & Saatkamp, H.W., 2021. "Actor analysis for sustainable soil management – A case study from the Netherlands," Land Use Policy, Elsevier, vol. 107(C).
    3. Berger, Niklas & Schulze-Schwering, Stefan & Long, Elisa & Spinler, Stefan, 2023. "Risk management of supply chain disruptions: An epidemic modeling approach," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1036-1051.
    4. Ioannis Sitaridis & Fotis Kitsios, 2020. "Competitiveness analysis and evaluation of entrepreneurial ecosystems: a multi-criteria approach," Annals of Operations Research, Springer, vol. 294(1), pages 377-399, November.
    5. Saeedeh Anvari & Metin Turkay, 2017. "The facility location problem from the perspective of triple bottom line accounting of sustainability," International Journal of Production Research, Taylor & Francis Journals, vol. 55(21), pages 6266-6287, November.
    6. Maureen S. Golan & Laura H. Jernegan & Igor Linkov, 2020. "Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic," Environment Systems and Decisions, Springer, vol. 40(2), pages 222-243, June.
    7. Hou, Yunzhang & Wang, Xiaoling & Wu, Yenchun Jim & He, Peixu, 2018. "How does the trust affect the topology of supply chain network and its resilience? An agent-based approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 229-241.
    8. Chun-Chieh Tseng & Jun-Yi Zeng & Min-Liang Hsieh & Chih-Hung Hsu, 2022. "Analysis of Innovation Drivers of New and Old Kinetic Energy Conversion Using a Hybrid Multiple-Criteria Decision-Making Model in the Post-COVID-19 Era: A Chinese Case," Mathematics, MDPI, vol. 10(20), pages 1-25, October.
    9. Munim, Ziaul Haque & Duru, Okan & Ng, Adolf K.Y., 2022. "Transhipment port's competitiveness forecasting using analytic network process modelling," Transport Policy, Elsevier, vol. 124(C), pages 70-82.
    10. Simwanda, Matamyo & Murayama, Yuji & Ranagalage, Manjula, 2020. "Modeling the drivers of urban land use changes in Lusaka, Zambia using multi-criteria evaluation: An analytic network process approach," Land Use Policy, Elsevier, vol. 92(C).
    11. Amelia Bilbao-Terol & Mar Arenas-Parra & Raquel Quiroga-García & Celia Bilbao-Terol, 2022. "An extended best–worst multiple reference point method: application in the assessment of non-life insurance companies," Operational Research, Springer, vol. 22(5), pages 5323-5362, November.
    12. Margolis, Joshua T. & Sullivan, Kelly M. & Mason, Scott J. & Magagnotti, Mariah, 2018. "A multi-objective optimization model for designing resilient supply chain networks," International Journal of Production Economics, Elsevier, vol. 204(C), pages 174-185.
    13. 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.
    14. 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).
    15. Gabrielle Gauthier Melançon & Philippe Grangier & Eric Prescott-Gagnon & Emmanuel Sabourin & Louis-Martin Rousseau, 2021. "A Machine Learning-Based System for Predicting Service-Level Failures in Supply Chains," Interfaces, INFORMS, vol. 51(3), pages 200-212, May.
    16. Abastante, Francesca & Corrente, Salvatore & Greco, Salvatore & Ishizaka, Alessio & Lami, Isabella M., 2018. "Choice architecture for architecture choices: Evaluating social housing initiatives putting together a parsimonious AHP methodology and the Choquet integral," Land Use Policy, Elsevier, vol. 78(C), pages 748-762.
    17. Theißen, Sebastian & Spinler, Stefan, 2014. "Strategic analysis of manufacturer-supplier partnerships: An ANP model for collaborative CO2 reduction management," European Journal of Operational Research, Elsevier, vol. 233(2), pages 383-397.
    18. Manolan Kandy, D. & Mörtberg, U. & Wretling, V. & Kuhlefelt, A. & Byström, G. & Polatidis, H. & Barney, A. & Balfors, B., 2024. "Spatial multicriteria framework for sustainable wind-farm planning – Accounting for conflicts," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    19. Jahani, Hamed & Abbasi, Babak & Sheu, Jiuh-Biing & Klibi, Walid, 2024. "Supply chain network design with financial considerations: A comprehensive review," European Journal of Operational Research, Elsevier, vol. 312(3), pages 799-839.
    20. Misbah Anjum & Vernika Agarwal & P. K. Kapur & Sunil Kumar Khatri, 2020. "Two-phase methodology for prioritization and utility assessment of software vulnerabilities," 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. 11(2), pages 289-300, July.

    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:222:y:2020:i:c:s0925527319303317. 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.