Effective and Sustainable Management of Risk Disrupting the Supply Chain Activities: The Case of the ETRAG Company

doi:10.23940/ijpe.22.11.p2.770780

Abstract

Abstract: ETRAG is a tractor manufacturing company that has been the pride of the Algerian agricultural equipment industry for decades. However, despite the advantages that distinguish them from other industries, the length and complexity of the supply chain make it vulnerable to a variety of unexpected risks that can directly affect supply chain activities. Therefore, it is essential to explore the key risks and formulate risk mitigation actions, which are the objectives of our research. On the basis of extensive bibliographic research, the House of Risk (HOR) model, through a SCOR-based approach, was selected as a working tool in this study in order to identify the priority of risk agents and risk preventive action. The study results showed there were 13 business activities, 29 risk events, and 30 risk agents. Based on the Aggregate risk potential (ARP) value, 16 risk agents are classified as priority risks in HOR1. In HOR2, 17 preventive actions are proposed for priority risk agents to be implemented in the company in descending order based on the effectiveness to difficulty (ETD) value. There are five strategies to implement the 17 mitigation actions: operations planning, supplier relationship management, multiple sourcing, demand management, and skill and efficiency development.

Key words: House of risk, SCRM, risk, mitigation, ETRAG Company

Besma Saker and Rachid Chaib. Effective and Sustainable Management of Risk Disrupting the Supply Chain Activities: The Case of the ETRAG Company [J]. Int J Performability Eng, 2022, 18(11): 770-780.

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References

1. Jüttner U., Peck H., andChristopher M.Supply Chain Risk Management: Outlining an Agenda for Future Research. International Journal of Logistics: research and applications, vol. 6, no. 4, pp. 197-210, 2003.
2. Ivanov D.Viable Supply Chain Model: Integrating Agility, Resilience and Sustainability Perspectives—Lessons from and Thinking beyond the COVID-19 Pandemic. Annals of operations research, pp. 1-21, 2020.
3. Chand M., Raj T., Shankar R., andAgarwal A.Select the Best Supply Chain by Risk Analysis for Indian Industries Environment using MCDM Approaches. Benchmarking: An International Journal, 2017.
4. Besma S., Rachid C., andAbdelaziz K., For an Effective Management of the Functional Capacities of Companies: A Study of Pharmaceutical Companies, International Journal of Safety and Security Engineering, vol. 11. no. 5, pp. 557-563, 2021.
5. Besma, S. and Rachid, C.Development of a Continuous Improvement Approach in the Management of Practical Problems in Logistics and Transport: Case Study in NUMILOG Constantine, Algeria. Safety and Security Engineering IX, vol. 206, pp. 201, 2022.
6. Tang C.S.Perspectives in Supply Chain Risk Management. International journal of production economics, vol. 103, no. 2, pp. 451-488, 2006.
7. Sodhi M.S., Son B.G., andTang C.S.Researchers’ Perspectives on Supply Chain Risk Management. Production and operations management, vol. 21, no. 1, pp. 1-13, 2012.
8. Pujawan, I.N. and Geraldin, L.H.House of Risk: A Model for Proactive Supply Chain Risk Management. Business Process Management Journal, 2009.
9. Chowdhury N.A., Ali S.M., Mahtab Z., Rahman T., Kabir G., andPaul S.K.A Structural Model for Investigating the Driving and Dependence Power of Supply Chain Risks in the Readymade Garment Industry. Journal of Retailing and Consumer Services, vol. 51, pp. 102-113, 2019.
10. Heckmann I., Comes T., andNickel S.A Critical Review on Supply Chain Risk-Definition, Measure and Modeling. Omega, vol. 52, pp. 119-132, 2015.
11. Baryannis G., Validi S., Dani S., andAntoniou G.Supply Chain Risk Management and Artificial Intelligence: State of the Art and Future Research Directions. International Journal of Production Research, vol. 57, no. 7, pp. 2179-2202, 2019.
12. Zimon, D. and Madzík, P.Standardized Management Systems and Risk Management in the Supply Chain. International Journal of Quality & Reliability Management, 2019.
13. Tarei P.K., Thakkar J.J., andNag B.Benchmarking the Relationship between Supply Chain Risk Mitigation Strategies and Practices: An Integrated Approach. Benchmarking: An International Journal, vol. 27, no. 5, pp. 1683-1715, 2020.
14. Jüttner U.Supply Chain Risk Management: Understanding the Business Requirements from a Practitioner Perspective. The international journal of logistics management, 2005.
15. Manuj, I. and Mentzer, J.T.Global Supply Chain Risk Management Strategies. International Journal of Physical Distribution & Logistics Management, 2008.
16. Tang, O. and Musa, S.N.Identifying Risk Issues and Research Advancements in Supply Chain Risk Management. International journal of production economics, vol. 133, no. 1, pp. 25-34, 2011.
17. Fan, Y. and Stevenson, M.A Review of Supply Chain Risk Management: Definition, Theory, and Research Agenda. International Journal of Physical Distribution & Logistics Management, 2018.
18. Enyinda, C.I. and Mbah, C.H.Quantifying Sources of Risk in Global Food Operations and Supply Chain. Thunderbird International Business Review, vol. 59, no. 6, pp. 653-661, 2017.
19. Cano-Olivos, P., Sosa-Gallardo, J.F., Sánchez-Partida, D., and Martínez-Flores, J.L. Risks in the Automotive Industry Supply Chain. International Journal of Supply and Operations Management, vol. 9, no. 1, pp. 56-79, 2022.
20. Alora, A. and Barua, M.K.Development of a Supply Chain Risk Index for Manufacturing Supply Chains. International Journal of Productivity and Performance Management, 2020.
21. Amarouche A., Chapellier P., andGeorge A.La Gestion Des Risques Dans Une Chaine D’Approvisionnement: Le Cas de la Filière D’Approvisionnement en Fruits ET LéGumes D’Une Entreprise d’Import-Export. In Rencontres internationales de la recherche en logistique et supply chain (RIRL), 2018.
22. Anggrahini D., Karningsih P.D., andSulistiyono M.Managing Quality Risk in a Frozen Shrimp Supply Chain: A Case Study. Procedia Manufacturing, vol. 4, pp. 252-260, 2015.
23. Boonyanusith, W. and Jittamai, P.Blood Supply Chain Risk Management Using House of Risk Model. Walailak Journal of Science and Technology (WJST), vol. 16, no. 8, pp. 573-591, 2019.
24. Paillin D.B., Tupan J.M., Paillin J.B., Latuny W., andLawalata V.O.Risk Assessment and Risk Mitigation in a Sustainable Tuna Supply Chain. Acta Logistica, vol. 9, no. 1, pp. 51-61, 2022.
25. Nalhadi A., Kurniasari A., Djamal N., Suryani S., andSupriyadi S.Supply Chain Risk Assessment of Cotton Shirt Production Uses the House of Risk Method. In Journal of Physics: Conference Series, IOP Publishing, vol. 1381, no. 1, pp. 012060, 2019.
26. Yin R.K.Case study research: Design and methods, Sage, vol. 5, 2009.
27. Ntabe E.N.,LeBel, L., Munson, A.D., and Santa-Eulalia, L.A. A Systematic Literature Review of the Supply Chain Operations Reference (SCOR) Model Application with Special Attention to Environmental Issues. International Journal of Production Economics, vol. 169, pp. 310-332, 2015.
28. Alshawabkeh R.,AL-Awamleh, H., Alkhawaldeh, M., Kanaan, R., Al-Hawary, S., Mohammad, A., and Alkhawalda, R. The Mediating Role of Supply Chain Management on the Relationship between Big Data and Supply Chain Performance using SCOR Model. Uncertain Supply Chain Management, vol. 10, no, 3, pp. 729-736, 2022.
29. ISO 310002005 International Standard Organisation
30. Ratnasari S., Hisjam M., andSutopo W.Supply Chain Risk Management in Newspaper Company: House of Risk Approach. In AIP Conference Proceedings, AIP Publishing LLC, vol. 1931, no. 1, pp. 030016, 2018.
31. Kilubi I.The Strategies of Supply Chain Risk Management-A Synthesis and Classification. International Journal of Logistics Research and Applications, vol. 19, no. 6, pp. 604-629, 2016.
32. ur Rehman, O. and Ali, Y. Enhancing Healthcare Supply Chain Resilience: Decision-Making in a Fuzzy Environment. The International Journal of Logistics Management, 2021.
33. Tukamuhabwa B., Stevenson M., andBusby J.Supply Chain Resilience in a Developing Country Context: A Case Study on the Interconnectedness of Threats, Strategies and Outcomes. Supply Chain Management: An International Journal, 2017.
34. Chowdhury M.M.H. and Quaddus, M.A. A Multiple Objective Optimization Based QFD Approach for Efficient Resilient Strategies to Mitigate Supply Chain Vulnerabilities: The Case of Garment Industry of Bangladesh. Omega, vol. 57, pp. 5-21, 2015.
35. Scholten, K. and Fynes, B.Risk and Uncertainty Management for Sustainable Supply Chains. In Sustainable supply chains, Springer, Cham, pp. 413-436, 2017.

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