نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار دانشکده مدیریت و حسابداری، دانشکدگان فارابی دانشگاه تهران، قم، ایران

2 دانشجوی کارشناسی ارشد مدیریت صنعتی، دانشکده مدیریت و حسابداری، دانشکدگان فارابی دانشگاه تهران، قم، ایران

چکیده

به منظور تبیین چارچوبی برای مدیریت ریسک حمل‌ونقل در زنجیره تأمین صنایع غذایی، ابتدا بر اساس پیشینه پژوهش، 12 ریسک که می‌توانند منجر به ایجاد اختلال در حمل و نقل در زنجیره تامین صنایع غذایی شوند شناسایی شده‌اند. با استفاده از روش دلفی و کسب نظر از 15 خبره دانشگاهی و صنعتی و در 3 مرحله، ریسک‌ها نهایی شده و با کسب نظر از خبرگان، راهکارهای مواجهه با ریسک‌های حاضر نیز معین شده است. با بکارگیری روش دیمتل خاکستری به بررسی نحوه تعامل ریسک‌ها پرداخته شد. نتایج نشان داد که ریسک‌های مشکلات آب و هوایی ، بلایای طبیعی، نیروی کار ماهر ناکافی/ اعتصابات کارگری، ظرفیت نامناسب زیرساخت‌هاو تورم و تغییر نرخ ارز از جمله ریسک‌هایی هستند که بیشتر روی سایر ریسک‌ها اثر می‌گذارند تا اینکه بخواهند از آنها اثر بپذیرند. سپس با استفاده از روش کوپراس خاکستری نیز به رتبه‌بندی راهکارهای مواجهه با ریسک که با کسب نظرات خبرگان شناسایی شدند؛ پرداخته شده است. نتایج نشان داد که تعریف شاخص‌های عملکردی کلیدی اولویت اول را در میان راهکارها دارد. به همین دلیل، به مدیران پیشنهاد می‌شود که به منظور دست‌یابی به زنجیره تامین استوار و مدیریت ریسک فعالانه، پس از شناسایی ذینفعان و فرآیندهای حیاتی، توافق بر سر جریان مالی در هر موقعیت به دست آمده و نقشه جریان ارزش ترسیم شود. به این صورت اقدامات پیشگیرانه برای کاهش ریسک زنجیره تامین ممکن شده و می‌توان برای شرایط پیش‌بینی نشده یک طرح اضطراری تهیه نمود تا تاب‌آوری افزایش یابد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Identifying and Prioritizing Factors Affecting Transportation Risk Management in the Food Supply Chain Using Gray Delphi And Gray COPRAS

نویسندگان [English]

  • Mahsa Pishdar 1
  • Atefeh Habibi 2

1 Assistant Professor, Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran

2 Master's student in Industrial Management, Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran

چکیده [English]

To explain a framework for managing the risk of transportation in the food industry supply chain, the initial step involves identifying 12 risks that can potentially lead to transportation disruptions, based on the research background. Utilizing the Delphi method and gathering opinions from 15 academic and industrial experts across 3 stages, the risks were ultimately defined. Furthermore, expert opinions were sought to determine solutions to address the identified risks. The grey DEMATEL method was employed to investigate the interaction of risks. The findings revealed that weather problems, natural disasters, insufficient skilled labor/labor strikes, infrastructure capacity, and inflation and exchange rate changes are among the risks that exert a more significant influence on other risks than they are influenced by them. Subsequently, using the grey COPRAS method, the prioritization of solutions to mitigate the identified risks, based on expert opinions, was undertaken. The results indicated that the top-ranked solution is the definition of key performance indicators. Therefore, it is recommended to managers that, in order to establish a robust supply chain and proactively manage risks, they should identify stakeholders and critical processes. Afterward, an agreement on the financial flow in each situation should be obtained, and a value flow map drawn. This approach enables the implementation of preventive measures to reduce supply chain risk and facilitates the preparation of an emergency plan for unforeseen conditions, thereby enhancing resilience.
Introduction
Disruption in transportation stands as the pivotal factor undermining the efficiency of the supply chain. Any significant interruption can result in delays or business flow cessation, leading to consequential impacts (Ali et al., 2021). The transportation supply network is susceptible to various technical, economic, and environmental factors (Tan et al., 2023). Simultaneously, research indicates that factors such as a workforce lacking sufficient skills, suboptimal selection of service providers, traffic accidents, and the inability to predict the systemic impact of these risks play a crucial role in transportation, causing disruptions in the flow. In addition to these factors, it is noteworthy that supply chain management in the food industry introduces its own complexities. Unlike other industries, the quality of products in this type of supply chain consistently diminishes during product movement and this issue of perishability intensifying the need for transportation risk management (Hosseini-Motlagh et al., 2019; Choe et al., 2021). Given this context, emphasis should be placed on establishing distribution channels with lower costs and implementing change management to enhance efficiency. However, existing studies have predominantly focused solely on analyzing transportation disruption within companies' supply chains. Clearly, it is insufficient to only address disorders or the risks that may lead to their occurrence. A comprehensive examination of the cause-and-effect relationships among these risks facilitates a systematic understanding of the risk network. This approach enables the development of a more effective program to enhance the resilience of the transportation system. Even in the face of risks and disruptions, this ensures minimal damage, a swift return to normal operational levels in the supply chain, or the application of knowledge management to learn from experiences and prevent the recurrence of disruptions through appropriate implementation solutions. Consequently, the overall performance of the supply chain can be improved. In consideration of these elements, this study aims to address the following main questions:
- What are the risks associated with transportation in the food supply chain?
- What are the pertinent solutions, according to expert opinions, and what is their prioritization?
Methodology
In terms of its objective, the current study falls within the domain of applied research as it aims to discover practical solutions to address a real-world problem. Regarding information collection, the study is categorized as survey research, wherein data is gathered based on the opinions of 15 experts. Among these experts, 9 are drawn from the industrial community, each possessing over 10 years of experience in the commercial sector and raw material procurement within the food industry. The remaining 6 experts are affiliated with the academic community and have published numerous articles in the field. The study unfolds in several stages. Initially, a compilation of transportation risks causing disruptions in the food supply chain is accomplished through a literature review. Subsequently, the Delphi method is employed to screen and refine these disruptions. The cause-and-effect relationships among the identified disruptions are then scrutinized using the Grey DEMATEL method. Experts are engaged to contribute not only by assessing the risks but also by providing their insights into coping strategies based on their experience. Finally, the coping strategies are prioritized using the Grey COPRAS method.
Results and Discussion
According to the obtained results, it is evident that the climate problems risks of failure to choose logistics service providers that care about sustainability principles (C6) and frequent change of product delivery time (C9) exhibit the highest degree of interconnectedness with other risks. The weights of these risks have also been determined. Notably, the failure to choose logistics service providers committed to sustainability principles has secured the top rank with a weight of 0.1443. Following closely, the frequent change of product delivery time holds the second position with a weight of 0.1384, while natural disasters rank third with a weight of 0.1039. Turning to coping strategies, it is noteworthy that the solution of "Definition of Key Performance Indicators (KPI)" has claimed the top position. In today's business landscape dominated by Logistics 4.0 and Omnichannel, simplifying processes can create significant added value for any business, particularly in minimizing transfer time. Concurrently, many manufacturing companies are leveraging various logistics transportation modes as a critical factor for promptly responding to demands, thereby enhancing service reliability and minimizing travel time (Foroozesh et al., 2022). The adoption of modern technologies such as the Internet of Things facilitates real-time inventory monitoring, contributing to dynamic pricing policies. As product quality diminishes along the chain, electronic labels enable adjusting product prices based on features (Kumar & Agrawal, 2023).
Conclusion
Studies indicate that supply chain managers, particularly in food supply chains, have demonstrated significant commitments to sustainability goals, leading to the pursuit of a diverse array of performance improvement projects. This study identifies various risks and corresponding coping strategies. Outsourcing logistics activities to 3PL allows leveraging their expertise in supply chain management, thereby enhancing stability and efficiency. This approach can contribute to reducing the carbon footprint, increasing order fulfillment, and lowering energy consumption throughout the supply chain.
For future research endeavors, it is recommended to prioritize strategies related to realizing the circular economy within the logistics system of the food industry. Providing a roadmap for the sustainable development of logistics clusters can enhance supply chain performance, minimize waste, and boost the social credibility of the supply chain. Additionally, attention to the concept of greenwashing in sustainable logistics, particularly concerning the fulfillment of social responsibility, can prove beneficial in improving overall supply chain performance

کلیدواژه‌ها [English]

  • Transportation Risk
  • Food Supply Chain
  • Gray DEMATEL Method
  • Gray COPRAS Method
  • Delphi method
  1. حمودی، سهیلا (1399). عوامل مؤثر بر کاهش هزینه حمل‌ونقل در شبکه زنجیره تأمین (مطالعه موردی: اداره بندر امام خمینی). اولین کنفرانس ملی بهینه‌سازی سیستم‌های تولیدی و خدماتی. رودسر.
  2. صلاحی، فریبا (1399). ارائه الگویی باهدف کاهش هزینه ریسک زنجیره تأمین با رویکرد ترکیبی. نشریه حسابداری مدیریت. 13 (45). 155-167.
  3. غیاثی، نیلوفر (1400). مدل‌سازی زنجیره تأمین پایدار صنایع غذایی – کشاورزی (مطالعه موردی مرکبات). ششمین کنفرانس بین‌المللی تکنیک‌های توسعه پایدار در مدیریت و مهندسی صنایع با رویکرد شناخت چالش‌های دائمی. تهران.
  4. محتشمی، علی. خوش نامی، سمیه (1398). تحلیل موانع اجرای زنجیره تأمین سبز در صنعت حمل‌ونقل زیرزمینی با استفاده از تکنیک دیمتل فازی و ISM مطالعه موردی مترو تهران. شانزدهمین کنفرانس بین‌المللی مدیریت (علمی-پژوهشی). تهران.
  5. همتی، مریم؛ ابراهیمی، ایلناز (1401). عبور نرخ ارز بر ‌بخش حمل‌ونقل در اقتصاد ایران: الگوی خود‌رگرسیون با وقفه توزیعی (ARDL). فصلنامه علمی پژوهشنامه حمل‌ونقل، 19(3)، 179-194، 22034/tri.2022.319625.2991.
  6. Abbas, H., Zhao, L., Gong, X., & Faiz, N. (2023). The perishable products case to achieve sustainable food quality and safety goals implementing on-field sustainable supply chain model. Socio-Economic Planning Sciences, 87(A), https:/‌/‌doi.org/‌10.1016/‌j.seps.2023.101562
  7. Abideen, A.Z., Sorooshian, S., Pandiyan Kaliani Sundram, V., & Mohammed, A. (2023). Collaborative insights on horizontal logistics to integrate supply chain planning and transportation logistics planning – A systematic review and thematic mapping. Journal of Open Innovation: Technology, Market, and Complexity, 9(2), https:/‌/‌doi.org/‌10.1016/‌j.joitmc.2023.100066.
  8. Ali, M., Rahman, S.M. & Frederico, G.F. (2021). Capability components of supply chain resilience for readymade garments (RMG) sector in Bangladesh during COVID-19. Modern Supply Chain Research and Applications, 3 (2), 127-144, https:/‌/‌doi.org/‌10.1108/‌MSCRA-06-2020-0015.
  9. Bø, E., Beate Hovi, I., & Pinchasik, D.R. (2023). COVID-19 disruptions and Norwegian food and pharmaceutical supply chains: Insights into supply chain risk management, resilience, and reliability. Sustainable Futures, 5, 100102, https:/‌/‌doi.org/‌10.1016/‌j.sftr.2022.100102.
  10. Burgos, D., & Ivanov, D. (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, 152, 102412, https:/‌/‌doi.org/‌10.1016/‌j.tre.2021.102412.
  11. Cao, Q., Song, J., Liu, C., & Yang, W. (2023). Evolving water, energy and carbon footprints in China's food supply chain. Journal of Cleaner Production, 423, 138716, https:/‌/‌doi.org/‌10.1016/‌j.jclepro.2023.138716
  12. Chen, j., Fan, T., Gu, Q., & Pan, F. (2022). Emerging technology-based online scheduling for instant delivery in the O2O retail era. Electronic Commerce Research and Applications, 51, 101115, https:/‌/‌doi.org/‌10.1016/‌j.elerap.2021.101115
  13. Choe, J.Y.(J)., Kim, J.J.,& Hwang, J. (2021). Perceived risks from drone food delivery services before and after COVID-19. International Journal of Contemporary Hospitality Management, 33 (4), 1276-1296. https:/‌/‌doi.org/‌10.1108/‌IJCHM-08-2020-0839.
  14. Chinnasamy, Manickam, R., Nanjundan, P., & Kaur, J. (2023). Building Logistics Capabilities through Third-party Logistics Relationships Using COPRAS Method. REST Journal on Data Analytics and Artificial Intelligence, 1(3), doi: http:/‌/‌doi.org/‌10.46632/‌jdaai/‌1/‌3/‌1
  15. Dalalah, D., Hayajneh, M., & Batieha, F. (2011). A fuzzy multi-criteria decision-making model for supplier selection. Expert Systems with Applications, 38(7). 8384–8391, https:/‌/‌doi.org/‌10.1016/‌j.eswa.2011.01.031
  16. Dubey, R., Gunasekaran, A., Papadopoulos, T., Childe, S.J., Shibin, K.T., & Wamba, S.F. (2017). Sustainable supply chain management: framework and further research directions. Journal of Cleaner Production, 142, 1119-1130
  17. El Ayoubi, M.S., & Radmehr, M. (2023). Green food supply chain management as a solution for the mitigation of food supply chain management risk for improving the environmental health level. Heliyon, 9(2), https:/‌/‌doi.org/‌10.1016/‌j.heliyon.2023.e13264.
  18. Fattahi, M., Govindan, K., & Keyvanshokooh, E. (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, 101, 176-200, https:/‌/‌doi.org/‌10.1016/‌j.tre.2017.02.004.
  19. Foroozesh, N., Karimi, B., & Mousavi, S.M. (2022). Green-resilient supply chain network design for perishable products considering route risk and horizontal collaboration under robust interval-valued type-2 fuzzy uncertainty: A case study in food industry. Journal of Environmental Management, 307, 114470, https:/‌/‌doi.org/‌10.1016/‌j.jenvman.2022.114470.
  20. Gardas, B.B., Raut, R.D., &Narkhede, B.E. (2019). Analyzing the 3PL service provider’s evaluation criteria through a sustainable approach. International Journal of Productivity and Performance Management, 68(5), 958-980. https:/‌/‌doi.org/‌10.1108/‌IJPPM-04-2018-0154.
  21. Garvey, M. D., Carnovale, S., & Yeniyurt, S. (2015). An analytical framework for supply network risk propagation: A Bayesian network approach. European Journal of Operational Research, 243(2), 618-627, 1016/‌j.ejor.2014.10.034.
  22. German, J.D., Ong, A.K.S., Redi, A.A.N.P., & Robas, K.P.E. (2022). Predicting factors affecting the intention to use a 3PL during the COVID-19 pandemic: A machine learning ensemble approach. Heliyon, 8(11), e11382, https:/‌/‌doi.org/‌10.1016/‌j.heliyon.2022.e11382.
  23. Ghavamifar, A., Makui, A., & Taleizadeh, A. A. (2018). Designing a resilient competitive supply chain network under disruption risks: A real-world application. Transportation Research Part E: Logistics and Transportation Review, 115, 87-109, https:/‌/‌doi.org/‌10.1016/‌j.tre.2018.04.014.
  24. Hong, J., Zhang, Y., & Ding, M. (2018). Sustainable supply chain management practices, supply chain dynamic capabilities, and enterprise performance. Journal of Cleaner Production, 172, 3508–3519, https:/‌/‌doi.org/‌10.1016/‌j.jclepro.2017.06.093.
  25. Hosseini-Motlagh, S.M., Samani, M.R.G., & Saadi, F.A. (2019). Strategic optimization of wheat supply chain network under uncertainty: a real case study. Operational Research, 21, 1487–1527, DOI:1007/‌s12351-019-00515-y.
  26. Karagiannis, G., Minis, L., Arampantzi, C., & Dikas, G. (2022). Warehousing and distribution network design from a Third-Party Logistics (3PL) company perspective. 55(10), 3106-3111, https:/‌/‌doi.org/‌10.1016/‌j.ifacol.2022.10.206
  27. Kraude, R., Narayanan, S., & Talluri, S. (2022). Evaluating the performance of supply chain risk mitigation strategies using network data envelopment analysis. European Journal of Operational Research, 303(3), 1168-1182, https:/‌/‌doi.org/‌10.1016/‌j.ejor.2022.03.016.
  28. Kumar, A., & Agrawal, S. (2023). Challenges and opportunities for agri-fresh food supply chain management in India. Computers and Electronics in Agriculture, 212, 108161, https:/‌/‌doi.org/‌10.1016/‌j.compag.2023.108161.
  29. Lin, Y., & Zhou, L. (2011). The impacts of product design changes on supply chain risk: a case study. International Journal of Physical Distribution & Logistics Management, 41(2), 162-186, 1108/‌09600031111118549.
  30. Mesa-Arango, R., Zhan, X., Ukkusuri, S.V., & Mitra, A. (2016). Direct transportation economic impacts of highway networks disruptions using public data from the United States. Journal of Transportation Safety & Security, 8(1), 36-55, https:/‌/‌doi.org/‌10.1080/‌19439962.2014.978962.
  31. Mitroshin, P., Shitova, Y., Shitov, Y., Vlasov, D., & Mitroshin, Y. (2022). Big Data and Data Mining Technologies Application at Road Transport Logistics. Transportation Research Procedia, 61, 462-466, https:/‌/‌doi.org/‌10.1016/‌j.trpro.2022.01.075.
  32. Ngussa, M. G., Mruma, F. V., Nawaz, A. (2020). Analysis of effects of e-commerce on Supply chain management to facilitate the entrepreneurship in Tanzania. Journal of Business and Management, 22 (4), 1-13.
  33. Qureshi, M.N., Kumar, D., & Kumar, P. (2008). An integrated model to identify and classify the key criteria and their role in the assessment of 3PL services providers. Asia Pacific Journal of Marketing and Logistics, 20(2), 227-249. https:/‌/‌doi.org/‌10.1108/‌13555850810864579.
  34. Qureshi, M.R.N.M. (2022). A Bibliometric Analysis of Third-Party Logistics Services Providers (3PLSP) Selection for Supply Chain
    Strategic Advantage
    . Sustainability, 4,
    https:/‌/‌doi.org/‌10.3390/‌su141911836.
  35. Shen, G., & Aydin, S.G. (2014). Highway freight transportation disruptions under an extreme environmental event: the case of Hurricane Katrina. International Journal of Environmental Science and Technology, 11(8), 2387-2402.
  36. Song, W., Ming, X., & Liu, H. C. (2017). Identifying critical risk factors of sustainable supply chain management: A rough strength-relation analysis method. Journal of Cleaner Production, 143, 100-115, 1016/‌j.jclepro.2016.12.145.
  37. Tan,H., Wang, C., Zhu, S., Liang, Y., He, X., Li, Y., Wu, C., Li, Q., Cui, Y., & Deng, X. (2023). Neonicotinoids in draining micro-watersheds dominated by rice-vegetable rotations in tropical China: Multimedia occurrence, influencing factors, transport, and associated ecological risks. Journal of Hazardous Materials, 446, 130716, https:/‌/‌doi.org/‌10.1016/‌j.jhazmat.2022.130716.
  38. Thun, J. H., & Hoenig, D. (2011). An empirical analysis of supply chain risk management in the German automotive industry. International Journal of Production Economics, 131(1),242-249, https:/‌/‌doi.org/‌10.1016/‌j.ijpe.2009.10.010
  39. Tummala, R., & Schoenherr, T. (2011). Assessing and managing risks using the supply chain risk management process (SCRMP). Supply Chain Management: An International Journal, 16(6), 474-483, https:/‌/‌doi.org/‌10.1108/‌13598541111171165.
  40. Wagner, S. M., & Bode, C. (2008). An empirical examination of supply chain performance along. Journal of business logistics, 29(1), 307-325, https:/‌/‌doi.org/‌10.1002/‌j.2158-1592.2008.tb00081.x
  41. Waters, D. (2011). Supply chain risk management: vulnerability and resilience in logistics. Kogan Page Publishers, London, United Kingdom.
  42. Wunderlich S M. (2021). Food supply chain during pandemic: changes in food production, food loss and waste. International Journal of Environmental Impacts, 4)2), 101–112, 10.2495/‌EI-V4-N2-101-112.
  43. Yuan, Y., Xu, Z., & Zhang, Y. (2023). The DEMATEL–COPRAS hybrid method under probabilistic linguistic environment and its application in Third Party Logistics provider selection. Fuzzy Optimization and Decision Making, 21, 137–156, https:/‌/‌doi.org/‌10.1007/‌s10700-021-09358-9.
  44. Zhang, Z., & Figliozzi, M. A. (2010). A survey of China’s logistics industry and the impacts of transport delays on importers and exporters. Transport Reviews, 30(2), 179-194.
  45. Zhang, J., Teixeira, Â. P., Guedes Soares, C., Yan, X., & Liu, K. (2016). Maritime transportation risk assessment of Tianjin Port with Bayesian belief networks. Risk analysis, 36(6), 1171-1187.
  46. Zhemchugova, O., Levshina, V., & Levshin, L. (2022). Application of risk-based approach methods of various levels of complexity in the quality management system of a transport company. Transportation Research Procedia, 63, 1-12, https:/‌/‌doi.org/‌10.1016/‌j.trpro.2022.05.001