نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، دانشگاه شهید بهشتی.

2 دانشجوی دکتری، دانشگاه شهید بهشتی.

10.52547/jimp.12.3.141

چکیده

نخستین گام در راستای تحلیل و ارزیابی ریسک‌های زنجیره تأمین شناسایی این ریسک‌ها است. روش‌های تحلیل مرسوم بر اساس فیلترهای دستی یا روش‌های خودکار داده‌­محور ارائه شده است. روش‌های فیلتر دستی به‌­دلیل محدودیت‌های نمونه‌گیری دارای مشکلات اعتبارسنجی هستند و از طرف دیگر روش‌های تحلیل خودکار مبتنی بر داده، در تحلیل داده‌های ریسک که پیچیده و مبهم هستند، عملکرد ضعیفی دارند. برای پرکرده خلل پژوهشی، در این پژوهش، چارچوبی تعاملی بین تحلیل‌­گر و ماشین برای تحلیل حجم وسیعی از داده‌های ریسک در حوزه زنجیره تأمین مواد غذایی با استفاده از تکنیک مدل‌سازی موضوع، تعبیه‌سازی کلمات، تحلیل همبستگی اصطلاح و نقشه دانشی ارائه شده است. هدف از سیستم نظارت بر داده‌های ریسک زنجیره تأمین، کمک به مدیران در شرکت‌های مواد غذایی برای نظارت و شناسایی خطر بحران‌ها و ارائه اطلاعات پشتیبانی تصمیم برای ایجاد یک زنجیره تأمین مواد غذایی پایدار است. نتایج تحلیل موضوعی فراداده‌ها، نقشه دانشی در پنج حوزه «برداشت»، «کشاورزی»، «خرده‌فروشی مواد غذایی»، «خدمات غذایی»، «توزیع» و «مصرف» را نشان داد که در هیئت خبرگان تأیید شد. نتایج نشان می‌دهد مدل تحلیل مخاطرات در استخراج واحدهای دانشی مرتبط با حوزه مدیریت بحران زنجیره تأمین مواد غذایی مفید است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Modeling the Impact of the Covid-19 Risks on Global Supply Chains using Text Mining Methods: A Case Study of the Food Supply Chain

نویسندگان [English]

  • Navid Nezafati 1
  • Mohammad Reza Sheikhattar 2

1 Assistant Professor, Shahid Beheshti University.

2 Ph.D Candidate, Shahid Beheshti University.

چکیده [English]

The first step in analyzing and evaluating supply chain risks is to identify these risks. Conventional analysis methods are based on manual filters or data-driven automated methods. Manual filtering methods suffer from validation problems due to sampling limitations, and automated data analysis methods, on the other hand, perform poorly in analyzing risk data that is complex and ambiguous. To fill the research gap, in this study, an interactive framework between the analyst and the machine is presented to analyze a large volume of risk data in the field of food supply chain using topic modeling techniques, word embedding, term correlation analysis, and knowledge map. The purpose of the supply chain risk monitoring system is to assist managers in food companies to monitor and identify crisis risks and provide a decision support information system to create a sustainable food supply chain. The results of the topic analysis of metadata showed a knowledge map in five areas of "harvest", "agriculture", "food retail", "food services", "distribution" and "consumption", which was approved by the expert panel. The results show that the risk analysis model is useful in extracting knowledge units related to the field of food supply chain crisis management.

کلیدواژه‌ها [English]

  • Supply Chain Crisis Management
  • Supply Chain Risk Monitoring
  • Pandemic Crisis
  • Topic Analysis Model
  • Text Mining
  1. Ariyanti, F. D. & Andika, A. (2016). Supply Chain Risk Management in the Indonesian Flavor Industry: Case Study from a Multinational Flavor Company in Indonesia. Proceedings of the 2016 International Conference on Industrial Engineering and Operations Management, 17(2), 18-29.
  2. Aven, T. & Renn, O. (2009).On Risk Defined as an Event Where the Outcome Is Uncertain.Journal of risk research, 12(1), 1-11.
  3. Baghersad, M. & Zobel, C.W. (2021). Assessing the Extended Impacts of Supply Chain Disruptions on Firms: An Empirical Study. International Journal of Production Economics, 18(3), 78-93.
  4. Balaid, A., Abd Rozan, M. Z., Hikmi, S. N., & Memon, J. (2016). Knowledge Maps: A Systematic Literature Review and Directions for Future Research. International journal of information management, 36(3), 451-75.
  5. Chavas, J- (2004). Risk Analysis in Theory and Practice. Elsevier, 52(2),451-575.
  6. Chu, C-Y., Park, K., & Kremer, G E. (2020). A Global Supply Chain Risk Management Framework: An Application of Text-Mining to Identify Region-Specific Supply Chain Risks. Advanced Engineering Informatics, 53(2), 302-324.
  7. Chu C-Y., Park, K., & Kremer, G E. (2019). Applying Text-Mining Techniques to Global Supply Chain Region Selection: Considering Regional Differences. Procedia Manufacturing, 43(1), 151-175.
  8. Dayton, B. W. & Bernhardsdottir, A. (2015).Crisis Management. International Encyclopedia of Peace, 11(3), 101-123.
  9. Faisal, M. N., Banwet, D. & Shankar, R. (2007). Management of Risk in Supply Chains: Scor Approach and Analytic Network Process. Supply Chain Forum: An International Journal: Taylor & Francis, 9(1), 75-97.
  10. Giannakis, M., & Papadopoulos, T. (2016) .Supply Chain Sustainability: A Risk Management Approach. International Journal of Production Economics, 9(2), 43-64.
  11. Gokhberg, L., Kuzminov, I., Bakhtin, P., Khabirova, E., Chulok, A., Timofeev A., & Lavrynenko, A. (2017). Big-Data-Augmented Approach to Emerging Technologies Identification: Case of Agriculture and Food Sector. Higher School of Economics Research Paper.,12(2), 12-32.
  12. Hong, J., Zhang, Y. & Shi, M. (2018). The Impact of Supply Chain Quality Management Practices and Knowledge Transfer on Organisational Performance: An Empirical Investigation from China. International Journal of Logistics Research and Applications, 21(3), 259-78.
  13. Hudnurkar, M., Deshpande, S., Rathod, U., & Jakhar, S. (2017). Supply Chain Risk Classification Schemes: A Literature Review. Operations and Supply Chain Management: An International Journal, 10(4), 182-99.
  14. Jakob-Hoff, R. M., MacDiarmid, S. C., Lees, C., Miller, P. S., Travis, D., & Kock, R. (2014). Manual of Procedures for Wildlife Disease Risk Analysis. Manual of procedures for wildlife disease risk analysis,11(2), 82-101.
  15. Jin, M., Wang, Y., & Zeng, Y. (2018). Application of Data Mining Technology in Financial Risk Analysis. Wireless Personal Communications, 12(4), 21-51.
  16. Karami, M., Samimi, A., & Jafari, M. (2020).The Necessity of Risk Management Evaluations in Petrochemical Industries. Advanced Journal of Chemistry-Section, 12(1), 72-99.
  17. Khanyle, D., & Cluett, J.D. (2018). Sensemaking and Unknowable in Risk Management. In Cross-Cultural Dialogue as a Conflict Management Strategy, Springer, 10(1), 56-82.
  18. Ligita, T., Nurjannah, I., Wicking, K., Harvey, N., & Francis, K. (2020). From Textual to Visual: The Use of Concept Mapping as an Analytical Tool in a Grounded Theory Study. Qualitative Research, 8(1), 121-143.
  19. Luckstead, J., Nayga, Jr R. M., & Snell, H.A. (2021). Labor Issues in the Food Supply Chain Amid the Covid‐19 Pandemic. Applied Economic Perspectives and Policy, 43(1), 382-400.
  20. Manuj, I. & Mentzer, J. T. (2008). Global Supply Chain Risk Management Strategies. International Journal of Physical Distribution & Logistics Management, 8(1), 103-133.
  21. Muhren, W. J. & Van de Walle, B. (2010). A Call for Sensemaking Support Systems in Crisis Management. Interactive Collaborative Information Systems, 18(1), 202-227.
  22. Ngo, Q. H., Kechadi, T., & Le-Khac, N-A. (2020). Oak: Ontology-Based Knowledge Map Model for Digital Agriculture. International Conference on Future Data and Security Engineering, Springer, 10(2), 785-802.
  23. Novak, J., & Wurst, M. (2004). Supporting Knowledge Creation and Sharing in Communities Based on Mapping Implicit Knowledge. UCS, 10(3), 235-51.
  24. Ponis, S. T. & Ntalla, A. (2016). Crisis Management Practices and Approaches: Insights from Major Supply Chain Crises. Procedia economics and finance, 15(2), 529-551.
  25. Rao, S., & Goldsby, T. J. (2009). Supply Chain Risks: A Review and Typology. The International Journal of Logistics Management.
  26. Shah, S. M., Lütjen, M. & Freitag, M. (2021). Text Mining for Supply Chain Risk Management in the Apparel Industry. Applied Sciences, 11(5), 285-306.
  27. Sharma, S. K., & Bhat, A. (2012). Identification and Assessment of Supply Chain Risk: Development of Ahp Model for Supply Chain Risk Prioritisation. International Journal of Agile Systems and Management, 5(4), 350-69.
  28. Song, B., Yan, W., & Zhang, T. (2019). Cross-Border E-Commerce Commodity Risk Assessment Using Text Mining and Fuzzy Rule-Based Reasoning. Advanced Engineering Informatics, 17(5), 69-85.
  29. Tang, O., & Musa, S. N. (2011).Identifying Risk Issues and Research Advancements in Supply Chain Risk Management. International Journal of Production Economics, 13(1), 25-34.
  30. Vilko, J., Ritala, P., & Edelmann, J. (2014). On Uncertainty in Supply Chain Risk Management. The International Journal of Logistics Management, 15(5), 85-101.
  31. Wang, Z., & Ye, X. (2018). Social Media Analytics for Natural Disaster Management. International Journal of Geographical Information Science, 32(1), 49-72.
  32. Zhu L, Bian W, Wu B, Feng W, Zhu Q, Song H&Hu L. (2019). Intelligent Graph Review System Based on Knowledge Map. 18th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES): IEEE, 15(1),49-72.
  33. Jangi Zahi & Maghaini A. (2021). The relationship between logistics capability and risk in the resilience of the supply chain of goods transportation with the focal correlation analysis approach. The Journal of Industrial Management Perspectives, 11(2), 70-247. (In Persian)
  34. Chamani R., Heydarieh, Abdullah S. & Zargar. (2022). Designing a model for the intelligent supply chain of services, with the database method (case study: Omid Entrepreneurship Fund). The Journal of Industrial management perspective,13(2), 251- 295. (In Persian)
  35. Sajdis S., Sarfaraz A., Bamdad S. & Khalili Damghani K. (2021). Presenting a mathematical model of location, multi-product and multi-period in a sustainable closed loop chain by considering risk and uncertainty in demand and quality. The Journal of Industrial Management Perspective, 11(2), 271- 304. (In Persian)