مدل‌سازی تأثیر ریسک‌های پاندمی کرونا بر زنجیره تأمین عمومی با استفاده از روش‌های متن‌کاوی (مورد مطالعه: زنجیره تأمین غذایی)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، دانشگاه شهید بهشتی.

2 دانشجوی دکتری، دانشگاه شهید بهشتی.

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

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