نویسندگان

1 استادیار، مدیریت صنعتی، دانشکده مدیریت، دانشگاه تهران، تهران، ایران

2 استادیار، مدیریت صنعتی، پردیس فارابی، دانشگاه تهران، قم، ایران

3 دانشجوی کارشناسی‌ارشد، دانشکده مدیریت، دانشگاه تهران، تهران، ایران

چکیده

ارزیابی ریسک زنجیره تأمین یکی از ارکان مدیریت ریسک زنجیره تأمین بوده و هدف آن تجزیه و تحلیل ریسک‌هاست. شناسایی و طبقه‌بندی نخستین گام جهت ارزیابی ریسک زنجیره تأمین است؛ بدین منظور در این مقاله پس از بررسی ادبیات تحقیق و استخراج ریسک‌های مربوط به زنجیره تأمین جهت شناسایی ریسک‌ها در زنجیره تأمین خدمات از دو روش گروه کانونی و کیوسرت استفاده شده و خروجی آن شناسایی 10 مؤلفه از مهم‌ترین ریسک‌ها به عنوان مشخصه‌های موقعیتی در مدل‌سازی راف است و متعاقباً با تلفیق روش‌های مختلف گسسته‌سازی داده، تولید بی‌زائده و تولید قوانین و با استفاده از نرم‌افزار Rosetta، پنج مدل قوانین راف برای مؤلفه‌های ریسک تولید گشت. با توجه به مدل‌های استخراج شده از بین مشخصه‌های موقعیتی، مؤلفه ریسک بازار و ریسک مالی اصلی‌ترین مؤلفه در تحلیل ریسک زنجیره تأمین خدمات شناسایی شدند و پس از اعتبارسنجی قوانین به دست آمده مدل تکمیل داده‌ها به روش میانگین و مد شرطی و تکمیل داده‌های ناقص بالاترین اعتبار را جهت پیش‌بینی مشاهده‌های جدید نشان دادند.

کلیدواژه‌ها

عنوان مقاله [English]

Service Supply Chain Risk Assessment Applying Rough Set Theory Approach: Case of Payment Service Providers

نویسندگان [English]

  • Touraj Karimi 2
  • Sahar bandesi 3

چکیده [English]

One of the pillars of supply chain risk management is risk assessment and its aim is to analyze the risk. The first step to assess supply chain risk is identifying and classifying them. In this paper, after reviewing the literature and extracting supply chain risks, service supply chain risks  identified by focus group and Q-sort method. As the result 10 components of the most important risks as conditional characteristics in Rough modeling identified. Using Rosetta software, 5 set of rule were produced. According to extracted models among conditional attributes, market and financial risks were most important attributes. After completion of various models validation, the model by Conditional Mean/Mode fill and incompletes for complement data showed the highest reliability for predicting new observations.

کلیدواژه‌ها [English]

  • supply chain risk classification
  • supply chain risk assessment
  • rough modeling
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