مدیریت ریسک‏های برون‏سپاری لجستیک به‏ شرکت‏های طرف سوم به کمک نقشه شناختی فازی (FCM)، خانه کیفیت (HOQ) و برنامه ریزی ریاضی چندهدفه

نوع مقاله : مدیریت تولید و عملیات (برنامه‌­ریزی تولید، چابکی، پایداری، ناب، سبز، برون­‌سپاری، زنجیره تأمین، زنجیره ارزش، کیفیت، بهره وری، صنعت چهار و ابعاد آن، . . .)

نویسندگان

1 استادیار، گروه مدیریت صنعتی، دانشکده ادبیات و علوم انسانی، دانشگاه خلیج فارس، بوشهر، ایران

2 استادیار، گروه مدیریت صنعتی، دانشکده علوم اقتصادی و اداری، دانشگاه مازندران

3 استادیار، گروه مدیریت صنعتی، دانشگاه خلیج فارس

چکیده

با توجه به ضرورت برون‏سپاری لجستیک در صنعت نفت، گاز و پتروشیمی و همراه بودن آن با ریسک‏های گوناگون، این تحقیق با دو هدف کلی شناسایی ریسک‏های برون‏سپاری و ارائه راهکارهای مناسب مدیریت ریسک صورت گرفت. در بخش اول تحقیق، ریسک‏های برون‏سپاری لجستیک به شرکت‏های طرف سوم شناسایی گردید و در هفت دسته ریسک‏های استراتژیک، مالی، عملیاتی، ریسک‏های مرتبط با طراحی، ریسک‏های مرتبط با کیفیت، ریسک‏های اقتصادی، تغییر در سیاست‏ها و ترکیب دولت تقسیم شدند. ابزارهای اصلی گردآوری داده ها در این پژوهش، پرسشنامه و مصاحبه بودند. به منظور تحلیل داده ها، با استفاده از نقشه شناختی فازی به تحلیل ریسک‏ها و تعیین اوزان نسبی ریسک ها مبتنی بر شاخص محوریت و مرکزیت پرداخته شد و در قسمت بعدی پژوهش، با استفاده از رویکرد ترکیبی خانه گسترش کیفیت و مدلسازی چند هدفه راهکارهای مهم جهت مدیریت ریسک‏های برون‏سپاری انتخاب گردید. نتایج پژوهش، نشان می دهد بین ریسک‏های شناسایی‏شده، ریسک عدم پاسخگویی به نیازهای مشتریان دارای بیشترین اهمیت است، و ریسک‏ اختلال در عملیات و از دست دادن کنترل بر عملیات لجستیک در رتبه‏های دوم و سوم اهمیت قرار می‏گیرند. راهکارهای لازم جهت مدیریت ریسک‏های برون‏سپاری به سه دسته استراتژی‏های پیشگیرانه، فرآیندی و انعطاف‏پذیری تقسیم شده و بر اساس ریسک های مختلف این استراتژی ها مورد بحث قرار گرفتند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Risk management in third-party logistics by using fuzzy cognitive mapping (FCM), House of Quality (HOQ) and multiple objective mathematical programming

نویسندگان [English]

  • Reza Jalali 1
  • Seyed Hamid Hashemi Petrudi 2
  • Hadi Balouei Jamkhaneh 3
1 Industrial management, Group,, Persian Gulf University,, Bushehr
2 Assistant Professor, Industrial Management Group, Faculty of Economics and Administrative Sciences
3 Industrial Management Group, Persian Gulf University, Bushehr
چکیده [English]

According to the importance of oil, gas and petrochemical industries and their related supply chain risks, this research with the aim of identifying logistics outsourcing risks, and proposing suitable risk management strategies is done. At first, logistics outsourcing to third party logistics (3PLs) providers were identified and classified in seven categories: strategic, financial, operational, design related, quality related, economic, changes in policies and government alteration. Main data gathering tools were questionnaire and interview. In data analysis phase, firstly, fuzzy cognitive mapping (FCM) was employed for analyzing risks and their interrelationships to determine their relative weight based on centrality measure and secondly, a combination of house of quality (HOQ) and multiple objective mathematical programming model were utilized to propose relevant risk mitigation strategies. Research findings show that the risk of unresponsiveness to customer needs is the most important one, following by disruption risks in operations, and uncontrollability on logistics operations. Further, requesting letter of credit from 3PL providers, creating black list, defining very strict quality standards and limits are among the most important preventive mitigation strategies. 3PLs ranking system design, using mixed contracts, holding tenders, and special attention to local suppliers have been identified as the most important flexibility strategies to mitigate risks. Finally, the most important process strategies to risk management were identified as improving the credit of company, establishing long-term relationships with 3PLs, enhancing resilience, and demand prediction and management system.

کلیدواژه‌ها [English]

  • third-party logistics
  • outsourcing
  • fuzzy cognitive mapping
  • multiple objective modeling
  • house of quality
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