شناسایی و رتبه بندی مخاطرات زنجیره تامین محصولات لبنی به کمک مدل سازی معادلات ساختاری
محورهای موضوعی : مدیریت صنعتی
Parisa Ehsani
1
(MS graduated, School of industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, IRAN)
Sadigh Raissi
2
(Associate Professor, School of industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran)
کلید واژه: Structural Equation Modeling (SEM), supply chain management, مدیریت زنجیره تامین, Risk Assessment, ارزیابی مخاطرات, رتبه بندی مخاطرات, مدل سازی معادلات ساختاری, Risk Prioritization,
چکیده مقاله :
عوامل مختلفی زنجیره های تامین محصولات لبنی را در معرض مخاطره قرار می دهند. در این موارد تحلیل مخاطرات همواره با عدم قطعیت روبرو بوده و می تواند برای سازمان ها هزینه آور شده و باعث اختلال در روند زنجیره تامین و نارضایتی مشتریان شوند. بدین منظور امروزه مدیریت مخاطرات یک موضوع اساسی در تحلیل زنجیره های تامین به شمار می آید. هدف اصلی مقاله حاضر تعیین عوامل مخاطره زا و تحلیل آثار آنها در شرکت صنایع شیر ایران پگاه است تا سیاست های بازدارندگی از بروز مخاطرات حاصل شود. در این مطالعه در انطباق و سازگاری با اهداف پژوهش، یک مدل مفهومی جهت ارزیابی همزمان شش فرضیه آماری ارائه شده است. اعتبار پرسشنامه با بهره گیری از روش تائید عاملی دنبال شده است و مدل تحلیلی مناسب به کمک مدل سازی معادلات ساختاری طراحی شده است. نتایج پژوهش نشانگر اهمیت بالای مخاطرات مربوط به تامین کنندگان می باشد که لزوم اتخاذ تصمیمات مدیریتی جهت محدود سازی مخاطرات بحرانی در این این حوزه را نشان می دهد.
Many factors may disrupt dairy products supply chains. Hence, risk analysis embedded uncertainties both in expenses and customer satisfaction. Consequently, risk management becomes a vital task in supply chain management. The main objective of this paper is to recognize different risk factors affecting on Iran’s dairy industries company (Pegah) and also evaluating their effects to address appropriate mitigation strategies. In this study, consistent with scientific research method principles, a conceptual model was presented for the hypothetical risk assessment method. The scale validity of the questionnaire was confirmed using confirmatory factor analysis. The analytic model was fitted through Structural Equation Modeling (SEM) technique to evaluate six hypothesis statistically. The results indicated that risks relevant to the suppliers have the most contribution in the current supply chain and it is necessary to properly trace critical risks using appropriate mitigation strategies.
1- Chopra , S., Sodhi, M. S., (2004). Managing risk to avoid supply chain breakdown. MIT Sloan Management Review, 53-61.
2- Christopher, M. (2005). Logistics and Supply Chain Management. London: Prentice Hall.
3- Christopher, M., & Peck, H. (2004). Building the resilient supply chain. International Journal of Logistics Management, 15, 1-13.
4- Christopher. M, & Lee. H. (2004). Mitigating supply chain risk through improved confidence. International Journal of Physical Distribution&LogisticsManagement, 34, 388-396.
5- Tiffany J. Harper. (2012). Agent Based Modeling and Simulation Framework For Supply Chain Risk Management, Department of Operational Sciences, Graduate School of Engineering and Management, Air Force Institute of Technology, Ohio, USA.
6- Goh, M., Lim, J. Y., & Meng, F. (2007). A stochastic model for risk management in global supply chain networks. European Journal of Operational Research, 164-173.
7- Hallikasa.Jukka, Karvonenb.Iris, Pulkkinenb.Urho, & Virolainen.Veli-Matti. (2004). Risk management processes in supplier networks. International Journal of Production economics, 90, 47-58.
8- Juttner, U., Peak, H., & Christopher, M. (2003). Supply Chain Risk Management: Outlining An Agenda For Future Research. International Journal of Logistics : Research & Applications, 6(4), 197-210.
9- Manuj, Ila., & Mentzer, J. T. (2008). Global supply chain risk management strategies. International Journal of Physical Distribution & Logistics Management, 38(3), 192 - 223.
10- Punniyamoorthy, Murugesan, Thamaraiselvan.N, & Lakshminarayanan.M. (2013). Assessment of supply chain risk: scale development and validation. International Journal of Benchmarking, 20, 79-105.
11- Tang, C. S. (2006). Perspectives in supply chain management. International Journal of Production Economics, 451-488.
_||_1- Chopra , S., Sodhi, M. S., (2004). Managing risk to avoid supply chain breakdown. MIT Sloan Management Review, 53-61.
2- Christopher, M. (2005). Logistics and Supply Chain Management. London: Prentice Hall.
3- Christopher, M., & Peck, H. (2004). Building the resilient supply chain. International Journal of Logistics Management, 15, 1-13.
4- Christopher. M, & Lee. H. (2004). Mitigating supply chain risk through improved confidence. International Journal of Physical Distribution&LogisticsManagement, 34, 388-396.
5- Tiffany J. Harper. (2012). Agent Based Modeling and Simulation Framework For Supply Chain Risk Management, Department of Operational Sciences, Graduate School of Engineering and Management, Air Force Institute of Technology, Ohio, USA.
6- Goh, M., Lim, J. Y., & Meng, F. (2007). A stochastic model for risk management in global supply chain networks. European Journal of Operational Research, 164-173.
7- Hallikasa.Jukka, Karvonenb.Iris, Pulkkinenb.Urho, & Virolainen.Veli-Matti. (2004). Risk management processes in supplier networks. International Journal of Production economics, 90, 47-58.
8- Juttner, U., Peak, H., & Christopher, M. (2003). Supply Chain Risk Management: Outlining An Agenda For Future Research. International Journal of Logistics : Research & Applications, 6(4), 197-210.
9- Manuj, Ila., & Mentzer, J. T. (2008). Global supply chain risk management strategies. International Journal of Physical Distribution & Logistics Management, 38(3), 192 - 223.
10- Punniyamoorthy, Murugesan, Thamaraiselvan.N, & Lakshminarayanan.M. (2013). Assessment of supply chain risk: scale development and validation. International Journal of Benchmarking, 20, 79-105.
11- Tang, C. S. (2006). Perspectives in supply chain management. International Journal of Production Economics, 451-488.