تحلیل کمّی فرصت‌های رشد و سودآوری سازمان های صنعتی با توجه به معیارهای انتخاب تأمین‌کنندگان در زنجیرۀ تأمین تاب‌آور
محورهای موضوعی : مدیریت بازرگانی
حمید شاه بندرزاده
1
(دانشیار، گروه مدیریت صنعتی، دانشکده ادبیات و علوم انسانی، دانشگاه خلیج فارس، بوشهر، ایران.)
محمد حسین کبگانی
2
(عضو هیات علمی دانشگاه خلیج فارس)
کلید واژه: فازی, تاب آوری, مدلسازی ریاضی, زنجیرۀ تأمین, غیرخطی,
چکیده مقاله :
امروزه رویکرد های ناب، تاب آور و سبز بودن به عنوان پارادایم های زنجیرۀ تأمین به سازمان ها این امکان را می دهند که به صورت رقابتی در بازار فعالیت کنند. تاب آوری، توانایی سیستم برای بازگشت به حالت اولیه خود و یا حالتی بهتر پس از ایجاد اختلال است. این امر می تواند موجب رشد انگیزه برای بررسی میزان تاب آوری در سطوح استراتژیک، ناشی از ریسک شود. در پژوهش حاضر به تبیین مدلی جهت شناسایی معیارهای انتخاب تأمین کنندگان در زنجیرۀ تأمین تاب آور پرداخته شدهاست. ابعاد اصلی مدل مذکور از بررسی پیشینه نظری زنجیرۀ تأمین تاب آور گرفته شدهاست که به وسیله تحلیل عاملی تأییدی مورد تأیید قرار گرفت. معیارهای اصلی این مدل عبارتند از: معیارهای عملیاتی، معیارهای کنترل ریسک و معیارهای پشتیبانی- زیست محیطی. در پژوهش حاضر جهت تعیین میزان اهمیت هر یک از ابعاد مدل، از نظر کارشناسان حوزه صنایع و استادان دانشگاه و همچنین مدلسازی غیرخطی فازی استفاده شده است. نتایج حاصل از مدلسازی ریاضی نشان می دهد که معیارهای عملیاتی در رتبه اول قرارگرفته است.
Nowadays, Lean, resilientandgreen approachesas the paradigmofsupply chain allow theorg anization stooperatecompetitivelyin themarket.Resilience, isthe ability ofthe system toreturntoits original orbetterstateafter creatingdisorder.This property can lead to motivation growth forinvestigating the amount of resilience at the strategic levels due to therisk. This study is an explanation of a model for recognizing thesuppliers' selection criteria in supply chain resilience. The maindimentions of stated model have been taken from the theoreticalliterature review about supply chain resilience which was confirmed byconfirmatory factor analysis. The main criteria of the model are:operational, risk control, and supportive- bio environmental criteria. Todetermine the importance of each of the dimensions of the model thenotion of industries experts and university professors and fuzzy nonlinearmodeling have been used. The results of mathematical modelingshowed that the mathematical measures take the lead.
Govindan, K., G., Azevedo, S., Carvalho, H., & Cruz-Machado, V.(2014), Impact of supply chain management practices on sustainability. Journal of Cleaner Production, 85 212-225.
Mitra, K., D., Gudi, R., C., Patwardhan, S., & Sardar, G. (2009), Towards resilient supply chains: Uncertainty analysis using fuzzy mathematical programming. Chemical engineering research and design, 87, 967–981.
Alzaman, C. (2014), Green supply chain modelling: literature review. Supply Chain Model, 6(1), 16-39.
Azevedo, S., Carvalho, H., Cruz-Machado, V., & Grilo, F. (2010), The influence of agile and resilient practices on supply chain performance: an innovative conceptual model proposal.
Carvalho, H., & Cruz-Machado, V. (2011), A supply chain resilience assessment model. 18th International Annual EurOMA Conference.
Christopher, M., & Lee, H. (2004), Mitigating supply chain risk through improved confidence. Int. J. Phys. Distrib. Logist. Manag, 34 (5), 388-396.
G. Azevedo, S., Govindan, K., Carvalho, H., & Cruz-Machado, V. (2013), Ecosilient Index to assess the greenness and resilience of the upstream automotive supply chain. Journal of Cleaner Production, 56, 131-146.
Hanna, J., Skipper, J., & Hall, D. (2010), Mitigating supply chain disruption: the importance of top management support to collaboration and flexibility. International Journal of Logistics Systems and Management, 6(4/5), 397-414.
Kristianto, Y., Gunasekaran, A., Helo, P., & Hao, Y. (2014), A model of resilient supply chain network design: A two-stage programming with fuzzy shortest path. Expert Systems with Applications, 41, 39–49.
Pettit, T, Fiksel, J.,&., Croxton, K.(2010),Ensuring supply chain resilience: development of a conceptual framework.Journal of Business Logistics, 31(1), 1-21.
Cardosoa, S. R., Barbosa-Póvoa, A., Relvasa, S., & Novais, A.Q. (2014), Network Design and Planning of Resilient Supply Chains. Proceedings of the 24th European Symposium on Computer Aided Process Engineering.
Rajesh, R., & Ravi, V. (2015), Supplier selection in resilient supply chains: a grey relational analysis approach. Journal of Cleaner Production, 86, 343-359.
Schmitt, A., & Singh, M. (2012), A quantitative analysis of disruption risk in a multi-echelon supply chain. International Journal of Production Economics,139(1), 22–32.
Wagner, S., & Neshat, N. (2010), Assessing the vulnerability of supply chains using graph theory. International Journal of Production Economics 126(1), 121-129.
Waters, D. (2011), Supply Chain Risk Management: Vulnerability and Resilience in Logistics. Kogan Page Publishers.
Zeballos, L., Gomesc, M., Barbosa-Povoad, A., & Novais, A. (2012), Optimum Design and Planning of Resilient and Uncertain Closed-Loop Supply Chains. Ian David Lockhart Bogle and Michael Fairweather (Editors), Proceedings of the 22nd European Symposium on Computer Aided Process Engineering.
Zanjirchi, S. M. (1390), FuzzyAnalytical Hierarchy Process. Tehran: Sanei Shahmirzadi publications,. (In Persian).
_||_Govindan, K., G., Azevedo, S., Carvalho, H., & Cruz-Machado, V.(2014), Impact of supply chain management practices on sustainability. Journal of Cleaner Production, 85 212-225.
Mitra, K., D., Gudi, R., C., Patwardhan, S., & Sardar, G. (2009), Towards resilient supply chains: Uncertainty analysis using fuzzy mathematical programming. Chemical engineering research and design, 87, 967–981.
Alzaman, C. (2014), Green supply chain modelling: literature review. Supply Chain Model, 6(1), 16-39.
Azevedo, S., Carvalho, H., Cruz-Machado, V., & Grilo, F. (2010), The influence of agile and resilient practices on supply chain performance: an innovative conceptual model proposal.
Carvalho, H., & Cruz-Machado, V. (2011), A supply chain resilience assessment model. 18th International Annual EurOMA Conference.
Christopher, M., & Lee, H. (2004), Mitigating supply chain risk through improved confidence. Int. J. Phys. Distrib. Logist. Manag, 34 (5), 388-396.
G. Azevedo, S., Govindan, K., Carvalho, H., & Cruz-Machado, V. (2013), Ecosilient Index to assess the greenness and resilience of the upstream automotive supply chain. Journal of Cleaner Production, 56, 131-146.
Hanna, J., Skipper, J., & Hall, D. (2010), Mitigating supply chain disruption: the importance of top management support to collaboration and flexibility. International Journal of Logistics Systems and Management, 6(4/5), 397-414.
Kristianto, Y., Gunasekaran, A., Helo, P., & Hao, Y. (2014), A model of resilient supply chain network design: A two-stage programming with fuzzy shortest path. Expert Systems with Applications, 41, 39–49.
Pettit, T, Fiksel, J.,&., Croxton, K.(2010),Ensuring supply chain resilience: development of a conceptual framework.Journal of Business Logistics, 31(1), 1-21.
Cardosoa, S. R., Barbosa-Póvoa, A., Relvasa, S., & Novais, A.Q. (2014), Network Design and Planning of Resilient Supply Chains. Proceedings of the 24th European Symposium on Computer Aided Process Engineering.
Rajesh, R., & Ravi, V. (2015), Supplier selection in resilient supply chains: a grey relational analysis approach. Journal of Cleaner Production, 86, 343-359.
Schmitt, A., & Singh, M. (2012), A quantitative analysis of disruption risk in a multi-echelon supply chain. International Journal of Production Economics,139(1), 22–32.
Wagner, S., & Neshat, N. (2010), Assessing the vulnerability of supply chains using graph theory. International Journal of Production Economics 126(1), 121-129.
Waters, D. (2011), Supply Chain Risk Management: Vulnerability and Resilience in Logistics. Kogan Page Publishers.
Zeballos, L., Gomesc, M., Barbosa-Povoad, A., & Novais, A. (2012), Optimum Design and Planning of Resilient and Uncertain Closed-Loop Supply Chains. Ian David Lockhart Bogle and Michael Fairweather (Editors), Proceedings of the 22nd European Symposium on Computer Aided Process Engineering.
Zanjirchi, S. M. (1390), FuzzyAnalytical Hierarchy Process. Tehran: Sanei Shahmirzadi publications,. (In Persian).
