ارزیابی چابکی زنجیره تأمین با رویکرد تصمیم‌گیری تلفیقی دلفی- فازی(مطالعه میدانی: شرکت های شهرک صنعتی اهواز)
محورهای موضوعی :
مدیریت صنعتی
Nadiya Akbari
1
,
Arman Sajedinejad
2
*
1 - Master of Industrial Engineering student at Azad University, Masjed Soleyman
2 - Assistant Professor, Iranian Research Institute for Information Science and Technology (IRANDOC)
تاریخ دریافت : 1395/01/31
تاریخ پذیرش : 1395/04/28
تاریخ انتشار : 1395/06/04
کلید واژه:
Supply Chain,
VIKOR,
فرآیند تحلیل شبکه,
دلفی فازی,
ANP,
چابکی,
ویکور,
Agility,
زنجیره تأمین,
دیماتل,
fuzzy Delphi,
DEMATELT,
چکیده مقاله :
چابکی زنجیره تأمین از جمله موضوعاتی است که در تحقیقات سال های اخیر مورد توجه قرار گرفته و فعالیت‌های تحقیقاتی متنوعی بروی آن انجام شده است. یکی از دلایل این مساله توجه صنعت به این چابکی به عنوان عاملی برای ورود سریع به بازار و جذب رضایت مشتریان از زنجیره است. ارزیابی موثر و کارای چابکی زنجیره تأمین شرکتها امری ضروری و چالش برانگیز برای شرکتها میباشد و هدف این پژوهش ارزیابی چابکی زنجیره تأمین شرکتهای تولیدی شهرک صنعتی اهواز با رویکرد تلفیقی دلفی فازی و تصمیم گیری با معیارهای چندگانه میباشد. در این تحقیق ضمن شناسایی شاخصها و طراحی مدل مفهومی با استفاده از تکنیک دلفی فازی، ارزیابی چابکی زنجیره تأمین شرکتها را با رویکرد تلفیقی دیماتل تجدید نظر شده، FANP و ویکور بررسی می‌نماییم. از نتایج این تحقیق به نقش با اولویت رضایت مشتری در بین ابعاد مختلف و پس از آن بهبود کیفیت و معرفی محصول جدید می‌توان اشاره نمود. لازم به ذکر است که نتایج حاصله با اسناد موجود و نظرات مدیران ارشد همخوانی داشته و این مطلب گویای کارایی بالای رویکرد پیشنهادی پژوهش در رتبه بندی عملکرد میباشد.
چکیده انگلیسی:
Supply chain agility has taken into consideration of research topics in recent years and it is done on a variety of research activities. One of the reasons of the mentioned focus is due to the need of industries for rapid entry into the market and attracts customer’s satisfaction. Efficient evaluation of supply chain agility is an essential and challenging matter for companies and manufacturing firms and therefore the aim of this study is to evaluate supply chain agility of industrial city of Ahvaz companies with fuzzy Delphi approach integrated with multiple criteria decision-making process. In this research, identification of agility indicators and designing the conceptual model using the Delphi technique are aimed as well as evaluating supply chain agility firms with an integrated DEMATEL, FANP, and VIKOR approach. The results of this research can be considered significant in the priority of customer satisfaction, improving quality and introducing new products among different dimensions. It should be noted the results are consistent with existing documents and opinions of senior experts and this indicates the efficiency of the proposed approach in this research.
منابع و مأخذ:
Chu, T. C., & Varma, R. (2012). Evaluating suppliers via a multiple levels multiple criteria decision making method under fuzzy environment. Computers & Industrial Engineering, 62(2), 653-660.
Haq, A. N., & Boddu, V. (2015). Analysis of agile supply chain enablers for Indian food processing industries using analytical hierarchy process. International Journal of Manufacturing Technology and Management, 29(1-2), 30-47.
Harrison, A., Christopher, M., & Hoek, R. v. (1999). Creating the Agile Supply Chain: Issues and Challenges. London: Institute of Logistics & Transport.
Jassbi, J., Seyedhosseini, S. M., & Pilevari, N. (2010). An Adaptive Neuro Fuzzy Inference System for Supply chain Agility Evaluation. International Journal of Industrial Engineering & Production Research, 20(4), 187- 196.
Keeney, R. L. (2006). Value-Focused Thinking: A Path to Creative Decision Making. Cambridge: Harvard University Press.
Lin, C. T., Chiu, H., & Tseng, Y. H. (2006). Agility Evaluation Using Fuzzy Logic. International Journal of Production Economics, 101(2), 101, 353- 368.
Mirsayafi, C. E. (2013). Identify and rank the factors affecting supply chain agility. Tehran: Islamic Azad University Central Tehran Branch Master’s thesis.
Mohaghar, E., Malaei, M., & Afzlyan, M. (2014). Ranking of the key factors in the success of agile supply chain design and production of cultural products. Supply Chain Management, 16(43), 54-61.
Ngai, E. W., Chau, D. C., & Chan, T. L. (2011). Information technology, operational, and management competencies for supply chain agility: Findings from case studies. Journal of Strategic Information Systems, 20, 232–249.
Novjavan, M., Hashemi, M., & Teimoori, E. (2014). Measurement of supply chain flexibility combined with AHP model and fuzzy TOPSIS (Case study: Garments). Tenth Conference international industrial Engineering (pp. 1-10). College of Industrial Engineering, 4.
Seyedhoseini, S. M., Jassbi, J., & Pilevari, N. (2010). Application of adaptive neuro fuzzy inference system in measurement of supply chain agility: Real case study of a manufacturing company. African Journal of Business Management, 4(1), 83-95.
Sharifi, H., & Zhang, Z. (1999). A methodology for achieving agility in manufacturing organisations: an introduction. International Journal of Production Economics, 62, 7–22.
Sherehiy, B., Karwowski, W., & Layer, J. K. (2007). A review of enterprise agility: Concepts, frameworks, and attributes. International Journal of Industrial Ergonomics, 37(5), 445-460.
Tizro, A., Azar, A., Ahmadi, R., & Rafie, M. (2010). A model of supply chain agility Case: steel company. Journal of Industrial Management, 3(7), 17-36.
Vinodh, S., & Devadasan, S. R. (2011). Twenty criteria based agility assessment using fuzzy logic approach. The International Journal of Advanced Manufacturing Technology, 54(9), 1219–1231.
Yang, J. (2014). Supply chain agility: Securing performance for Chinese manufacturers. International Journal of Production Economics, 150, 104–
Yusuf, Y. Y., Gunasekaran, A., Musa, A., Dauda, M., El-Berishy, N. M., & Cang, N. (2014). A relational study of supply chain agility, competitiveness and business performance in the oil and gas industry. International Journal of Production Economics, 14, 531-543.
_||_
Chu, T. C., & Varma, R. (2012). Evaluating suppliers via a multiple levels multiple criteria decision making method under fuzzy environment. Computers & Industrial Engineering, 62(2), 653-660.
Haq, A. N., & Boddu, V. (2015). Analysis of agile supply chain enablers for Indian food processing industries using analytical hierarchy process. International Journal of Manufacturing Technology and Management, 29(1-2), 30-47.
Harrison, A., Christopher, M., & Hoek, R. v. (1999). Creating the Agile Supply Chain: Issues and Challenges. London: Institute of Logistics & Transport.
Jassbi, J., Seyedhosseini, S. M., & Pilevari, N. (2010). An Adaptive Neuro Fuzzy Inference System for Supply chain Agility Evaluation. International Journal of Industrial Engineering & Production Research, 20(4), 187- 196.
Keeney, R. L. (2006). Value-Focused Thinking: A Path to Creative Decision Making. Cambridge: Harvard University Press.
Lin, C. T., Chiu, H., & Tseng, Y. H. (2006). Agility Evaluation Using Fuzzy Logic. International Journal of Production Economics, 101(2), 101, 353- 368.
Mirsayafi, C. E. (2013). Identify and rank the factors affecting supply chain agility. Tehran: Islamic Azad University Central Tehran Branch Master’s thesis.
Mohaghar, E., Malaei, M., & Afzlyan, M. (2014). Ranking of the key factors in the success of agile supply chain design and production of cultural products. Supply Chain Management, 16(43), 54-61.
Ngai, E. W., Chau, D. C., & Chan, T. L. (2011). Information technology, operational, and management competencies for supply chain agility: Findings from case studies. Journal of Strategic Information Systems, 20, 232–249.
Novjavan, M., Hashemi, M., & Teimoori, E. (2014). Measurement of supply chain flexibility combined with AHP model and fuzzy TOPSIS (Case study: Garments). Tenth Conference international industrial Engineering (pp. 1-10). College of Industrial Engineering, 4.
Seyedhoseini, S. M., Jassbi, J., & Pilevari, N. (2010). Application of adaptive neuro fuzzy inference system in measurement of supply chain agility: Real case study of a manufacturing company. African Journal of Business Management, 4(1), 83-95.
Sharifi, H., & Zhang, Z. (1999). A methodology for achieving agility in manufacturing organisations: an introduction. International Journal of Production Economics, 62, 7–22.
Sherehiy, B., Karwowski, W., & Layer, J. K. (2007). A review of enterprise agility: Concepts, frameworks, and attributes. International Journal of Industrial Ergonomics, 37(5), 445-460.
Tizro, A., Azar, A., Ahmadi, R., & Rafie, M. (2010). A model of supply chain agility Case: steel company. Journal of Industrial Management, 3(7), 17-36.
Vinodh, S., & Devadasan, S. R. (2011). Twenty criteria based agility assessment using fuzzy logic approach. The International Journal of Advanced Manufacturing Technology, 54(9), 1219–1231.
Yang, J. (2014). Supply chain agility: Securing performance for Chinese manufacturers. International Journal of Production Economics, 150, 104–
Yusuf, Y. Y., Gunasekaran, A., Musa, A., Dauda, M., El-Berishy, N. M., & Cang, N. (2014). A relational study of supply chain agility, competitiveness and business performance in the oil and gas industry. International Journal of Production Economics, 14, 531-543.
