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  • ارزیابی کارایی تأمین‌کننده‌ها با تحلیل پوششی داده‌های خاکستری به همراه اعمال ترجیحات تصمیم‌گیرنده

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کد مقاله : IMJ-2106-1642 (R2) بازدید : 282 صفحه: 60 - 75

10.30495/imj.2022.1933549.1642

نوع مقاله: پژوهشی

ارزیابی کارایی تأمین‌کننده‌ها با تحلیل پوششی داده‌های خاکستری به همراه اعمال ترجیحات تصمیم‌گیرنده

محورهای موضوعی : مدیریت صنعتی

Shahab Rafiazadeh 1 ( Department of Industorial Engineering )
Dariush Mohammadi Zanjirani 2 ( دانشیار گروه مدیریت، دانشکده علوم اداری و اقتصاد، دانشگاه اصفهان، اصفهان، ایران )

تاریخ دریافت : 1400/04/21 تاریخ پذیرش : 1400/11/12 تاریخ انتشار : 1401/03/05

کلید واژه: تحلیل پوششی داده‌ها, زنجیره تأمین, داده‌های خاکستری, کارایی نسبی, کارایی بازه ای,

چکیده مقاله :

اغلب شرکت‌های تجاری به منظور افزایش توان رقابت پذیری در فضای رقابتی، در عین حال که سطح برون‌سپاری فعالیت‌های خود را افزایش می دهند؛ به وابستگی بیشتر خود به تأمین‌کنندگان‌ نیز تن می دهند. به همین خاطر شرکت ها همواره برای ارائه محصولی با کیفیت بالا و در قیمتی نسبتاً پایین ناچارند نسبت به کنترل کارایی تأمین‌کنندگان خود، توجه داشته باشند. با این حال، ارزیابی و انتخاب تأمین‌کنندگان کارا، یک مزیت رقابتی را نیز برای شرکت‌ها ایجاد می‌نماید. در چارچوب نظری، از روش‌های و فنون بسیاری جهت ارزیابی و انتخاب تأمین‌کننده نام برده شده است. تحلیل پوششی داده‌ها به دلیل پایه برنامه‌ریزی ریاضی و نیز جبر خطی آن، یکی از روش‌های توانمند و مقبول در این حوزه است که با چندین ورودی و خروجی به سنجش کارایی واحدهای همگن می‌پردازد. از طرفی در کاربردهای واقعی و در فضای تصمیم نادقیق یا خاکستری با توجه به عدم قطعیت موجود در داده‌ها، این مسئله به یک تصمیم‌گیری پیچیده تبدیل شده است. در مطالعه حاضر، یک مدل ریاضی توسعه یافته برای تحلیل پوششی داده‌های خاکستری و نیز روش رتبه‌بندی جدیدی برای زمانی که کارایی حاصل از مدل مورد اشاره به صورت بازه‌ای حاصل می شود، پیشنهاد و به اجرا گذاشته شده است. الگوهای مورد اشاره به منظور ارزیابی سطح کارایی تأمین‌کنندگان شیشه اتومبیل طرف قرارداد با شرکت ایران‌خودرو آزمون شده و علاوه بر مقبول بودن نتایج، پایداری بیشتری نیز در نتایج ارزیابی و واحدهای مرجع، مشاهده شده است.

چکیده انگلیسی:

Organizations and companies in order to increase their capabilities in the business environment must increase the level of outsourcing of their activities, and this increases the dependence on suppliers., they must pay attention to the performance of their suppliers, and also the evaluation and selection of efficient suppliers creates a competitive advantage for companies. Recent studies have suggested many methods for evaluating and selecting suppliers. Data envelopment analysis due to the basis of mathematical programming and linear algebra is one of the powerful methods in this regard that measures the efficiency of homogeneous units with multiple inputs and outputs. On the other hand, due to the uncertainty of the data in real applications, this issue has become a complex decision-making. Gray number theory is a method used to deal with uncertainty conditions that has been used in this research. In the present study, two models for evaluating and selecting efficient suppliers and a method for ranking efficient units based on data envelopment analysis and gray data are presented. The first model is proposed in order to achieve real results by applying the priority of inputs and outputs by decision makers and the second model is proposed by reducing the volume of calculations and solving them in the issue of evaluation and selection of efficient suppliers. The two mentioned models have been used to evaluate the car glass suppliers of IranKhodro Company and the implementation of the model shows acceptable results from the implementation of the proposed models.

منابع و مأخذ:


  1. Amiri, M., & Hadinejad, F. (2017). Evaluation and prioritization of suppliers adopting a combined approach of entropy, analytic hierarchy process, and revised Promethee (Case study: Youtab Company). Journal of Operational Research In Its Applications (Applied Mathematics)-Lahijan Azad University, 14(4), 1-20.
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  5. Ghasemi, A., Farokhpour, GH & Rezayazdi, P. (2015). Evaluation of suppliers based on indicators involved in supply chain productivity (survey o f supply companies for Toos Automotive Group). Journal of Organizational Culture Management, 13(2):515-535. (in persian).
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Kolagar Daronkola, M., & Hosseini, S. (2019). Evaluating The Suppliers Based On A Hybrid Approach Of Einstein Choquet Integral And Promethee Ii With Respect To Scor Metrics (Case Study: Medical Laboratory. Industrial Engineering & Management Sharif (Sharif: Engineering), 35-1(1/1 ), 105-117. https://www.sid.ir/en/journal/ViewPaper.aspx?id=698600

  1. Lia, GD., Yamaguchi, D., & Nagai, M. (2007). A grey-based decision-making approach to the supplier selection problem. Mathematical and Computer Modelling, 46:573–581.
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  2. Liao, Z., & Rittscher, J. (2007). A multi-objective supplier selection model under stochastic demand conditions. International Journal of Production Economics, 105(1), 150-159.
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  3. Lin, C. T., Chang, C. W., & Chen, C. B. (2006). The worst ill-conditioned silicon wafer slicing machine detected by using grey relational analysis. The International Journal of Advanced Manufacturing Technology, 31(3-4), 388-395.
    4.

Luthra, S., Govindan, K., Kannan, D., Mangla, S. K., & Garg, C. P. (2017). An integrated framework for sustainable supplier selection and evaluation in supply chains. Journal of Cleaner Production, 140, 1686-1698.

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  3. Rashidi, K., & Cullinane, K. (2019). A comparison of fuzzy DEA and fuzzy TOPSIS in sustainable supplier selection: Implications for sourcing strategy. Expert Systems with Applications, 121(1):266-281.
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Stević, Ž., Pamučar, D., Puška, A., & Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS). Computers & Industrial Engineering, 140, 106231.

  1. Sunil, L., Kannan, G., Devika, K., Sachin, K & Mangla, Ch. (2017). An integrated framework for sustainable supplier selection and evaluation in supply chains. Journal of Cleaner Production, 140(3):1686-1698.
    2.
  2. Talluri, S., & Narasimhan, R. (2004). A methodology for strategic sourcing. European Journal of Operational Research, 154:236-250.
    3.
  3. Wu, D. D., & Olson, D. L. (2010). Fuzzy multiattribute grey related analysis using DEA. Computers & Mathematics with Applications, 60(1), 166-174.
    4.
  4. Yazdani, M., Prasenjit, C., Kazimieras Zavadskas, E., & Hashemkhani Zolfanid, S. (2017). Integrated QFD-MCDM framework for green supplier selection. Journal of Cleaner Production, 142(4):3728-3740.
    5.
_||_

  1. Amiri, M., & Hadinejad, F. (2017). Evaluation and prioritization of suppliers adopting a combined approach of entropy, analytic hierarchy process, and revised Promethee (Case study: Youtab Company). Journal of Operational Research In Its Applications (Applied Mathematics)-Lahijan Azad University, 14(4), 1-20.
    2.
  2. Du, J., Liu, S., & Liu, Y. (2021). A novel grey multi-criteria three-way decisions model and its application. Computers & Industrial Engineering, 158, 107405.
    3.
  3. Esmaeilian, M., Hemmatgir, H., & Ghaenian, R. (2018). Designing and implementing suppliers evaluating process in supplier relationship management system (case study: Mobarake Steel Co.). Industrial Management Perspective, 8 (31), 37-61.
    4.
  4. Fallah, H., & Kazemi, F. (2017). Evaluating the efficiency of suppliers in the sustainable supply chain using data envelopment analysis approach, Case Study: Wood Plast Tosca Company. International Conference on Industrial Management, Mazandaran University, 2:1-18. (In Persian).
    5.
  5. Ghasemi, A., Farokhpour, GH & Rezayazdi, P. (2015). Evaluation of suppliers based on indicators involved in supply chain productivity (survey o f supply companies for Toos Automotive Group). Journal of Organizational Culture Management, 13(2):515-535. (in persian).
    6.
  6. Jahanshahloo, G.R., Hosseinzadeh Lotfi, F., Sanei, M., & Fallah Jelodar, M. (2008). Reviw of Rankg Models in Data Envelopment Analysis. Applied Mathematical Sciences, 2:1431-1448.
    7.
  7. Jiuping, X., Bin, L., & Desheng, W. (2009). Rough data envelopment analysis and its application to supply chain performance evaluation. J. Production Economics, 122:628–638.
    8.

Kolagar Daronkola, M., & Hosseini, S. (2019). Evaluating The Suppliers Based On A Hybrid Approach Of Einstein Choquet Integral And Promethee Ii With Respect To Scor Metrics (Case Study: Medical Laboratory. Industrial Engineering & Management Sharif (Sharif: Engineering), 35-1(1/1 ), 105-117. https://www.sid.ir/en/journal/ViewPaper.aspx?id=698600

  1. Lia, GD., Yamaguchi, D., & Nagai, M. (2007). A grey-based decision-making approach to the supplier selection problem. Mathematical and Computer Modelling, 46:573–581.
    2.
  2. Liao, Z., & Rittscher, J. (2007). A multi-objective supplier selection model under stochastic demand conditions. International Journal of Production Economics, 105(1), 150-159.
    3.
  3. Lin, C. T., Chang, C. W., & Chen, C. B. (2006). The worst ill-conditioned silicon wafer slicing machine detected by using grey relational analysis. The International Journal of Advanced Manufacturing Technology, 31(3-4), 388-395.
    4.

Luthra, S., Govindan, K., Kannan, D., Mangla, S. K., & Garg, C. P. (2017). An integrated framework for sustainable supplier selection and evaluation in supply chains. Journal of Cleaner Production, 140, 1686-1698.

  1. Memon, M.S., Lee, Y.H & IrshadMari, S. (2015). Group multi-criteria supplier selection using combined grey systems theory and uncertainty theory. Expert Systems with Applications, 42(21): 7951-7959.
    2.
  2. Moaazez, H., Fathi, R., & Ramezani, D. (2019). Evaluation of Resilence suppliers using fuzzy relative system and network analysis process. Journal of Environmental Science and Technology, 10: 57-86.
    3.
  3. Rashidi, K., & Cullinane, K. (2019). A comparison of fuzzy DEA and fuzzy TOPSIS in sustainable supplier selection: Implications for sourcing strategy. Expert Systems with Applications, 121(1):266-281.
    4.
  4. Saen, R. F. (2010). Developing a new data envelopment analysis methodology for supplier selection in the presence of both undesirable outputs and imprecise data. The International Journal of Advanced Manufacturing Technology, 51(9), 1243-1250.
    5.

Stević, Ž., Pamučar, D., Puška, A., & Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS). Computers & Industrial Engineering, 140, 106231.

  1. Sunil, L., Kannan, G., Devika, K., Sachin, K & Mangla, Ch. (2017). An integrated framework for sustainable supplier selection and evaluation in supply chains. Journal of Cleaner Production, 140(3):1686-1698.
    2.
  2. Talluri, S., & Narasimhan, R. (2004). A methodology for strategic sourcing. European Journal of Operational Research, 154:236-250.
    3.
  3. Wu, D. D., & Olson, D. L. (2010). Fuzzy multiattribute grey related analysis using DEA. Computers & Mathematics with Applications, 60(1), 166-174.
    4.
  4. Yazdani, M., Prasenjit, C., Kazimieras Zavadskas, E., & Hashemkhani Zolfanid, S. (2017). Integrated QFD-MCDM framework for green supplier selection. Journal of Cleaner Production, 142(4):3728-3740.
    5.

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