استفاده از مدل تحلیل پوششی داده ها برای ارزیابی عملکرد زنجیره تأمین سه سطحی
محورهای موضوعی : مدیریت صنعتی
Saviz Saei
1
(Student of M.s Degree, Department of Industrial Engineering, Science & Research Branch, Islamic Azad University, Tehran, Iran)
Esmaeil Najafi
2
(Assistant Professor, Department of Industrial Engineering, Science & Research Branch, Islamic Azad University, Tehran, Iran)
کلید واژه: Data envelopment analysis (DEA), Two-stage, Supply Chain Management (SCM), مدیریت زنجیره تأمین, تحلیل پوششی داده ها(DEA), تحلیل پوششی دو مرحله ای,
چکیده مقاله :
تحلیل پوششی داده ها ابزاری برای محاسبه کارایی در واحدهای تصمیم گیری از جمله زنجیره تأمین است. از آنجا که اخیراً مدل تحلیل پوششی دو مرحله ای تمرکز بیشتری روی فرآیندهای زنجیره تأمین در دو سطح داشته است؛ بدین منظور ضرورت ارزیابی زنجیره تأمین در سطوح بالاتر برای دستیابی به کارایی دقیق تر محسوس است. در این مقاله مدل وانگ(2010) را در سه سطح جهت ارزیابی یک زنجیره تأمین، گسترش داده ایم تا اهمیت یکپارچگی در کل زنجیره تأمین را نشان دهیم. در این مدل، تحلیل پوششی داده های دو مرحله ای کو و هوانگ(2010) با فرض بازده به مقیاس متغیر و کارایی مدل چن(2009) با در نظر گرفتن اهمیت نسبی وزن ها در هر مرحله به طور مجزا لحاظ شده است. مدل ارائه شده در یک مثال عددی اجرا گردیده و نتایج در قالب جداولی تحلیل گردیده است.
Data envelopment analysis is the tool for computation in decision-making units such as supply chain. Since the model of two-stage data envelopment analysis has more focused on supply chain processes in two levels; the evaluation in higher levels has been essential to obtain more accurate efficiency. This paper extends Wang & Chin (2010) model into three levels for evaluation the supply chain to show the importance of integration in the overall supply chain. The model is considered the extended two-stage DEA model of Koa and Hwang(2008) to variable returns to scale (VRS) assumption and the additive efficiency decomposition model of Chen (2009) generalized to take into account the relative importance weights of two individual stages. The presented model is executed in numerical illustration and the results are analyzed in tables.
1- Banker, R., Charnes, A., & Cooper, W. (1984). Some models for the estimation of technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9)1078-1092.
2- Castelli, L., Pesenti, R., & Ukovich, W. (2004). DEA-like models for the efficiency evaluation of hierarchically structured units. European Journal of Operation Research, 154,456-476.
3- Charnes, A., Cooper, W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operation Research, 2(6), 429-444.
4- Chen, M.-C., Yang, T., & Yen, C.-T. (2007). Investing the value of information sharing in multi-echelon supply chains. Qual Quant, 41,497-511.
5- Chen, Y., Cook, W., Li, N., & Zhu, J. (2009). Additive efficiency decomposition in two-stage DEA. European Journal of Operation Research, 196(3), 1170-1176.
6- ] Chen, Y., Liang, L., & Yang, F. (2006). A DEA game model approach to supply chain efficiency . Annals of Operation Research, 1451, 5-13.
7- Chen, Y.-J. (2011). Structured methodology for supplier selection and evaluation in a supply chain. Information Science, 181(9), 1651-1670.
8- Chopra, S., & Meindl, P. (2004). Supply chain Management, strategy, planning and operation. Prentice Hall.
9- Easton, L., Murphy, D., & Pearson, J. (2002). Purchasing performance evaluation: with data envelopment analysis. European Journal of Purchasing & Supply Management, 8,123-134.
10- Fare, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences.
11- Farrel, J. (1957). The Measurement of Productivity Efficiency. The Royal Statistical Society, Series A, 120, 253-290.
12- Feng, Y., Dexiang, W., Liang, L., Gongbing, B., & Desheng, D. (2011). Supply chain DEA: production possibility set and performance evaluation model. Ann Operation Research, 195-211.
13- Handfield, L., & Nicholas, E. (1999). Introduction to supply chain management. Prentice Hall.
14- Koa, C., & Hwang, S. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operation Research, 185(1), 418-429.
15- Narasimhan, R., Talluri, S., & Das, A. (2004). Exploring flexibility and execution competencies of manufacturing firms. Journal of Operation Management, 22, 91-106.
16- Ross, A., & Droge, C. (2004). An analysis of operations efficiency in large-scale distribution systems. Journal of Operation Management, 21,673-688.
17- Ross, A., & Droge, C. (2002). An integrated benchmarking approach to distribution center performance using DEA modeling. Journal of Operations Management, 20, 19-32.
18- Seiford, L., & Zhu, J. (1999). Profitability and marketability of the top 55 US commercial banks . Management Science, 45(9), 1270-1288.
19- Simchi-Levi, D., Kaminsky, P., & Simchi-levi, E. (2000). Designing and Managing the supply chain concepts, and Strategies and Case studies. McGraw-Hill.
20- Talluri, S., & Baker, R. (2002). A multi-phase mathematical programming approach for effective supply chain design. European Journal of Operation Research, 141(3), 546-560.
21- Troutt, M., Ambrose, P., & Chan, C. (2004). Multi-stage efficiency tools for goal setting and monitoring in supply chains. Successful strategies in supply chain management Hershey: Idea Group Publishing Co.
22- Troutt, M., Ambrose, P., & Chan, C. (2001). Optimal throughput for multistage input-output processes. International Journal of Operations and Production Management, 21(1), 148-158.
23- Ying-Ming Wang, A., & Kwai-Sang Chin, B. (2010). Some alternative DEA models for two-stage process. Expert System with Applications, 37, 8799-8808.
_||_1- Banker, R., Charnes, A., & Cooper, W. (1984). Some models for the estimation of technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9)1078-1092.
2- Castelli, L., Pesenti, R., & Ukovich, W. (2004). DEA-like models for the efficiency evaluation of hierarchically structured units. European Journal of Operation Research, 154,456-476.
3- Charnes, A., Cooper, W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operation Research, 2(6), 429-444.
4- Chen, M.-C., Yang, T., & Yen, C.-T. (2007). Investing the value of information sharing in multi-echelon supply chains. Qual Quant, 41,497-511.
5- Chen, Y., Cook, W., Li, N., & Zhu, J. (2009). Additive efficiency decomposition in two-stage DEA. European Journal of Operation Research, 196(3), 1170-1176.
6- ] Chen, Y., Liang, L., & Yang, F. (2006). A DEA game model approach to supply chain efficiency . Annals of Operation Research, 1451, 5-13.
7- Chen, Y.-J. (2011). Structured methodology for supplier selection and evaluation in a supply chain. Information Science, 181(9), 1651-1670.
8- Chopra, S., & Meindl, P. (2004). Supply chain Management, strategy, planning and operation. Prentice Hall.
9- Easton, L., Murphy, D., & Pearson, J. (2002). Purchasing performance evaluation: with data envelopment analysis. European Journal of Purchasing & Supply Management, 8,123-134.
10- Fare, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences.
11- Farrel, J. (1957). The Measurement of Productivity Efficiency. The Royal Statistical Society, Series A, 120, 253-290.
12- Feng, Y., Dexiang, W., Liang, L., Gongbing, B., & Desheng, D. (2011). Supply chain DEA: production possibility set and performance evaluation model. Ann Operation Research, 195-211.
13- Handfield, L., & Nicholas, E. (1999). Introduction to supply chain management. Prentice Hall.
14- Koa, C., & Hwang, S. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operation Research, 185(1), 418-429.
15- Narasimhan, R., Talluri, S., & Das, A. (2004). Exploring flexibility and execution competencies of manufacturing firms. Journal of Operation Management, 22, 91-106.
16- Ross, A., & Droge, C. (2004). An analysis of operations efficiency in large-scale distribution systems. Journal of Operation Management, 21,673-688.
17- Ross, A., & Droge, C. (2002). An integrated benchmarking approach to distribution center performance using DEA modeling. Journal of Operations Management, 20, 19-32.
18- Seiford, L., & Zhu, J. (1999). Profitability and marketability of the top 55 US commercial banks . Management Science, 45(9), 1270-1288.
19- Simchi-Levi, D., Kaminsky, P., & Simchi-levi, E. (2000). Designing and Managing the supply chain concepts, and Strategies and Case studies. McGraw-Hill.
20- Talluri, S., & Baker, R. (2002). A multi-phase mathematical programming approach for effective supply chain design. European Journal of Operation Research, 141(3), 546-560.
21- Troutt, M., Ambrose, P., & Chan, C. (2004). Multi-stage efficiency tools for goal setting and monitoring in supply chains. Successful strategies in supply chain management Hershey: Idea Group Publishing Co.
22- Troutt, M., Ambrose, P., & Chan, C. (2001). Optimal throughput for multistage input-output processes. International Journal of Operations and Production Management, 21(1), 148-158.
23- Ying-Ming Wang, A., & Kwai-Sang Chin, B. (2010). Some alternative DEA models for two-stage process. Expert System with Applications, 37, 8799-8808.