ارزیابی ساختارهای دومرحله‌ای متوالی: رویکرد تحلیل پوششی داده‌های شبکه‌ای چندهدفه (MO-NDEA)

نوع مقاله: مقاله علمی پژوهشی

نویسنده

استادیار، گروه مدیریت، دانشکده علوم اداری و اقتصاد دانشگاه ولی‌عصر (عج)، رفسنجان، ایران.

چکیده

هدف: مدل‌های اولیه تحلیل پوششی داده‌ها در مواجهه با ساختارهای با بیش از یک مرحله (شبکه‌ای) نمی‌توانند منبع ناکارایی را به‌خوبی مشخص کنند. ساختارهای شبکه‌ای دو مرحله‌ای متوالی، یکی از ساختارهای شبکه‌ای مبنا و پرکاربرد است. چالش اصلی بررسی این ساختار، ارتباط بین کارایی کل و کارایی مراحل و تعیین مقدار بهینه متغیرهای میانی است. مدل‌های موجود در حل این چالش‌ها و محاسبه کارایی‌ها یا همراه با خطا هستند یا امکان توسعه به انواع ساختارهای دومرحله‌ای را ندارند. هدف این مقاله، توسعه یک مدل چندهدفه تحلیل پوششی داده‌های شبکه‌ای است که ضعف‌های مدل‌های موجود را ندارد.
روش: در این مقاله تلاش شده است که با رویکرد ترکیب، مدل چندهدفه‌ای که هم‌زمان کارایی مراحل را مدنظر قرار می‌دهد توسعه داده شده و به بیان تعبیر هندسی و مقایسه آن با مدل‌های موجود پرداخته شود. مدل ارائه‌شده برای شرایط جواب بهینه چندگانه و بازده به مقیاس متغیر نیز تعمیم داده شده است.
یافته‌ها: در تمامی مدل‌های توسعه داده‌شده در مقاله، کارایی‌هایی که برای مراحل و کل محاسبه شده است، بین صفر تا 1 به‌دست آمده است، فقط در صورتی یک واحد کارای شبکه‌ای می‌شود که در هر دومرحله کارا باشد.
نتیجه‌گیری: از مدل ارائه‌شده در مثالی کاربردی برای ارزیابی پایداری 17 زنجیره تأمین استفاده شد. نتایج نشان داد که مدل موجود در مقایسه با مدل‌های سنتی و شبکه‌ای، ارزیابی واقع‌بینانه‌تری انجام می‌دهد. در نهایت با مثال‌هایی برتری مدل در مقایسه با مدل‌های ادبیات پژوهش نشان داده شد.

کلیدواژه‌ها


عنوان مقاله [English]

Evaluation of Continuous Two-stage Structures: A New Multi-objective Network Data Envelopment Analysis (MO-NDEA) Approach

نویسنده [English]

  • Reza Soleymani Damaneh
Assistant Prof., Department of Management, Faculty of Economic and Administrative Sciences, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran.
چکیده [English]

Objective: Traditional DEA models cannot determine the source of inefficiency for structures with more than one stage (network structures). Continuous two-stage structures are one of the most applicable and basic network structures, and one of their main challenges is determining the relationship between the total efficiency and the efficiency of the stage and also determining the optimum amount of intermediate variables. The available models in solving the challenges and calculating the efficiency have orib or aren’t applicable for all two-stage structures. The purpose of this study is developing a multi-objective network DEA model that doesn’t have the weaknesses of the previous model.
Methods: In this study, it is attempted to develop a multi-objective model with a composition approach that considers the efficiency of the stages simultaneously, and also to interpret the results geometrically and compare it with the available models. The presented model was developed to multi-optimal and VRS conditions.
Results: In all the models, efficiencies are between zero to one and a unit is network efficient only and only when it is efficient in both stages.
Conclusion: The presented model was used in an applicable example to evaluate the sustainability of 17 supply chains and the results showed that the model does a realistic evaluation in comparison to the traditional models. In the end, the model priority over the literature review models was mentioned with examples.

کلیدواژه‌ها [English]

  • Network DEA
  • Performance Evaluation
  • Two-stage structures
  • Multi-objective models
  • Efficiency
رضوی، سیدمصطفی؛ شهریاری، سلطانعلی؛ احمدپور داریانی، محمود (1394). ارزیابی عملکرد نوآورانه شرکت‌های دانش‌‌نیان با استفاده از تحلیل پوششی داده‌ای شبکه‌‌‌ای ـ رویکرد تئوری بازی. مدیریت صنعتی، 7(4)، 721-742.

زارعی محمودابادی، محمد (1395). ارزیابی چندسطحی کارایی در صنعت بانکداری (رویکرد SBM شبکه‌ای). مدیریت صنعتی، 8(3)، 359- 380.

شهریاری، سلطانعلی؛ لاهیجی، ساینا (1396). ارزیابی کارایی نظام ملی نوآوری با استفاده از تحلیل پوششی داده‌های شبکه‌ای. مدیریت صنعتی، 9(3)، 455- 474.

 

References

An, Q., Chen, H., Xiong, B., Wu, J., & Liang, L. (2017). Target intermediate products setting in a two-stage system with fairness concern. Omega, 73, 49-59.

Ang, S., & Chen, C.M. (2016). Pitfalls of decomposition weights in the additive multi-stage DEA model. Omega, 58, 139-153.

Banker, R.D., Chanres, A., Cooper, W. W. (1984). Some models for estimating technical and scale efficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092.

Byrnes, P., Fare, R., Grosskopf, S. (1984). Measuring productive efficiency: An application to Illinois mines. Management Science, 30, 671-681.

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 2(6), 429-444.

Chen, Y., Cook, W. D., & Zhu, J. (2010). Deriving the DEA frontier for two-stage processes. European Journal of Operational Research, 202(1), 138-142.

Chen, Y., Cook, W. D., Li, N., & Zhu, J. (2009). Additive efficiency decomposition in two-stage DEA. European Journal of Operational Reaearch, 196(3), 1170-1176.

Cook, W. D., Zhu, J., Bi, G. B., & Yang, F. (2010). Network DEA: Additive efficiency decomposition. European Journal of Operational Research, 207(2), 1122-1129.

Despotis, K., D., Koronakos, G., Sotiros, D. (2016). Composition versus decomposition in two-stage network DEA: a reverse approach. Journal of Productivity Analysis, 45(1), 71-87.

Despotis, K., Sotiros D., Koronakos G. (2016). A network DEA approach for series multi-stage processes. Omega, 61, 35-48.

Du, J., Liang, L., Chen, Y., Cook, W. D., & Zhu, J. (2011). A bargaining game model for measuring performance of two-stage network structures. European Journal of Operational Research, 210(2), 390–397.

Emrouznejad, A, Parker BR, Tavates, G. (2008). Evaluation of research in efficiency and productivity: a survey and analysis of the first 30 years of scholarly literature in DEA. Socio-Economic Plan sciences, 42(3), 151-158.

Emrouznejad, A. & Yang, G. L. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978-2016. Socio-Economic Planning Sciences, 61, 4-8.

Färe, R., & Grosskopf, S. (1996). Productivity and intermediate products: A frontier approach. Economics Letters, 50(1), 65–70.

Fare, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34, 35-49.

Färe, R., & Whittaker, G. (1995). An intermediate input model of dairy production using complex survey data. Journal of Agricultural Economics, 46(2), 201–213.

Fare, R., Primont, D. (1984). Efficiency measures for multiplant firms. Operations Research Letters, 3, 257-260.

Fare, R., Primont, D. (1993). Measuring the efficiency of multiunit banking: An activity analysis approach, journal of Banking & Finance, 17(2), 539-544.

Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, 120(3), 253-290.

Fukuyama, H., & Matousek, R. (2017). Modelling Bank Performance: A Network DEA Approach, European Journal of Operational Research, 259(2), 721-732.

Guan, J. C., & Chen, K. H. (2012). Modeling the relative efficiency of national innovation systems. Research Policy, 41(1), 102–115.

Halkos, G.E., Tzeremes, N.G., & Kourtzidis, S. A. (2014). A unified classification of two-stage DEA models. Surveys in Operations Research and Management Science, 19(1), 1-16.

Ho, C. T. B., & Oh, K. B. (2008). Measuring online stockbroking performance. Industrial Management and Data Systems, 108(7), 988–1004.

Kao, C. (1995). Some properties of Pareto efficiency under the framework of data envelopment analysis. International Journal of Systems Science, 26, 1549-1558.

Kao, C. (2009). Efficiency decomposition in network data envelopment analysis: A relational model. European Journal of Operational Research, 192(3), 949-962.

Kao, C. (2014a). Efficiency decomposition for general multi-stage systems in data envelopment analysis. European Journal of Operational Research, 232(1),117-124.

Kao, C. (2014b). Network data envelopment analysis: A review. European Journal of Operational Research, 239(1), 1-16.

Kao, C., & Hwang, S. N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185, 418-429.

Kao, C., & Hwang, S. N. (2010). Efficiency measurement for network systems: IT impact on firm performance. Decision Support Systems, 48(3), 437-446.

Kao, C., & Hwang, S. N. (2011). Decomposition of technical and scale efficiencies in two-stage production systems. European Journal of Operational Research, 211(3), 515–519.

Lee, B. & Worthington, A.C. (2016). A network DEA quantity and quality-orientated production model: an application to Australian university research services. Omega, 60, 26-33.

Li, F., Zhu, Q., & Zhuang, J. (2017). Analysis of fire protection efficiency in the United States: a Two-stage DEA-based approach. OR Spectrum, DOI: 10.1007/s00291-017-0490-2.

Li, H., Chen, C., Cook, W. D., Zhang, J. & Zhu, J. (2018). Two-Stage network DEA: Who is the leader? Omega, 74, 15-19.

Liang, L., Cook, W. D., & Zhu, J. (2008). DEA models for two-stage processes: Game approach and efficiency decomposition. Naval Research Logistics, 55(7), 643–653.

Lim, S. & Zhu, J. (2018). Primal-dual correspondence and frontier projections in two-stage network DEA models. Omega, 1-13.

Lim, S., & Zhu J. (2013). Integrated data envelopment analysis:Global vs local optimum. European Journal of Operational Research, 229(1), 276-278.

Liu, J. S., Lu, L. Y., Lu, W. M. & Lin, B. j. (2013). A survey of DEA applications. Omega, 41, 893-902.

Lo, S.F. (2010). Performance evaluation for sustainable business: A profitability and marketability framework. Corporate Social Responsibility and Environmental Management, 17(6), 311–319.

Ma, J., Qi, Linan, Deng, L. (2017). Efficiency measurement and decomposition in hybrid two-stage DEA with additional inputs. Exper Systems with Applications, 79(3), 348-357.

Mirdehghan, S.M. & Fukuyama, H. (2016). Pareto-Koopmans efficiency and network DEA. Omega, 61, 78-88.

Premachandra, I. M., Zhu, J., Watson, J., & Galagedera, D. U. A. (2012). Bestperforming US mutual fund families from 1993 to 2008: Evidence from a novel two-stage DEA model for efficiency decomposition. Journal of Banking and Finance, 36(12), 3302–3317.

Razavi, S. M., Shahriari, S., Ahmadpour, M. (2015). Evaluation of innovative Performance of Knowledge based Company by Network Data Envelopment Analysis- Game Theory Approach. Journal of Industrial Management, 7(4), 721-742. (in Persian)

Seiford, L. M., & Zhu, J. (1999). Profitability and marketability of the top 55 US commercial banks. Management Science, 45(9), 1270-1288.

Shahriari, S., Lahiji, S. (2017). Performance Evaluation of the National Innovation Systems by Network Data Envelopment Analysis. Journal of Industrial Management, 9(3), 455-474. (in Persian)

Shokri, V., Yousefi, S., Shabanpour, H. & Farzipoor, S, R. (2017). How to evaluate sustainability of supply chains? A dynamic network DEA approach. Industrial Management & Data Systems, 117(9), 1866-1889.

Sotiros, D., Koronakos, G. & Despotis D.k. (2018). Dominance at the divisional efficiencies level in network DEA: the case of two-stage processes, Omega, doi: 10.1016/j.omega.2018.06.007.

Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197, 243-252.

Tsolas, I. E. (2013). Modeling profitability and stock market performance of listed construction firms on the Athens Exchange: Two-stage DEA approach. Journal of Construction Engineering and Management, 139(1), 111–119.

Wacker, J. G. (1998). A definition of theory: research guidelines for different theory-building research methods in operations management. Journal of Operations Management, 16(4), 361-385.

Zarei, M. (2016). Multilevel Measuring of Efficiency in Banking Industry (Network Slacks-Based Measure (NSBM) Approach). Journal of Industrial Management, 8(3), 359-380. (in Persian)

Zhang, L., & Chen, Y. (2018). Equivalent solutions to additive two-stage network data envelopment analysis. European Journal of Operationa Research, 264(3), 1189-1191.

Zhu, J. (2000). Multi-factor performance measure model with an application to fortune 500 companies. European Journal of Operational Research, 123(1), 105-124.