ارزیابی کارایی سازمانی بر اساس تلفیق «کارت امتیازی متوازن» و «تحلیل پوششی داده‌های فازی» (مطالعه موردی: شعب بانک سپه شهر تبریز)
محورهای موضوعی : مدیریت بازرگانیعلیرضا بافنده 1 , سمیرا رفیعی 2
1 - استادیار، گروه مدیریت صنعتی، واحد تبریز،دانشگاه آزاد اسلامی، تبریز، ایران
2 - دانش آموخته کارشناسی ارشد مهندسی صنایع موسسه آموزش عالی الغدیر
کلید واژه: کارایی, کارت امتیازی متوازن, تحلیل پوششی داده های فازی, بانک سپه,
چکیده مقاله :
در عصر کنونی تحولات شگرف دانش مدیریت، وجود نظام ارزیابی را اجتناب‌ناپذیر نموده است؛ به گونه‌ای که فقدان نظام ارزیابی در ابعاد مختلف سازمان را، اعم از ارزیابی در استفاده از منابع و امکانات، کارکنان، اهداف و استراتژی ها؛ به عنوان یکی از علائم بیماریهای سازمان قلمداد می‌نمایند. تکنیک های مختلفی برای این کار وجود دارد که هر کدام نقاط قوت و ضعف خود را دارند. در این مقاله با ترکیب کارت امتیازی متوازن[1] با تحلیل پوششی داده های فازی[2]الگویی برای ارزیابی عملکرد شعب بانک ها ارائه می شود. جامعه آماری تحقیق، کلیه شعب بانک سپه شهر تبریز به تعداد 42 شعبه می باشد. شاخص های اندازه گیری کارایی با استفاده از کارت امتیازی متوازن و با توجه به پیشینة موضوع تعیین گردیده است. برای جمع آوری داده های مربوط به هر شاخص از پرسش نامه محقق ساخته استفاده شده است که پس از تأیید روایی و پایایی آن در بین جامعه آماری توزیع شده است. برای تجزیه و تحلیل داده ها و ارزیابی کارایی شعب، تحلیل پوششی داده های فازی به کار رفته است. نتایج بررسی ها نشان می دهد که تعداد 10 شعبه از مجموع شعب فعال در سطح شهر تبریز، کارای قوی، 27 شعبه در رده کارا و 5 شعبه باقی مانده ناکارا می باشند. [1]. Balanced Score Card (BSC) [2]. DEA-Like Model
In the current era, the amazing changes in the management knowledge necessitate the existence of measurement systems so much so that the absence of a measurement system in the different dimensions of an organization such measurement of the use facilities and resources, personnel, goals and strategies is considered one of the signs of organization illness. There are different techniques for performance measurement, each of which has strong and weak points. In the present paper, a model for measuring the performance of the bank branches by combining “balance score card” with “fuzzy data envelopment analysis”. The statistical population includes all the branches of Sepah Bank of Tabriz totaling 42. The efficiency measurement indices have been specified through the use of balanced scorecard and with regard to the research literature. For data collection, a researcher – devised questionnaire was used after its validity and reliability had been confirmed. Data envelopment analysis was applied for data analysis. The results show that 10 branches out of the 42 around Tabriz are specified as strongly efficient, 27 as efficient, and the remaining 5 branches as inefficient.
- Adler, N., Friedman, L., Sinuany Stern, Z. (2002). Review of Ranking Methods in the Data Envelopment Analysis Context. European Journal of Operational Research, 140, 249-265.
- Amirteimoori, A., & Kordrostami, S. (2005). DEA-Like Models for Multi-Component Performance Measurement. Mathematics and Computation, 163, 735-43.
- Azar, A., & Faraji, H. (2008). Fuzzy Management Science. Tehran: Nahrian (In Persian).
- Bala, K., & Cook, W. D. (2003). Performance Measurement with Classification Information: An Enhanced Additive DEA Model. The International Journal of Management Science, 31, 439-550.
- Bernolak, I. (1997). Effective Measurement and Successful Elements of Company Productivity: The Basis of Competitiveness and World Prosperity. International Journal of Production Economics, 52, 203-213.
- Charnes, A., Cooper, W. W., & Lewin, A. Y. (1994). Data Envelopment Analysis: Theory, Methodology and Applications. Boston: Kluwer Academic Publishers.
- Chen, T. Y., Chen, L. H. (2007). DEA Performance Evaluation Based on BSC Indicators Incorporated: The Case of Semiconductor Industry. International Journal of Productivity and Performance Management, 56(4), 335-57.
- Gheysari, K. (2007). Introduction to Fuzzy Data Envelopment Analysis. Ghazvin: Islamic Azad university, (In Persian).
- Guixia, W. , Jinbo, W., & Lanxiang, Z. (2014). Strategy Map for Chinese Science Parks with KPIs of BSC. Journal of Science and Technology Policy Management, 5(2), 82-105.
- Haizhi, W. (2014). Guest Editorial: Banking and Finance in China. Managerial Finance, 40(10), 10-46.
- Khalily, M., Alirezayi, M. R., Mirhasani, S. A., & Keshvary, A. (2007). Introducing the Combinational Method of DEA and BSC in Order to Productivity Monitoring. 6th Congress of Productivity and Quality in Electricity Industry.
- Paul, L. (2002). Niven/John Wiley, Balanced Scored Step-by-Stey.
- Norman, M., & Stoker, B. (1998). Performance, Efficiency and Data Envelopment Analysis. Translation by: N., Michael, & B., Stoker, New York: Jonh Wiley & Sons Ltd.
- Kaplan, R. S., & Norton, D. P. (1992). The Balanced Scorecard: Measures that Drive Performance. Harvard Business Review, 71-9.
- Khodadad Kashi, F., & Hajian, M. (2013). The Evaluation of Cost Efficiency in Banking Industry of Iran during the period of 2001-2007. The Journal of Planning and Budgeting, 1, 3-24, (In Persian).
- Li, P. (2001). Design of Performance Measurement Systems: A Stakeholder Analysis Framework. The Academy of Management Review.
- Maureen, J. (2001). What’s So Important About Evaluation?. Library Management, 22(1/2), 50-58.
- Martins, R. A. (2002). The Use of Performance Measurement Information as a Driver in Designing a Performance Measurement System. Decision Support Systems, 25(1), 71-78.
- Maskell, B. (1989). Performance Measures for World Class Manufacturing. Management Accounting, 32-33.
- Michael, A. C. (2013). Building Operational Efficiencies. The Bottom Line, 26(1), 21- 24.
Mirhosseyni, S. A. (2010). Envelopment Analysis of Data,Models and Applications. Tehran: Amirkabir Univercity, (In Persian).
- Neely, A., Marr, B., Roos, G., Pike, S., & Gapta, O. (2003). Towards the Third Generation of Performance Measurement. Controlling Haft.
- Edgar, P. Blanco, E., & Laureano, E. (2014). Supply Chain Management in Latin America. International Journal of Physical Distribution & Logistics Management, 44(7).
- Safari, S. (2003). Designing a Math Model to Evaluation the Performance with Emphasis on Quality Management Indices in Manufacturing Organizations to approach Data Envelopment Analysis. PhD Thesis, Tehran: Tarbiat Modares University., (In Persian).
- Geertshuis, S., Holmes, M., Geertshuis, H., Clancy, D., & Amanda, A. (2002). Evaluation of Workplace Learning. Journal of Workplace Learning, 14(1), 11-18.
- Taeho, K. (2010). Efficiency of Trucks in Logistics: Technical Efficiency and Scale Efficiency. Asian Journal on Quality, 11(1), 89 – 96.
- Taghizadeh Mehrjuyi, R. A., Fazele Yazdi, A., & Mohebbi, A. (2013). Modeling and Predicting the Efficiency of Private and Public Banks in Iran Using Artificial Neural Networks, Fuzzy Neural Network and Genetic Algorithms. Journal of Asset Management and Financing. 2, 103-126, (In Persian).
- Tangen, S. (2004). Professional Practice Performance Measurement: From Philosophy to Practice. International Journal of Productivity and performance Management, 53(8), 726-37.
- Wang, Y. M., Liu, J., & Elhag, T. M. S. (2011). An Integrated AHP-DEA Methodology for Bridge Risk Assessment. Computer & Industrial Engineering, 54, 513-525.
- Zamani Farahani, M. (2012). Money, Currency and Banking. Tehran: Samt, (In Persian).
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- Adler, N., Friedman, L., Sinuany Stern, Z. (2002). Review of Ranking Methods in the Data Envelopment Analysis Context. European Journal of Operational Research, 140, 249-265.
- Amirteimoori, A., & Kordrostami, S. (2005). DEA-Like Models for Multi-Component Performance Measurement. Mathematics and Computation, 163, 735-43.
- Azar, A., & Faraji, H. (2008). Fuzzy Management Science. Tehran: Nahrian (In Persian).
- Bala, K., & Cook, W. D. (2003). Performance Measurement with Classification Information: An Enhanced Additive DEA Model. The International Journal of Management Science, 31, 439-550.
- Bernolak, I. (1997). Effective Measurement and Successful Elements of Company Productivity: The Basis of Competitiveness and World Prosperity. International Journal of Production Economics, 52, 203-213.
- Charnes, A., Cooper, W. W., & Lewin, A. Y. (1994). Data Envelopment Analysis: Theory, Methodology and Applications. Boston: Kluwer Academic Publishers.
- Chen, T. Y., Chen, L. H. (2007). DEA Performance Evaluation Based on BSC Indicators Incorporated: The Case of Semiconductor Industry. International Journal of Productivity and Performance Management, 56(4), 335-57.
- Gheysari, K. (2007). Introduction to Fuzzy Data Envelopment Analysis. Ghazvin: Islamic Azad university, (In Persian).
- Guixia, W. , Jinbo, W., & Lanxiang, Z. (2014). Strategy Map for Chinese Science Parks with KPIs of BSC. Journal of Science and Technology Policy Management, 5(2), 82-105.
- Haizhi, W. (2014). Guest Editorial: Banking and Finance in China. Managerial Finance, 40(10), 10-46.
- Khalily, M., Alirezayi, M. R., Mirhasani, S. A., & Keshvary, A. (2007). Introducing the Combinational Method of DEA and BSC in Order to Productivity Monitoring. 6th Congress of Productivity and Quality in Electricity Industry.
- Paul, L. (2002). Niven/John Wiley, Balanced Scored Step-by-Stey.
- Norman, M., & Stoker, B. (1998). Performance, Efficiency and Data Envelopment Analysis. Translation by: N., Michael, & B., Stoker, New York: Jonh Wiley & Sons Ltd.
- Kaplan, R. S., & Norton, D. P. (1992). The Balanced Scorecard: Measures that Drive Performance. Harvard Business Review, 71-9.
- Khodadad Kashi, F., & Hajian, M. (2013). The Evaluation of Cost Efficiency in Banking Industry of Iran during the period of 2001-2007. The Journal of Planning and Budgeting, 1, 3-24, (In Persian).
- Li, P. (2001). Design of Performance Measurement Systems: A Stakeholder Analysis Framework. The Academy of Management Review.
- Maureen, J. (2001). What’s So Important About Evaluation?. Library Management, 22(1/2), 50-58.
- Martins, R. A. (2002). The Use of Performance Measurement Information as a Driver in Designing a Performance Measurement System. Decision Support Systems, 25(1), 71-78.
- Maskell, B. (1989). Performance Measures for World Class Manufacturing. Management Accounting, 32-33.
- Michael, A. C. (2013). Building Operational Efficiencies. The Bottom Line, 26(1), 21- 24.
Mirhosseyni, S. A. (2010). Envelopment Analysis of Data,Models and Applications. Tehran: Amirkabir Univercity, (In Persian).
- Neely, A., Marr, B., Roos, G., Pike, S., & Gapta, O. (2003). Towards the Third Generation of Performance Measurement. Controlling Haft.
- Edgar, P. Blanco, E., & Laureano, E. (2014). Supply Chain Management in Latin America. International Journal of Physical Distribution & Logistics Management, 44(7).
- Safari, S. (2003). Designing a Math Model to Evaluation the Performance with Emphasis on Quality Management Indices in Manufacturing Organizations to approach Data Envelopment Analysis. PhD Thesis, Tehran: Tarbiat Modares University., (In Persian).
- Geertshuis, S., Holmes, M., Geertshuis, H., Clancy, D., & Amanda, A. (2002). Evaluation of Workplace Learning. Journal of Workplace Learning, 14(1), 11-18.
- Taeho, K. (2010). Efficiency of Trucks in Logistics: Technical Efficiency and Scale Efficiency. Asian Journal on Quality, 11(1), 89 – 96.
- Taghizadeh Mehrjuyi, R. A., Fazele Yazdi, A., & Mohebbi, A. (2013). Modeling and Predicting the Efficiency of Private and Public Banks in Iran Using Artificial Neural Networks, Fuzzy Neural Network and Genetic Algorithms. Journal of Asset Management and Financing. 2, 103-126, (In Persian).
- Tangen, S. (2004). Professional Practice Performance Measurement: From Philosophy to Practice. International Journal of Productivity and performance Management, 53(8), 726-37.
- Wang, Y. M., Liu, J., & Elhag, T. M. S. (2011). An Integrated AHP-DEA Methodology for Bridge Risk Assessment. Computer & Industrial Engineering, 54, 513-525.
- Zamani Farahani, M. (2012). Money, Currency and Banking. Tehran: Samt, (In Persian).