ارائه مدل ارزیابی عملکرد هوش تجاری برمبنای فرآیند تحلیل شبکه فازی
محورهای موضوعی : مدیریت بازرگانیجلال حقیقت منفرد 1 , آزاده رضایی 2
1 - استادیار گروه مدیریت ، واحد تهران مرکزی ،دانشگاه آزاد اسلامی ،تهران،ایران
2 - کارشناس ارشد گروه مدیریت ، واحد تهران مرکزی ،دانشگاه آزاد اسلامی ،تهران،ایران
کلید واژه: ارزیابی عملکرد, هوش تجاری, فرآیند تحلیل سلسله مراتبی فازی, فاکتورهای موثر بر هوشمندی تجاری سازمان, چارچوب ارزیابی عملکرد هوش تجاری, فرآیند تحلیل شبکه ای فازی,
چکیده مقاله :
در محیط تجاری سریعا در حال تغییر امروز، نیاز به اطلاعات تجاری سودمند برای سازمانها نه تنها به منظور کسب موفقیت بلکه برای بقاء حیاتی می باشد. با توجه به ناتوانایی سیستمهای اطلاعاتی مدیریت در برآورده کردن انتظارات تصمیم گیرندگان سازمانی در عرصه رقابت در سالهای اخیر، فن آوریهای هنر گونه ای نظیر هوش تجاری به یکی از مفاهیم مهم در مدیریت سیستمهای اطلاعاتی تبدیل شده و با فرهنگ سازمانهای پیشرو عجین شده است و در خط مقدم فناوری اطلاعات برای پشتیبانی تصمیم گیری مدیریت قرار دارد. در این مقاله سعی شده است تا با شناسایی و معرفی مهمترین فاکتورها و عوامل مؤثر در عملکرد یک سیستم هوش تجاری، یک مدل ارزیابی عملکرد هوش تجاری (در قالب یک مطالعه موردی در یک سازمان تولید کننده نرم افزار) ارایه گردد و با توجه به عدم استقلال و وجود وابستگی بین عاملهای مؤثر، از روش فرآیند تحلیل شبکه فازی برای شناسایی وابستگی های ممکن بین عوامل و اندازه گیری آنها برای توسعه مدل ارزیابی استفاده شده و در ادامه، نتایج حاصله با روشهای فرآیند تحلیل سلسله مراتبی فازی[1]و فرآیند تحلیل شبکه غیر فازی[2] مقایسه شده است. [1] - Fuzzy Analytic Hierarchy Process (FAHP) [2] - Analytic Network Process (ANP)
In today’s fast expanding business climate, the need for useful business information seems to be vital not only for achieving success but also for the survival of an organization. Considering the failure of management information systems (MIS) in satisfying the requirements of organizational decision makers on the competition issues during recent years, the art-like technologies such as business intelligence have become one of the major concepts of management information systems, and by mixing with the culture of advanced organizations, are playing an important role at the frontline of information technology for supporting management decision making. In this article an attempt has been made, with the identification and introduction of the most important factors and parameters affecting the performance of a business intelligence system, to present a model for the measurement of a business intelligence performance (within the framework of a case study in a software production organization ). Taking into consideration the lack of independence and the existence of the relationship between effective factors, the method of Fuzzy Analytic Network Process was used for identifying the possible relationship between the factors as well as measuring them for the development of an evaluating model. In the conclusion, the results gained have been compared with the methods of Fuzzy Analytic Hierarchy Process and Non-Fuzzy Analytic Network Process.
Azoff, M., Charlesworth, I. (2004), the New Business Intelligence. A European Perspective, Butler Group, White Paper.
Bellman R. E. & Zadeh, L. A. (1970). Decision making in fuzzy environment, Management Science, 17 (4) (1970) 141-164
Gilad, B., Gilad, T. (1986), SMR Forum: Business Intelligence- the Quiet Revolution, Sloan Management Review, Vol. 27, No. 4, pp. 53–61.
Hannula, M. (1999), Expedient Total Productivity Measurement, Acta Polytechnica Scandinavica, Industrial Management and Business Administration Series, No. 1.
Herring, J. (1996), measuring the Value of Competitive Intelligence: Accessing & Communicating CI’s Value to Your Organization, SCIP Monograph Series, and Alexandria, VA.
Jharkharia,s.,&Shankar,R.(2007).Selection of logistics service provider:An analytic network process (ANP) approach .Omega,35,274-289
Kaplan, R. S., Norton, D. P. (1996), the Balanced Scorecard. Translating Strategy into Action, Harvard Business School Press, and Boston, Massachusetts.
Kaufmann, A., & Gupta, M. M. (1988). Fuzzy mathematical models in engineering and management science. North Holland.
Lee,J.W.,&Kim,S.H.(2000).Using analytic network process and goal programming for interdependent information system project selection.Computers and Operations Research,367-382
Lin, H. F. (2009). An application of fuzzy AHP for evaluating course website quality. Computers & Education.
Liou, T. S, and M. J.J Wang. (1992). “Ranking fuzzy numbers with integral value.” Fuzzy Sets and Systems 50:247–255.
Lönnqvist, A. (2004), Measurement of Intangible Success Factors: Case Studies on the Design, Implementation and Use of Measures, Tampere University of Technology, Publication 475, Tampere.
Meade, L.M., &Sarkis, J. (1999).Analyzing organizational project alternatives for agile manufacturing processes:An analytical network approach.International Jornal of production Research, 37(2),241-261.
Neely, A. (1999), The Performance Measurement Revolution: Why Now and What Next? International Journal of perations & Production Management, Vol. 19, No. 2. pp. 205–228.
Neely, A., Adams, C., Kennerley, M. (2002), the Performance Prism. The Scorecard for Measuring and Managing Business Success, Prentice Hall.
Saaty, T. L. (1996). Decision making with dependence and feedback: the analytic network process.
Tuomela, T.-S. (2000), Customer Focus and Strategic Control. A Constructive Case Study of Developing a Strategic Performance Measurement System at FinABB, Publications of the Turku School of Economics and Business Administration, Series D-2:2000.
Wang ,(2005).Bussiness intelligence.Taiwan:Dr Master Culture Limited Company
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338–353.
Zhu, K. J., Jing, Y., & Chang, D. Y. (1999). A discussion on extent analysis method and applications of fuzzy AHP. European Journal of Operational Research, 116(2), 450–456.
_||_Azoff, M., Charlesworth, I. (2004), the New Business Intelligence. A European Perspective, Butler Group, White Paper.
Bellman R. E. & Zadeh, L. A. (1970). Decision making in fuzzy environment, Management Science, 17 (4) (1970) 141-164
Gilad, B., Gilad, T. (1986), SMR Forum: Business Intelligence- the Quiet Revolution, Sloan Management Review, Vol. 27, No. 4, pp. 53–61.
Hannula, M. (1999), Expedient Total Productivity Measurement, Acta Polytechnica Scandinavica, Industrial Management and Business Administration Series, No. 1.
Herring, J. (1996), measuring the Value of Competitive Intelligence: Accessing & Communicating CI’s Value to Your Organization, SCIP Monograph Series, and Alexandria, VA.
Jharkharia,s.,&Shankar,R.(2007).Selection of logistics service provider:An analytic network process (ANP) approach .Omega,35,274-289
Kaplan, R. S., Norton, D. P. (1996), the Balanced Scorecard. Translating Strategy into Action, Harvard Business School Press, and Boston, Massachusetts.
Kaufmann, A., & Gupta, M. M. (1988). Fuzzy mathematical models in engineering and management science. North Holland.
Lee,J.W.,&Kim,S.H.(2000).Using analytic network process and goal programming for interdependent information system project selection.Computers and Operations Research,367-382
Lin, H. F. (2009). An application of fuzzy AHP for evaluating course website quality. Computers & Education.
Liou, T. S, and M. J.J Wang. (1992). “Ranking fuzzy numbers with integral value.” Fuzzy Sets and Systems 50:247–255.
Lönnqvist, A. (2004), Measurement of Intangible Success Factors: Case Studies on the Design, Implementation and Use of Measures, Tampere University of Technology, Publication 475, Tampere.
Meade, L.M., &Sarkis, J. (1999).Analyzing organizational project alternatives for agile manufacturing processes:An analytical network approach.International Jornal of production Research, 37(2),241-261.
Neely, A. (1999), The Performance Measurement Revolution: Why Now and What Next? International Journal of perations & Production Management, Vol. 19, No. 2. pp. 205–228.
Neely, A., Adams, C., Kennerley, M. (2002), the Performance Prism. The Scorecard for Measuring and Managing Business Success, Prentice Hall.
Saaty, T. L. (1996). Decision making with dependence and feedback: the analytic network process.
Tuomela, T.-S. (2000), Customer Focus and Strategic Control. A Constructive Case Study of Developing a Strategic Performance Measurement System at FinABB, Publications of the Turku School of Economics and Business Administration, Series D-2:2000.
Wang ,(2005).Bussiness intelligence.Taiwan:Dr Master Culture Limited Company
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338–353.
Zhu, K. J., Jing, Y., & Chang, D. Y. (1999). A discussion on extent analysis method and applications of fuzzy AHP. European Journal of Operational Research, 116(2), 450–456.