Developing a New Approach for Product Performance Evaluation Based on Balanced Scorecard: An empirical study of a Dairy Company

Document Type: Research Paper


1 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, I.R. Iran

2 M.Sc., School of Industrial Engineering, Faculty Engineering, University of Tehran

3 Ph.D. Candidate, School of Industrial Engineering, Faculty Engineering, University of Tehran


     To survive in today’s competitive environment, companies need continues improvement to achieve goals such as increasing the accuracy of targeting, improving product or service. However, there is no improvement guaranty without performance evaluation. An organization must assess current performance before embarking on any improvement. If it does not, it will have no baseline from which to determine if its efforts have yielded any improvement. Product development team must evaluate the product performance to enhance existing products so that they continue to meet changing customer requirements. Product performance evaluate the company to maintain market share and leadership. Innovation and new product development are important to build market breakthroughs. Product performance evaluation is necessary before making any change because new product development require high levels of investment and involve significant risk of product failure.
     One of the proper evaluation methods is Balance Scorecard. In the Balanced Scorecard approach actual performance is measured, the measured value is compared to an expected value and based on the difference between the two corrective interventions are made as required. Such measurement requires three things to be effective: I) a choice of data to measure, II) the setting of an expected value for the data, and III) the ability to make a corrective intervention. However this approach cannot consider the combination of measurement factors. In order to overcome this problem, in this paper a novel approach has been developed for performance evaluation based on balanced scorecard and fuzzy analytic network process (F-ANP). Considering the interdependency of measurement factors, the proposed method increases the efficiency of performance evaluation process.   
The proposed method is implemented in product development department of a dairy company. Measurement factors are defined based on literature and also expert’s opinions. Then the weights are calculated by F-ANP. Finally, the performance of 4 new products is evaluated by the proposed approach. Results obtained, shows the efficiency of proposed method as a practical approach for product evaluation process.


Main Subjects

1. Hayri, Ü. and Mirze, S. K. (2004). İşletmelerde Stratejik Yönetim. Literatür Yayıncılık, İstanbul.

2. Kaplan, R. S. and Norton, D. P. (1992). The balanced scorecard: Measures that drive performance. Harvard Business Review, 70, 71–79.

3. Kaplan, R. S. and Norton, D. P. (1996). Using the balanced scorecard as a strategic management systems. Harvard Business Review, 74, 75–85.

4. Kaplan, R. S., Norton, D. P., Egeli, S., and Şirket stratejisini eyleme dönüştürmek. (1996). Balanced scorecard: Şirket stratejisini eyleme dönüştürmek. Sistem Yayıncılık.

5. Lee, A. H. I., Chen, W. C., and Chang, C. J. (2008). “A fuzzy AHP and BSC approach for evaluating performance of IT department in the manufacturing industry in Taiwan. Expert Systems with Applications.” 34, 96–107.

6. Leung, L. C. and Cao, D. (2000). “On consistency and ranking of alternatives in fuzzy AHP.” European Journal of Operational Research, 124, 102–113.

7. Yüksel, İ. And Dağdeviren, M. (2010). Using the fuzzy analytic network process (ANP) for Balanced Scorecard (BSC): A case study for a manufacturing firm. Expert Systems with Applications, 37(2), 1270-1278.

8. Razmi, J., Ghaderi, S. F., Zairi, M., and Keyno, H. S. (2008). “Compiling and prioritizing strategies for electrical energy production from fossil fuel with support of benchmarking.” Benchmarking: An International Journal, 15(6), 794-804.

9. Bentes, A. V., Carneiro, J., da Silva, J. F., and Kimura, H. (2012). “Multidimensional assessment of organizational performance: Integrating BSC and AHP.” Journal of Business Research, 12(65): 1790-1799.

10. Wu, H. Y., Tzeng, G. H., and Chen, Y. H. (2009). “A fuzzy MCDM approach for evaluating banking performance based on Balanced Scorecard.” Expert Systems with Applications, 36(6), 10135-10147.

11. Cebeci, U. (2009). “Fuzzy AHP-based decision support system for selecting ERP systems in textile industry by using balanced scorecard.” Expert Systems with Applications, 36(5), 8900-8909.

12. Wu, H. Y., Lin, Y. K., and Chang, C. H. (2011). “Performance evaluation of extension education centers in universities based on the balanced scorecard.” Evaluation and Program Planning, 34(1), 37-50.

13. Tjader, Y., May, J. H., Shang, J., Vargas, L. G., and Gao, N. (2013). “Firm-Level Outsourcing Decision Making: A Balanced Scorecard-Based Analytic Network Process Model.” International Journal of Production Economics.

14. Zareinejad, M. and Hojjati, S. M. H. (2013). “Application of Integrated Concept of IFAHP and FSIR in Balance Score Card for Evaluating Performance of Information Technology Department of Bank Systems”. Journal of Industrial Engineering, University of Tehran, 47(2), 183-200.

15. Wong-On-Wing, B., Guo, L., Li, W., and Yang, D. (2007). “Reducing conflict in balanced scorecard evaluations.” Accounting, Organizations and Society, 32(4), 363-377.

16. Razmi, J., Rabbani, M., Jolai, F., BeygVerdi, M., and Ezati, B. (2008). “Implementing Fuzzy set coverage approach for proper strategy in balanced evaluation approach.” Sharif Journal, 49(1), 65-72.

17. Dağdeviren, M. and Yüksel, İ. (2010). “A fuzzy analytic network process (ANP) model for measurement of the sectoral competititon level (SCL).” Expert systems with applications, 37(2), 1005-1014.

18. Kahraman, C., Ertay, T., and G. Buyukozkan, (2006). “A fuzzy optimization model for QFD planning processusing analytic network approach.” European Journal of Operational Research, 171, 411-390.

19. Dağdeviren, M. and Yüksel, İ. (2010). “A fuzzy analytic network process (ANP) model for measurement of the sectoral competititon level (SCL).” Expert Systems with Applications, 37(2), 1005-1014.