Quantification of Balanced Scorecard Using Crisp and Fuzzy Multi Attribute Decision Making: Application to Banking

Abstract

This study proposes a methodology for quantification of Balanced Scorecard (BSC) for performance evaluation of banks in India using crisp and Fuzzy Multi Attribute Decision Making (FMADM). The four perspectives of balanced scorecard, also known as, performance indicators have been designed through the expert opinion. This performance indicator of a perspective assesses the performance of that particular indicator only and hence we do not get a holistic view of the overall organization performance. In order to get the holistic view of the organization's overall performance in terms of a unified number, we need to combine all the performance indicators of the BSC. So, we applied crisp methods like Technique for Order Preference by Similarity to the Ideal Solutions (TOPSIS) and a modified FMADM. We applied these methods to e-commerce industry data and to a real life Indian public sector bank data. The results of the methods are compared. The proposed FMADM model can benefit the banking sector in assessing and enhancing the business performance of banks, making it highly useful for bank's top management.