Using fuzzy logic to quantify cause-and-effect relationships in the balanced scorecard: A case study at a car financing company
This study examines the quantification of strengths of influences between performance measures in the Balanced Scorecard (BSC). Starting from the hypothesis that many companies do not have enough historical data about performance measures to perform a statistical analysis, especially if these measures did change over time, I present a methodology that translates a qualitative description of relationships between BSC measures into a quantitative expression by using a fuzzy logic based inference system. I test the methodology in a case study at an Australian car financing company. My findings indicate that the approach is very suitable for quantifying influences within the BSC. Retrieved knowledge could be incorporated into a tool for senior managers, which allows the evaluation of different strategies by providing projections of resulting changes in performance measures. I also find that executing the proposed approach already helps in communicating the strategy and creating awareness of the effects of one’s own work. However, high attention has to be paid to the careful and complete qualitative formulation of the relationships between BSC measures, especially with respect to company politics and personal opinions.