The balanced scorecard (BSC) is a performance management system that supplements traditional financial measures with the criteria that measure the performance from different perspectives. For strategists, they need to predict the KPIs future values to make good decisions during the design of BSC and the determination of the suitable target for each objective and KPI. From historical data, the dependency between KPIs can be discovered through developing traditional prediction model. Hence, the KPI future values can be predicted. However, such prediction does not consider the nature of KPIs in the BSC. The historical values of KPIs depend on the previously settled targets for objectives and KPIs. This raises the challenge of finding a solution to make more accurate prediction that considers the real values of KPIs beside the previous settled targets. For achieving that, we propose a solution that uses fuzzy logic to categorize the KPI values and then predict the future KPI values. Then, we develop a third predictor model as a data fusion module to predict the KPI values depending on both previous values and category predictors. We find that the prediction accuracy of our proposed solution significantly overcomes the normal values prediction of KPIs.

Data Mining Balanced Scorecard Key Performance Indicators Association Rule Fuzzy Data Fusion Decision Tree and KPI Prediction