Understanding key performance indicators through driver measures

Abstract

Performance management is a vital part of every organization for its success and long term survival in the current competitive global market place. An organization needs a sound performance management framework to analyze its data to make strategic and tactical decisions. The weaknesses in today's performance management frameworks are linked to their inability to integrate strategy formulation with implementation. In our thesis, we introduce D river Measure Models that can define cause-and-effect relationship between d river measures and Key Performance Indicators (KPIs) for effective performance management and strategic alignment. Driver Measure Models make the performance management more dynamic as the operational activities are linked to strategies. Another contribution of the thesis is the identification of mathematical techniques to quantify relationships between KPIs and driver measures. Thesis makes an effort to show how mathematical techniques can be used for planning and forecasting outcomes while changing strategies. After conducting analysis using the mathematical techniques, organization can arrive at a set of operational tasks associated to driver measures which need to be executed to achieve its optimal result. Finally, we identified the essential set of criteria that a performance management framework needs to address through a literature survey and a gap analysis of existing frameworks. We created an extension to the Balanced Scorecard framework based on Driver Measure Models and support for the management of external factors to address these criteria and compared it to existing frameworks using a case study.