An Improved Performance Measurement Approach for Knowledge-Based Companies Using Kalman Filter Forecasting Method

Abstract

Performance measurement and forecasting are crucial for effective management of innovative projects in emerging knowledge-based companies. This study proposes an integrated performance assessment and forecasting model based on a combination of earned schedule methodology and the learning curve theory under risk condition. The operational performance is measured in terms of time and cost at completion indicators. As a novelty, the learning effects and Kalman filter forecasting method are employed to accurately estimate the future performance of the company. Furthermore, in order to predict the cost performance accurately, a logistic growth model is utilized. The validity of this integrated performance measurement model is demonstrated based on a case study. The computational results confirmed that the developed performance measurement framework provides, on average, more accurate forecast in terms of mean and standard deviation of the forecasting error for the future performance as against the traditional deterministic performance measurement methods.