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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 4831867, 15 pages
Research Article

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

Department of Industrial & Systems Engineering, Isfahan University of Technology, Isfahan, Iran

Received 3 July 2016; Accepted 25 August 2016

Academic Editor: Alessandro Lo Schiavo

Copyright © 2016 Mohammad Reza Hasanzadeh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.