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

机译：基于卡尔曼滤波预测方法的知识型公司绩效评估的改进方法

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摘要

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.
机译：绩效衡量和预测对于有效管理新兴知识型公司中的创新项目至关重要。这项研究提出了一个综合的绩效评估和预测模型，该模型结合了在风险情况下的进度计划方法和学习曲线理论。运营绩效是根据时间和完成指标上的成本来衡量的。作为一种新颖性，采用学习效果和卡尔曼滤波器预测方法来准确地估计公司的未来绩效。此外，为了准确地预测成本绩效，使用了物流增长模型。基于案例研究证明了这种综合绩效评估模型的有效性。计算结果证实，相对于传统的确定性绩效衡量方法，相对于传统的确定性绩效衡量方法，所开发的绩效衡量框架在平均水平和预测误差的标准偏差方面提供了更准确的预测。

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