Keywords: project performances, earned value, statistical control charts, data mining.

Most construction professionals agree that the earned value management (EVM) method is considered to be a powerful tool that supports the management of project scope, time, and cost. The earned value has been a part of the cost and schedule control systems for the past few decades. It is a powerful approach for quantitative measure of work performance in terms of cost deviation and schedule deviation. It also provodes a quantitative basis for estimating actual completion time and actual cost at completion. However, due to several reason, the effective use of this technique is relatively rare outside of the U.S. government and its contractors. In order to improve this situation, organizational, managerial, and technical efforts are required to achieve the goal of successful EVM.

Traditional monitoring of project performances is based on the principles of cost variance (CV) and schedule variance (SV), or the cost performance index (CPI) and the schedule performance index (SPI). Here CV and SV will be taken as explanation examples. Whether the project performance is in control is based on wether the CV and SV are positive or negative values at the report date. Generally, zero value of CV and SV indicates that the performance is on target. A positive value indicates good performance. A negative value indicates poor performance.

However, there are several disadvantages to the conventional performance monitoring techniques. First, when there are assignable causes that affect the performance trend, it cannot easily be detected by traditional EVM. Second, although project performances are plotted in a visual manner, it is difficult to replicate analysis results because different performance managers may have a different point of view on the performace trend. Finally, feedback of the impact of assignable causes on actions may not be provided quite effectively. The above-mentioned situations can obviously be improved by providing the construction industry with an efficient aid which supports the functions of continuous monitoring on the trend of project performances and effective analysis of cause and effect to provide corrective action recommendations. Statistical process control (SPC) techniques address several quality management capabilities including adverse trends and corrective action management. Based on the output of SPC, corrective measures need to be taken to prevent further non-compliance.

This paper attempts to refine and improve the performance of the traditional EVM method by the introduction of statistical control chart techniques. Individual control charts are used as a tool to monitor project performance data so that adverse changes can be detected in a timely manner. It allows analysis of the trend of project costs and the schedule in progress and highlights the possible needs for corrective action. A comparative analysis between the traditional EVM and the new approach is undertaken to identify the effectiveness of the new approach.

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