مقایسه روش‌های مختلف پیش‌بینی مدت زمان اجرای پروژه با استفاده از شاخص‌های ارزش کسب شده

Earned value project management is a well-known management system that integrates cost, schedule and technical performance. It allows the calculation of cost and schedule variances and performance indices and forecasts of project cost and schedule duration. The earned value method provides early indications of project performance to highlight the need for eventual corrective action. Earned value management was originally developed for cost management and has not widely been used for forecasting project duration. However, recent research trends show an increase of interest to use performance indicators for predicting total project duration. In this paper, we give an overview of the state-of-the-art knowledge for this new research trend to bring clarity in the often confusing terminology. The purpose of this paper is 3-fold. First, we compare the classic earned value performance indicators SV and SPI with the newly developed earned schedule performance indicators SV(t) and SPI(t). Next, we present a generic schedule forecasting formula applicable in different project situations and compare the three methods from literature to forecast total project duration. Finally, we illustrate the use of each method on a simple one activity example project and on real-life project data.

Earned Value Management (EVM) is a methodology used to measure and communicate the real physical progress of a project and to integrate the three critical elements of project management (scope, time and cost management). It takes into account the work complete, the time taken and the costs incurred to complete the project and it helps to evaluate and control project risk by measuring project progress in monetary terms. The basic principles and the use in practice have been comprehensively described in many sources (for an overview, see, e.g. [1] or [2]). Although EVM has been setup to follow-up both time and cost, the majority of the research has been focused on the cost aspect (see, e.g. the paper written by Fleming and Koppelman [3] who discuss earned value management from a price-tag point-of-view). Nevertheless, earned value management provides two well-known schedule performance indices, the schedule variance (SV) and the schedule performance index (SPI), to measure project progress. The SV is the difference between the earned value (EV) and the planned value (PV), i.e. SV = EV − PV (for a graphical presentation, see Fig. 1). Note that the PV is often denoted as the BCWS (Budgeted Cost for Work Scheduled) and the EV as the BCWP (Budgeted Cost Work Performed). The SV measures a volume of work done (i.e. earned) versus a volume of work planned. However, the SV does not measure time but is expressed in a monetary unit. If SV < 0, a lower volume of work has been earned as planned, and the work is behind plan. If SV > 0, a higher volume of work has been earned as planned, and the work is ahead of plan. If SV = 0, the earned work is exactly as planned. At the end of a project, the EV = PV = BAC (budget at completion), and hence, the SV always equals 0. The SPI is the ratio between the earned value and the planned value, i.e. SPI = EV/PV, and is a dimensionless indicator to measure the efficiency of the work. If SPI < 1 (=1, >1), the schedule efficiency is lower than (equal to, higher than) planned. At the end of a project, the SPI is always equal to 1.

In this paper, we compared three different project duration methods using earned value metrics and evaluate them on fictive and real-life project data. We presented a generic formula to forecast the duration of a project and linked them to different project situations. Each method can be further sub-divided into three different forecasting models as a function of the project situation. We applied each method on a fictive single-activity project with linear and non-linear increasing periodic values reflecting the absence or presence of learning curves as well as three real-life project from Fabricom Airport Systems, Belgium. We summarized the often confusing terminology of the different methods in two tables. The results show a similar forecasting accuracy for each method in the linear planned value case. However, the introduction of learning curves, which is much more realistic in the project world, results in a different forecasting accuracy for the three methods. The three real-life projects reveal that the earned schedule method was the only method which showed satisfying and reliable results during the whole project duration. Consequently, the results confirm the previously found results that the results obtained by the planned value rate and the earned duration method are unreliable at the end of the project. Instead, the earned schedule method seems to provide valid and reliable results along the project’s lifespan. As a conclusion, we believe that the use the planned value method, the earned duration method or the earned schedule method depending on the need and knowledge of the project manager might lead to similar results for project monitoring in the early and middle stages. However, we recommend to shift to the earned schedule method for monitoring project progress at the final stage of the project. Moreover, we recommend to use these schedule forecasting methods at least at the cost account level or at higher levels of the work breakdown structure. This is contradictory to the statements given by Jacob [6] who argues that the schedule forecast metrics should only be used at the level of the activity. Although we recognize that, at higher WBS levels, effects (delays) of non-performing activities can be neutralized by well performing activities (ahead of schedule), which might result in masking potential problems, we believe that this is the only approach that can be taken by practitioners. Indeed, the earned value metrics are set-up as early warning signals to detect in an easy and efficient way (i.e. at the cost account level, or even higher), rather than a simple replacement of the critical-path based scheduling tools. This early warning signal, if analyzed properly, defines the need to eventually “drill-down” into lower WBS-levels. In conjunction with the project schedule, it allows to take corrective actions on those activities which are in trouble (especially those tasks which are on the critical path). Our forecasting results on the three real-life projects demonstrate that forecasting project duration with earned value metrics at higher WBS levels provides reliable early warning signals.