Improving the Forecasting Process in Project Control

Improving the Forecasting Process in Project Control

Franco Caron (Politecnico di Milano, Italy)
Copyright: © 2014 |Pages: 9
DOI: 10.4018/978-1-4666-5202-6.ch108
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Abstract

In the project control process, the role of the ETC (Estimate to Complete) is critical, since, given a feed forward control loop, the only way to influence the overall project performance is to take actions affecting the work remaining. The forecasting accuracy related to ETC is linked to the ability of the project team to exploit all the knowledge available in order to anticipate the future development of the project. According to the classification of the knowledge sources it is possible to identify three different approaches to determining the ETC: (1) using data records related to the work completed by highlighting possible trends (2) adjusting the trend stemming from data records using experts' judgment and (3) integrating the internal view of the project, i.e. data records related to the work completed and experts' judgment related to work remaining, with data records deriving from similar projects completed in the past. A Bayesian approach may represent a possible way of integrating the different knowledge sources.
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Introduction

Among the typical project management processes, forecasting plays a decisive role in reducing a project’s uncertainty. Since uncertainty arises from a lack of knowledge, it is strictly linked to the inability of the project team to exploit all the available knowledge in order to anticipate the future development of the project (Williams & Samset, 2010; Williams et al., 2009).

In fact, planning represents a forecasting exercise (Soderholm, 2008). Developing a project plan is strictly related to making assumptions about the near and long term future, since the project plan reflects a summary of the assumptions taken about the future (Dvir & Lechler, 2004). Planning and forecasting are interrelated since they allow us, for instance, to fix milestones, e.g. critical dates for the project’s stakeholders, in order to coordinate material/services delivery, provided that forecasting lead times are consistent with delivery lead times. In particular, planning is necessary for each supplier in order to deliver the required contribution to the project in a timely way (Kleim & Ludin, 1998).

Forecasting capability remains at the heart of project control. At a specific Time-Now (TN), a part of the work is completed (WC) and a part of the work, the work remaining (WR), still has to be done. Based on the Earned Value Management System (EVMS) (Fleming, 1992), the two components of the estimate at completion (EAC) are given by the Actual Cost AC of the WC and the Estimate To Complete (ETC) concerning the Work Remaining (WR). Analogous considerations may be applied to the estimate of Time at Completion (TAC). It should be noted that in the project control process the role of ETC is critical, since the only way to influence the overall project performance is to take actions affecting the WR. The information drawn from the ETC may highlight the possible need for corrective actions that may adjust the project plan (Anbari, 2003; Christensen, 1996). This approach corresponds to a feed-forward type control loop (see Figure 1).

Figure 1.

Estimation at completion at Time Now (internal view)

As a consequence, during the project control process, the project manager plays a twofold role: the “historian,” attempting to grasp the drivers that have determined the past evolution of the project, and the “wizard,” attempting to grasp the future evolution of the project and to exploit all the lessons learned from the past (Makridakis & Taleb, 2009; Makridakis et al., 2009).

This chapter will address the question of identifying the possible knowledge sources and integrate their different contribution in order to improve the forecasting capability during the project control process.

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Background

As Project Management Institute (2008) stated, the main processes involved in project management are: initiating, planning, executing, monitoring, controlling and closing. In particular, Earned Value Management (EVM) represents an effective way of addressing the project control process. EVM is an efficient performance measurement and reporting technique for estimating cost and time at completion (PMI, 2011; Marshall et al., 2008). The following basic parameters are used in EVM, where TN indicates Time Now, i.e. the time along the project life cycle at which the control process is implemented:

  • Planned Value (PV): The budget cost of work scheduled at TN;

  • Earned Value (EV): The budget cost of work completed at TN;

  • Actual Cost (AC): The actual cost of work completed at TN.

Key Terms in this Chapter

Trend Analysis: Linear extrapolation to the work remaining of the data records related to indices of project performance, e.g. productivity, during the execution of the work completed in order to estimate cost and time to complete the project.

Earned Value Management System (EVMS): Management system aiming at an integrated control of project cost and schedule based on the concept of earned value i.e. budget cost of work performed at Time Now.

Project Risk: Uncertain event that may have a positive/negative impact on the project.

Monte Carlo Simulation: “What if” analysis of the future project scenarios, provided a mathematical/ logical model of the project implemented on a computer.

Subjective Probability: The degree of belief in the occurrence of an event, by a given person at a given time and with a given set of information.

Bayes Theorem: Represents a rigorous approach to update a prior distribution, which expresses the experts’ preliminary opinion, through the experimental data gathered on the field.

Project Control: Project management process aiming at identifying and implementing possible corrective actions based on the expected performance of the project.

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