Measuring Time: Review

Book review

In the latest edition of the Measurable News (edition 2, 2017), someone wrote a review on my first book "Measuring Time". At the time of writing, following by the award in Rome in 2008, I didn't know that this book would literally change my professional life and would lead to many more books around similar topics (visit the bookstore), but the review in the Measurable News (about a special topic, i.e. 50 years of EVM) came as a happy surprise. 

I'm grateful to the the author (don't know who, but have a vague idea who it might be) who wrote at the beginning of the article "This book (Measuring time), and Mario’s other book, Integrated Project Management and Control, should be in the top ten list for the Earned Value Management professional" and concluded the summary with "With this book, the EVM professional has access to tools and techniques to increase the probability of project success". Nice comments! :-).

Read here the complete review below or download the edition here:

Measuring Time: Improving Project Performance Using Earned Value, Mario Vanhoucke, Springer International Series in Operations Research and Management Science, 2009.

This book, and Mario’s other book, Integrated Project Management and Control, should be in the top ten list for the Earned Value Management professional. The book deals with project performance and control processes of the project lifecycle. It is a detailed investigation of time performance measurement methods and risk analysis techniques. An evaluation of existing and new methods are examined in terms of their abilities to improve the corrective actions needed for decision making processes during project performance management.

Individual chapters start with simulation studies for forecast accuracy, schedule adherence and time sensitivity, and approaches to top-down and bottom-up progress tracking. The book provides a case study, a tutorial on the use of the ProTrack software developed from Vanhoucke’s research in Earned Value Management, and conclusions of the e ectiveness for each technique.

While Earned Value Management has been around for 50 years, in recent years there is focus on compliance rather than program performance management. IPMRs produced on month end and sent to the customer, contain cost information and the attached Integrated Master Schedule. Rarely is this information risk adjusted, connected to the Technical Performance Measures, Measures of E ectiveness, and Measures of Performance of the deliverables from the work produced by the project.
This book shows how to make those connections as well as conduct the analysis to make informed decisions needed to take corrective actions to Keep the Program Green.

Traditional Earned Value Management can provide Estimates to Complete (ETC) and Estimates at Completion (EAC) based on project assumptions. These estimates usually don’t take into account the uncertainties that exist in all projects. These uncertainties are of two types – (1) reducible uncertainties (Epistemic) and (2) irreducible uncertainties (Aleatory).

Reducible uncertainty and the reducible risk it creates is handled with explicit intervention activities that are contained in the baseline. Work to buy down the risk resulting from these uncertainties is contained in work packages and paid for from the PMB’s budget. Irreducible uncertainty and the risk it creates is handled with margin (schedule, performance, and/or cost margin). These margins are part of program baselines.

The book describes how performance indicators for predicting the total project duration
can be applied to increase the probability of project success. These indicators have been developed from research studies used to validate the Earned Value Management methods to forecast the total duration of the project.

Typically Earned Value Management is the domain of Program Planning and Controls. This
is usually relegated, with little interest in the larger problem of managing and delivering
the project’s technical outcomes. It’s typically a reporting role on the project along with
the compliance role in our EIA-748-C domain. Little has been done to critically analyze the behavior of Earned Value Management processes – calculations and the data – for a wide set of diverse projects and programs from their activity networks.

This book takes an academic approach and tests the behavior of EVM metrics on a large set of synthetic data rather than a small set of actual data. Some might suggest this data is not real, but with this data, actual project data can be matched to assess the e ectiveness of the metric in ways not possible with a selected set of small projects. The risk with a small set of data is not only cherry picking the projects needed to support a hypothesis, but a statistical limitation when drawing conclusions applicable across the a broad set of projects.

There are eight chapters that guide the reader through the principles and processes of decision making using Earned Value Management in the presence of uncertainty.

  • Chapter 1. Provides an overview of common terms. Here the notion of Earned Schedule (ES) is introduced. For those not familiar with Earned Schedule, this books is a good starting point to understand that the raw data used by ES is the same
as the raw data used by EVM. The result is a forecast of project performance in terms of schedule adherence not found in EVM without looking at the Integrated Master Schedule. This is one of the dark secrets of EVM. What does it mean to have an unfavorable Schedule Variance of $250,000? This depends on the burn rate of the project. It could be many weeks or months, or in my experience in a nuclear decommissioning program, a few minutes.
  • Chapter 2. Reviews the p-factor assessment metric used to measure schedule adherence based on raw data from the Earned Value Management processes. This p-factor provides detection methods for project impediments for the portion of work performed under risk, based again on raw data from EVM measures. A discussion of how the p-factor can be modi ed to improve the accuracy of forecasts is provided.
  • Chapter 3. Three actual project case studies are shown from Mario’s work at Fabricom Airport Systems.
  • Chapter 4. Reviews and evaluations of Earned Value Management methods using Monte Carlo Simulation are presented. While our DID 81861A calls out Schedule Risk
  • Analysis (SRA), it does not specify the method used for this analysis. This chapter details the processes and the care needed to de ne and model the network of activities to produce a credible forecast of future performance based on the behavior of individual tasks and their past performance.
  • Chapter 5. The relation between forecast accuracy and project sensitivity is reviewed in detail. The ability of activity sensitivity information to improve the tracking processes of the project is addressed along with the needed corrective actions to address performance problems or respond to opportunities.
  • Chapter 6. A simulation study is provided from the data presented in the previous chapters. This validates the tracking methods with the EVM comparing a bottom-up approach and a top-down approach.
  • Chapter 7. The tool ProTrack is presented here.
  • Chapter 8. Sums up the chapters and reviews the results from the four simulation studies from the point of view of project performance tracking. The conclusion shows the di erences between top-down and bottom-up processes that provide credible performance forecasting needed to make informed decisions in the presence of uncertainty.

The book shows that Earned Value Management and the Schedule Risk Analysis resulting from the Earned Schedule techniques are two complementary techniques, using the same raw data.
With this book, the EVM professional has access to tools and techniques to increase the probability of project success.