ProjectFlightDeck

Paulo became aware of Earned Schedule in 2007 through course work on his master’s degree. With expertise in technical translation, he translated some of Walt Lipke’s articles and both of his books into Portuguese.

Paulo published articles and gave presentations on Earned Schedule. Some Brazilian companies began using ES, but adoption has been slow. Once use of EVM increases, ES adoption will increase.

Paulo developed Abacus, an Excel spreadsheet, to serve as a demo and instructional tool for ES. It’s available for download from Walt’s Earned Schedule website.

Paulo then developed an executive project reporting service through his company Techisa Abacus. It features an Excel/VBA application that performs ES analysis and presents results in tabular and graphical form.

Paulo conducts research on ES. He’s pursuing a Ph.D. with Prof. Mario Vanhoucke at Ghent University. Paulo’s research focuses on determining the reliability of a project’s forecasted completion. He’s exploring whether or not the shape of the Performance Measurement Baseline (PMB) affects estimate reliability.

Paulo proposed a reliability categorizer based on the PMB shape. First, he created a measure of schedule topology: the degree to which the schedule is serial (S) or parallel (P).

Next, he introduced Batselier and Vanchoucke’s Regularity Indicator (RI). Projects with higher RI yield better forecast reliability than other projects. And, they are more accurate than the SP indicator.

Paulo combined SP and RI to produce a third categorizer, the PMB Limits Categorizer (PLC). Rather than taking a theoretical approach, he pursued an empirical one. He used empirical data and statistical analysis techniques to identify limits for the PMB curves.

Regular (R) PMB curves were contained within Inner Limits. Irregular (I) PMB curves fell outside the Outer Limits. All other curves were called Medium-regular (M). The position of the PMB curve within the limits was determined by its deviation from a central line.

Paulo used the Batselier and Vanhoucke repository of real project data for his research. He extracted 100 projects out of the 133 projects in the data base, using selection criteria developed by Vanhoucke and Martens. He normalized the data and used statistical analysis to identify the central line. From this, he constructed the limits and moved to categorize projects.

Paulo used real data to test the approach. The average curve for the data had an unexpected shape. Rather than an S-curve, it approximated a straight line (with some irregularities). Construction projects constituted 75% of the sample, and they are known to be mostly serial. That accounted for the shape of the curve. And, they became the focus of his additional research.

Paulo applied statistical techniques to smooth out irregularities in the construction data. The smoothed line was the basis for the limits. They are placed at a fixed distance from the central line. The distance varies depending on the data, and a single parameter is used to control variation in the calculations.

Given the limits, Paulo partitioned the sample projects into R and I categories. Further statistical analysis produced a measure of “forecasting goodness”.

Applied to the sample, the categorizers were ranked. PLC showed the best results in categories balance, clustering quality, and correlation. Also, for the R category, the PLC showed the best mean absolute percentage error.

Conclusions: PLC is superior to SP and RI in the study. It best fits construction projects and small projects rather than large ones.

For future research: validate PLC for megaprojects, sectorize the central line and limits (once more data is available), and create guidelines for using PLC in project planning.

Keith has a 30-year career in project management and control. He has broad experience on large-scale projects and programs. In his current role at NASA, he is the lead scheduler on the Artemis project.

The Artemis project is well-known, given its recent successful launch. A brief video gave an overview of the project. Its immediate mission is to again set foot on the Moon—this time, on the southern pole. Its ultimate goal is Mars.

The Artemis rocket is the only one that’s capable of sending people into deep space. The rocket generates 39 million newtons of thrust. [I believe that’s about 9 million pounds. By comparison, the Saturn V, the retired moon-shot rocket, had about 7.7 million pounds of thrust.]

There are six variations of the Artemis rocket. Planning is underway for all of the variants. The size of the planning effort is formidable: 150K activities at the summary level! The plan is broken up by components.

A second video explained the mission further. It was summed up by a phrase that echoes Apollo 11:

"We came in peace. We return for all humanity.”

Keith then described his role as lead scheduler for the Space Launch System (SLS). He oversaw the summary level schedule. Keith noted that the schedule did not include contractor details.

Keith’s company provides over 200 controls specialists to NASA. In his role, Keith brought to bear his experience in large construction projects. Through one of those projects, he had contact with SpaceX. Helping SpaceX plan the testing of their project led to his work for NASA.

Keith then addressed Earned Schedule’s fit into the overall controls paradigm on Artemis.The prime contractor on the Critical Path reported the BCWS and BCWP in labour hours, not dollars. The numbers did not match the contracted schedule.

Keith’s team tracked ES week by week. They used ES predictive metrics. And, they fed the ES metrics into Monte Carlo analysis for comparative estimates. They found the ES numbers to be highly accurate.

Keith noted that the difference between accounting and project control/engineering: it’s the difference between an historian and a fortune teller. One lives in the past, the other in the future.

Keith finds the math behind ES simple, but he says that it still yields good information. He sees the current use of ES as a test bed for other projects, as it offers a model of practice and is supported by tools and techniques.

For the future, Keith sees a need to move beyond the hard skills of scheduling to the soft skills of working with teams and communicating results.

Kym recounted his history with Earned Schedule.

Kym first became aware of ES through the Measurable News. He read Walt Lipke’s 2003 article, “Schedule Is Different”, and noticed that there was information missing. (Later, a corrected version was published.)

Kym contacted Walt, requesting the missing pieces. That led to an offer to test the theory on his project archive. Kym went on to publish early confirmation of the theory. While doing so, he criticized existing measures as “algebraically flawed”. That, he speculated, might have inspired early debate on ES.

Despite the debate (or, perhaps, because of it), adoption of ES was quick: in 2005, PMI-CPM identified ES as an emerging practice. Since then, ES has been featured in the following:

• a PMI EVM Practice Standard
• an NDIA-IPMD Guide
• an ISO EVM Standard
• an Australian National Standard
• an ISO Implementation Guide

Earned Schedule is used in government programs, studied by academics, supported by tools, translated into multiple languages, and made available for use and distribution without charge.

Over time, it seems that the debate on ES credibility has shifted to a debate on whether it is an “add on” to EVM or simply part of EVM. 

Why has Earned Schedule been successful? Kym’s view is that first, and foremost, it works!

• It requires no additional data collection beyond Planned Value and Earned Value.
• It tells the likely finish date for a project.
• It can be used at different skill levels. It offers simple metrics for past performance and likely future outcome and advanced metrics for project recovery, schedule adherence, and statistical analysis.
• ES still ties back to EVM basics, closely aligning with familiar EVM terminology.
• Finally, ES enjoys empirical validation, both by practitioners and academics.

Kym concluded by commenting on Walt Lipke.

He applauded Walt’s commitment to placing ES in the public domain, rather than making it proprietary.

Kym believes that ES has not been oversold, as has happened with other techniques. It has relied on control-professionals to make their own decisions on adoption. And, it’s been responsive to their comments and critiques.

It was Walt’s “brilliant insight” into the relation between PV, EV, and time that has powered ES.

To Kym, it’s been a “wild ride” that has benefited him personally. He’s proud of his role in ES and its achievements.