For more than 40 years the discipline of earned value management (EVM) has gone through a number of changes in its descriptions, governance, and procedures. During that same time its community has been resistant to improvements in its methodology or to changes that extend its value when taking into account other methods that either augment its usefulness, or that potentially provide more utility in the area of performance management. This has been especially the case where it is suggested that EVM is just one of many methodologies that contribute to this assessment under a more holistic approach.
Instead, it has been asserted that EVM is the basis for integrated project management. (I disagree–and solely on the evidence that if it was so, then project managers would more fully participate in its organizations and conferences. This would then pose the problem that PMs might then propose changes to EVM that, well…default to the second sentence in this post). As evidence it need only be mentioned that there has been resistance to such recent developments in using earned schedule, technical performance, and risk–most especially risk based on Bayesian analysis).
Some of this resistance is understandable. First, it took quite a long time just to get to a consensus on the application of EVM, though its principles and methods are based on simple and well proven statistical methods. Second, the industries in which EVM has been accepted are sensitive to risk, and so a bureaucracy of practitioners have grown to ensure both consensus and compliance to accepted methods. Third, the community that makes up practitioners of EVM consist mostly of cost analysts, trained in simple accounting, arithmetic, and statistical methodology. It is thus a normal human bias to assume that the path of one’s previous success is the way to future success, though our understanding of the design space (reality) that we inhabit has been enhanced through new knowledge. Fourth, there is a lot of data that applies to project management, and the EVM community is only now learning of the ways that this other data impacts our understanding of measuring project performance and the probability of reaching project goals in rolling out a product. Finally, there is the less defensible reason that a lot of people and firms have built their careers that depends on maintaining the status quo.
Our ability to integrate disparate datasets is accelerating on a yearly basis thanks to digital technology, and the day in achieving integration of all relevant factors in project and enterprise performance is inevitable. To be frank, I am personally engaged in such projects and am assisting organizations in moving in this direction today. Regardless, we can make an advance in the discipline of performance management by pulling down low hanging fruit. The most reachable one, in my opinion, is technical performance measurement.
The literature of technical performance has come quite a long way, thanks largely to the work of the Institute for Defense Analyses (IDA) and others, particularly the National Defense Industrial Association through the publication of their predictive measures guide. This has been a topic of interest to me since its study was part of my duties back when I was still wearing a uniform. The early results of these studies resulted in a paper that proposed a method of integrating technical performance, earned value, and risk. A pretty comprehensive overview of the literature and guidance for technical performance can be found at this presentation by Glen Alleman and Tom Coonce given at EVM World in 2015. It must be mentioned that Rick Price of Lockheed Martin also contributed greatly to this literature.
Keep in mind what is meant when we decide to assess technical performance within the context of R&D. It is an assessment against expected or specified:
a. Measures of Effectiveness (MoE)
b. Measures of Performance (MoP), and
c. Key Performance Parameters (KPP)
The opposition from the project management community to widespread application of this methodology took two forms. First, it was argued, the method used to adjust the value of earned (CPI) seemed always to have a negative impact. Second, there are technical performance factors that transcend the WBS, and so it is hard to properly adjust the individual control accounts based on the contribution of technical performance. Third, some performance measures defy an assessment of value in a time-phased manner. The most common example has been tracking weight of aircraft, which has contributors from virtually all components that go into it.
Let’s take these in order. But lest one think that this perspective is an artifact from 1997, just a short while ago, in the A&D community, the EVM policy office at DoD attempted to apply a somewhat modest proposal of ensuring that technical performance was included as an element in EVM reporting. Note that the EIA 748 standard states this clearly and has done so for quite some time. Regardless, the same three core objections were raised in comments from the industry. Thus, this caused me to ask some further in-depth questions and my revised perspective follows below.
The first condition occurred, in many cases, due to optimism bias in registering earned value, which often occurs when using a single point estimate of percent complete by a limited population of experts contributing to an assessment of the element. Fair enough, but as you can imagine, its not a message that a PM wants to hear or will necessarily accept or admit, regardless of the merits. There are more than enough pathways to second guessing and testing selection bias at other levels of reporting. Glen Alleman in his Herding Cats blog post of 12 August has a very good post listing the systemic reasons for program failure.
Another factor is that the initial methodology did possess a skewing toward more pessimistic results. This was not entirely apparent at the time because the statistical methods applied did not make that clear. But, to critique that first proposal, which was the result of contributions from IDA and other systems engineering technical experts, the 10-50-90 method in assessing probability along the bandwidth of the technical performance baseline was too inflexible. The graphic that we proposed is as follows and one can see that, while it was “good enough”, if rolled up there could be some bias that required adjustment.
Note that this range around 50% can be interpreted to be equivalent to the bandwidth found in the presentation given by Alleman and Coonce (as well as the Predictive Measures Guide), though the intent here was to perform an assessment based on a simplified means of handicapping the handicappers–or more accurately, performing a probabilistic assessment on expert opinion. The method of performing Bayesian analysis to achieve this had not yet matured for such applications, and so we proposed a method that would provide a simple method that our practitioners could understand that still met the criteria of being a valid approach. The reason for the difference in the graphic resides in the fact that the original assessment did not view this time-phasing as a continuous process, but rather an assessment at critical points along the technical baseline.
From a practical perspective, however, the banding proposed by Alleman and Coonce take into account the noise that will be experienced during the life cycle of development, and so solves the slight skewing toward pessimism. We’ll leave aside for the moment how we determine the bands and, thus, acceptable noise as we track along our technical baseline.
The second objection is valid only so far as any alignment of work-related indicators vary from project to project. For example, some legs of the WBS tree go down nine levels and others go down five levels, based on the complexity of the work and the organizational breakdown structure (OBS). Thus where we peg within each leg of the tree the control account (CA) and work package (WP) level becomes relative. Do the schedule activities have a one-to-one relationship or many-to-one relationship with the WP level in all legs? Or is the lowest level that the alignment can be made in certain legs at the CA level?
Given that planning begins with the contract spec and (ideally) proceed from IMP –> IMS –> WBS –> PMB in a continuity, then we will be able to determine the contributions of TPM to each WBS element at their appropriate level.
This then leads us to another objection, which is that not all organizations bother with developing an IMP. That is a topic for another day, but whether such an artifact is created formally or not, one must achieve in practice the purpose of the IMP in order to get from contract spec to IMS under a sufficiently complex effort to warrant CPM scheduling and EVM.
The third objection is really a child of the second objection. There very well may be TPMs, such as weight, with so many contributors that distributing the impact would both dilute the visibility of the TPM and present a level of arbitrariness in distribution that would render its tracking useless. (Note that I am not saying that the impact cannot be distributed because, given modern software applications, this can easily be done in an automated fashion after configuration. My concern is in regard to visibility on a TPM that could render the program a failure). In these cases, as with other indicators that must be tracked, there will be high level programmatic or contract level TPMs.
So where do we go from here? Alleman and Coonce suggest adjusting the formula for BCWP, where P is informed by technical risk. The predictive measures guide takes a similar approach and emphasizes the systems engineering (SE) domain in getting to an assessment to determine the impact of reported EVM element performance. The recommendation of the 1997 project that I headed in assignments across Navy and OSD, was to inform performance based on a risk assessment of probable achievement at each discrete performance milestone. What all of these studies have in common, and in common with common industry practice using SE principles, is an intermediate assessment, informed by risk, of a technical performance index against a technical performance baseline.
So let’s explore this part of the equation more fully.
Given that we have MoE, MoP, and KPP are identified for the project, different methods of determining progress apply. This can be a very simplistic set of TPMs that, through the acquisition or fabrication of compliant materials, meet contractual requirements. These are contract level TPMs. Depending on contract type, achievement of these KPPs may result in either financial penalties or financial reward. Then there are the R&D-dependent MoEs, MoPs, and KPPs that require more discrete time-phasing and ties to the physical completion of work documented by through the WBS structure. As with EVM on the measurement of the value of work, our index of physical technical achievement can be determined through various methods: current EVM methods, simulated Monte Carlo technical risk, 10-50-90 risk assessment, Bayesian analysis, etc. All of these methods are designed to militate against selection bias and the inherent limitations of limited sample size and, hence, extreme subjectivity. Still, expert opinion is a valid method of assessment and (in cases where it works) better than a WAG or coin flip.
Taken together these TPMs can be used to determine the technical achievement of the project or program over time, with a financial assessment of the future work needed to bring it in line. These elements can be weighted, as suggested by Coonce, Alleman, and Price, through an assessment of relative risk to project success. Some of these TPIs will apply to particular WBS elements at various levels (since their efforts are tied to specific activities and schedules via the IMS), and the most important project and program-level TPMs are reflected at that level.
What about double counting? A comparison of the aggregate TPIs and the aggregate CPI and SPI will determine the fidelity of the WBS to technical achievement. Furthermore, a proper baseline review will ensure that double counting doesn’t occur. If the element can be accounted for within the reported EVM elements, then it need not be tracked separately by a TPI. Only those TPMs that cannot be distributed or that represent such overarching risk to project success need be tracked separately, with an overall project assessment made against MR or any reprogramming budget available that can bring the project back into spec.
My last post on project management concerned the practices at what was called Google X. There incentives are given to teams that identify an unacceptably high level of technical risk that will fail to pay off within the anticipated planning horizon. If the A&D and DoD community is to become more nimble in R&D, it needs the necessary tools to apply such long established concepts such as Cost-As-An-Independent-Variable (CAIV), and Agile methods (without falling into the bottomless pit of unsupported assertions by the cult such as elimination of estimating and performance tracking).
Even with EVM, the project and program management community needs a feel for where their major programmatic efforts are in terms of delivery and deployment, in looking at the entire logistics and life cycle system. The TPI can be the logic check of whether to push ahead, or finishing the low risk items that are remaining in R&D to move to first item delivery, or to take the lessons learned from the effort, terminate the project, and incorporate those elements into the next generation project or related components or systems. This aligns with the concept of project alignment with framing assumptions as an early indicator of continued project investment at the corporate level.
No doubt, existing information systems, many built using 1990s technology and limited to line-and-staff functionality, do not provide the ability to do this today. Of course, these same systems do not take into account a whole plethora of essential information regarding contract and financial management: from the tracking of CLINs/SLINs, to work authorization and change order processing, to the flow of funding from TAB to PMB/MR and from PMB to CA/UB/PP, contract incentive threshold planning, and the list can go on. What this argues for is innovation and rewarding those technology solutions that take a more holistic approach to project management within its domain as a subset of program, contract, and corporate management–and such solutions that do so without some esoteric promise of results at some point in the future after millions of dollars of consulting, design, and coding. The first company or organization that does this will reap the rewards of doing so.
Furthermore, visibility equals action. Diluting essential TPMs within an overarching set of performance metrics may have the effect of hiding them and failing to properly identify, classify, and handle risk. Including TPI as an element at the appropriate level will provide necessary visibility to get to the meat of those elements that directly impact programmatic framing assumptions.