Ground Control from Major Tom — Breaking Radio Silence: New Perspectives on Project Management

Since I began this blog I have used it as a means of testing out and sharing ideas about project management, information systems, as well to cover occasional thoughts about music, the arts, and the meaning of wisdom.

My latest hiatus from writing was due to the fact that I was otherwise engaged in a different sort of writing–tech writing–and in exploring some mathematical explorations related to my chosen vocation, aside from running a business and–you know–living life.  There are only so many hours in the day.  Furthermore, when one writes over time about any one topic it seems that one tends to repeat oneself.  I needed to break that cycle so that I could concentrate on bringing something new to the table.  After all, it is not as if this blog attracts a massive audience–and purposely so.  The topics on which I write are highly specialized and the members of the community that tend to follow this blog and send comments tend to be specialized as well.  I air out thoughts here that are sometimes only vaguely conceived so that they can be further refined.

Now that that is out of the way, radio silence is ending until, well, the next contemplation or massive workload that turns into radio silence.

Over the past couple of months I’ve done quite a bit of traveling, and so have some new perspectives that and trends that I noted and would like to share, and which will be the basis (in all likelihood) of future, more in depth posts.  But here is a list that I have compiled:

a.  The time of niche analytical “tools” as acceptable solutions among forward-leaning businesses and enterprises is quickly drawing to a close.  Instead, more comprehensive solutions that integrate data across domains are taking the market and disrupting even large players that have not adapted to this new reality.  The economics are too strong to stay with the status quo.  In the past the barrier to integration of more diverse and larger sets of data was the high cost of traditional BI with its armies of data engineers and analysts providing marginal value that did not always square with the cost.  Now virtually any data can be accessed and visualized.  The best solutions, providing pre-built domain knowledge for targeted verticals, are the best and will lead and win the day.

b.  Along these same lines, apps and services designed around the bureaucratic end-of-month chart submission process are running into the new paradigm among project management leaders that this cycle is inadequate, inefficient, and ineffective.  The incentives are changing to reward actual project management in lieu of project administration.  The core fallacy of apps that provide standard charts based solely on user’s perceptions of looking at data is that they assume that the PM domain knows what it needs to see.  The new paradigm is instead to provide a range of options based on the knowledge that can be derived from data.  Thus, while the options in the new solutions provide the standard charts and reports that have always informed management, KDD (knowledge discovery in database) principles are opening up new perspectives in understanding project dynamics and behavior.

c.  Earned value is *not* the nexus of Integrated Project Management (IPM).  I’m sure many of my colleagues in the community will find this statement to be provocative, only because it is what they are thinking but have been hesitant to voice.  A big part of their hesitation is that the methodology is always under attack by those who wish to avoid accountability for program performance.  Thus, let me make a point about Earned Value Management (EVM) for clarity–it is an essential methodology in assessing project performance and the probability of meeting the constraints of the project budget.  It also contributes data essential to project predictive analytics.  What the data shows from a series of DoD studies (currently sadly unpublished), however, is that it is planning (via a Integrated Master Plan) and scheduling (via an Integrated Master Schedule) that first ties together the essential elements of the project, and will record the baking in of risk within the project.  Risk manifested in poorly tying contract requirements, technical performance measures, and milestones to the plan, and then manifested in poor execution will first be recorded in schedule (time-based) performance.  This is especially true for firms that apply resource-loading in their schedules.  By the time this risk translates and is recorded in EVM metrics, the project management team is performing risk handling and mitigation to blunt the impact on the performance management baseline (the money).  So this still raises the question: what is IPM?  I have a few ideas and will share those in other posts.

d.  Along these lines, there is a need for a Schedule (IMS) Gold Card that provides the essential basis of measurement of programmatic risk during project execution.  I am currently constructing one with collaboration and will put out a few ideas.

e.  Finally, there is still room for a lot of improvement in project management.  For all of the gurus, methodologies, consultants, body shops, and tools that are out there, according to PMI, more than a third of projects fail to meet project goals, almost half to meet budget expectations, less than half finished on time, and almost half experienced scope creep, which, I suspect, probably caused “failure” to be redefined and under-reported in their figures.  The assessment for IT projects is also consistent with this report, with CIO.com reporting that more than half of IT projects fail in terms of meeting performance, cost, and schedule goals.  From my own experience and those of my colleagues, the need to solve the standard 20-30% slippage in schedule and similar overrun in costs is an old refrain.  So too is the frustration that it need take 23 years to deploy a new aircraft.  A .5 CPI and SPI (to use EVM terminology) is not an indicator of success.  What this indicates, instead, is that there need to be some adjustments and improvements in how we do business.  The first would be to adjust incentives to encourage and reward the identification of risk in project performance.  The second is to deploy solutions that effectively access and provide information to the project team that enable them to address risk.  As with all of the points noted in this post, I have some other ideas in this area that I will share in future posts.

Onward and upward.

Rear View Mirror — Correcting a Project Management Fallacy

“The past is never dead. It’s not even past.” —  William Faulkner, Requiem for a Nun

Over the years I and others have briefed project managers on project performance using KPPs, earned value management, schedule analysis, business analytics, and what we now call predictive analytics. Oftentimes, some set of figures will be critiqued as being ineffective or unhelpful; that the analytics “only look in the rear view mirror” and that they “tell me what I already know.”

In approaching this critique, it is useful to understand Faulkner’s oft-cited quote above.  When we walk down a street, let us say it is a busy city street in any community of good size, we are walking in the past.  The moment we experience something it is in the past.  If we note the present condition of our city street we will see that for every building, park, sidewalk, and individual that we pass on that sidewalk, each has a history.  These structures and the people are as much driven by their pasts as their expectations for the future.

Now let us take a snapshot of our street.  In doing so we can determine population density, ethnic demographics, property values, crime rate, and numerous other indices and parameters regarding what is there.  No doubt, if we stop here we are just “looking in the rear view mirror” and noting what we may or may not know, however certain our anecdotal filter.

Now, let us say that we have an affinity for this street and may want to live there.  We will take the present indices and parameters that noted above, which describe our geographical environment, and trend it.  We may find that housing pricing are rising or falling, that crime is rising or falling, etc.  If we delve into the street’s ownership history we may find that one individual or family possesses more than one structure, or that there is a great deal of diversity.  We may find that a Superfund site is not too far away.  We may find that economic demographics are pointing to stagnation of the local economy, or that the neighborhood is becoming gentrified.  Just by time-phasing and delving into history–by mapping out the trends and noting the significant historical background–provides us with enough information to inform us about whether our affinity is grounded in reality or practicality.

But let us say that, despite negatives, we feel that this is the next up-and-coming neighborhood.  We would need signs to make that determination.  For example, what kinds of businesses have moved into the neighborhood and what is their number?  What demographic do they target?  There are many other questions that can be asked to see if our economic analysis is valid–and that analysis would need to be informed by risk.

The fact of the matter is that we are always living with the past: the cumulative effect of the past actions of numerous individuals, including our own, and organizations, groups of individuals, and institutions; not to mention larger economic forces well beyond our control.  Any desired change in the trajectory of the system being evaluated must identify those elements that can be impacted or influenced, and an analysis of the effort that must be expended to bring about the change, is also essential.

This is a scientific fact, proven countless times by physics, biology, and other disciplines.  A deterministic universe, which provides for some uncertainty at any given point at our level of existence, drives the possible within very small limits of possibility and even smaller limits of probability.  What this means in plain language is that the future is usually a function of the past.

Any one number or index, no doubt, does not necessarily tell us something important.  But it could if it is relevant, material, and prompts further inquiry essential to project performance.

For example, let us look at an integrated master schedule that underlies a typical medium-sized project.

 

We will select a couple of metrics that indicates project schedule performance.  In the case below we are looking at task hits and misses and Baseline Execution Index, a popular index that determines efficiency in meeting baseline schedule planning.

Note that the chart above plots the performance over time.  What will it take to improve our efficiency?  So as a quick logic check on realism, let’s take a look at the work to date with all of the late starts and finishes.

Our bow waves track the cumulative effort to date.  As we work to clear missed starts or missed finishes in a project we also must devote resources to the accomplishment of current work that is still in line with the baseline.  What this means is that additional resources may need to be devoted to particular areas of work accomplishment or risk handling.

This is not, of course, the limit to our analysis that should be undertaken.  The point here is that at every point in history in every system we stand at a point of the cumulative efforts, risk, failure, success, and actions of everyone who came before us.  At the microeconomic level this is also true within our project management systems.  There are also external constraints and influences that will define the framing assumptions and range of possibilities and probabilities involved in project outcomes.

The shear magnitude of the bow waves that we face in all endeavors will often be too great to fully overcome.  As an analogy, a bow wave in complex systems is more akin to a tsunami as opposed to the tidal waves that crash along our shores.  All of the force of all of the collective actions that have preceded present time will drive our trajectory.

This is known as inertia.

Identifying and understanding the contributors to the inertia that is driving our performance is important to knowing what to do.  Thus, looking in the rear view mirror is important and not a valid argument for ignoring an inconvenient metric that may only require additional context.  Furthermore, knowing where we sit is important and not insignificant.  Knowing the factors that put us where we are–and the effort that it will take to influence our destiny–will guide what is possible and not possible in our future actions.

Note:  All charted data is notional and is not from an actual project.

Post-Blogging NDIA Blues — The Latest News (Project Management Wonkish)

The National Defense Industrial Association’s Integrated Program Management Division (NDIA IPMD) just had its quarterly meeting here in sunny Orlando where we braved the depths of sub-60 degrees F temperatures to start out each day.

For those not in the know, these meetings are an essential coming together of policy makers, subject matter experts, and private industry practitioners regarding the practical and mundane state-of-the-practice in complex project management, particularly focused on the concerns of the the federal government and the Department of Defense.  The end result of these meetings is to publish white papers and recommendations regarding practice to support continuous process improvement and the practical application of project management practices–allowing for a cross-pollination of commercial and government lessons learned.  This is also the intersection where innovation among the large and small are given an equal vetting and an opportunity to introduce new concepts and solutions.  This is an idealized description, of course, and most of the petty personality conflicts, competition, and self-interest that plagues any group of individuals coming together under a common set of interests also plays out here.  But generally the days are long and the workshops generally produce good products that become the de facto standard of practice in the industry. Furthermore the control that keeps the more ruthless personalities in check is the fact that, while it is a large market, the complex project management community tends to be a relatively small one, which reinforces professionalism.

The “blues” in this case is not so much borne of frustration or disappointment but, instead, from the long and intense days that the sessions offer.  The biggest news from an IT project management and application perspective was twofold. The data stream used by the industry in sharing data in an open systems manner will be simplified.  The other was the announcement that the technology used to communicate will move from XML to JSON.

Human readable formatting to Data-focused formatting.  Under Kendall’s Better Buying Power 3.0 the goal of the Department of Defense (DoD) has been to incorporate better practices from private industry where they can be applied.  I don’t see initiatives for greater efficiency and reduction of duplication going away in the new Administration, regardless of what a new initiative is called.

In case this is news to you, the federal government buys a lot of materials and end items–billions of dollars worth.  Accountability must be put in place to ensure that the money is properly spent to acquire the things being purchased.  Where technology is pushed and where there are no commercial equivalents that can be bought off the shelf, as in the systems purchased by the Department of Defense, there are measures of progress and performance (given that the contract is under a specification) that are submitted to the oversight agency in DoD.  This is a lot of data and to be brutally frank the method and format of delivery has been somewhat chaotic, inefficient, and duplicative.  The Department moved to address this by a somewhat modest requirement of open systems submission of an application-neutral XML file under the standards established by the UN/CEFACT XML organization.  This was called the Integrated Program Management Report (IMPR).  This move garnered some improvement where it has been applied, but contracts are long-term, so incorporating improvements though new contractual requirements tends to take time.  Plus, there is always resistance to change.  The Department is moving to accelerate addressing these inefficiencies in their data streams by eliminating the unnecessary overhead associated with specifications of formatting data for paper forms and dealing with data as, well, data.  Great idea and bravo!  The rub here is that in making the change, the Department has proposed dropping XML as the technology used to transfer data and move to JSON.

XML to JSON. Before I spark another techie argument about the relative merits of each, there are some basics to understand here.  First, XML is a language, JSON is simply data exchange format.  This means that XML is specifically designed to deal with hierarchical and structured data that can be queried and where validation and fidelity checks within the data are inherent in the technology. Furthermore, XML is known to scale while maintaining the integrity of the data, which is intended for use in relational databases.  Furthermore, XML is hard to break.  It is meant for editing and will maintain its structure and integrity afterward.

The counter argument encountered is that JSON is new! and uses fewer characters! (which usually turns out to be inconsequential), and people are talking about it for Big Data and NoSQL! (but this happened after the fact and the reason for shoehorning it this way is discussed below).

So does it matter?  Yes and no.  As a supplier specializing in delivering solutions that normalize and rationalize data across proprietary file structures and leverage database capabilities, I don’t care.  I can adapt quickly and will have a proof-of-concept solution out within 30 days of receiving the schema.

The risk here, which applies to DoD and the industry, is that the decision to go to JSON is made only because it is the shiny new thing used by gamers and social networking developers.  There has also been a move to adapt to other uses because of the history of significant security risks that had been found in Java, so much so that an entire Wikipedia page is devoted to them.  Oracle just killed off Java applets, though Java hangs on.  JSON, of course, isn’t Java, but it was designed from birth as JavaScript Object Notation (hence the acronym JSON), with the purpose of handling relatively small bits of data across web servers in a number of proprietary settings.

To address JSON deficiencies relative to XML, a number of tools have been and are being developed to replicate the fidelity and reliability found in XML.  Whether this is sufficient to be effective against a structured LANGUAGE is to be seen.  Much of the overhead that technies complain about in XML is due to the native functionality related to the power it brings to the table.  No doubt, a bicycle is simpler than a Formula One racer–and this is an apt comparison.  Claiming “simpler” doesn’t pass the “So What?” test knowing the business processes involved.  The technology needs to be fit to the solution.  The purpose of data transmission using APIs is not only to make it easy to produce but for it to–you know–achieve the goals of normalization and rationalization so that it can be used on the receiving end which is where the consumer (which we usually consider to be the customer) sits.

At the end of the day the ability to scale and handle hierarchical, structured data will rely on the quality and strength of the schema and the tools that are published to enforce its fidelity and compliance.  Otherwise consuming organizations will be receiving a dozen different proprietary JSON files, and that does not address the present chaos but simply adds to it.  These issues were aired out during the meeting and it seems that everyone is aware of the risks and that they can be addressed.  Furthermore, as the schema is socialized across solutions providers, it will be apparent early if the technology will be able handle the project performance data resulting from the development of a high performance aircraft or a U.S. Navy destroyer.

Takin’ Care of Business — Information Economics in Project Management

Neoclassical economics abhors inefficiency, and yet inefficiencies exist.  Among the core issues that create inefficiencies is the asymmetrical nature of information.  Asymmetry is an accepted cornerstone of economics that leads to inefficiency.  We can see in our daily lives and employment the effects of one party in a transaction having more information than the other:  knowing whether the used car you are buying is a lemon, measuring risk in the purchase of an investment and, apropos to this post, identifying how our information systems allow us to manage complex projects.

Regarding this last proposition we can peel this onion down through its various levels: the asymmetry in the information between the customer and the supplier, the asymmetry in information between the board and stockholders, the asymmetry in information between management and labor, the asymmetry in information between individual SMEs and the project team, etc.–it’s elephants all the way down.

This asymmetry, which drives inefficiency, is exacerbated in markets that are dominated by monopoly, monopsony, and oligopoly power.  When informed by the work of Hart and Holmström regarding contract theory, which recently garnered the Nobel in economics, we have a basis for understanding the internal dynamics of projects in seeking efficiency and productivity.  What is interesting about contract theory is that it incorporates the concept of asymmetrical information (labeled as adverse selection), but expands this concept in human transactions at the microeconomic level to include considerations of moral hazard and the utility of signalling.

The state of asymmetry and inefficiency is exacerbated by the patchwork quilt of “tools”–software applications that are designed to address only a very restricted portion of the total contract and project management system–that are currently deployed as the state of the art.  These tend to require the insertion of a new class of SME to manage data by essentially reversing the efficiencies in automation, involving direct effort to reconcile differences in data from differing tools. This is a sub-optimized system.  It discourages optimization of information across the project, reinforces asymmetry, and is economically and practically unsustainable.

The key in all of this is ensuring that sub-optimal behavior is discouraged, and that those activities and behaviors that are supportive of more transparent sharing of information and, therefore, contribute to greater efficiency and productivity are rewarded.  It should be noted that more transparent organizations tend to be more sustainable, healthier, and with a higher degree of employee commitment.

The path forward where there is monopsony power, where there is a dominant buyer, is to impose the conditions for normative behavior that would otherwise be leveraged through practice in a more open market.  For open markets not dominated by one player as either supplier or seller, instituting practices that reward behavior that reduces the effects of asymmetrical information, and contracting disincentives in business transactions on the open market is the key.

In the information management market as a whole the trends that are working against asymmetry and inefficiency involve the reduction of data streams, the construction of cross-domain data repositories (or reservoirs) that allow for the satisfaction of multiple business stakeholders, and the introduction of systems that are more open and adaptable to the needs of the project system in lieu of a limited portion of the project team.  These solutions exist, yet their adoption is hindered because of the long-term infrastructure that is put in place in complex project management.  This infrastructure is supported by incumbents that are reinforcing to the status quo.  Because of this, from the time a market innovation is introduced to the time that it is adopted in project-focused organizations usually involves the expenditure of several years.

This argues for establishing an environment that is more nimble.  This involves the adoption of a series of approaches to achieve the goals of broader information symmetry and efficiency in the project organization.  These are:

a. Instituting contractual relationships, both internally and externally, that encourage project personnel to identify risk.  This would include incentives to kill efforts that have breached their framing assumptions, or to consolidate progress that the project has achieved to date–sending it as it is to production–while killing further effort that would breach framing assumptions.

b. Institute policy and incentives on the data supply end to reduce the number of data streams.  Toward this end both acquisition and contracting practices should move to discourage proprietary data dead ends by encouraging normalized and rationalized data schemas that describe the environment using a common or, at least, compatible lexicon.  This reduces the inefficiency derived from opaqueness as it relates to software and data.

c.  Institute policy and incentives on the data consumer end to leverage the economies derived from the increased computing power from Moore’s Law by scaling data to construct interrelated datasets across multiple domains that will provide a more cohesive and expansive view of project performance.  This involves the warehousing of data into a common repository or reduced set of repositories.  The goal is to satisfy multiple project stakeholders from multiple domains using as few streams as necessary and encourage KDD (Knowledge Discovery in Databases).  This reduces the inefficiency derived from data opaqueness, but also from the traditional line-and-staff organization that has tended to stovepipe expertise and information.

d.  Institute acquisition and market incentives that encourage software manufacturers to engage in positive signalling behavior that reduces the opaqueness of the solutions being offered to the marketplace.

In summary, the current state of project data is one that is characterized by “best-of-breed” patchwork quilt solutions that tend to increase direct labor, reduces and limits productivity, and drives up cost.  At the end of the day the ability of the project to handle risk and adapt to technical challenges rests on the reliability and efficiency of its information systems.  A patchwork system fails to meet the needs of the organization as a whole and at the end of the day is not “takin’ care of business.”

Technical Foul — It’s Time for TPI in EVM

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.

TPM Graphic

 

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.

Technical Ecstacy — Technical Performance and Earned Value

As many of my colleagues in project management know, I wrote a series of articles on the application of technical performance risk in project management back in 1997, one of which made me an award recipient from the institution now known as Defense Acquisition University.  Over the years various researchers and project organizations have asked me if I have any additional thoughts on the subject and the response up until now has been: no.  From a practical standpoint, other responsibilities took me away from the domain of determining the best way of recording technical achievement in complex projects.  Furthermore, I felt that the field was not ripe for further development until there were mathematics and statistical methods that could better approach the behavior of complex adaptive systems.

But now, after almost 20 years, there is an issue that has been nagging at me since publication of the results of the project studies that I led from 1995 through 1997.  It is this: the complaint by project managers in resisting the application of measuring technical achievement of any kind, and integrating it with cost performance, the best that anyone can do is 100%.  “All TPM can do is make my performance look worse”, was the complaint.  One would think this observation would not only not face opposition, especially from such an engineering dependent industry, but also because, at least in this universe, the best you can do is 100%.*  But, of course, we weren’t talking about the same thing and I have heard this refrain again at recent conferences and meetings.

To be honest, in our recommended solution in 1997, we did not take things as far as we could have.  It was always intended to be the first but not the last word regarding this issue.  And there have been some interesting things published about this issue recently, which I noted in this post.

In the discipline of project management in general, and among earned value practitioners in particular, the performance being measured oftentimes exceeds 100%.  But there is the difference.  What is being measured as exceeding 100% is progress against both a time-based and fiscally-based linear plan.  Most of the physical world doesn’t act nor can it be measured this way.  When measuring the attributes of a system or component against a set of physical or performance thresholds, linearity against a human-imposed plan oftentimes goes out the window.

But a linear progression can be imposed on the development toward the technical specification.  So then the next question is how do we measure progress during the development curve and duration.

The short answer, without repeating a summarization of the research (which is linked above) is through risk assessment, and the method that we used back in 1997 was a distribution curve that determined the probability of reaching the next step in the technical development.  This was based on well-proven systems engineering techniques that had been used in industry for many years, particularly at pre-Lockheed Martin Martin Marietta.  Technical risk assessment, even using simplistic 0-50-80-100 curves, provides a good approximation of probability and risk between each increment of development, though now there are more robust models.  For example, the use of Bayesian methodology, which introduces mathematical rigor into statistics, as outlined in this post by Eliezer Yudkowsky.  (As an aside, I strongly recommend his blogs for anyone interested in the cutting edge of rational inquiry and AI).

So technical measurement is pretty well proven.  But the issue that then presents itself (and presented itself in 1997) was how to derive value from technical performance.  Value is a horse of a different color.  The two bugaboos that were presented as being impassible roadblocks were weight and test failure.

Let’s take weight first.  On one of my recent trips I found myself seated in an Embraer E-jet.  These are fairly small aircraft, especially compared to conventional commercial aircraft, and are lightweight.  As such, they rely on a proper distribution and balance of weight, especially if one finds oneself at 5,000 feet above sea level with the long runway shut down, a 10-20 mph crosswind, and a mountain range rising above the valley floor in the direction of takeoff.  So the flight crew, when the cockpit noted a weight disparity, shifted baggage from belly stowage to the overhead compartments in the main cabin.  What was apparent is that weight is not an ad hoc measurement.  The aircraft’s weight distribution and tolerances are documented–and can be monitored as part of operations.

When engineering an aircraft, each component is assigned its weight.  Needless to say, weight is then allocated and measured as part of the development of subsystems of the aircraft.  One would not measure the overall weight of the aircraft or end item without ensuring that the components and subsystems did not conform to the weight limitations.  The overall weight limitation of an aircraft will very depending on mission and use.  If a commercial-type passenger airplane built to takeoff and land from modern runways, weight limitations are not as rigorous.  If the aircraft in question is going to takeoff and land from a carrier deck at sea then weight limitations become more critical.  (Side note:  I also learned these principles in detail while serving on active duty at NAS Norfolk and working with the Navy Air Depot there).  Aside from aircraft weight is important in a host of other items–from laptops to ships.  In the latter case, of which I am also intimately familiar, weight is important in balancing the ship and its ability to make way in the water (and perform its other missions).

So given that weight is an allocated element of performance within subsystem or component development, we achieve several useful bits of information.  First off, we can aggregate and measure weight of the entire end item to track if we are meeting the limitations of the item.  Secondly, we can perform trade-off.  If a subsystem or component can be made with a lighter material or more efficiently weight-wise, then we have more leeway (maybe) somewhere else.  Conversely, if we need weight for balance and the component or subsystem is too light, we need to figure out how to add weight or ballast.  So measuring and recording weight is not a problem. Finally, we allocate and tie performance-wise a key technical specification to the work, avoiding subjectivity.

So how to do we show value?  We do so by applying the same principles as any other method of earned value.  Each item of work is covered by a Work Breakdown Structure (WBS), which is tied (hopefully) to an Integrated Master Schedule (IMS).  A Performance Management Baseline (PMB) is applied to the WBS (or sometimes thought a resource-loaded IMS).  If we have properly constructed our Integrated Management Plan (IMP) prior to the IMS, we should clearly have tied the relationship of technical measures to the structure.  I acknowledge that not every program performs an IMP, but stating so is really an acknowledgement of a clear deficiency in our systems, especially involving complex R&D programs.  Since our work is measured in short increments against a PMB, we can claim 100% of a technical specification but be ahead of plan for the WBS elements involved.

It’s not as if the engineers in our industrial activities and aerospace companies have never designed a jet aircraft or some other item before.  Quite a bit of expertise and engineering know-how transfers from one program to the next.  There is a learning curve.  The more information we collect in that regard, the more effective that curve.  Hence my emphasis in recent posts on data.

For testing, the approach is the same.  A test can fail, that is, a rocket can explode on the pad or suffer some other mishap, but the components involved will succeed or fail based on the after-action report.  At that point we will know, through allocation of the test results, where we are in terms of technical performance.  While rocket science is involved in the item’s development, recording technical achievement is not rocket science.

Thus, while our measures of effectiveness, measures of performance, measures of progress, and technical performance will determine our actual achievement against a standard, our fiscal assessment of value against the PMB can still reflect whether we are ahead of schedule and below budget.  What it takes is an understanding of how to allocate more rigorous measures to the WBS that are directly tied to the technical specifications.  To do otherwise is to build a camel when a horse was expected or–as has been recorded in real life in previous programs–to build a satellite that cannot communicate, a Navy aircraft that cannot land on a carrier deck, a ship that cannot fight, and a vaccine that cannot be delivered and administered in the method required.  We learn from our failures, and that is the value of failure.

 

*There are colloquial expressions that allow for 100% to be exceeded, such as exceeding 100% of the tolerance of a manufactured item or system, which essentially means to exceed its limits and, therefore, breaking it.

The Revolution Will Not Be Televised — The Sustainability Manifesto for Projects

While doing stuff and living life (which seems to take me away from writing) there were a good many interesting things written on project management.  The very insightful Dave Gordon at his blog, The Practicing IT Project Manager, provides a useful weekly list of the latest contributions to the literature that are of note.  If you haven’t checked it out please do so–I recommend it highly.

While I was away Dave posted to an interesting link on the concept of sustainability in project management.  Along those lines three PM professionals have proposed a Sustainability Manifesto for Projects.  As Dave points out in his own post on the topic, it rests on three basic principles:

  • Benefits realization over metrics limited to time, scope, and cost
  • Value for many over value of money
  • The long-term impact of our projects over their immediate results

These are worthy goals and no one needs to have me rain on their parade.  I would like to see these ethical principles, which is what they really are, incorporated into how we all conduct ourselves in business.  But then there is reality–the “is” over the “ought.”

For example, Dave and I have had some correspondence regarding the nature of the marketplace in which we operate through this blog.  Some time ago I wrote a series of posts here, here, and here providing an analysis of the markets in which we operate both in macroeconomic and microeconomic terms.

This came in response to one my colleagues making the counterfactual assertion that we operate in a “free market” based on the concept of “private enterprise.”  Apparently, such just-so stories are lies we have to tell ourselves to make the hypocrisy of daily life bearable.  But, to bring the point home, in talking about the concept of sustainability, what concrete measures will the authors of the manifesto bring to the table to counter the financialization of American business that has occurred of the past 35 years?

For example, the news lately has been replete with stories of companies moving plants from the United States to Mexico.  This despite rising and record corporate profits during a period of stagnating median working class incomes.  Free trade and globalization have been cited as the cause, but this involves more hand waving and the invocation of mantras, rather than analysis.  There has also been the predictable invocations of the Ayn Randian cult and the pseudoscience* of Social Darwinism.  Those on the opposite side of the debate characterize things as a morality play, with the public good versus greed being the main issue.  All of these explanations miss their mark, some more than others.

An article setting aside a few myths was recently published by Jonathan Rothwell at Brookings, which came to me via Mark Thoma’s blog, in the article, “Make elites compete: Why the 1% earn so much and what to do about it”.  Rothwell looks at the relative gains of the market over the last 40 years and finds that corporate profits, while doing well, have not been the driver of inequality that Robert Reich and other economists would have it be.  In looking at another myth that has been promulgated by Greg Mankiw, he finds that the rewards of one’s labors is not related to any special intelligence or skill.  On the contrary, one’s entry into the 1% is actually related to what industry one chooses to enter, regardless of all other factors.  This disparity is known as a “pay premium”.  As expected, petroleum and coal products, financial instruments, financial institutions, and lawyers, are at the top of the pay premium.  What is not, against all expectations of popular culture and popular economic writing, is the IT industry–hardware, software, etc.  Though they are the poster children of new technology, Bill Gates, Mark Zuckerburg, and others are the exception to the rule in an industry that is marked by a 90% failure rate.  Our most educated and talented people–those in science, engineering, the arts, and academia–are poorly paid–with negative pay premiums associated with their vocations.

The financialization of the economy is not a new or unnoticed phenomenon.  Kevin Phillips, in Wealth and Democracy, which was written in 2003, noted this trend.  There have been others.  What has not happened as a result is a national discussion on what to do about it, particularly in defining the term “sustainability”.

For those of us who have worked in the acquisition community, the practical impact of financialization and de-industrialization have made logistics challenging to say the least.  As a young contract negotiator and Navy Contracting Officer, I was challenged to support the fleet when any kind of fabrication or production was involved, especially in non-stocked machined spares of any significant complexity or size.  Oftentimes my search would find that the company that manufactured the items was out of business, its pieces sold off during Chapter 11, and most of the production work for those items still available done seasonally out of country.  My “out” at the time–during the height of the Cold War–was to take the technical specs, which were paid for and therefore owned by the government, to one of the Navy industrial activities for fabrication and production.  The skillset for such work was still fairly widespread, supported by the quality control provided by a fairly well-unionized and trade-based workforce–especially among machinists and other skilled workers.

Given the new and unique ways judges and lawyers have applied privatized IP law to items financed by the public, such opportunities to support our public institutions and infrastructure, as I was able, have been largely closed out.  Furthermore, the places to send such work, where possible, have also gotten vanishingly smaller.  Perhaps digital printing will be the savior for manufacturing that it is touted to be.  What it will not do is stitch back the social fabric that has been ripped apart in communities hollowed out by the loss of their economic base, which, when replaced, comes with lowered expectations and quality of life–and often shortened lives.

In the end, though, such “fixes” benefit a shrinkingly few individuals at the expense of the democratic enterprise.  Capitalism did not exist when the country was formed, despite the assertion of polemicists to link the economic system to our democratic government.  Smith did not write his pre-modern scientific tract until 1776, and much of what it meant was years off into the future, and its relevance given what we’ve learned over the last 240 years about human nature and our world is up for debate.  What was not part of such a discussion back then–and would not have been understood–was the concept of sustainability.  Sustainability in the study of healthy ecosystems usually involves the maintenance of great diversity and the flourishing of life that denotes health.  This is science.  Economics, despite Keynes and others, is still largely rooted in 18th and 19th century pseudoscience.

I know of no fix or commitment to a sustainability manifesto that includes global, environmental, and social sustainability that makes this possible short of a major intellectual, social or political movement willing to make a long-term commitment to incremental, achievable goals toward that ultimate end.  Otherwise it’s just the mental equivalent to camping out in Zuccotti Park.  The anger we note around us during this election year of 2016 (our year of discontent) is a natural human reaction to the end of an idea, which has outlived its explanatory power and, therefore, its usefulness.  Which way shall we lurch?

The Sustainability Manifesto for Projects, then, is a modest proposal.  It may also simply be a sign of the times, albeit a rational one.  As such, it leaves open a lot of questions, and most of these questions cannot be addressed or determined by the people to which it is targeted: project managers, who are usually simply employees of a larger enterprise.  People behave as they are treated–to the incentives and disincentives presented to them, oftentimes not completely apparent on the conscious level.  Thus, I’m not sure if this manifesto hits its mark or even the right one.

*This term is often misunderstood by non-scientists.  Pseudoscience means non-science, just as alternative medicine means non-medicine.  If any of the various hypotheses of pseudoscience are found true, given proper vetting and methodology, that proposition would simply be called science.  Just as alternative methods of treatment, if found effective and consistent, given proper controls, would simply be called medicine.