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Lifecycle Variables

I have written a couple of posts now (here and here) about the new PMBOK v4 guide due out soon. One of the new graphs included helps describe how project characteristics change over the project life time.
The top blue line of the graph is used to illustrate how Stakeholder Influence, Risk and Uncertainty start off high and then reduce as the project progresses. The Escalating orange line illustrates how the Cost of Changes increase dramatically over the project timeline.

Quite a lot has already been written on flattening the Cost of Change curve within agile, so I will leave that for now and focus first on the top line.

Before discussing ideas such as how ongoing business input in, for example, prioritization of the remaining work prolongs their ability to influence the project, we should take a moment to understand the PMBOK audience. The PMBOK is not just for software projects, or IT projects, it is an industry agnostic guide relevant to construction, engineering, and manufacturing among other disciplines.

As a general guide, I think these curves make sense, especially outside of software projects. The ability to influence does decline rapidly once designs are committed and construction begins. Likewise, Risks and Uncertainty also reduce generally later in the project once technical obstacles have been overcome.

Software though is different, actually I would hazard a guess that every industry is different really, however software is the one that I know about. Software exhibits a characteristic known as “Extreme Modifiability” meaning we can make many changes, even late in the lifecycle and still be successful. While it would be difficult to move a bridge 3 miles upstream when it was 75% complete; we could choose to move validation logic from the presentation layer, to a middle tier, or a database trigger late into a project.

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Velocity Signature Analysis

Velocity Analysis Most agile projects track their velocity. For the past 10 years or so I have been studying the velocity profiles of my projects and any other projects I can get data from. Velocity profiles tell a story about the project, and like signatures are unique.

Tracking stories, or more normally points completed per iteration, gives the classic velocity graphs such as the one shown below.

Iteration Velocity sample

Here we can see the Projected Velocity shown by the dotted blue line and the Actual Velocity shown in dark blue.

From observing 15-20 projects I have noticed the following reoccurring patterns. Am I the only one, or are these common?

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Living the Theory of Constraints

Hourglass This past week I have had an opportunity to experience some hospital process control and contrast it with traditional project process controls. In doing so, I saw many instances of where today’s projects that exhibit uncertainty could be better managed via prioritization and collaborative decision making than preset plans.

How did we get to Traditional Project Management?
Project management is a fairly young discipline, yet because its repeatable process scales so well, and is easy to duplicate and automate; it rapidly became the dominant process for running projects. Frederick Taylor published his studies on “Scientific Management” in 1911 outlining the process of decomposing complex work into simpler and simpler steps until localized labour could be employed to perform each simple task. Embraced by Henry Ford and others, Scientific Management became the prevailing way of problem solving for entire industries.

It was not until the 1950’s when Peter Drucker and then later Michael Porter convinced the world that centralization and command-and-control structures were flawed. Respect for workers and a holistic value based view of systems can produce better results and more sustainable organizations. Yet traditional project management persisted.

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