Concept: Schedule Adherence, aka, P-Factor, measures how well or poorly the planned sequence of deliverables is being followed.
There are limits on the possible values of the P-Factor. Those limits imply limitations on the applicability of the concept.
Practice: The limits on the value of the P-Factor are easy to state and explain.
**Limits**
Like the wheel alignment of a car, for your car to run straight, the wheels must be properly aligned. The wheels are either in alignment or they are not. Although they can be wildly out of line, they cannot be better-than-aligned. The analogy suggests the limits on P-Factor.
For Schedule Adherence, it matters very much which deliverables are completed. If the completions exactly align with the plan, the Schedule Adherence is a perfect 1.0. If there is no alignment with the plan, the Schedule Adherence is 0.0. At most times, the value lies between the two extremes.
And, that is where the limitations on P-Factor come in.
**Limitations**
The rationale behind the limitations is neither quick nor easy. For a full treatment, click here. For this post, I’ll offer only a summary of the key points, excluding the mathematical details.
The explanation starts with Theorem 1.
Theorem 1. The total accrued Earned Value equals the sum of the sum of values earned as of the Actual Time which in turn equals the sum of the values planned as of the Earned Schedule time.
Theorem 1 follows from Earned Schedule’s definition: ES is the time at which the value currently earned should have been earned.
Theorem 2 follows from Theorem 1 and the P-Factor definition.
First, the P-Factor definition: P-Factor equals the amount of value actually earned in conformance with the schedule divided by the total value that should have been earned, i.e., the planned value for the same set of tasks.
Second, it follows that the value earned according to the schedule equals the fraction that’s aligned (P) times the total earned (EV).
Theorem 3 follows from the first two theorems.
Theorem 3. The earned value not aligned with the schedule, EV(r), is the difference between the total value earned (from Theorem 1) and the value earned in alignment with the schedule (from Theorem 2).
From this theorem and more derivations comes the critical factor: f(r)—the fraction of *work* that is a candidate for rework. That fraction is multiplied times value to get the *value* of rework.
This is the point at which mathematical modeling comes into play.
To represent the fraction mathematically, we first need to understand its behaviour. At the start of a project, the knowledge gap is large, and the opportunity for rework is also large—so large that the starting point for the fraction is 1.
At the end, the knowledge gap has shrunk to nothing, and there is no opportunity for rework—the fraction is 0. In between, the knowledge gap decreases, and so does the opportunity for rework. The decline speeds up as the amount of work dwindles.
A line tracing the fraction on a graph would curve from 1 to 0, dropping off more sharply at the end. Such a graph is familiar to mathematicians. Historically, it is tied to the number “e”. That number is special, like π (pi).
In the full post, I explain the use of “e” at this point. For now, I’ll abbreviate the explanation to this:
First, the fraction of rework depends on how close the project is to the end. As the end represents 100% completion, 1.0 minus the fraction complete stands for the fraction of time remaining.
Second, the fraction of rework needs to reflect the drop in rework over the remaining time. How much decline is to be expected? Used in an equation, “e” gives an answer.
Over the project’s timeline, the “e”-equation yields a curve. To keep things simple, Walt sets values in the equation to produce a relatively flat curve. It falls between the straight line and a “rainbow”.
The curve tells us the f(r), and with it, we can calculate the value of Rework (R).
It took a while to get to R, but now I need to pull the rug from under your feet: the derivation of R is an interesting exercise in applied math, but R is not useful for managing a project. "Why not?", you say.
Here comes the limitation. It’s best to use Walt’s own words (Lipke, 2019, p. 6):
“The equation for R computes the amount of rework forecast to occur from the present status point to project completion due to the current measure of schedule adherence. It is an intriguing computation, but it is not a useful indicator for project managers (PM). Recall that P increases as the project progresses and concludes at the value of 1.0 at completion, regardless of efforts by managers or workers to cause improvement. Thus, the computed value of R from one status point to the next cannot provide trend information concerning improvement and neither can it lead to a forecast of the total amount of rework caused by lack of schedule adherence.
At this point R appears to be a useless calculation.”
By inference, the P-Factor is equally suspect, at least for the later parts of a project.
Walt then goes on to propose a solution to the problem. That’s the focus of next month’s post.
References
Lipke, W. (2019) Schedule Adherence and Rework, PM World Journal, Vol. VIII, Issue VI.Lipke, W. (2013). Schedule Adherence …a useful measure for project management. *PM World Journal*, *Vol II, Issue VI*.
Lipke, W. (2012). Schedule Adherence and Rework. *CrossTalk, November-December*.
Lipke, W. (2011b) Schedule Adherence and Rework. *PM World Today, July.*
Lipke, W. (2011a) Schedule Adherence and Rework. *The Measurable News, Issue 1 (corrected version).* |