The Earned Schedule Exchange


August 31, 2018
Schedule Adherence: Stop Wasting Time

Concept: When work is done out of sequence, knowledge gaps are inevitable. To bridge the gaps, performers must make assumptions about missing inputs. At some point, the missing information becomes available, and when it does, it often differs from the assumptions. That means what has already been done must be reworked.

Rework is doubly wasteful. First, and obviously, re-doing what has already been done is lost time and money—time that could be spent completing new deliverables. Second, and less obviously, the time spent making the assumptions is also lost time and money—it too could have been spent on completing new deliverables. In both cases, performers feel frustration with a work process that is the exact opposite of “one and done”. They want to stop wasting time.

Knowing rework will be required and even knowing which tasks are likely to cause rework are important but only part of what you need to know to stop losing time.  What’s lacking is a fix on the impact of rework.

Stop_Wasting_Time_Cropped.jpg

Practice: Although the P-Factor implies when rework is likely, it does not tell how much rework is at stake.[1] Given the connection between rework and waste, it’s important for projects to know the amount of rework in order to minimize waste—if you can’t measure it, you can’t make it better.[2]

Walt Lipke has identified a metric that quantifies rework’s impact. That metric, the Total Rework Forecast (Rtot), is the estimated cost of rework. The estimate can be made at any given point in the project schedule. It can then be used in conjunction with allowances for uncertainty to make decisions affecting project execution.

Before jumping into the calculation and use of the Rework Forecast, some set up is required. The first step is to determine the likely amount of rework (R). Then, we can move to its cost.

Recall that the P-Factor measures the degree of adherence to the schedule. If all tasks are done on schedule, adherence is perfect, and the P-Factor equals 1. If no tasks are done on schedule, there is no adherence, and the P-Factor equals 0. Most often, there’s a mix: some tasks are on schedule and others are either late or ahead of schedule, causing the P-Factor to land between 1 and 0.

In those cases, R can be quantified. It is a function of the % Complete, the degree of Schedule Adherence (the P-Factor), and the amount of value earned (the BCWP, aka EV).  Here’s the logic behind each factor.

  • % Complete: As a project proceeds, the amount of rework that is possible declines, not only because more information is available but also because there is less work to go astray. In fact, ultimately, rework goes to 0 because there is nothing left to rework. So, % Complete must be included to identify how much work is left that might go wrong.
  • P-Factor: The lower the P-Factor, the higher the probability of rework. Reason: low P-Factor indicates that work on the project is not adhering to the schedule. With more work being done out of sequence, the likelihood of rework increases. On the other hand, the more closely work adheres to the schedule, the less likely rework becomes. So, P-Factor must figure in the calculation to identify how much Schedule Adherence is being achieved.
  • Earned Value: The total value that has been earned must be adjusted to reflect the portion liable for rework. Including this portion of the EV ties rework to value—a critical connection, given that the target impact is on cost. So, EV must be included to identify how much cost stems from imperfect Schedule Adherence. 

As the project proceeds, R necessarily declines, not only because more information is available but also because there is less work to go astray. Ultimately, R goes to zero because there is nothing left to rework.

One implication of the trend to 0 is that R cannot provide reliable trend information. After all, if R must trend downward to 0, no matter what actions are taken to improve Schedule Adherence, they will look good—hardly a reliable indicator of performance.

Walt suggests a way to rework R that creates meaningful trends. That is the topic of next month’s post.

Notes:

[1] When is the P-Factor low enough to warrant investigation? There are rules-of-thumb to guide us. Walt Lipke states the “normally, P-Factor values are expected to be greater than 0.8 before 20% completion” (Lipke, 2011a, p 12). LinkedIn posts cite 0.9 as a cut off without specifying a % Complete. All of these guidelines are subject to the limits of anecdotal evidence. A more systematic, objective approach is described in my Reworking Rework posts on this topic.

[2] Often attributed to one of the “double D’s” (i.e., Drucker or Deming), the aphorism actually does not belong to either. For Drucker’s contra-indication, see Zak, 2013 and for Deming’s, see Hunter, 2015. In both cases, the authors argue that while Drucker and Deming both valued measurement’s role in management, they recognized it has limits. Here, it is enough to say, if you don’t know how much rework is at stake, you don’t know if it’s worth the time to reduce it.

References:

Hunter, J. (2015, 13 August). Myth: If You Can’t Measure It, You Can’t Manage It. Retrieved from https://blog.deming.org/2015/08/myth-if-you-cant-measure-it-you-cant-manage-it/.

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).

Zak, P. (2013, 04 July). Measurement Myopia. Retrieved from http://www.druckerinstitute.com/2013/07/measurement-myopia/.

Add new comment

All fields are required.

*

*

*

No Comments




Archives