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.
Figure 1
Practice: Assessing the impact of rework starts with identifying the amount of rework that's at stake. The previous post explained how the amount of rework can be estimated at any point in the project. The post also pointed out a shortfall in R (the indicator for the amount of rework): it does not provide sufficient information for managing a schedule. Again, the reason is its behaviour toward the end of a project.
As explained in my last post, R depends on the P-Factor, and as a project proceeds, P is increasingly unresponsive to improvement efforts. Regardless of what steps are taken, P ends up at a perfect 1.0. So, R will consistently improve toward the end of a project, regardless of what actions are taken. (See the Rework line in Figure 1.) [1] That means R is neither a good indicator of what improves performance nor a sound basis for forecasting the cost of rework.
Fortunately, there’s a way to address both of these concerns. It leads to a new rework indicator that can, in turn, be used to generate a forecast of associated costs. With the forecast, we have, at last, a way to manage schedule adherence systematically throughout a project’s life cycle.
The first step is to note that the amount of rework is distributed over the remainder of the project. If R is therefore normalized to the remaining work, its inevitable decrease to 0 is avoided. Normalization scales the value of R to the time left, canceling the effect of increases in knowledge and decreases in number of candidates for rework.
The normalization can be formulated as follows. Relate the amount of rework (R) to the baseline project budget (BAC) less the value already earned (EV). The new indicator is called the Schedule Adherence Index (SAI):
Equation 17[2]
Scaling R to the remaining budget means that, as the project proceeds, the divisor shrinks. As it shrinks, the impact of remaining rework increases, offsetting the downward pressure of more knowledge and fewer candidates for rework.
Ultimately, the SAI ends up at 0 as rework runs out, but until then, as shown in Figure 1, it can fluctuate. Freed from an unrelenting drop in the latter part of the project, SAI is a viable tool for managing schedule performance.
Here's what it tells us:
If SAI increases, schedule adherence is suffering. As the BAC is fixed and the amount of EV cannot decrease from one period to the next, the amount of rework must be increasing. A larger numerator over a fixed denominator spells an increase in the ratio. Thus, schedule adherence is worse.
If SAI decreases, adherence is improving. Either the amount of rework is staying the same and value is being added in alignment with the schedule, or the amount of rework is decreasing, and so the EV is increasing. (If R decreases, work is being done, and the EV must grow.) In both cases, the fraction has shrunk. Thus, schedule adherence is better.
Equally important, SAI offers a way to forecast the cost of rework, and that is the topic of the next post.
Notes:
[1] The data behind Figure 1 come from an actual project.
[2] The equation numbering scheme continues from the previous post, as the equations are all connected.
References:
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). |
