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.
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 missing is the impact of rework.
The first step toward the impact is an indicator of schedule adherence that is reliable throughout the project lifecycle.
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.
The indicator 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 improve toward the end of project, regardless of what actions are taken. 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.
Note that the amount of rework is distributed over the remainder of the project. If R is normalized to the remaining work, its inevitable increase to 1.0 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):
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, it can fluctuate. Freed from a necessary rise toward the end of the project, SAI is a viable tool for managing schedule performance.
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. Once we have that, we can quantitatively assess the impact of work done out-of-sequence. |
