The Capacity Constraint Buffer

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The capacity-constraint buffer assures that the constraint resource is available when needed by the project. Conceptually, you place it between the use of the constraint resource in the prior project and the first use of the resource in the project you are scheduling. It does not take lead-time out of the project you are scheduling, but it defines the start date for the resource-using activity.

The previous paragraph uses the word conceptually because the actual process of sizing the capacity-constraint buffer with multiple projects can be much more complex than visualized by delaying one or two tasks relative to another set of tasks in another project. Think more in terms of filling a bucket. The bucket represents your resource capacity. You will usually want to put the "big rocks" in first. These projects have a firm deadline and perhaps are contracted with delivery penalties. You must do these projects as soon as possible. The big rocks do not fill all the space in the bucket. There is space between the rocks. Your bucket still has some room. So next, pour in the gravel, the smaller projects, in accordance with their priority. After that, there is still some more room to pour in the sand, the nonproject work. That still leaves room for you to add water to the bucket, the crises that do not really involve project work.

Some project software allows you to specify the time bucket for resource leveling (e.g., weekly or monthly). It will allow overallocations and not try to level resources, as long as the average demand for the time bucket is within the average

Push the overflow later in time, until you can "drop it in" to start with a CCB

Drop into next slot /_

C start date determined by "backing up" from this point: the constraint use date

Yellow and green can start immediately

Figure 7.4 The drum schedule accommodates all project demands, including capacity-constraint buffers.

supply of the resource. This fits well with CCPM, as we know that those apparent overlaps are not real; they are an artifact of presenting a reality with variation using a deterministic drawing method.

You should consider queuing theory and your resource-leveling approach when sizing the capacity-constraint buffer. Queuing theory suggests that the capacity-constraint buffer should be at least 25% of the capacity-constraint-resource capability. Otherwise, your projects will slow down to a crawl.

Everyone is familiar with queues. We wait in queues at the supermarket, at the bank, and, one after another, in airports. Some of us even sometimes wait in queues to go to the bathroom. We all know that queues can form very rapidly, and they can dissipate rapidly when extra servers are applied.

I ask project-management groups, "Suppose that the average time to process each person through a queue exactly equals the average arrival rate of people to be served by the queue. How long will the line be?" Most people answer that there will not be a line, or that the line will average one person waiting to be served. Unfortunately, this is an excellent example of the human mind's inability to intuitively understand variation. For this case, over time, the line approaches an infinite length. Of course, it takes an infinite time to get that long, but it can grow surprisingly rapidly and, once there, will not dissipate until the server capacity is increased or the arrival rate decreases. That may be a reason stores close the doors at night.

Figure 7.5 illustrates the classic queuing curve for one line and one server. It plots the length of the line versus the ratio of the average arrival rate to the average processing rate. The curve for wait time has the same shape. A value of x = 1 means

Utilization

Figure 7.5 The queuing model predicts an infinite line when the average arrival rate approaches the average processing rate. [For project resources, utilization = (average arrival rate of tasks)/(average duration of tasks).]

Utilization

Figure 7.5 The queuing model predicts an infinite line when the average arrival rate approaches the average processing rate. [For project resources, utilization = (average arrival rate of tasks)/(average duration of tasks).]

that the average arrival rate equals the average processing rate. Note that the line is infinitely long at that point and rises very rapidly as x approaches that level. The queuing model has certain statistical assumptions that underlie it, but the overall behavior is quite robust. The line begins to grow very rapidly as the ratio gets beyond about 0.7, or 70%, average utilization of the resource, which corresponds to a 30% capacity-constraint buffer.

You might appreciate the following to help understand this surprising result. Consider that you are working at 90% capacity and are sick for a day. It will take you nine days to catch up because you only have 10% excess capacity available each day to catch up. Now suppose you are working at 95% capacity. It will take more than twice as long to catch up because you have half as much time each day to make up the loss, and you have lost a little more. At 99%, it takes 99 days to catch up. At 100%, you can never catch up.

The reality is that people make up for a lack of capacity buffer by making excess capacity. They will work additional hours, paid or not. They will find innovative ways to move the work on. They may cut corners so that the backlog does not get too large. They may send on incomplete work. They may send on lower-quality work. Although some amount of overtime for a limited duration (i.e., a couple of weeks) can be beneficial when focused by CCPM, research consistently demonstrates that extended overtime leads to a total throughput that goes back to or declines lower than preovertime levels.

If you do not want your projects to wait an infinite amount of time for the drum resource, you should use a capacity-constraint buffer in the range of 25% to 30%.

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