Completion queue as incremental throttle

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In the last two posts, we’ve discussed some useful properties of internal workflow queues:

  • queue states between processes can provide an early warning of process breakdowns
  • local work-in-process limits serve to slow down a malfunctioning workflow and free up resources to fix it
  • queues can sometimes be combined to reduce the total work-in-process while still preserving their buffering function

I gave an example of workflow throttling, and suggested there was another configuration of those internal queues that could respond more smoothly and gracefully than the simple, independent queues given in the example.

In order to pull a work item, there has to be a place to pull it from, and there should be some way to distinguish work that is eligible to be pulled from work that is still in process. At the same time, there has to be a place to put completed work when you are done with it. A completion queue serves both these functions.

In this case, we can have up to 3 items in the “specify” state AND we can have up to 3 items waiting for the next state in the workflow. The team can pull new work into “specify” whenever there are fewer than 3 work items in process. If there are already 3 work items in process then the team will have to wait until something is moved into the completion queue. If there is some kind of blockage downstream, first the completion queue will fill up, THEN the specify queue will fill up, THEN the specify process will stall. And when it stalls, it stalls all at once. The flow is either on or off, there’s no middle speed, and it keeps going until it stalls.

In another example, we still have a busy state and a complete state, but the token limit is shared between them. In this case, we can have 4 items in process OR 4 waiting. Or we can have (3 busy + 1 waiting) OR (1 busy + 3 waiting).

In the ideal case of 3 busy and 1 waiting, this queue works just like the first example does. However, if work starts to accumulate in the “complete” state, then the “specify” state will incrementally throttle down. The effective WIP limit for “specify” goes from 4->3->2->1->0 as more items are completed ahead of the rate of downstream intake. So, the process slows before it stops, and it slows much sooner than it would have under the independent queues.

What’s more, even though it operates in the same way in the normal case, it does it with two fewer kanban in the system. Fewer kanban, with gradual throttling and smoother flow, should result in lower lead times.

With this in mind, let’s reconsider our scenario from the previous topic:

1. Something is going wrong in the design process, but nobody knows it yet.
2. The specify-complete queue starts to back up, thereby throttling down the WIP limit for specify. A resource is freed as a result, who should now inquire into the cause of the backup, which may only be random variation. The code process continues to complete work and pull from the existing backlog.
3. Code state begins to starve and specify state throttles down another level. Two more people are released as a result. There’s more than enough free resources now to either fix the problem or shut down the process.
4. The stall completes by flushing out the specify and code states.

It still takes a while for the system to stall completely. The difference is that it begins stalling immediately, and when it does stall, it stalls with less WIP. For equivalent throughput, this pipeline should operate with fewer kanban and less variation in WIP, and therefore should have smoother flow and shorter lead times. It should respond faster to problems and free up resources earlier to correct those problems.

These shared completion queues might be the most common type of workflow queue. There are a couple of other types that we use, and we’ll take a look at those in a future post.