Insight #7: Capacity Is Finite


(If the following paragraph is hard to parse, try reading it out loud)

Re-prioritizing doesn't give back time is one of those statements that's designed to call attention to something easily overlooked - the kind of overlooking where when what's hidden is revealed it becomes apparent that its hidden-that-its-hidden.

Prioritizing seems so legitimate - it answers what to work on first. What could be more important than working on the highest priority items first?

What's missing from this notion of prioritization is any recognition that capacity is finite.

To illustrate the pitfall and to underline the insight that's available from recognizing that capacity is finite I first ask people to prioritize their backlogs using whatever method for prioritizing they wish.

Some use value to customers, some use complexity (a measure of risk), still others use WSJF - regardless at the end of this step in the exercise what's presented is a backlog ordered by priority. 

Next I introduce the notion of finite capacity and inform them that the total capacity available for delivery can deliver no more than 40% of the demand and ask them to separate the backlog into three groups - items that are definitely going to be completed (must-have), items that are never going to be started (dropped) and items that may be started and may not be completed (may have).

Unsurprisingly, reworking the backlog to fit this constraint is never as simple as drawing 2 lines representing the boundaries between three groupings.

What gets revealed is that the finite capacity shapes and informs what's definitely in, potentially in and will never be delivered from the backlog. What also becomes clear is the otherwise hidden and very real cost that a backlog that does not take into account finite capacity has on a program when items that will never be finished are started, especially at the expense of items that must be finished.

The name of the game with finite capacity is not prioritization - rather its allocation - or more specifically answering the question how might I allocate my finite capacity for maximum advantage, where we say maximum advantage is highest value in market soonest.

The Elephant in the Room

There is something else to acknowledge about the insight that capacity is finite. That is - that operating as if our capacity were not finite is perhaps the most widely observable example of failing to think accurately. Its effects are everywhere to be seen...

  • Long cycle times from too much WIP
  • Very long queues that introduce extraordinary amounts of delay
  • The cordial hypocrisy that accompanies the relentless acceptance of requests that ought to be declined
  • The practice of making use of buffers in project schedules to deal with the above breakdowns

Frequently there comes a point in the transformation when people discover within themselves an authentic level of disgust for having played their part in being complicit in failing to call out the elephants in the room.

This typically marks a kind of turning point in the tone and the level of commitment as people begin to step up and step into their own willingness to deal with this form of not-thinking-accurately and to do so fearlessly, decisively and completely.