Is Your Process at Best or at Rest?
The purpose of this article is to provide readers with some insight to the shape of two common process distributions and to quickly understand whether their target process is currently at "Best" (close to entitlement) or at "Rest" (much room for improvement). This is especially important for operational leaders.
To provide this insight, it's very important for leaders to understand the natural shape of the distribution of the target process. In most Lean Six Sigma or continuous improvement initiatives, processes that are targeted for improvement rarely have a bi-lateral specification (upper and lower specification limit or expectation) which will typically drive a bell-shaped or "normal" distribution.
In a manner of speaking, most of us are engaged in business optimization efforts to maximize "good stuff" and minimize "bad stuff". For example ....
Minimize - defects, cycle time, lead time, errors, lost customers, call handling time, rework, scrap, days sales outstanding, inventory, etc.
Maximize - rolled throughput yield, sales, close rates, throughput, net promoter score, inventory turns, etc.
If you are a continuous improvement practitioner, you may have been taught to compare your current process data to the shape of a theoretical "normal" distribution. This can be tricky or even misleading if the measure of the target process you are managing or improving should actually exhibit a left-skewed (maximize stuff) or a right-skewed distribution (minimize stuff).
First, let's examine the "right-skewed" distribution in Figure 1 below for processes that "minimize" bad stuff as described above.
As you can see in the callout above, processes that minimize stuff like defects, scrap, average call handling time, etc. should run up against a wall of process entitlement (process at "Best") if the process is stressed for improvement. This means there are expectations and/or organizational commitments to keep these measures active in that direction. What's important here is the shape of this distribution should reveal a natural boundary to the left and skew out to the right as Figure 1 illustrates.
Why is this important to understand? Let's take a gander. Let's suggest that the distribution in Figure 2 is a process measure that minimizes scrap, cycle time, etc. This process should then resemble the distribution shape in Figure 1, however, if it looks more like a "normal" distribution, we should examine the reason why.
In my past experience, the most common reason is what I consider a process at "rest"or one that is not stressed to get the best results. In other words, expectations may not be clear or required by the organization responsible. I usually describe this as a process that is very "comfortable" with their performance. There is a lot of white space between the distribution and the natural boundary to the left.
Figure 3 below illustrates the opportunity space and desired action. The goal for processes that hope to minimize bad stuff, is to stress the process and push it towards its natural boundary. Conceptually, one should be able to envisage that a process that performs closer to its natural boundary will look closer to the distribution in Figure 1.
What's the punchline?
If your process is minimizing or maximizing its unit of output, your process distribution shape should be left skewed or right skewed as illustrated above. Otherwise, you may have significant opportunity to close performance gaps just by communicating expectations and closely managing these processes. Leaders in charge of performance objectives and outcomes should become knowledgeable enough about these inherent distribution shapes in order to ask the right questions and keep their organizations performing "at Best".
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