I'm a fan of small batches. Partially, this is explained by my appreciation of the many fine single-barrel and small-batch bourbons produced in good ol' Kentucky. But principally, my bias towards small batches is due to the positive impact that batch-size reduction has on process flow, quality, etc.
Normally, we associate batch-size reduction with process improvement. But if we take a step back and look at our process for conducting process improvement, batch-size reduction is equally as applicable. Specifically, the way we go about testing countermeasures via PDCA can be enhanced by batch-size reduction. I call this principle Small-Batch PDCA.
What is Small Batch PDCA?
When we're in the planning phase of of PDCA, we have to decide how many countermeasures we want to test during the current PDCA cycle. There's a trade-off between the number of countermeasures we test and the amount of time, effort, and resources that will be required to conduct the test. More countermeasures equals more testing complexity. In order to properly execute a complex test, we might feel the need to utilize a complex tool such as Design of Experiments (DOE). My bias is to avoid this testing complexity by testing in smaller batches when possible.
By reducing the complexity involved with carrying out a test, Small-Batch PDCA allows us to compress the lead time from idea generation to idea testing. This gives us the chance to perform more iterations of PDCA, which in turn gives us a chance to adjust our model more frequently.
Is there a downside to Small-Batch PDCA?
One of the drawbacks of Small-Batch PDCA is that we don't get to test the future-state in a holistic manner, at least not during the first few rounds of testing. This means that any data we collect early on might not show the dramatic improvement we want, and in fact, it may be impossible to detect any statistically significant changes in performance. This is a valid concern, but this drawback is partially mitigated by the fact that if we are willing to go to the gemba and observe the test with our own eyes, we don't have to rely on data as much.
Plus, there are some important things that just can't be measured, so we usually need to go to the gemba regardless. In other words, data isn't everything. Subjective feedback from those involved with the process can be extremely valuable. Insights gained from direct observation can also be extremely valuable. Small-Batch PDCA provides us with most of the feedback we need to effectively carry out process improvements, even if the data is not as perfect as we would like.
