I listen to the Gemba Academy Podcast, and a recent entry reflects back to a blog post from one of their contributors, Why Can’t We See the Financial Impact of Continuous Improvement? I love the list of mistakes people make that make CI efforts seem to vanish on the balance sheet:
Not agreeing upfront on how to measure success. Specifically, if the people holding the purse strings don’t see the results on their measures, then is it a success? I love the comment that those leading the effort should be fluent in the financial measures that leadership are using. (There was a recent TOCICO webinar on this same topic, connected to the new Throughput Economics book by Schragenheim, Surace and Camp.)
Improving the links but not the chain. If the flow through the entire system doesn’t improve, then it is not likely to be a real improvement from the system perspective. This is a central tenant that is easily forgotten in “cost saving” efforts, instead of continuous improvement. I like how the upcoming Unicorn Project by Gene Kim (my review) exemplified this by continuously attacking the one thing that was preventing flow - and eventually looping back to an improvement made early in the book that was now the source of blockage.
Declaring victory too soon. Or deciding that it hasn’t worked too soon. Some changes make a difference right away. Other changes need an opportunity to stabilize, particularly if the “activation energy” to make the change is high. Another key element of continuous improvement is that we monitor the change to ensure we are operating in the new way AND that the results are as expected.
Not making changes to supporting systems. In my mind, this connects strongly to #2. Making changes without understanding how the entire system flows is a perfect opportunity for local optima that don’t help (or even damage) the overall flow. Think of all the automation that technology has created, and yet people still operate under the previous ways of working.
Assuming that physical results will be visible as financial results. Again, this ties back to #1. Understanding how the system is measured (and checking if those measures are valid) will ensure that the changes we make show up on the bottom line.