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When Change Is Not an Improvement

Have you ever felt conflicted about a project? Ever thought, this time, change is not an improvement?

Part of our Consultants’ role is to sit on review panels within our client organisations to ensure that improvement projects are done correctly and that the outcomes are accurately documented so that they can learn from the outputs. 

One project stands out in particular, where a recommendation was made to invest in some new technology they had been testing. The problem in question was around improving the energy efficiency of a process core to each of their 26 manufacturing sites. The organisation had a group-wide target to reduce energy consumption while still delivering high-quality products.  

The potential solution that had been favoured was a tech solution costing €50k investment per plant. They had a total of 26 sites, equating to €1.3m. They had also calculated that there would be a decent 10% reduction in direct energy costs, equating to €2m on an annual basis. This meant a return on investment of less than 2 years, so clearly, we were very interested to hear their story. 

A Statistical Process Control Chart showing a stable and predictable initial learning phase.

Learning data

The Project Leader had done a great job of analysing the problem, investigating potential solutions, engaging the team, and testing.

This was the initial learning data that the team had gathered on a single plant. As you can see, it was stable and predictable. They felt the limits sensibly reflected the steady state conditions of their process. Thus, they locked the control limits as they now wanted to see how the data would develop in the testing phase. 

A Statistical Process Control Chart showing test data added and limits recalculated

Changed limits – is change an improvement? 

Next, they added the test data to the Control Chart and recalculated the limits from when the change was made. This showed a marginal improvement. Based on this, the project leader felt that even though it wasn’t quite the 10% improvement they had been after, it was still a worthwhile investment and recommended that they go ahead, procure the new technology and roll it out worldwide.

Now, the alarm bells were going off. They had recalculated. But was the change truly an improvement? One that we could justify statistically?  

A Statistical Process Control Chart with no staging

The change was not an improvement

We asked them to undo the recalculation of the limits post-change, and this was the resulting picture. As you can see, there was no signal of an assignable cause—no run of 8, no data point outside the control limits. Thus, we asked the project leader on which basis they decided that the change was an improvement, and this is how the conversation prevailed…

“Why?” they asked. “I have made a change, I can see a change, and thus, I recalculated.” 

“But did you see any signals?” 

“No, I did not see any signals” 

“So the change was statistically speaking not an improvement?” 


“So, in terms of applying PDSA, is this an Adopt, Adapt or Abandon?” 

“Well… it’s not an Adopt, possibly an Adapt or Abandon”. 

So, in that review meeting, we decided not to proceed with the recommendation to invest in this new technology just yet but to do some more testing of other solutions. 

In conclusion

Our desire to be better tells us that when we create a change, this surely must be an improvement. We have invested personal time and energy into this project. We desire to see a positive change, so the moment the change is deployed, we recalculate the Control Chart.  The head gets ignored as the heart overrules our decision-making processes. We want to prove that what we have done is right and that it has made a positive benefit. So, what is the right way to manage this emotional overruling?

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3 Steps to manage the heart and the mind to ensure your changes are improvements.

Reframe your mind: Instead of being pressured that what you are doing on an improvement project has to deliver a bottom line saving to the organisation, pressure yourself to do a good project to learn from.

Use control charts: Don’t fall back to old-fashioned run-charts. You will see patterns where no patterns truly exist. Control charts were designed to help people make better decisions.

Stick to the recalculation rules: Look for signals and remember the rules as you were taught during your improvement training.  Not all changes are an improvement, so don’t recalculate unless you comply with the pre-conditions for recalculating:

  • Is there evidence of an assignable cause?
  • Is the cause of the signal known?
  • Is the change desirable?
  • Is the change sustainable?


Empower your team to harness data for informed decision-making through our new Data Fundamentals On-Demand Learning.

Offering a comprehensive introduction, learners will be capable and confident in the use and interpretation of Control Charts as part of Process Improvement.

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“[Control Charts] provide, under a wide range of unknowable circumstances, future and past, a rational and economic guide to minimum economic loss from two types of mistakes.”

– W. Edwards Deming


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