Blog posts on variation

Posts selected fromManagement Blog - Engineering Blog - Investing Blog and other blogs

• Understanding Variation Doesn't Mean Crushing Any Variety

  Dr. Deming understood the organization as a system and how understanding variation fit within that system. When variation within the system causes problems and reduces efficiency then reducing variation important. It is a mistake to attempt to take thinking that is part of a system and analyze it without understanding the context within which it has meaning.

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• Understanding and Misunderstanding Variation

  ... one of the central aspects of Deming’s ideas on management: that we need to view the organization as a system. It is not as simple as adopting a couple practices and getting the same results another organization gets from those same practices. The interactions between the existing organization and the new practices will be different. This makes management challenging but it also makes our jobs of improving management continually interesting and full of opportunities to learn.

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• Rethinking Statistics for Quality Control with George Box

  George Box shared a presentation on Rethinking Statistics for Quality Control at the 2008 Deming Institute Conference in Madison, Wisconsin.

  In the presentation George discusses how to look at data from a process. He mentions why it was so important to understand what Shewhart understood about process data: the order of the data is extremely important; which is why run charts and control (process behavior) charts are plotted in time order...

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• Fooled by Randomness

  When people are asked to explain random variations in data they will make up special causes (that they often even believe are special causes even when they are not) but you can improve management a great deal by just stopping the requirement to “explain” common cause variation...

  Use that time instead to standardize processes. Create control charts for critical processes. Run experiments using PDSA cycle…

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• Stratification and Systemic Thinking

  This is a great example of a positive special cause. How would you identify this? First you would have to stratify the data. It also shows that sometimes looking at the who is important (the problem is just that we far too often look at who instead of the system so at times some get the idea that it is not ok to stratify data based on who – it is just be careful because we often do that when it is not the right approach and we can get fooled by random variation into thinking there is a cause...

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• Data is Important and You Must Confirm What the Data Actually Says

  Without an appreciation for the gemba, where the data was collected, it is easy to be mislead by the data.

  The most common waste of effort in examining data is reacting to the expected variation of a system as if it is something special. We have discussed this in many previous posts, for example, We Need to Understand Variation to Manage Effectively. After that, I think there is a good chance of failure to appreciate what the data is, and is not, telling us based on mistaken assumptions about what the operational definitions were...

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