Service Metrics: What You Need to Understand
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I overheard a fellow say "I've got Deming's principles down pat, now all I have to do is understand this variation thing." Hmmm, Dr. Deming was a statistician and his philosophy did come from his understanding of variation as taught to him at the Western Electric plant (Chicago, IL) in the late 1920s from Dr. Walter Shewhart.
What W. Edwards Deming learned was how to evaluate data using a statistical process control (SPC) chart. To me, it’s the difference between knowledge and tampering or guessing.
Early in my career I was a corporate director of operations where I learned to evaluate income statements and compare last months revenue, expenses, etc. to this months and all types of dictates and commands came from this naive view of data.
Managing Data for Process Excellence
After attending Dr. Deming's four-day seminar and learning from the likes of Dr. Don Wheeler and Dr. "Frony" Ward, I learned a better way to manage with data.
In statistical terms understanding the differences between common and special causes of variation. Let's pretend we have sales of 15, 19, 14, 16, 12, 17, 15, 17 and 11 (in thousands). A manager might conclude that the month with 19,000 in sales is a celebratory moment best month on record and the last month with 11,000 is reason to "bark" at the salespeople for poor sales.
Common Cause Variation
By plotting data using the SPC chart (below), we can tell that we can expect anywhere from 5.1 (LCL-Lower Control Limit) to 25.1 (UPL-Upper Control Limit) with an average of 15.1. A manager celebrating 19,000 or getting upset over 11,000 is foolishness. In a matter of fact, we can expect between 5,100 and 25,100 (the control limits) in sales and it wouldn't be unusual.
This is called common cause variation.
INSERT GRAPH HERE
Conversely, if the next month showed 28,000 in sales (see chart below) this would be outside the UCL (Upper Control Limit). The $28,000 month is unusual (outside the limits) meaning we have a special cause. Something unusual has happened. Now is the time to investigate the reason there is overwhelming evidence that we should investigate the "special cause." There are other indicators of special causes (run of 8 and others) that need to be accounted for, but this is a column.
Not understanding the differences between common and special causes leads a manager to tamper with the system.
Two Mistakes Identified By Edward Deming
1. Reacting to an outcome as if it came from a special cause, when it came from common causes of variation.
2. To treat an outcome as if it came from common causes of variation, when it was from a special cause.
A systems thinking organization (or any other organization) must understand the differences between special and common causes of variation in order to manage effectively. Leadership development, organization change management programs and even technology implemented devoid of these basics are keeping service organizations from making better decisions.
This isn't just for Lean Six Sigma Black Belts and Master Black Belts, we all use data. We must know how to use this data to make better decisions and avoiding the mistakes Dr. Deming warned us about.
First published on the New Systems Thinking Blog
What W. Edwards Deming learned was how to evaluate data using a statistical process control (SPC) chart. To me, it’s the difference between knowledge and tampering or guessing.
Early in my career I was a corporate director of operations where I learned to evaluate income statements and compare last months revenue, expenses, etc. to this months and all types of dictates and commands came from this naive view of data.
Managing Data for Process Excellence
After attending Dr. Deming's four-day seminar and learning from the likes of Dr. Don Wheeler and Dr. "Frony" Ward, I learned a better way to manage with data.
In statistical terms understanding the differences between common and special causes of variation. Let's pretend we have sales of 15, 19, 14, 16, 12, 17, 15, 17 and 11 (in thousands). A manager might conclude that the month with 19,000 in sales is a celebratory moment best month on record and the last month with 11,000 is reason to "bark" at the salespeople for poor sales.
Common Cause Variation
By plotting data using the SPC chart (below), we can tell that we can expect anywhere from 5.1 (LCL-Lower Control Limit) to 25.1 (UPL-Upper Control Limit) with an average of 15.1. A manager celebrating 19,000 or getting upset over 11,000 is foolishness. In a matter of fact, we can expect between 5,100 and 25,100 (the control limits) in sales and it wouldn't be unusual.
This is called common cause variation.
INSERT GRAPH HERE
Conversely, if the next month showed 28,000 in sales (see chart below) this would be outside the UCL (Upper Control Limit). The $28,000 month is unusual (outside the limits) meaning we have a special cause. Something unusual has happened. Now is the time to investigate the reason there is overwhelming evidence that we should investigate the "special cause." There are other indicators of special causes (run of 8 and others) that need to be accounted for, but this is a column.
Not understanding the differences between common and special causes leads a manager to tamper with the system.
Two Mistakes Identified By Edward Deming
1. Reacting to an outcome as if it came from a special cause, when it came from common causes of variation.
2. To treat an outcome as if it came from common causes of variation, when it was from a special cause.
A systems thinking organization (or any other organization) must understand the differences between special and common causes of variation in order to manage effectively. Leadership development, organization change management programs and even technology implemented devoid of these basics are keeping service organizations from making better decisions.
This isn't just for Lean Six Sigma Black Belts and Master Black Belts, we all use data. We must know how to use this data to make better decisions and avoiding the mistakes Dr. Deming warned us about.
First published on the New Systems Thinking Blog
Tags:
quality
statistical analysis
data
variation
six sigma
blackbelt
causes of variation
lean
new systems thinking
Edward Deming
process excellence
Tripp Babbitt
