Every system has variation. Common cause variation is the variation due to the current system. Dr. Deming increased his estimate of variation due to the system (common cause variation) to 97% (earlier in his life he cited figures around 80%). Special cause variation is that due to some special (not part of the system) cause.
The control chart (in addition to other things) helps managers to avoid tampering (taking action on common cause variation as though it were a special cause). In order to take action against the results of common cause variation the performance of the system the system itself must be changed. A systemic improvement approach is needed.
To take action against a special cause, that isolated special cause can be examined. Unfortunately that approach (the one we tend to use almost all the time) is the wrong approach for systemic problems (which Deming estimated at 97% of the problems).
That doesn’t mean it is not possible to improve results by treating all problems as some special event. Examining each failure in isolation is just is not as effective. Instead examine the system that produced those results is the best method. The control chart provides a measurement of the system. The chart will show what the process is capable of producing and how much variation is in the system now.
If you would like to reduce the variation picking the highest data values (within the control limits) and trying to study them to figure out why they are so high is not effective. Instead you should study the whole system and figure out what systemic changes to make. One method to encourage this type of thinking is asking why 5 times. It seeks to find the systemic reasons for individual results.
At the Hunter Conference, years ago, a speaker (I forget who) talked about how to get useful feedback. He discussed how asking “how is everything” normally will get the response: “fine” (which is often that is exactly what the staff wants so they can move on without wasting any time). However, if you really want to improve that doesn’t help.
He explained how he worked with Disney to improve their restaurants. Using the “how is everything” question had not alerted the restaurant to any issues. So he visited the tables with the manager and asked – “What one thing could we do to improve?” Over 50% of the people said the rolls were stale: clear information that is actionable. And in fact they were able to adjust the system to remove that problem. A small thing, in this case, but a clear example of a good method to help target improvement.
To encourage useful feedback, specifically give the customer permission to mention something that could be improved. What one thing could we do better?
This post was sparked by Seth’s post: This must be hard. I think he was on the right track, but I think the results could be even better using a question like: what one thing could we do better?
One-Factor-at-a-Time Versus Designed Experiments (site broke link so I removed it -when will people learn how to manage web content?) by Veronica Czitrom:
The advantages of designed experiments over [One Factor at a Time] OFAT experiments are illustrated using three real engineering OFAT experiments, and showing how in each case a designed experiment would have been better. This topic is important because many scientists and engineers continue to perform OFAT experiments.
I still remember, as a child, asking what my father was going to be teaching the company he was going to consult with for a few days. He said he was going to teach them about using designed factorial experiments. I said, but you explained that to me and I am just a kid, how can you be teaching adults that? Didn’t they learn it in school? The article is a good introduction to the idea of why one factor at a time experiments are an ineffective way to learn.
The “Illusion of Explanatory Depth”: How Much Do We Know About What We Know? (broken link 🙁 was removed) is an interesting post that touches on psychology and theory of knowledge.
Often (more often than I’d like to admit), my son… will ask me a question about how something works, or why something happens the way it does, and I’ll begin to answer, initially confident in my knowledge, only to discover that I’m entirely clueless. I’m then embarrassed by my ignorance of my own ignorance.
I wouldn’t be surprised, however, if it turns out that the illusion of explanatory depth leads many researchers down the wrong path, because they think they understand something that lies outside of their expertise when they don’t.
I really like the title – it is more vivid than theory of knowledge. It is important to understand the systemic weaknesses in how we think in order to improve our thought process. We must question (more often than we believe we need to) especially when looking to improve on how things are done. Many things that we believe we have good reasons for, we will find we don’t if we question those beliefs.
We have discovered that creating a common experience in the classroom is absolutely essential. To accomplish this we implemented a modified production simulation exercise and in doing so, bring the opportunity to Go & See to the students. These types of simulations are quite common and are usually done with building blocks or paper airplanes. We chose paper airplanes and created a simulation that we run with the class as part of our very first class session. The exercise takes about 4 hours to run, during which time students build paper airplanes in groups of 4 or 5.
From lean tools to lean management (link broken by site so I removed it) by Jim Womack:
Only management by science through constant experimentation to answer questions can produce sustainable improvements in value streams. (Toyota’s A3 is a wonderful management tool for putting science to work and I’ll have more to say about it in the next few months.)
Please understand: Lean tools are great. We all need to master and deploy them, and our efforts of the last 15 years to do so are not wasted. But just as a carpenter needs a vision of what to build in order to get the full benefit of a hammer, we need a clear vision of our organizational objectives and better management methods before we pick up our lean tools.
In the Curious Cat Science and Engineering blog, The Future is Engineering points to 2 great essays on the secret of Silicon Valley. Guy Kawasaki puts it well, though in my opinion far to kind to our current MBA system (the inordinate focus on accounting does actual harm above and beyond the harm of ignoring what managers should learn):
If I had to point to the single biggest reason for Silicon Valley’s existence, it would be Stanford University—specifically, the School of Engineering. Business schools are not of primary importance because MBAs seldom sit around discussing how to change the world with great products.
Germany utility E.On AG said Wednesday that a European-wide blackout earlier this month that left millions without power was the result of human error and not any technical glitches.
The Duesseldorf-based company said the power outage, which led to blackouts in parts of Germany, France, Belgium, Italy, Portugal and Spain on Nov. 4, was not caused by a lack of proper maintenance or enough investment in transmission grids and facilities.
The blackout was caused after a high-voltage transmission line over a German river was turned off in an aborted attempt to allow a newly built Norwegian cruise ship to pass safely under it.
That triggered a blackout that briefly left 10 million people without power, stopping trains in their tracks and trapping people in elevators.
Ok, the focus seems to be that we didn’t do anything wrong, just some “human” made an error, which seems to be implied is out of their control. Why would the organization not be responsible for the people and the system working together? Management needs to create systems that works. That system includes people and equipment and process management and suppliers… Continue reading →
So a team of assembly employees made a real decision. Don’t make the worker pick the parts; let him focus on installing them. The idea seems obvious in retrospect: Deliver a kit of presorted visors and seat belts–one kit per car, each containing exactly the right parts. The team applied the simplest technology available, the blue Rubbermaid caddy. “We went just down the road to Wal-Mart and bought them,” Artrip says. Now, the line worker doesn’t have to make any decisions at all. Just grab the handle of the blue tote like a lunch pail and step into the car.