Category Archives: Quality tools

Special Cause Signal Isn’t Proof A Special Cause Exists

One of my pet peeves is when people say that a point outside the control limits is a special cause. It is not. It is an indication that it likely a special cause exists, and that special cause thinking is the correct strategy to use to seek improvement. But that doesn’t mean there definitely was a special cause – it could be a false signal.

This post relies on an understand of control charts and common and special causes (review these links if you need some additional context).

Similarly, a result that doesn’t signal a special cause (inside the control limits without raising some other flag, say a run of continually increasing points) does not mean a special cause is not present.

The reason control charts are useful is to help us maximize our effectiveness. We are biased toward using special cause thinking when it is not the most effective approach. So the control chart is a good way to keep us focused on common cause thinking for improvement. It is also very useful in flagging when it is time to immediately start using special cause thinking (since timing is key to effective special cause thinking).

However, if there is result that is close to the control limit (but inside – so no special cause is indicated) and the person that works on the process everyday thinks, I noticed x (some special cause) earlier, they should not just ignore that. It very well could be a special cause that, because of other common cause variation, resulted in a data point that didn’t quite reach the special cause signal. Where the dot happened to land (just above or just below the control limit – does not determine if a special cause existed).

The signal is just to help us systemically make the best choice of common cause or special cause thinking. The signal does not define whether a special cause (an assignable cause) exists of not. The control chart tool helps guide us to use the correct type of improvement strategy (common cause or special cause). But it is just a signaling device, it isn’t some arbiter of whether a special cause actually exists.

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5s at NASA

NASA did some amazing things culminating with landing on Moon. Much of what they did was doing many small things very well. They used 5s, checklists, gemba thinking, usability, simplicity, testing out on a small scale and much more.

Here are a few photos from the Smithsonian Air and Space museum in Washington DC. I also have some nicer NASA 5s photos from the new Annex near Dulles Airport, but, ironically, I can’t find them.

photo of container labeled with many compartments for NASA

These kits were used by NASA astronauts on the Apollo 11 mission to the moon. Obviously NASA had to have everything that might be needed where it was needed (picking up something from the supply closet in building 2 wasn’t an option).

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My New Book: Management Matters

Image of the book cover of Management Matters by John Hunter

Management Matters by John Hunter is now available.

I have a new book in progress: Management Matters. It is now available in “pre-release format” via leanpub. The idea I am experimenting with (supported by leanpub) is pre-publishing the book online. The ebook is available for purchase now, and comes with free access to the updates.

My plan is to continue working on the book for the next few months and have it “release ready” by October, 2012. One of the advantages of this method is that I can incorporate ideas based on feedback from the early readers of the book.

There are several other interesting aspects to publishing in this way. Leanpub allows a suggested retail price, and a minimum price. So I can set a suggested price and a minimum price and the purchaser gets to decide what price to pay (they can even pay over suggested retail price – which does happen). The leanpub model provides nearly all the revenue to the author (unlike traditional models) – the author gets 90% of the price paid, less 50 cents per book (so $8.50 of a $10 purchase).

They provide the book in pdf, mobi (Kindle) and epub (iPad, Nook, etc.) formats. And the books do not have any Digital Rights Management (DRM) entanglements.

Management Matters covers topics familiar to those who have been reading this blog for years. It is an attempt to put in one place the overall management system that is most valuable (which as you know, based on the blog, is largely based upon Dr. Deming’s ideas – which means lean manufacturing are widely covered too).

I hope the book is now in a state where those who are interested would find it useful, but it is in what I consider draft format. I still have much editing to do and content to add.

Leanpub also provides a sample book (where a portion of the content can be downloaded to decide if you want to buy). If you are interested please give it a try and let me know your thoughts.

Value Stream Mapping for Fun and Profit

Guest post by Evan Durant, author of the Kaizen Notebook blog [The broken link was removed].

I tend to get a little preachy about the importance of value stream maps, but they really can be useful tools not only to plan an improvement effort but also to monitor your progress going forward. In particular they provide a way to quantify the impact of changes to your process. Here’s a real life example as a case in point.

For a particular value stream a team went to gemba, followed the flow of material and information, collected process cycle times, and counted inventory. When everything was mapped and all the data tallied, here was the current state that they came up with:

Total Lead Time:		16.8 days
Process Lead Time:		2.2 days
Process Time:		1.9 days
Operator Cycle Time:		8.2 minutes

So what does all this mean? First of all the Total Lead Time represents the amount of time that a new piece of raw material would take to enter the value stream, be worked on, wait around with all the rest of the material in process, and then finally make its way to the customer. This number is usually driven higher by large amounts of in-process inventory caused by pushing between operations.

Second, the Process Lead Time is the amount of time it would take to process a single batch through the process, if it didn’t have to wait behind any other batches. Note that even though parts are processed one at a time through all of the manual operations, a certain amount of batching is required to overcome long machine cycle times in automatic operations. Also we do not ship parts to the customer one at a time, but rather in standard package sizes.

Third, we have the Process Time. This is the total amount of value added time, manual and automatic processing, that a part sees in the value stream.

Finally the Operator Cycle Time (also called manual time) is the amount of actual “touch” time required to make a part. The difference between the Process Time and the Operator Cycle Time is the Machine Cycle Time (also called automatic time). This is when a batch of parts is on a machine that does not require any operator intervention during a cycle. (We have a lot of machine cycle time in this value stream.)

Then the team applied the concepts of flow and pull to reduce overproduction and pace the value stream to the rate of customer demand. The results of the future state map were as follows:

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Richard Feynman Explains the PDSA Cycle

Ok, really Richard Feynman Explains the scientific method. But his thoughts make the similarity between the PDSA cycle and the scientific method obvious.

1) Plan, hypothesis.
You make a guess about a theory (in using the PDSA cycle this step is often missed, while in the scientific method this is of the highest priority). You make a prediction based on that theory.

2) Do the experiment

3) Study the results

If the results disprove the theory you were wrong. If they results don’t disprove the theory you may have a useful theory (it can also be that your theory is still wrong, but this experiment happened not to provide results that disprove it).

Step 4, Act, only exists for PDSA. In science the aim is to learn and confirm laws. While the PDSA cycle has an aim to learn and adopt methods that achieve the desired results.

Richard Feynman: “If it disagrees with experiment it is wrong, in that simple statement is the key to science, it doesn’t make any difference how beautiful your guess is, it doesn’t make a difference how smart you are (who made the guess), or what his name is, if it disagrees with experiment it is wrong.”

Actually far to often “PDSA” fails to adopt this understanding. Instead it become PA: no study of the results, just implement and we all already agree it is going to work so don’t bother wasting time testing that it actually does. Some organization do remember to study results of the pilot experiments but then forget to study the results when the new ideas are adopted on a broader scale.

Management Blog Posts From November 2006

I have selected a few great posts from the Curious Cat Management Blog back in November 2006.

What Could we do Better? – There are many important ideas to improve management. This is one of the most important tips to aid improvement that I know of: it is easy to do, brings huge benefits and most organizations fail to do it. Ask your customers: “What one thing could we do to improve?”
Ackoff’s F-laws: Common Sins of Management presents 13 common sins of management, such as: Managers who don’t know how to measure what they want settle for wanting what they can measure
Common Cause Variation – “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 as low as 80%). Special cause variation is that due to some special (not part of the system) cause.”
Sub-Optimize by Interrupting Knowledge Workers – “The general consensus is that the loss from interrupting [software] developers is much greater than for interrupting most other forms of work and therefor a great deal of effort is placed on improving the system to allow developers to focus.”
Amazon Innovation – “I believe Amazon uses technology very well. They have done many innovative things. They have been less successful at turning their technology into big profits. But I continue to believe they have a good shot at doing so going forward (and their core business is doing very well I think).” [Amazon announced great sales numbers today, continuing their long term tread. They are also continuing to be very slow to grow profits (CEO, Jeff Bezos remains willing to challenge common practices – such as his willingness to build business and sacrifice current profits)].

Keys to the Effective Use of the PDSA Improvement Cycle

The PDSA improvement cycle was created by Walter Shewhart where Dr. Deming learned about it. An improvement process is now part of many management improvement methods (A3 for lean manufacturing, DMAIC for six sigma and many other modifications). They are fairly similar in many ways. The PDSA cycle (Plan, Do, Study, Act) has a few key pieces that are either absent in most others processes of greatly de-emphasized which is why I prefer it (A3 is my second favorite).

The PDSA cycle is a learning cycle based on experiments. When using the PDSA cycle, it is important to predict the results. This is important for several reasons but most notably due to an understanding of the theory of knowledge. We will learn much more if we write down our prediction. Otherwise we often just think (after the fact); yeah that is pretty much what I expected (even if it wasn’t). Also we often fail to think specifically enough at the start to even have a prediction. Forcing yourself to make a prediction gets you to think more carefully up front and can help you set better experiments.

PDSA Improvement cycle graphic from my book – Management Matters

An organization using PDSA well will turn the PDSA cycle several times on any topic and do so quickly. In a 3 month period turning it 5 times might be good. Often those organizations that struggle will only turn it once (if they are lucky and even reach the study stage). The biggest reason for effective PDSA cycles taking a bit longer is wanting more data than 2 weeks provides. Still it is better to turn it several times will less data – allowing yourself to learn and adjust than taking one long turn.

The plan stage may well take 80% (or even more) of the effort on the first turn of the PDSA cycle in a new series. The do stage may well take 80% of of the time (when you look at the whole process, multiple turns through the PDSA cycle) – it usually doesn’t take much effort (to just collect a bit of extra data) but it may take time for that data to be ready to collect. In the 2nd, 3rd… turns of the PDSA cycle the Plan stage often takes very little time. Basically you are just adjusting a bit from the first time and then moving forward to gather more data. Occasionally you may learn you missed some very important ideas up front; then the plan stage may again take some time (normally if you radically change your plans).

Remember to think of Do as doing-the-experiment. If you are “doing” a bunch of work (not running an experiment and collecting data) that probably isn’t “do” in the PDSA sense.

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Agile Story Point Estimation

In agile software development tasks are documented as user stories. Then the level of effort for those stores can be estimated by assigning each story points. The velocity that can be produced in a period (called a sprint, for us 2 weeks) can be estimated. Thus you can predict what can be delivered in the next sprint (which can help business managers make priority decisions).

I have found estimation to be worthwhile. In doing so, we accept there is a great amount of variation but points give a hint to scale. They can help prioritize (if you have 5 things you want but 1 is much harder you may well drop that to the bottom). I have always accepted a great amount of variation in the velocity, worry about the variation I don’t find worthwhile. I do think trying to act as though the velocity is precise can lead to problems. At the same time having a measure of velocity, even accepting understanding variation was present, was useful.

Over time reducing variation (probably largely through better estimation and perhaps a few better tools, reduced technical debt, better documentation, testing…) is helpful and laudable. We made improvement but still lots of variation existed. The biggest help in reducing the measured velocity was breaking down large stories to more manageable sizes. The challenge of estimating user stories, I suspect, has some fairly high variation (even with good system improvements that can help reduce variation).

Large stories just can hide huge variation in what is really required once getting into implementing it.

The way we did estimation (discussing in a sprint planning meeting) did take some time (but not a huge amount). It was agreed by those involved that the time spent was worthwhile. Sometimes we did slip and spend too much time on this, that was an area we had to pay attention to. The discussions were educational and helped provide guidance on how to approach the story. The value of discussions around estimations was probably the biggest surprise I have had in implementing any agile ideas. The value of those discussion was much higher than I imagined (I basically anticipated them just as non-value added time to get the result of an estimate, but they were a source of learning and consensus building).

These thoughts were prompted by: Story Points Considered Harmful – Or why the future of estimation is really in our past…

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Eliminate the Waste of Waiting in Line with Queuing Theory

One thing that frustrates me is how managers fail to adopt proven strategies for decades. One very obvious example is using queuing theory to setup lines.

Yes it may be even better to adopt strategies to eliminate as much waiting in line as possible, but if there is still waiting in line occurring and you are not having one queue served by multiple representatives shame on you and your company.

Dr. Deming in 1980 on Product Quality in Japan and the USA

I posted an interesting document to the Curious Cat Management Library: it includes Dr. Deming’s comments as part of a discussion organized by the Government Accounting Office in 1980 on Quality in Japan and the United States.

The document provides some interesting thoughts from Dr. Deming and others; Dr. Deming’s statements start on page 52 of the document. For those really interested in management improvement ideas it is a great read. I imagine most managers wouldn’t enjoy it though (it isn’t giving direct advice for today, but I found it very interesting).

Some selected quotes from the document follow. On his work with Japan in 1950:

This movement, I told them, will fail and nothing will happen unless management does their part. Management must know something about statistical techniques and know that if they are good one place, they will work in another. Management must see that they are used throughout the company.

Quality control must take root with simple statistical techniques that management and everyone in the company must learn. By these techniques, people begin to understand the different kinds of variation. Then quality control just grow with statistical theory and further experience. All this learning must be guided by a master. Remarkable results may come quick, but one has no right to expect results in a hurry. The learning period never ends.

The statistical control of quality is not for the timid and the halfhearted. There is no way to learn except to learn it and do it. You can read about swimming, but you might drown if you had to learn it that way!

One of the common themes at that time was Deming’s methods worked because Japanese people and culture were different. That wasn’t why the ideas worked, but it was an idea many people that wanted to keep doing things the old way liked to believe.

There may be a lot of difference, I made the statement on my first visit there that a Japanese man was never too old nor too successful to learn, and to wish to learn; to study and to learn. I know that people here also study and learn. I’ll be eighty next month in October. I study every day and learn every day. So you find studious people everywhere, but I think that you find in Japan the desire to learn, the willingness to learn.

You didn’t come to hear me on this; there are other people here much better qualified than I am to talk. But in Japan, a man works for the company; he doesn’t work to please somebody. He works for the company, he can argue for the company and stick with it when he has an idea because his position is secure. He doesn’t have to please somebody. It is so here in some companies, but only in a few. I think this is an important difference.

At the time the way QC circles worked in Japan was basically employee led kaizen. So companies that tried to copy Japan told workers: now go make things better like the workers we saw in Japan were doing. Well with management not changing (and understanding Deming’s ideas, lean thinking, variation, systems thinking…) and staff not given training to understand how to improve processes it didn’t work very well. We (those reading this blog) may all now understand the advantages one piece flow. I can’t imagine too many people would jump to that idea sitting in their QC circle without having been told about one piece flow (I know I wouldn’t have), and all the supporting knowledge needed to make that concept work.

QC circles can make tremendous contributions. But let me tell you this, Elmer. If it isn’t obvious to the workers that the managers are doing their part, which only they can do, I think that the workers just get fed up with trying in vain to improve their part of the work. Management must do their part: they must learn something about management.

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