Tag Archives: Design of Experiments

Understanding Design of Experiments (DoE) in Protein Purification

This webcast, from GE Life Sciences, seeks to provide an understanding Design of Experiments (DoE) using an example of protein purification. It begins with a good overview of the reason why multi-factorial experiments must be used while changing multiple factors at the same time in order to see interactions between factors. These interactions are completely missed by one-factor-at-a-time experiments.

While it is a good introduction it might be a bit confusing if you are not familiar with multi-factorial designed experiments. You may want to read some of the links below or take advantage of the ability to pause the video to think about what he says or to replay portions you don’t pick up immediately.

I have discussed the value of design of experiments in multiple posts on this blog in the past, including: Introductory Videos on Using Design of Experiments to Improve Results by Stu Hunter, Design of Experiments: The Process of Discovery is Iterative and Factorial Designed Experiment Aim.

He also provides a good overview of 3 basic aims of multivariate experiment (DoE):

  • screening (to determine which factors have the largest impact on the results that are most important)
  • optimization (optimize the results)
  • robustness testing (determine if there are risks in variations to factors)

Normally an experiment will focus on one of these aims. So you don’t know the most important factors you may choose to do a screening experiment to figure out which factors you want to study in detail in an optimization experiment.

It could be an optimized set of values for factors provides very good results but is not robust. If you don’t have easy way to make sure the factors do not vary it may be worthwhile to choose another option that provides nearly as good results but is much more robust (good results even with more variation within the values of the factors).

Related: YouTube Uses Multivariate Experiment To Improve Sign-ups 15% (2009)Combinatorial Testing for Software (2009)Marketers Are Embracing Statistical Design of Experiments (2005)

Using Technology to Improve The Sharing of Knowledge

This month the ASQ is asking Influential Voices to discuss methods to aid in sharing knowledge. Manu Vora kicked the discussion off with his post on The Gift of Knowledge Transfer Through Technology.

My career has been largely shaped by the pursuit of better ways to communicate. I grew up surrounded by those seeking to improve management (Bill Hunter, George Box, Brian Joiner, Peter Scholtes…). When I was in grade school that focus was largely on statistics and the value of multi-factor experiments (Dad was a statistician who wrote the “bible” on design of experiments, with George Box and Stu Hunter: Statistics for Experimenters). As I moved into high school Dad was doing much more direct management consulting (it was also a combination of statistics, engineering and management but the emphasis shifted over time) based on Deming’s ideas.

The knowledge of how to properly experiment on system with multiple important factors to experiment with (nearly all experiments) has been around for almost 100 years. Yet, even so, still many college level courses talk about the need to adjust one factor at a time (OFAT) and many businesses still experiment this way. The rate at which we incorporate new knowledge is still very poor.

Technology can help improve our adoption of better understanding. Creating a climate and expectation of continued learning is also important, but I won’t talk about that in this post.

I published and presented (I think at an ASQ conference though I can’t recall which one right now) a paper on Using Quality to Develop an Internet Resource in 1999. The purpose of that internet resource was to share knowledge about quality management and the article provides insight into both those ways of looking at what was done (using quality ideas to create a resource and using the internet to spread quality ideas).

A few years later I started this blog to help people find knowledge that would make them more likely to succeed with efforts to improve management. I believe deeply in the value of Deming’s ideas on management but see so many companies make poor attempts to improve management. There are many things needed to improve the success of organizations improvement efforts but I believe the right knowledge (the ideas talked about by Deming, Ackoff, Ohno, Scholtes, etc.) will help a great deal.

Intranets are great tools to share knowledge within your organization. They can also be powerful tools to connect people to internal resources within your organization.

Wikis are a great tool to share a knowledge base (and to maintain things like standardized work, visual job instructions etc.). Wikis are a wonderful technology because of how easy they make the management of shared knowledge. It may well be you print out various things to post and make more visible (depending on what makes sense for the work environment).

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George Box Webcast on Statistical Design in Quality Improvement

George Box lecture on Statistical Design in Quality Improvement at the Second International Tampere Conference in Statistics, University of Tampere, Finland (1987).

Early on he shows a graph showing the problems with American cars steady over a 10 years period. Then he overlays the results for Japanese cars which show a steady and significant decline of the same period.

Those who didn’t get to see presentations before power point also get a chance to see old school, hand drawn, overhead slides.

He discusses how to improve the pace of improvement. To start with informative events (events we can learn from) have to be brought to the attention of informed observers. Otherwise only when those events happen to catch the attention of the right observer will we capture knowledge we can use to improve. This results in slow improvement.

A control chart is an example of highlighting that something worth studying happened. The chart will indicate when to pay attention. And we can then improve the pace of improvement.

Next we want to encourage directed experimentation. We intentionally induce informative events and pay close attention while doing so in order to learn.

Every process generates information that can be used to improve it.

He emphasis the point that this isn’t about only manufacturing but it true of any process (drafting, invoicing, computer service, checking into a hospital, booking an airline ticket etc.).

He then discussed an example from a class my father taught and where the students all when to a TV plant outside Chicago to visit. The plant had been run by Motorola. It was sold to a Japanese company that found there was a 146% defect rate (which meant most TVs were taken off the line to be fixed at least once and many twice) – this is just the defect rate before then even get off the line. After 5 years the same plant, with the same American workers but a Japanese management system had reduced the defect rate to 2%. Everyone, including managers, were from the USA they were just using quality improvement methods. We may forget now, but one of the many objections managers gave for why quality improvement wouldn’t work in their company was due to their bad workers (it might work in Japan but not here).

He references how Deming’s 14 points will get management to allow quality improvement to be done by the workforce. Because without management support quality improvement processes can’t be used.

With experimentation we are looking to find clues for what to experiment with next. Experimentation is an iterative process. This is very much the mindset of fast iteration and minimal viable product (say minimal viable experimentation as voiced in 1987).

There is great value in creating iterative processes with fast feedback to those attempting to design and improve. Box and Deming (with rapid turns of the PDSA cycle) and others promoted this 20, 30 and 40 years ago and now we get the same ideas tweaked for startups. The lean startup stuff is as closely related to Box’s ideas of experimentation as an iterative process as it is to anything else.

Related: Ishikawa’s seven quality control tools

He also provided a bit of history that I was not aware of saying the first application of orthogonal arrays (fractional factorial designs) in industry was by Tippett in 1933. And he then mentioned work by Finney in 1945, Plackett and Burman in 1946 and Rao in 1947.

Stu Hunter Discussing Bill Hunter, Statistics for Experimenters and EVOP

In this clip, Stu Hunter talks about Bill Hunter (my father, and no relation to Stu Hunter), Statistics for Experimenters and EVolutionary OPerations (EVOP).

Stu mentions Bill Hunter’s work with the City of Madison, which started with the First Street Garage (Out of the Crisis included a short write up on this effort by Dad, which, I believe, was the first application of Deming’s ideas in the public sector).

There was also a great deal of work done with the Police department, as the police chief, David Couper, saw great value in Deming’s ideas. The Police department did some great work and David’s blog shares wonderful ideas on improving policing. I don’t think Dad was that directly involved in what happened there, but it is one of the nice benefits of seeding new ideas: as they take root and grow wonderful things happen without any effort on your part.

As to why Dad got involved with the city, he returned from a summer teaching design of experiments and quality improvement methods in China (this is just before China was really open, a few outsiders were let in to teach). We had also lived overseas several other times, always returning to Madison. He decided he wanted to contribute to the city he loved, Madison, and so he talked to the Mayor about helping improve performance of the city.

The mayor listened and they started with a pilot project which Dad work on with Peter Scholtes. Dad talked to Peter, who he had know for years, and who worked for the city, before talking to the mayor. Read more about the efforts in Madison via the links at the end of this post.

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Design of Experiments: The Process of Discovery is Iterative

This video is another excerpt on the design of experiments videos by George Box, see previous posts: Introduction to Fractional Factorial Designed Experiments and The Art of Discovery. This video looks at learning about experimental design using paper helicopters (the paper linked there may be of interest to you also).

In this example a screening experiment was done first to find those factors that have the largest impact on results. Once the most important factors are determined more care can be put into studying those factors in greater detail.

The video was posted by Wiley (with the permission of George’s family), Wiley is the publisher of George’s recent autobiography, An Accidental Statistician, and many of his other books.

The importance of keeping the scope (in dollars and time) of initial experiments down was emphasized in the video.

George Box: “Always remember the process of discovery is iterative. The results of each stage of investigation generating new questions to answered during the next.”

Soren Bisgaard and Conrad Fung also appear in this except of the video.

The end of the video includes several suggested resources including: Statistics for Experimenters, Out of the Crisis and The Scientific Context of Quality Improvement.

Related: Introductory Videos on Using Design of Experiments to Improve Results (with Stu Hunter)Why Use Designed Factorial Experiments?brainstormingWhat Can You Find Out From 12 Experimental Runs?

George Box

I would most likely not exist if it were not for George Box. My father took a course from George while my father was a student at Princeton. George agreed to start the Statistics Department at the University of Wisconsin – Madison, and my father followed him to Madison, to be the first PhD student. Dad graduated, and the next year was a professor there, where he and George remained for the rest of their careers.

George died today, he was born in 1919. He recently completed An Accidental Statistician: The Life and Memories of George E. P. Box which is an excellent book that captures his great ability to tell stories. It is a wonderful read for anyone interested in statistics and management improvement or just great stories of an interesting life.

photo of George EP Box

George Box by Brent Nicastro.

George Box was a fantastic statistician. I am not the person to judge, but from what I have read one of the handful of most important applied statisticians of the last 100 years. His contributions are enormous. Several well know statistical methods are known by his name, including:

George was elected a member of the American Academy of Arts and Sciences in 1974 and a Fellow of the Royal Society in 1979. He also served as president of the American Statistics Association in 1978. George is also an honorary member of ASQ.

George was a very kind, caring and fun person. He was a gifted storyteller and writer. He had the ability to present ideas so they were easy to comprehend and appreciate. While his writing was great, seeing him in person added so much more. Growing up I was able to enjoy his stories often, at our house or his. The last time I was in Madison, my brother and I visited with him and again listened to his marvelous stories about Carl Pearson, Ronald Fisher and so much more. He was one those special people that made you very happy whenever you were near him.

George Box, Stuart Hunter and Bill Hunter (my father) wrote what has become a classic text for experimenters in scientific and business circles, Statistics for Experimenters. I am biased but I think this is acknowledged as one of (if not the) most important books on design of experiments.

George also wrote other classic books: Time series analysis: Forecasting and control (1979, with Gwilym Jenkins) and Bayesian inference in statistical analysis. (1973, with George C. Tiao).

George Box and Bill Hunter co-founded the Center for Quality and Productivity Improvement at the University of Wisconsin-Madison in 1984. The Center develops, advances and communicates quality improvement methods and ideas.

The Box Medal for Outstanding Contributions to Industrial Statistics recognizes development and the application of statistical methods in European business and industry in his honor.

All models are wrong but some are useful” is likely his most famous quote. More quotes By George Box

A few selected articles and reports by George Box

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Introductory Videos on Using Design of Experiments to Improve Results

The video shows Stu Hunter discussing design of experiments in 1966. It might be a bit slow going at first but the full set of videos really does give you a quick overview of the many important aspects of design of experiments including factorial designed experiments, fractional factorial design, blocking and response surface design. It really is quite good, if you find the start too slow for you skip down to the second video and watch it.

My guess is, for those unfamiliar with even the most cursory understanding of design of experiments, the discussion may start moving faster than you can absorb the information. One of the great things about video is you can just pause and give yourself a chance to catch up or repeat a part that you didn’t quite understand. You can also take a look at articles on design of experiments.

I believe design of experiments is an extremely powerful methodology of improvement that is greatly underutilized. Six sigma is the only management improvement program that emphasizes factorial designed experiments.

Related: One factor at a time (OFAT) Versus Factorial DesignsThe purpose of Factorial Designed Experiments

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One factor at a time (OFAT) Versus Factorial Designs

Guest post by Bradley Jones

Almost a hundred years ago R. A. Fisher‘s boss published an article espousing OFAT (one factor at a time). Fisher responded with an article of his own laying out his justification for factorial design. I admire the courage it took to contradict his boss in print!

Fisher’s argument was mainly about efficiency – that you could learn as much about many factors as you learned about one in the same number of trials. Saving money and effort is a powerful and positive motivator.

The most common argument I read against OFAT these days has to do with inability to detect interactions and the possibility of finding suboptimal factor settings at the end of the investigation. I admit to using these arguments myself in print.

I don’t think these arguments are as effective as Fisher’s original argument.

To play the devil’s advocate for a moment consider this thought experiment. You have to climb a hill that runs on a line going from southwest to northeast but you are only allowed to make steps that are due north or south or due east or west. Though you will have to make many zig zags you will eventually make it to the top. If you noted your altitude at each step, you would have enough data to fit a response surface.

Obviously this approach is very inefficient but it is not impossible. Don’t mistake my intent here. I am definitely not an advocate of OFAT. Rather I would like to find more convincing arguments to persuade experimenters to move to multi-factor design.

Related: The Purpose of Factorial Designed ExperimentsUsing Design of Experimentsarticles by R.A. Fisherarticles on using factorial design of experimentsDoes good experimental design require changing only one factor at a time (OFAT)?Statistics for Experimenters

Factorial Designed Experiment Aim

Multivariate experiments are a very powerful management tool to learn and improve performance. Experiments in general, and designed factorial experiments in particular, are dramatically underused by managers. A question on LinkedIn asks?

When doing a DOE we select factors with levels to induce purposely changes in the response variable. Do we want the response variable to move within the specs of the customers? Or it doesn’t matter since we are learning about the process?

The aim needs to consider what you are trying to learn, costs and potential rewards. Weighing the various factors will determine if you want to aim to keep results within specification or can try options that are likely to return results that are outside of specs.

If the effort was looking for breakthrough improvement and costs of running experiments that might produce results outside of spec were low then specs wouldn’t matter much. If the costs of running experiments are very high (compared with expectations of results) then you may well want to try designed experiment values that you anticipate will still produce results within specs.

There are various ways costs come into play. Here I am mainly looking at the costs as (costs – revenue). For example the case where if the results are withing spec and can be used the costs (net costs, including revenue) of the experiment run are substantially lower.
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Combinatorial Testing – The Quadrant of Massive Efficiency Gains

My brother, Justin Hunter, gives a lightning talk on Combinatorial Testing – The Quadrant of Doom and The Quadrant of Massive Efficiency Gains in the video above. The following text is largely directly quoted from the talk – with a bit of editing by me.

When you have a situation that has many many many possible parameters and each time only a few possible choices (a few items you are trying to vary and test – in his example in the video, 2 choices) you wind up with a ridicules number of possible tests. But you can cover all the possibilities in just 30 tests if your coverage target is all possible pairs. When you have situations like that you will see dramatic efficiency gains. What we have found in real world tests is greatly reduced time to create the tests and consistently 2 to 3 times as many defects found compared to the standard methods used for software testing.

You can read more on these ideas on his blog, where he explores software testing and combinatorial testing. The web base software testing application my brother created and shows in the demo is Hexawise. It is free to try out. I recommend it, though I am biased.

Related: Combinatorial Testing for SoftwareVideo Highlight Reel of Hexawise – a pairwise testing tool and combinatorial testing toolYouTube Uses Multivariate Experiment To Improve Sign-ups 15%What Else Can Software Development and Testing Learn from Manufacturing? Don’t Forget Design of Experiments (DoE)Maximize Test Coverage Efficiency And Minimize the Number of Tests Needed

Justin posted the presentation slides online at for anyone who is interested in seeing more details about the test plan he reviewed that had 1,746,756,896,558,880,852,541,440 possible tests. The slides are well worth reading.
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Highlights from Recent George Box Speech

The JMP blog has posted some highlights from George Box’s presentation at Discovery 2009

Infusing his entire presentation with humor and fascinating tales of his memories, Box focused on sequential design of experiments. He attributed much of what he knows about DOE [design of experiments] to Ronald A. Fisher. Box explained that Fisher couldn’t find the things he was looking for in his data, “and he was right. Even if he had had the fastest available computer, he’d still be right,” said Box. Therefore, Fisher figured out how to study a number of factors at one time. And so, the beginnings of DOE.

Having worked and studied with many other famous statisticians and analytic thinkers, Box did not hesitate to share his characterizations of them. He told a story about Dr. Bill Hunter and how he required his students to run an experiment. Apparently a variety of subjects was studied [see 101 Ways to Design an Experiment, or Some Ideas About Teaching Design of Experiments]

According to Box, the difficulty of getting DOE to take root lies in the fact that these mathematicians “can’t really get the fact that it’s not about proving a theorem, it’s about being curious about things. There aren’t enough people who will apply [DOE] as a way of finding things out. But maybe with JMP, things will change that way.”

George Box is a great mind and great person who I have had the privilege of knowing my whole life. My father took his class at Princeton, then followed George to the University of Wisconsin-Madison (where Dr. Box founded the statistics department and Dad received the first PhD). They worked together building the UW statistics department, writing Statistics for Experimenters and founding the Center for Quality and Productivity Improvement among many other things.

Statistics for Experimenters: Design, Innovation, and Discovery shows that the goal of design of experiments is to learn and refine your experiment based on the knowledge you gain and experiment again. It is a process of discovery. If done properly it is very similar to the PDSA cycle with the application of statistical tools to aid in determining the impact of various factors under study.

Related: Box on QualityGeorge Box Quotationsposts on design of experimentsUsing Design of Experiments

YouTube Uses Multivariate Experiment To Improve Sign-ups 15%

Google does a great job of using statistical and engineering principles to improve. It is amazing how slow we are to adopt new ideas but because we are it provides big advantages to companies like Google that use concepts like design of experiments, experimenting quickly and often… while others don’t. Look Inside a 1,024 Recipe Multivariate Experiment

A few weeks ago, we ran one of the largest multivariate experiments ever: a 1,024 recipe experiment on 100% of our US-English homepage. Utilizing Google Website Optimizer, we made small changes to three sections on our homepage (see below), with the goal of increasing the number of people who signed up for an account. The results were impressive: the new page performed 15.7% better than the original, resulting in thousands more sign-ups and personalized views to the homepage every day.

While we could have hypothesized which elements result in greater conversions (for example, the color red is more eye-catching), multivariate testing reveals and proves the combinatorial impact of different configurations. Running tests like this also help guide our design process: instead of relying on our own ideas and intuition, you have a big part in steering us in the right direction. In fact, we plan on incorporating many of these elements in future evolutions of our homepage.

via: @hexawiseMy brother has created a software application to provide much better test coverage with far fewer tests using the same factorial designed experiments ideas my father worked with decades ago (and yet still far to few people use).

Related: Combinatorial Testing for SoftwareStatistics for ExperimentersGoogle’s Website Optimizer allows for multivariate testing of your website.Using Design of Experiments

Combinatorial Testing for Software

Combinatorial testing of software is very similar to the design of experiments work my father was involved in, and which I have a special interest in. Combinatorial testing looks at binary interaction effects (success or failure), since it is seeking to find bugs in software, while design of experiments captures the magnitude of interaction effects on performance. In the last several years my brother, Justin Hunter, has been working on using combinatorial testing to improve software development practices. He visited me this week and we discussed the potential value of increasing the adoption of combinatorial testing, which is similar to the value of increasing the adoption of the use of design of experiments: both offer great opportunities for large improvements in current practices.

Automated Combinatorial Testing for Software

Software developers frequently encounter failures that occur only as the result of an interaction between two components. Testers often use pairwise testing – all pairs of parameter values – to detect such interactions. Combinatorial testing beyond pairwise is rarely used because good algorithms for higher strength combinations (e.g., 4-way or more) have not been available, but empirical evidence shows that some errors are triggered only by the interaction of three, four, or more parameters

Practical Combinatorial Testing: Beyond Pairwise by Rick Kuhn, US National Institute of Standards and Technology; Yu Lei, University of Texas, Arlington; and Raghu Kacker, US National Institute of Standards and Technology.

the detection rate increased rapidly with interaction strength. Within the NASA database application, for example, 67 percent of the failures were triggered by only a single parameter value, 93 percent by two-way combinations, and 98 percent by three-way combinations.2 The detection-rate curves for the other applications studied are similar, reaching 100 percent detection with four- to six-way interactions.
These results are not conclusive, but they suggest that the degree of interaction involved in faults is relatively low, even though pairwise testing is insufficient. Testing all four- to six-way combinations might therefore provide reasonably high assurance.

Related: Future Directions for Agile ManagementThe Defect Black MarketMetrics and Software DevelopmentFull and Fractional Factorial Test DesignGoogle Website Optimizer

Statistics for Experimenters in Spanish

book cover of Estadística para Investigadores

Statistics for Experimenters, second edition, by George E. P. Box, J. Stuart Hunter and William G. Hunter (my father) is now available in Spanish.

Read a bit more can find a bit more on the Spanish edition, in Spanish. Estadística para Investigadores Diseño, innovación y descubrimiento Segunda edición.

Statistics for Experimenters – Second Edition:

Catalyzing innovation, problem solving, and discovery, the Second Edition provides experimenters with the scientific and statistical tools needed to maximize the knowledge gained from research data, illustrating how these tools may best be utilized during all stages of the investigative process. The authors’ practical approach starts with a problem that needs to be solved and then examines the appropriate statistical methods of design and analysis.

* Graphical Analysis of Variance
* Computer Analysis of Complex Designs
* Simplification by transformation
* Hands-on experimentation using Response Service Methods
* Further development of robust product and process design using split plot arrangements and minimization of error transmission
* Introduction to Process Control, Forecasting and Time Series

Book available via Editorial Reverte

Related: Statistics for Experimenters ReviewCorrelation is Not CausationStatistics for Experimenters Dataposts on design of experiments

Full and Fractional Factorial Test Design

An Essential Primer on Full and Fractional Factorial Test Design

Since full factorial gathers additional data, it reveals all possible interactions, but as seen by the numbers above, there is a trade-off. More data equals more information but more data also equals a longer test duration. The minimum data requirements for full factorial are very high since you are showing every experiment.

Even if you are using full factorial to get the same amount of information as a fractional factorial test, it will take more time since you need more data to see statistically relevant differences between the many experiments. You might be wondering how fractional factorial can be accurate if interactions are possible?

Random interactions of high relevance are very rare, especially when looking for interactions of more than 2 factors. You really need to design tests where you look for meaningful interactions that are based on true business requirements rather than hoping for a random and low influence interaction between a red button, a hero shot and a headline.

I am a fan of design of experiments as long time readers know (see posts on design of experiments).

Some good resources for more on the topics discussed above: What Can You Find Out From 8 and 16 Experimental Runs? by George Box – Statistics for ExperimentersDesign of Experiments in Advertising.

Related: Google Website Optimizerfactorial experiment articlesUsing Design of ExperimentsMarketers Are Embracing Statistical Design of Experiments

Printer Product Development Using Design of Experiments

MEMS development in less than half the time by Christopher N. Delametter, Eastman Kodak Company

The traditional approach to optimizing a product or process using computer simulation is to evaluate the effects of one design parameter at a time. The problem with this approach is that interactions between design factors and second-order effects are likely to result in a locally optimized design that will provide far less performance than the global optimum. Kodak researchers use DOE to develop tests that examine first-order, second-order, and multiple factor effects simultaneously with relatively few simulation runs. The result is that the analyst can iterate to a globally optimized design with a far higher level of certainty and in much less time than the traditional approach.

By using DOE to drive CFD, Kodak researchers were able to optimize the design of the printhead in considerably less time than competitors. The advantages of simulation were especially apparent late in the project when researchers discovered a more optimal ink formulation for one of the colors.

Related: Design of Experiments articlesUsing Design of ExperimentsStatistics for ExperimentersWhy Use Designed Factorial Experiments?Kodak Debuts Printers With Inexpensive Cartridges

Management Improvement Carnival #34

Please submit your favorite management posts to the carnival. Read the previous management carnivals.

  • Introduction to Factorial Designs by Jonathan Mendez – “I like the idea of velocity in marketing — test, learn, test, learn, test. Instead of one large test I prefer focusing attention on certain areas or elements to achieve deeper understanding.”
  • MIT’s Message about Lean Enterprise Transformation by Mark Edmondson- “1. Market leaders are good at embracing enterprise change; 2. Enterprise change requires a holistic approach that engages all stakeholders. This includes employees, suppliers, customers, unions, and investors/owners”
  • Two Types of Bottleneck by David J. Anderson – “I now teach that there are two types of bottleneck: capacity constrained resources CCRs; and non-instant availability resources”
  • Oranges, Pebbles, and Sand by Ron Pereira – “In this video my daughters and I demonstrate how meeting an objective is just the beginning to improvement.”
  • Why errorproof when you can double-check? – “If you are in the position to prevent the error in the first place, why wouldn’t you? And, I’d argue, if you can write a tool to detect the screw up – ie, it is possible to programmatically figure out that the template is wrong,”
  • Systems and Improvement by John Dowd – “Thus did Deming, over sixty years ago, show a basic model about how to think about quality and improvement.”
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Using Design of Experiments

How to Institute DOE in Your Company (link broken – removed) by Davis Balestracci:

DOE works, but I don’t need to sell that to the readers of this newsletter. But as certain as we all are, no one can deny that design of experiments faces resistance even in environments where it is a proven tool. Every research scientist or engineer who has had a major success from DOE can tell you story after story of how management still wanted problems solved one-factor-at-a-time.

Design of Experiments (DoE) was developed by R.A. Fisher in the 1920s (related terms: factorial design, multivariate expertness). Six Sigma was the first general management approach that specifically highlighted the use of Designed Experiments for improvement. Still the use of factorial designed experiments is much less than it could be.
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Box on Quality

Bill Hunter and George Box

Dr. George Box is not as well known in the general management community as his ideas merit (in my biased opinion – photo of Bill Hunter and George Box). He is well know in the statistics field as one of the leading statistical minds. Box on Quality is an excellent book that gathers his essays from his 65th to 80th year. The book has just been issued in paperback (which helps as the hardback was pricey).

While some of the essays are aimed at a reader with an advanced understanding of statistics, many of the articles are aimed at any manager attempting to apply Quality Management principles (SPC, Deming, process improvement, six sigma, etc.). An except from the book provides a table of contents and an introduction.

Some of the articles from the book are available online. I encourage you to take a look at several of the articles and then go ahead and add this book to your prized management resources, if you find them worthwhile.

Design of Experiments Articles

We have added several Design of Experiments articles to the Curious Cat Management Improvement Library recently, including:

See more Design of Experiments related online resources.