Divine Wisdom and Paradigm Shifts:

cancer

One of the best books I have read in recent times is The Emperor of All Maladies by the talented Siddhartha Mukherjee. Mukherjee won the 2011 Pulitzer Prize for this book. The book is a detailed history of Cancer and humanity’s battle with it. Amongst many things that piqued my interest, was one of the quotes I had heard attributed to Dr. Deming – In God we trust, all others must bring data.

To tell this story, I must first talk about William S. Halsted. Halsted was a very famous surgeon from John Hopkins who came up with the surgical procedure known as the “Radical Mastectomy” in the 1880’s. This is a procedure to remove the breast, the underlying muscles and attached lymph nodes to treat breast cancer. He hypothesized that the breast cancer spreads centrifugally from the breast to other areas. Thus, the removal of the breast, underlying muscles and lymph nodes would prevent the spread of cancer. He called this the “centrifugal theory”. Halsted called this procedure as “radical” to notate that the roots of the cancer are removed. Mukherjee wrote in his book that the intent of radical mastectomy was to arrest the centrifugal spread by cutting every piece of it out of the body. Physicians all across America identified the Radical Mastectomy as the best way to treat breast cancer. The centrifugal theory became the paradigm for breast cancer treatment for almost a century.

There were skeptics of this theory. The strongest critics of this theory were Geoffrey Keynes, a London based surgeon in the 1920s, and George Barney Crile, an American surgeon who started his career in the 1950s. They noted that even with the procedures that Halsted had performed, many patients died within four or five years from metastasis (cancer spreading to different organs). The surgeons overlooked these flaws, as they were firm believers in the Radical Mastectomy. Daniel Dennett, the famous American Philosopher, talks about the concept of Occam’s Broom, which might explain the thinking process for ignoring the flaws in a hypothesis. When there is a strong acceptance of a hypothesis, any contradicting information may get swept under the rug with Occam’s Broom. The contradictory information gets ignored and not confronted.

Keynes was even able to perform a local surgery of the breast and together with radiation treatment achieve some success. But Halsted’s followers in America ridiculed this approach, and came up with the name “lumpectomy” to call the local surgery. In their minds, the surgeon was simply removing “just” a lump, and this did not make much sense. They were aligning themselves with the paradigm of Radical Mastectomy. In fact, some of the surgeons even went further to come up with “superradical” and “ultraradical” procedures that were morbidly disfiguring procedures where the breast, underlying muscles, axillary nodes, the chest wall, and occasionally the ribs, part of the sternum, the clavicle and the lymph nodes inside the chest were removed. The idea of “more was better” became prevalent.

Another paradigm with clinical studies during that time was trying to look only for positive results – is treatment A better than treatment B? However, this approach did not show that treatment A was no better than treatment B. Two statisticians, Jerry Neyman and Egon Pearson, changed the approach with their idea of using the statistical concept of power. The sample size for a study should be based on the power calculated. Loosely stated, more independent samples mean higher power. Thus, with a large sample size of randomized trials, one can make a claim of “lack of benefit” from a treatment. The Halsted procedure did not get challenged for a long time because the surgeons were not willing to take part in a large sample size study.

A Philadelphia surgeon named Dr. Bernard Fisher was finally able to shift this paradigm in the 1980s. Fisher found no reason to believe in the centrifugal theory. He studied the cases put forth by Keynes and Crile. He concluded that he needed to perform a controlled clinical trial to test the Radical Mastectomy against Simple Mastectomy and Lumpectomy with radiation. The opposition from the surgeons slowly shifted with the strong advocacy from the women who wanted a less invasive treatment. Mukherjee cites the Thalidomide tragedy, the Roe vs Wade case, along with the strong exhortation from Crile to women to refuse to submit to a Radical Mastectomy, and the public attention swirling around breast cancer for the slow shift in the paradigm. Fisher was finally able to complete the study, after ten long years. Fisher stated that he was willing to have faith in divine wisdom but not in Halsted as divine wisdom. Fisher brusquely told a journalist – “In God we trust. All other must have data.”

The results of the study proved that all three cases were statistically identical. The group treated with Radical Mastectomy however paid heavily from the procedure but had no real benefits in survival, recurrence or mortality. The paradigm of Radical Mastectomy shifted and made way to better approaches and theories.

While I was researching this further, I found that the quote “In God we trust…” was attributed to another Dr. Fisher. Dr. Edwin Fisher, brother of Dr. Bernard Fisher, when he appeared before the Subcommittee on Tobacco of the Committee on Agriculture, House of Representatives, Ninety-fifth Congress, Second Session, on September 7, 1978. As part of presentation Dr. Fisher said – “I should like to close by citing a well-recognized cliche in scientific circles. The cliche is, “In God we trust, others must provide data. This is recorded in “Effect of Smoking on Nonsmokers. Hearing Before the Subcommittee on Tobacco of the Committee on Agriculture House of Representatives. Ninety-fifth Congress, Second Session, September 7, 1978. Serial Number 95-000”. Dr. Edwin Fisher unfortunately was not a supporter of the hypothesis that smoking is bad for a non-smoker. He even cited that people traveling on an airplane are more bothered by crying babies than the smoke from the smokers.

fisher

Final Words:

This past year, I was personally affected by a family member suffering from the scourge of breast cancer. During this period of Thanksgiving in America, I am thankful for the doctors and staff who facilitated her recovery. I am thankful for the doctors and experts in the medical field who were courageous to challenge the “norms” of the day for treating breast cancer. I am thankful for the paradigm shift(s) that brought better and effective treatments for breast cancer. More is not always better! I am thankful for them for not accepting a hypothesis based on just rationalism, an intuition on how things might be working. I am thankful for all the wonderful doctors and staff out there who take great care in treating all cancer patients.

I am also intrigued to find the quote of “In God we trust…” used with the statement that smoking may not have a negative impact on non-smokers.

I will finish with a story of another paradigm shift from Joel Barker in The Business of Paradigms.

A couple of Swiss watchmakers in Centre Electronique Horloger (CEH) in Neuchâtel, Switzerland, developed the first Quartz based watch. They went to different Swiss watchmakers with the technology that would later revolutionize the watch industry. However, the paradigm at that time was the intricate Swiss watch making process with gears and springs. No Swiss Watch company was interested in this new technology which did not rely on gears or springs for keeping time. The Swiss watchmakers with the new idea then went to a Clock convention and set up a booth to demonstrate their new idea. Again, no Swiss watch company was interested in what they had to offer. Two representatives, one from the Japanese company Seiko, and the other from Texas Instruments took notice of the new technology. They purchased the patents and as they say – the rest is history. The new paradigm then became Quartz watches. The Swiss, who were on the top of watch making with over 50% of the watch market in the 1970s, stepped aside for the Quartz watch revolution marking the decline of their industry. This was later termed as the Quartz Revolution.    

Always keep on learning…

In case you missed it, my last post was The Best Attribute to Have at the Gemba:

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Which Way You Should Go Depends on Where You Are:

compass

I recently read the wonderful book “How Not To Be Wrong, The Power of Mathematical Thinking” by Jordan Ellenberg. I found the book to be enlightening and a great read. Jordan Ellenberg has the unique combination of being knowledgeable and capable of teaching in a humorous and engaging way. One of the gems in the book is – “Which way you should go depends on where you are”. This lesson is about the dangers of misapplying linearity. When we are thinking in terms of abstract concepts, the path from point A to point B may appear to be linear. After all, the shortest path between two points is a straight line. This type of thinking is linear thinking.

To illustrate this, let’s take the example of poor quality issues on the line. The first instinct to improve quality is to increase inspection. In this case, point A = poor quality, and point B = higher quality. If we plot this incorrect relationship between Quality and Inspection, we may assume it as a linear relationship – increasing inspection results in better quality.

Inspection and Quality

However, increasing inspection will not result in better quality in the long run and will result in higher costs of production. We must build quality in as part of the normal process at the source and not rely on inspection. In TPS, there are several ways to do this including Poka Yoke and Jidoka.

In a similar fashion, we may look at increasing the number of operators in the hopes of increasing productivity. This may work initially. However, increasing production at the wrong points in the assembly chain can hinder the overall production and decrease overall productivity. Taiichi Ohno, the father of Toyota Production System, always asked to reduce the number of operators to improve the flow. Toyota Production System relies on the thinking of the people to improve the overall system.

The two cases discussed above are nonlinear in nature. Thus increasing one factor may increase the response factor initially. However, continually increasing the factor can yield negative results. One example of a non-linear relationship is shown below:

productivity

The actual curve may of course vary depending on the particularities of the example. In nonlinear relationships, which way you should go depends on where you are. In the productivity example, if you are at the Yellow star location on the curve, increasing the operators will only decrease productivity. You should reduce the number of operators to increase productivity. However, if you are at the Red star, you should look into increasing the operators. This will increase productivity up to a point, after which the productivity will decrease. Which Way You Should Go Depends on Where You Are!

In order to know where you are, you need to understand your process. As part of this, you need to understand the significant factors in the process. You also need to understand the boundaries of the process where things will start to breakdown. The only way you can truly learn your process is through experimentation and constant monitoring. It is likely that you did not consider all of the factors or the interactions. Everything is in flux and the only constant thing is change. You should be open for input from the operators and allow improvements to happen from the bottom up.

I will finish off with the anecdote of the “Laffer curve” that Jordan Ellenberg used to illustrate the concept of nonlinearity. One polical party in America have been pushing for lowering taxes on the wealthy. The conservatives made this concept popular using the Laffer curve. Arthur Laffer was an economics professor at the University of Chicago. The story goes that Arthur Laffer drew the curve on the back of a napkin during dinner in 1974 with the senior members of then President Gerald Ford’s administration. The Laffer Curve is shown below:

Laffer curve

The horizontal axis shows the tax rate and the vertical axis shows the revenue that is generated from taxation. If there is no taxation, then there is no revenue. If there is 100% taxation, there is also no revenue because nobody would want to work and make money, if they cannot hold on to it. The argument that was raised was that America was on the right hand side of the curve and thus reducing taxation would increase revenue. It has been challenged whether this assumption was correct. Jordan used the following passage from Greg Manikiw, a Harvard economist and a Republican who chaired the Council of Economic Advisors under the second President Bush:

Subsequent history failed to confirm Laffer’s conjecture that lower tax rates would raise tax revenue. When Reagan cut taxes after he was elected, the result was less tax revenue, not more. Revenue from personal income taxes fell by 9 percent from 1980 to 1984, even though average income grew by 4 percent over this period. Yet once the policy was in place, it was hard to reverse.

The Laffer curve may not be symmetric as shown above. The curve may not be smooth and even as shown above and could be a completely different curve altogether. Jordan states in the book – All the Laffer curve says is that lower taxes could, under some circumstances, increase tax revenue; but figuring out what those circumstances are requires deep, difficult, empirical work, the kind of work that doesn’t fit on a napkin.

Always keep on learning…

In case you missed it, my last post was Epistemology at the Gemba:

Concept of Constraints in Facing Problems:

220px-Atlas_Santiago_Toural_GFDL

In today’s post, I will be looking at the concept of constraints in facing problems. Please note that I did not state “solving problems”. This is because not all problems are solvable. There are certain problems, referred to as “wicked problems” or complex problems that are not solvable. These problems have different approaches and none of the approaches can solve the problems completely. Some of the alternatives are better than the others, but at the same time they may have their own unintended consequences. Some examples of this are global warming and poverty.

My post is related to the Manufacturing world. Generally in the manufacturing world, most of the problems are solvable. These problems have a clear cause and effect relationships. They can be solved by using the best practice or a good practice. The best practice is used for obvious problems, when the cause and effect relationship is very clear, and there is truly one real solution. A good practice is employed where the cause and effect relationship is evident only with the help of subject-matter-experts. These are called “complicated problems”. There are also complex problems where the cause and effect relationships are not evident. These may be understood only after-the-fact. An example for this is launching a new product and ensuring a successful launch. Most of the time, the failures are studied and the reasons for the failure are “determined” after the fact.

The first step in tackling these problems is to understand what type of problem it is. Sometimes, the method to solve a problem is prescribed before the problem is understood. Some of the methods assume that the problem has a linear cause and effect relationship. An example is 5 why. 5 why assumes that there is a linear relationship between cause and effect. This is evident in the question – “why did x happen?”  This works fine for the obvious problems. This may not work that well for complicated problems and never for a complex problem. One key thing to understand is that the problems can be composite problems, some aspects may be obvious while some aspects may be complicated. Using a prescribed method can be ineffective in these cases.

The concept of constraints is tightly related to the concept of variety. The best resource for this is Ross Ashby’s “An Introduction to Cybernetics” [1]. Ashby defined variety as the number of distinct elements in a set of distinguishable elements or as the logarithm to base 2 of the number of distinct elements. Thus, we can say that the variety of genders is 2 (male or female) or as 1 bit (based on the logarithm calculation). Ashby defined constraint as a relation between two sets. Constraint only exists when one set’s variety is lower than the other set’s variety. Ashby gives the axample of a school that only admits boys. Compared to the set of gender (boys and girls), the school’s variety is less (only boys). Thus the school has a constraint imposed on itself.

A great resource for this is Dave Snowden and his excellent Cynefin framework [2]. Snowden says that ontology precedes epistemology or in other words data precedes framework. The fundamental properties of the problem must be understood before choosing a “tool” to address the problem. Prescribing a standard tool to use in all situations is constraining oneself and this will lead to ineffective attempts at finding a solution. When the leader says we need to use lean or six sigma, this is an attempt to add constraints by removing variety. Toyota’s methodologies referred to as Toyota Production System, was developed for their problems. They identified the problems and then proceeded to find ways to address them. They did not have a framework to go by. They created the framework based on decades of experience and tweaking. Thus blindly copying their methodologies are applying constraints on yourself that may be unnecessary. As the size or scope of a project increases, it tends to increase the complexity of the project. Thus enterprise wide applications of “prescribed solutions” are not generally effective since the cause-effect relationships cannot be completely predicted leading to unintended consequences, inefficiency and ineffectiveness. On the other hand, Ashby advises to take note of any existing constraints in a system, and to take advantage of the constraints to improve efficiency and effectiveness.

A leader should thus first understand the problem to determine the approach to proceed. Sometimes, one may have to use a composite of tools. One needs to be open for modifications by having a closed loop(s) with a feedback mechanism so that the approach can be modified as needed. It is also advisable to use heuristics like genchi genbutsu since they are general guidelines or rules of thumb. This does not pose a constraint. Once a methodology is chosen, then a constraint is being applied since the available number of tools to use (variety) has now diminished.  This thinking in terms of constraints prevents the urge to treat everything as a nail when your preferred tool is a hammer.

I will finish with a great story from the great Zen master Huangbo Xiyun;

Huangbo once addressed the assembly of gathered Zen students and said; “You are all partakers of brewer’s grain. If you go on studying Zen like that, you will never finish it. Do you know that in all the land of T’ang there is no Zen teacher?”
Then a monk came forward and said, “But surely there are those who teach disciples and preside over the assemblies. What about that?”
Huangbo said, “I do not say that there is no Zen, but that there is no Zen teacher…”

Always keep on learning…

In case you missed it, my last post was Jidoka, the Governing Principle for Built-in-Quality:

[1] https://www.amazon.com/Introduction-Cybernetics-W-Ross-Ashby/dp/1614277656

[2] http://cognitive-edge.com/blog/part-two-origins-of-cynefin/

Process Validation and the Problem of Induction:

EPSON MFP image

From “The Simpsons”

Marge: I smell beer. Did you go to Moe’s?

Homer: Every time I have beer on my breath, you assume I’ve been drinking.[1]

In today’s post, I will be looking at process validation and the problem of induction.  I have looked at process validation through another philosophical angle by using the lesson of the Ship of Theseus [4] in an earlier post.

US FDA defines process validation [2] as;

“The collection and evaluation of data, from the process design stage through commercial production, which establishes scientific evidence that a process is capable of consistently delivering quality product.”

My emphases on FDA’s definition are the two words – “capability” and “consistency”. One of the misconceptions about process validation is that once the process is validated, then it achieves almost an immaculate status. One of the horror stories I have heard from my friends in the Medical Devices field is that the manufacturer stopped inspecting the product since the process was validated. The problem with validation is the problem of induction. Induction is a process in philosophy – a means to obtain knowledge by looking for patterns from observations and coming to a conclusion. For example, the swans that I have seen so far are white, thus I conclude that ALL swans are white. This is a famous example to show the problem of induction because black swans do exist. However, the data I collected showed that all of the swans in my sample were white. My process of collection and evaluation of the data appears capable and the output consistent.

The misconception that the manufacturer had in the example above was the assumption that the process is going to remain the same and thus the output also will remain the same. This is the assumption that the future and present are going to resemble the past. This type of thinking is termed the assumption of “uniformity of nature” in philosophy. This problem of induction was first thoroughly questioned and looked at by the great Scottish philosopher David Hume (1711-1776). He was an empiricist who believed that knowledge should be based on one’s sense based experience.

One way of looking at process validation is to view the validation as a means to develop a process where it is optimized such that it can withstand the variations of the inputs. Validation is strictly based on the inputs at the time of validation. The 6 inputs – man, machine, method, materials, inspection process and the environment, all can suffer variation as time goes on. These variations reveal the problem of induction – the results are not going to stay the same. There is no uniformity of nature. The uniformities observed in the past are not going to hold for the present and future as well.

In general, when we are doing induction, we should try to meet five conditions;

  1. Use a large sample size that is statistically valid
  2. Make observations under different and extreme circumstances
  3. Ensure that none of the observations/data points contradict
  4. Try to make predictions based on your model
  5. Look for ways and test your model to fail

The use of statistics is considered as a must for process validation. The use of a statistically valid sample size ensures that we make meaningful inferences from the data. The use of different and extreme circumstances is the gist of operational qualification or OQ. OQ is the second qualification phase of process validation. Above all, we should understand how the model works. This helps us to predict how the process works and thus any contradicting data point must be evaluated. This helps us to listen to the process when it is talking. We should keep looking for ways to see where it fails in order to understand the boundary conditions. Ultimately, the more you try to make your model to fail, the better and more refined it becomes.

The FDA’s guidance on process validation [2] and the GHTF (Global Harmonized Task Force) [3] guidance on process validation both try to address the problem of induction through “Continued Process Verification” and “Maintaining a State of Validation”. We should continue monitoring the process to ensure that it remains in a state of validation. Anytime any of the inputs are changed, or if the outputs show a trend of decline, we should evaluate the possibility of revalidation as a remedy for the problem of induction. This brings into mind the quote “Trust but verify”. It is said that Ronald Reagan got this quote from Suzanne Massie, a Russian writer. The original quote is “Doveryai, no proveryai”.

I will finish off with a story from the great Indian epic Mahabharata, which points to the lack of uniformity in nature.

Once a beggar asked for some help from Yudhishthir, the eldest of the Pandavas. Yudhishthir told him to come on the next day. The beggar went away. At the time of this conversation, Yudhishthir’s younger brother Bhima was present. He took one big drum and started walking towards the city, beating the drum furiously. Yudhishthir was surprised.

He asked the reason for this. Bhima told him:
“I want to declare that our revered Yudhishthir has won the battle against time (Kaala). You told that beggar to come the next day. How do you know that you will be there tomorrow? How do you know that beggar would still be alive tomorrow? Even if you both are alive, you might not be in a position to give anything. Or, the beggar might not even need anything tomorrow. How did you know that you both can even meet tomorrow? You are the first person in this world who has won the time. I want to tell the people of Indraprastha about this.”

Yudhishthir got the message behind this talk and called that beggar right away to give the necessary help.

Always keep on learning…

In case you missed it, my last post was If a Lion Could Talk:

[1] The Simpsons – Season 27; Episode 575; Every Man’s Dream

[2] https://www.fda.gov/downloads/drugs/guidances/ucm070336.pdf

[3] https://www.fda.gov/OHRMS/DOCKETS/98fr/04d-0001-bkg0001-10-sg3_n99-10_edition2.pdf

[4] https://harishsnotebook.wordpress.com/2015/03/08/ship-of-theseus-and-process-validation/

[5] Non-uniformity of Nature Clock drawing by Annie Jose

In-the-Customer’s-Shoes Quality:

shoes

I had a conversation recently with a Quality professional from another organization. The topic somehow drifted to the strict Quality standards in Japan. The person talked about how the product gets rejected by his Japanese counterparts for small blemishes, debris etc. The “defects” met the corporate standards, yet the product gets rejected at their Japanese warehouse. This conversation led me to write this post. My response was that the Japanese were looking at the product from the eyes of the customer. The small blemishes and debris impact the perception of quality, and can bring distaste as the product is being used.

In Japanese, the term for quality is Hinshitsu (hin = goods, and shitsu = quality). With the advent of TQM (Total Quality Movement), the idea of two “Qualities” was made more visible by Professor Noriaki Kano. He termed these;

  1. Miryokuteki Hinshitsu, or Attractive Quality
  2. Atarimae Hinshitsu, Must-Be Quality

These concepts were not exactly new, but Prof. Kano was able to put more focus on this. The “Attractive Quality” refers to something that fascinates or excites the customer and the “Must-Be Quality” refers to everything that is expected from the item by the customer. For example, a new phone in the market is expected to function out of the box. It should be able to make calls, connect to the internet, take pictures, play games etc. But if the phone came with the case or if the phone came with the name of the owner etched on the back, then that particular attribute is exciting for the customer. It was not something that he was expecting, and thus it brings “joy” to the customer. The interesting thing about the Attractive Quality is that today’s Attractive Quality becomes tomorrow’s Must-Be Quality. Would you purchase a phone today without the ability to browse the internet or take pictures? These features were added as Attractive Quality features in the past, and they have become Must-Be Quality features today.

The Japanese Quality guru Kaoru Ishikawa called these “Forward-looking qualities” and “Backward-looking qualities”. He called the special features like “easy to use”, “feels good to use” etc. as forward looking qualities. In contrast, “absence of defects” was called as backward looking. The father of Statistical Quality Control, Walter Shewhart called these as Objective and Subjective qualities.

Sometimes the Miryokuteki Hinshitsu also refers to the “Aesthetic Quality” of the product. Apple products are famous for this. There is a lot of attention paid by the Apple Designers for the Aesthetic Quality of their products. The IPhone should feel and look good. Even the package it comes in should say that it contains a “quality product”. In the Japanese culture, the concept of Aesthetics is rooted in “Shibui” and “Mononoaware”. Shibui can be defined as a quality associated with physical beauty “that has a tranquil effect on the viewer”. It brings to attention the naturalness, simplicity and subdued tone. Mononoaware on the other hand refers to the merging of one’s identity with that of an object. (Source: The Global Business by Ronnie Lessem, 1987).

The Total Quality Movement (Or Total Quality Control Movement as it is often referred to in the Japanese books) was taken quite seriously by the Japanese manufacturers. The following concepts were identified as essential;

  1. Customer orientation
  2. The “Quality first” approach
  3. Quality is everyone’s responsibility – from top management down
  4. Continual improvement of Quality
  5. Quality assurance is the responsibility of the producer, not of the purchaser or the inspection department
  6. Quality should be extended from the hardware (i.e., the product) to the software (i.e., services, work, personnel, departments, management, corporations, groups, society and the environment)

Source: Kaoru Ishikawa

Rather than relying on inspection, the Japanese manufacturers, including Toyota and Nissan, believed in building in quality throughout the entire process. The awareness of quality was seen as essential by the operator involved in making the product. It became a matter of owning the process and taking pride in what the operator did. Kenichi Yamamoto, the previous chairman of Mazda, is quoted to have said by BusinessWeek – “any manufacturer can produce according to statistics.”Yamamoto’s remark is about not focusing simply on quantities. Even when we are focusing on quality we should focus on both the objective and subjective quality. This reflects how our company culture views the ownership of quality.

Final Words:

I have always wondered why the windows in an airplane are not aligned with the airplane’s seats. It appears that the plane’s body is built based on a standard, and the seats are later added based on what the plane carriers want. There is not always a focus on what the customer wants, which explains why the seats are not aligned with the windows. I refer to the idea of the quality of a product as “in-the-customer’s-shoes quality”. If you were the customer, how would you like the product?

I will finish off with a story I heard from one of the episodes of the delightful TV show, “Japanology Plus”. This story perfectly and literally captures the concept of in-the-customer’s-shoes quality.

The episode was interviewing a “Japanophile” who was living in Japan for quite a long time. He talked about one incident that truly changed his view on Japan. He went to a small tea house in Japan. He was requested to remove his shoes before entering the room. After the tea, when he came out he was pleasantly surprised to see that his shoes were now moved to face away from the room. This way, he did not have to turn around and fumble to put his shoes on. He can simply put the shoes on his way out without turning around. He was taken aback by the thoughtfulness of the host.

Always keep on learning…

In case you missed it, my last post was “Four Approaches to Problem Solving”.

Four Approaches to Problem Solving:

dc

As a Quality professional, I am always interested in learning about problem solving. In today’s post I will be looking at the four approaches to Problem Solving as taught by the late great Systems Thinker, Russell Ackoff. He called these “Problem Treatments” – the ways one deals with problems. They are;

  1. Absolution – This is a common reaction to a problem. This means to ignore a problem with the hope that it will solve by itself or it will go away of its own accord.
  2. Resolution – This means to do something that yields an outcome that is “good enough”, in other words, that “satisfices”. This involves a clinical approach to problems that relies heavily on past experience, trial and error, qualitative judgment, and so-called common sense.
  3. Solution – This means to do something that yields the best outcome that “optimizes”. This involves a research approach to problems, one that often relies on experimentation, quantitative analysis, and uncommon sense. This is the realm of effective counterintuitive solutions.
  4. Dissolution – This means to redesign either the entity that has the problem or its environment in such a way as to eliminate the problem and enable the entity involved to do better in the future that the best it can do today – in a word, to “idealize”.

I see it also as the progression of our reaction to a big problem. At first, we try to ignore it. Then we try to put band aids on it. Then we try to make the process better, and finally we change a portion of the process so that the problem cannot exist in the new process. Ackoff gave a story in his book, “The Democratic Corporation”, to further explain these ideas. Ackoff was called in by a consultant to help with a problem in a large city in Europe. The city used double-decker buses for public transportation that had a bus driver and a conductor in it. The driver got paid extra based on how efficiently he could keep up with the schedule, and the conductor got paid extra based on how efficiently he could collect fares and keeps track of receipts. The conductor was also in charge of letting the driver know when the bus was ready to move by signaling to them from the rear entrance to the bus. During peak hours, problems arose. To meet the high volume of passengers, conductors started to let passengers in without collecting fares with the thought that they could be collected between stops. The conductors could not always get back to the entrance to signal to the driver that they were ready to move. The drivers started to determine themselves when they could move by trying to see that no one was getting off or on to the bus. All this caused delays that were costly to the driver. This resulted in great hostility between the drivers and the conductors. The drivers were trying to do what was best for them, and the conductors were trying to do what was best for them.

The management at first tried to “absolve” by pretending that the problem would go away on its own. When things got worse, the management tried to “resolve” by proposing to retract the incentives. This was not met well by both the drivers and conductors, and the management was not willing to increase their wages to offset the incentives. Next the management tried to “solve” the problem by proposing that the driver and the conductor share the total sum of incentives. This also was not met well by the drivers and the conductors because of lack of trust and unwillingness to increase their interdependence.

Finally, Ackoff proposed a modification to the process. He proposed that during the peak hours the conductors should be taken off the bus and placed at the stops. This way he can collect the fares from the people already at the stop, and he can verify the receipts of the people getting off the bus. He also can easily signal the bus driver. The problem was “dissolved” by this modification to the process.

Final Words:

One of the best teachings from Ackoff for Management is that to manage a system effectively, you must focus on the interactions of the parts rather than their behaviors (actions) taken separately. The next time you are facing a problem, think and understand if you are trying to absolve, resolve, solve or dissolve the problem. I will finish with a great story from Osho about the butcher who never had to sharpen his knife.

There was a great butcher in Japan and he was said to be a Zen master. After hearing about him, the emperor came to see him at his work. The emperor asked only one thing, about the knife that he used to kill the animals. The knife looked so shiny, as if it had just been sharpened.

The emperor asked, “Do you sharpen your knife every day?”

He said, “No, this is the knife my father used, and his father used, and it has never been sharpened. But we know exactly the points where it has to cut the animal so there is a minimum of pain possible — through the joints where two bones meet. The knife has to go through the joint, and those two bones that meet there go on sharpening the knife. And that is the point where the animal is going to feel the minimum pain. I am aware of the interactions.”

“For three generations we have not sharpened the knife. A butcher sharpening a knife simply means he does not know his art.”

Always keep on learning…

In case you missed it, my last post was Respect for People in light of Systems Thinking.

The Pursuit of Quality – A Lesser Known Lesson from Ohno:

Ohno

In today’s post, I will be looking at a lesser known lesson from Taiichi Ohno regarding the pursuit of Quality.

“The pursuit of quantity cultivates waste while the pursuit of quality yields value.”

Ohno was talking about using andons and the importance of resisting mass production thinking. Andon means “lantern” in Japanese, and is a form of visual control on the floor. Toyota requires and requests the operators to pull the andon cord to stop the line if a defect is found and to alert the lead about the issue. Ohno said the following about andons;

“Correcting defects is necessary to reach our goal of totally eliminating waste.”

Prior to the oil crisis, in the early 1970’s in Japan, all the other companies were buying high-volume machines to increase output. They reasoned that they could store the surplus in the warehouse and sell them when the time was right. Toyota, on the other hand, resisted this and built only what was needed. According to Ohno, the companies following mass-production thinking got a rude awakening in the wake of the oil crisis since they could not dispose off their high inventory. Meanwhile Toyota thrived and their profits increased. The other companies started taking notice of the Toyota Production System.

Ohno’s lesson of the pursuit of quality to yield value struck a chord with me. This concept is similar to Dr. Deming’s chain reaction model. Dr. Deming taught us that improvement of quality begets the natural and inevitable improvement of productivity. His entire model is shown below (from his book “Out of the Crisis”).

Deming Chain reaction

Dr. Deming taught the Japanese workers that the defects and faults that get into the hands of the customer lose the market and cost him his job. Dr. Deming taught the Japanese management that everyone should work towards a common aim – quality.

Steve Jobs Story:

I will finish with a story I heard from Tony Fadell who worked as a consultant for Apple and helped with the creation of the IPod. Tony said that Steve Jobs did not like the “Charge Before Use” sticker on all of the electronic gadgets that were available at that time. Jobs argued that the customer had paid money anticipating using the gadget immediately, and that the delay from charging takes away from the customer satisfaction. The normal burn-in period used to be 30 minutes for the IPod. The burn-in is part of the Quality/Reliability inspection where the electronic equipment runs certain cycles for a period of time with the intent of stressing the components to weed out any defective or “weak” parts. Jobs changed the burn-in time to two hours so that when the customer got the IPod, it was fully charged for him to use right away. This was a 300% increase in the inspection time and would have impacted the lead time. Traditional thinking would argue that this was not a good decision. However, this counterintuitive approach was welcomed by the customers and nowadays it is the norm that electronic devices come charged so that the end user can start using it immediately.

Always keep on learning…

In case you missed it, my last post was Challenge and Kaizen.

Eight Lessons from Programming – At the Gemba:

At the gemba - coding

In today’s post, I will be writing about the eight lessons I learned from Programming. I enjoy programming, and developing customer centric programs. I have not pursued a formal education in programming, although I did learn FORTRAN and BASIC as part of my Engineering curriculum. Whatever I have learned, I learned with an attitude of “let’s wing it and see”.

  • Be Very Dissatisfied with Repetitive Activities:

Our everyday life is riddled with repetition. This is the operative model of a business. Design a product, and then make them again and again. This repetitive way of doing things can be sometimes very inefficient. The programmer should have a keen eye to recognize the repetitive non-value adding activities that can be easily automated. If you have to generate a report every week, let’s automate it so that it is generated every week with minimal effort from you.

  • There is Always a Better Way of Doing Things:

Along the same lines as the first lesson, you must realize that there is always a better way of doing things. The best is not here yet, nor will it ever be. This is the spirit of kaizen. Even when a process has been automated, there is still big room left for improvement. The biggest room certainly is the room for improvement.

  • Never Forget Making Models:

When a Lean Practitioner is looking at a system, creating a model is the first step. This model could be a mental model, a mathematical model or even a small scale physical model. This model can even be a basic flowchart. This is part of the Plan phase of PDCA. How do the components work with each other? How does the system interact with the environment? What happens when step A is followed by Step B? A good programmer should understand the system first before proceeding with creating programs. A good programmer is also a good Systems Thinker.

  • Keep Memory in Mind:

A good programmer knows that using up a lot of memory and not freeing up memory can cause the program to hang and sometimes crash. Memory Management is an important lesson. This is very much akin to the concept of Muri in Lean. Overburdening the resources has an adverse impact on productivity and quality, and it is not a sustainable model in the long run.

  • Walk in Their Shoes:

A good programmer should look at the program from the end user’s viewpoint. Put yourself in their shoes, and see if your program is easy to use or not. Programmers are sometimes very focused on adding as many features as possible, when the end user is requiring only a few features. There is some similarity with the use of lean or six sigma tools at the Gemba. If it is not easy to use, the end users will try to find a way around it. This brings us to the next lesson.

  • Listen to the Gemba:

One of the lessons I learned early in my career is that I am not the owner of the program I write. The person using the program is the owner. If I do not listen to the end user then my program is not going to be used. I do not make the program for me; I make it for the end user. Less can be more and more can be less. The probability of a program being successful is inversely proportional to the distance of gemba from the source of program creation.

  • Documentation:

I wrote at the beginning that I learned programming from a “winging it” attitude. However, I soon learned the importance of documentation. A good programmer relies on good documentation. The documentation should explain the logic of the program, the flow of the program, how it will be tested and qualified, how the program changes will be documented and how the bugs will be tracked. The simplest tool for documentation can be a checklist. My favorite view on using checklists is – not using a checklist for a project is like shopping without a shopping list. You buy several things that are not needed, and do not buy the things that you actually need.

  • Keep a Bugs List – Learn from Mistakes:

Bugs to a programmer are like problems on a factory floor to a lean practitioner- it depends on how you view them. For a lean practitioner, problems are like gold mine. They are all opportunities to improve. In this same line of thinking, bugs are also a programmer’s friends. You learn the most from making mistakes. No program is 100% bug free. Each bug is unique and provides a great lesson. The goal is to learn from them so that you do not repeat them.

Another important lesson is – ensure that fixing a problem does not cause new problems. A programmer is prone to the law of unintended consequences. Any change to a program should be tested from a system standpoint.

Final Words:

I will finish off with my favorite anecdote about programming:

When Apple introduced the IPod, they were very proud of its “shuffle” feature. There is no accurate way of truly randomizing songs. However, there are several algorithms that can generate a pretty good random order. Apple utilized such an algorithm. It was so good that the users started complaining because sometimes the same song was repeated, or the same artist was played repeatedly. That is not how random should be – the end users argued. Steve Jobs then asked his programmers to change the algorithm so that it is less random.

The Digital Music Service company, Spotify faced the same problem. As they explained on their blog;

“If you just heard a song from a particular artist, that doesn’t mean that the next song will be more likely from a different artist in a perfectly random order. However, the old saying says that the user is always right, so we decided to look into ways of changing our shuffling algorithm so that the users are happier. We learned that they don’t like perfect randomness.”

The perception of random for the end user meant that the songs are equally spaced from one another based on how similar they are. The end user did not want randomness in a theoretical sense. They wanted random from a human practical sense.

Spotify changed their algorithm in 2014. “Last year, we updated it with a new algorithm that is intended to feel more random to a human.”

Always keep on learning…

In case you missed it, my last post was Be Like Coal At the Gemba.

Dharma, Karma and Quality:

Dharma

In today’s post I will be looking at the statement – quality is everyone’s responsibility. This is an interesting preachy statement. There are two questions that can be answered by this statement;

  1. Who is responsible for quality?
  2. What is everyone responsible for?

The first question (who is) is a wrong question to ask because it leads to blaming and never results in an improvement of current state. The second question is just too broad to answer. Everyone is surely responsible for more than just quality.

Dharma and Karma:

The best way to explain responsibility is by looking at “dharma”. “Dharma” is an ancient Sanskrit term, and goes back to about 1500 BC. The word was first explained in the ancient Indian script Rig Veda. This was explained as a means to achieve a sense of order in the world. The term loosely can be translated as “responsibility”, or “something that needs to be done from a sense of duty”. The main purpose of dharma is to preserve or uphold the order in a system. For example, the dharma of a plant is to bloom.

This brings me to the next word – “karma”. “Karma” is more commonly used in the English language, and everybody has some understanding of this word. The term actually means “action” in Sanskrit. The action can be in the past, present or in the future. However, every one of your actions has a consequence. This attaches the “cause and effect” meaning to the word “karma”.  There are three types of karma identified in the Sanskrit texts;

  1. Karma = action
  2. Vikarma = wrong action
  3. Akarma = no action (doing nothing is a form of action, and sometimes this is the right thing to do)

If everybody performs karma according to their dharma, then the system is sustained successfully.

Top Management – 85% or 100% Responsible?

The answer to the question, “who is responsible for quality” is sometimes answered as “Top Management”. Dr. Deming taught that “85% of all quality problems are management problems”. He is also supposed to have stated “85% of TQC’s (Total Quality Control program) success depends on the president.” This can be depicted as the chart below.

Responsibility

I have viewed this as – patient zero is in the board room.

Taiichi Ohno’s, the father of Toyota Production System, view on this was as follows;

“In reality, TQC’s success depends on the president’s resolution to assume 100% responsibility. The president should imagine him or herself taken hostage by TQC and become devoted to human quality control.”

Dr. Deming has also said that – Quality is made in the board room. However, he goes on to clarify this. Quality is everyone’s responsibility, but top management has the most leverage of all to make a meaningful impact with their decisions.

In this light, the answer to the question – “what is your responsibility?” is “You are responsible for what you can control.”

Top management’s dharma is to lay down the framework for the entire organization to grow. This involves strong vision, big and drastic improvements (innovation) and growth. Middle Management’s dharma is to enforce and reinforce the framework through maintaining the status quo while encouraging small improvements (kaizen) and developing people. The operator’s dharma is to aid middle management to maintain status quo while looking for opportunities for improvements. The push for maintaining status quo is to provide a temporary structure for the process so that it can be studied for improvements. The main goal is destruction of the status quo so that a new standard can be achieved. If the karma aligns with the dharma, then the organization will sustain itself, grow and be successful.

Final Words:

I have recently rediscovered Dr. Deming’s definition of quality – Quality is the pride of workmanship. I will use Dr. Deming to succinctly summarize this post.

“In a well organized system all the components work together to support each other. In a system that is well led and managed, everybody wins. This is what I taught Japanese top management and engineers beginning in 1950.”

I will finish off with a Zen monk story;

A monk was driving his car when a dog from nowhere crossed the road. Although the monk tried stopping his car, he ran over the dog, killing it. The monk stopped his car and parked it. He looked around and saw a temple across from the road. He went to the temple and knocked at the door. Another monk opened the door.

The first monk bowed his head and said “I am so sorry.”

He pointed to where the accident happened and continued; “My karma ran over your dogma over dharma”. (My car ran over your dog over there.)

Always keep on learning…

In case you missed it, my last post was To Be or Not To Be.

To Be or Not To Be:

decide

In today’s post, I will be looking at the process of decision making and the use of a modified Pugh Chart to quantitatively conduct decision making.

The general process for decision making looks like something below;

  1. What do I have to decide? What is it about?
  2. What are my choices?
  3. What are the pros and cons for each?
  4. Act upon the decision and see if any further action is needed.

Decision Making is an Emotional Process:

As you go deeper into the decision making process, you can see that it gets more and more interesting. The neuroscientist, Antonio Damasio made the striking discovery that decision making is emotional in nature, and is rarely logical. He studied several patients who suffered injuries to their brains which impaired their emotions. Their reasoning capabilities were not impacted. They all had difficulty making decisions. The patients were all cognitively normal except that they had lost their ability to experience emotions, and this significantly impacted their ability to make decisions.

So at the point of decision, emotions are very important for choosing. In fact even with what we believe are logical decisions, the very point of choice is arguably always based on emotion.   

Complexity in Decision Making:

As a leader in your organization, you are required to make decisions on a daily basis. The types of decisions can be broken down into three classes;

  1. Surface – Simple situations requiring routine decisions
  2. Shallow – Complicated situations requiring supervisorial or managerial level decisions
  3. Deep – Complex situations requiring Upper management level decision making

This approach is adopted from Bennet and Bennet. The surface decisions are made on a daily basis, and do not have a high risk associated with them. The shallow decisions are more infrequent and have a medium level of risk associated with them. Finally, deep decisions are rare and have high risk associated with them.

bennet

Jeff Bezos, founder of Amazon.com, talks about a similar approach. He argues that there is no one-size-fits-all approach for decision making. In his opinion, there are two levels of decisions to be made. “Type 1 decisions” are those decisions that have critical consequences and are irreversible or nearly irreversible. He calls them “one-way doors”. He advises making Type 1 decisions carefully, slowly and with great thought and deliberation. The other kind is the “Type 2 decisions”. These are simple and easily reversible decisions. These decisions should be made much faster and frequently. A wise man knows the difference between the two.

To Include or to Exclude:

When you think about it, decision making is a process of deciding whether to include or exclude something. I came across a great article on this involving the custom of arranged marriages in India. The decision making process in an arranged marriage uses the approach of inclusion or exclusion. The two types of thinking are;

  • Inclusion – After careful thought, out of the 100 applicants choose the few that you think are most suitable for your child.
  • Exclusion – After careful thought, out of the 100 applicants eliminate the applicants that you think are not suitable for your child.

The counterintuitive outcome is that if you utilize the inclusion approach, you will select much fewer candidates. If you use the exclusion approach, you will retain a higher number of candidates, even though you are using eliminating criteria. Additionally, when the exclusion approach is used, you are highly likely to choose an “average” candidate. On the other hand, when the inclusion approach is used, you are highly likely to choose a candidate who is very strong in certain categories.

Quantitative Pugh Matrix Method:

My favorite tool for decision making is a version of the Pugh Matrix method. The steps for the Pugh Matrix are as follows;

  1. Decide upon the categories that are most important for making the decision
  2. Assign a weighted scale for each category
  3. Choose a scale for each category. This can be 1 – 5, where 1 = worst and 5 = best
  4. Score each category for the different options
  5. Find the final weighted score for each option. The option with the highest weighted score wins.

As an example, let’s look at the highly complicated decision of where to go for dinner. The following categories maybe suitable for this example – food taste, service, pricing, dessert quality and drinks. The next step is to assign the weighted scale. The sum of all the weighted scales should add up to 1 (100%). I have shown this below.

categories

The next step is to assign the scores (1 to 5) for each category for the different options (Restaurant A, Restaurant B, and Restaurant C). This is shown below.

scores

The final step is to multiply each score with its associated weighted scale, and sum it all up. This is shown below.

Pugh

This shows that Restaurant A is the best choice for me based on the initial categories I chose. This tool is applicable for all kinds of scenarios. I have attached the excel spreadsheet I used for the example here. Even in the Pugh matrix, some values carry an emotional component.

I will part with a teaching from the great Zen master Shunryu Suzuki.

In the beginner’s mind there are many possibilities, but in the expert’s there are few.”

Always keep on learning…

In case you missed it, my last post was Talking Trash.