The Cybernetic View of Quality Control:

Shewhart cycle1

My last post was a review of Mark Graban’s wonderful book, Measures of Success. After reading Graban’s book, I started rereading Walter Shewhart’s books, Statistical Method from the Viewpoint of Quality Control (edited by Dr. Deming) and Economic Control of Quality of Manufactured Product. Both are excellent books for any Quality professional. One of the themes that stood out for me while reading the two books was the concept of Cybernetics. Today’s post is a result from studying Shewhart’s books and articles on cybernetics by Paul Pangaro.

The term “cybernetics” has its origins from the Greek word, κυβερνήτης, which means “navigation”. Cybernetics is generally translated as “the art of steering”. Norbert Wiener, the great American mathematician, wrote the 1948 book, Cybernetics: Or Control and Communication in the Animal and the Machine. Wiener made the term “cybernetics” famous. Wiener adapted the Greek word to evoke the rich interaction of goals, predictions, actions, feedback, and response in systems of all kinds.

Loosely put, cybernetics is about having a goal and a self-correcting system that adjusts to the perturbations in the environment so that the system can keep moving towards the goal. This is referred to as the “First Order Cybernetics”. An example (remaining true to the Greek origin of the word), we can use is a ship sailing towards a destination. When there are perturbations in the form of wind, the steersman adjusts the path accordingly and maintains the course. Another common example is a thermostat. The thermostat is able to maintain the required temperature inside the house by adjusting according to the external temperature. The thermostat “kicks on” when a specified temperature limit is tripped and cools or heats the house. An important concept that is used for cybernetics is the “law of requisite variety” by Ross Ashby. The law of requisite variety states that only variety can absorb variety. If the wind is extreme, the steersman may not be able to steer the ship properly. In other words, the steersman lacks the requisite variety to handle or absorb the external variety. The main mechanism of cybernetics is the closed feedback loop that helps the steersman adjust accordingly to maintain the course. This is also the art of a regulation loop –compare, act and sense.

Warren McCulloch, the American cybernetician, explained cybernetics as follows:

Narrowly defined it (cybernetics) is but the art of the helmsman, to hold a course by swinging the rudder so as to offset any deviation from that course. For this the helmsman must be so informed of the consequences of his previous acts that he corrects them – communication engineers call this ‘negative feedback’ – for the output of the helmsman decreases the input to the helmsman. The intrinsic governance of nervous activity, our reflexes, and our appetites exemplify this process. In all of them, as in the steering of the ship, what must return is not energy but information. Hence, in an extended sense, cybernetics may be said to include the timeliest applications of the quantitative theory of information.

Walter Shewhart’s ideas of statistical control works well with the cybernetic ideas. Shewhart purposefully used the term “control” for his field. The term control or regulation is a key concept in cybernetics, as explained above. Shewhart defined control as:

A phenomenon is said to be controlled when, through the use of past experience, we can predict at least within limits, how the phenomenon may be expected to vary in the future. Here it is understood that prediction within limits means that we can state, at least approximately, the probability that the observed phenomenon will fall within the given limits.

Shewhart expanded further:

The idea of control involves action for the purpose of achieving a desired end. Control in this sense involves both action and a specified end.

..We should keep in mind that the state of statistical control is something presumable to be desired, something to which one may hope to attain; in other words it is an ideal goal.

Shewhart’s view of control aligns very well with the teleological aspects of cybernetics. From here, Shewhart develops his famous Shewhart cycle as a means to maintain statistical control. Shewhart wrote:

Three steps in quality control. Three senses of statistical control. Broadly speaking, there are three steps in a quality control process: the specification of what is wanted, the production of things to satisfy the specification, and the inspection of things produced to see if they satisfy the specification.

The three steps (making a hypothesis, carrying out an experiment, and testing the hypothesis) constitute a dynamic scientific process of acquiring knowledge. From this viewpoint, it is better to show them as a forming a sort of spiral gradually approach a circular path to which would represent the idealized case, where no evidence is found in the testing of hypothesis indicates a need for changing the hypothesis. Mass production viewed in this way constitutes a continuing and self-corrective method for making the most efficient use of raw and fabricated materials.

The Shewhart cycle as he proposed is shown below:

Shewhart cycle1

One of the criterions Shewhart developed for his model was that the model should be as simple as possible and adaptable in a continuing and self-corrective operation of control. The idea of self-correction is a key point of cybernetics as part of maintaining the course.

The brilliance of Shewhart was in providing guidance on when we should react and when we should not react to the variations in the data. He stated that a necessary and sufficient condition for statistical control is to have a constant system of chance causes… It is necessary that differences in the qualities of a number of pieces of a product appear to be consistent with the assumption that they arose from a constant system of chance causes… If a cause system is not constant, we shall say that an assignable cause is present.

Shewhart continued:

My own experience has been that in the early stages of any attempt at control of a quality characteristic, assignable causes are always present even though the production operation has been repeated under presumably the same essential conditions. As these assignable causes are found and eliminated, the variation in quality gradually approaches a state of statistical control as indicated by the statistics of successive samples falling within their control limits, except in rare instances.

We are engaging in a continuing, self-corrective operation designed for the purpose of attaining a state of statistical control.

The successful quality control engineer, like the successful research worker, is not a pure reason machine but instead is a biological unit reacting to and acting upon an everchanging environment.

James Wilk defined cybernetics as:

Cybernetics is the study of justified intervention.”

This is an apt definition when we look at quality control, as viewed by Shewhart. We have three options when it comes to quality control:

  1. If we have an unpredictable system, then we work to eliminate the causes of signals, with the aim of creating a predictable system.
  2. If we have a predictable system that is not always capable of meeting the target, then we work to improve the system in a systematic way, aiming to create a new a system whose results now fluctuate around a better average.
  3. When the range of predictable performance is always better than the target, then there’s less of a need for improvement. We could, however, choose to change the target and then continue improving in a systematic way.

Source: Measures of Success (Mark Graban, 2019)

Final Words:

Shewhart wrote “Statistical Method from the Viewpoint of Quality Control” in 1939, nine years before Wiener’s Cybernetics book. The use of statistical control allows us to have a conversation with a process. The process tells us what the limits are, and as long as the data points are plotted randomly within the two limits, we can assume that whatever we are seeing is due to chance or natural variation. The data should be random and without any order. When we see some manner of order in the likes of a trend or an outside data point, then we should look for an assignable cause. The data points are not necessarily due to chance anymore. As we keep plotting, we should improve our process, and recalculate the limits.

I will finish off with Dr. Deming’s enhancement of Shewhart’s cycle. This is taken from a presentation by Clifford L. Norman. This was part of the evolution of the PDSA (Plan-Do-Study-Act) cycle which later became famous as PDCA cycle (Plan-Do-Check-Act). This showed only 3 steps with a decision point after step 3.

Shewhart cycle2

The updated cycle has lots of nuggets in it such as experimenting on a small scale, reflecting on what we learned etc.

Always keep on learning…

In case you missed it, my last entry was My Recent Tweets:

Note: The updated Shewhart cycle was added to the post after a discussion with Benjamin Taylor (Syscoi.com).

Book Review – Measures of Success:

Measures-of-Success-Cover-Dark-Green-Final-copy-1

In today’s post, I am reviewing the book, “Measures of Success”, written by Mark Graban. Graban is a Lean thinker and practitioner. Graban has written several books on Lean including Lean Hospitals and Healthcare Kaizen. Graban was kind enough to send me a preview copy of his latest book, Measures of Success. As Graban writes in the Preface, his goal is to help managers, executives, business owners, and improvement specialists in any industry use limited time available more effectively.

The book is about Process Behavior Charts or PBC (Statistical Process Control or SPC). Graban teaches in an easy way how to use Process Behavior Charts to understand a process, and truly see and listen to the process. The use of PBC is a strategy of prevention, and not a strategy of detection alone. PBCs help us see when a process is in control and whether what we see is indicative of normal noise present in a process in control or not. Walter Shewhart, who created and pioneered SPC, defined control as:

A phenomenon is said to be controlled when, through the use of past experience, we can predict at least within limits, how the phenomenon may be expected to vary in the future. Here it is understood that prediction within limits means that we can state, at least approximately, the probability that the observed phenomenon will fall within the given limits.

 Shewhart proceeded to state that a necessary and sufficient condition for statistical control is to have a constant system of chance causes… It is necessary that differences in the qualities of a number of pieces of a product appear to be consistent with the assumption that they arose from a constant system of chance causes… If a cause system is not constant, we shall say that an assignable cause is present.

Graban has written a great book to help us decide what is noise and what is meaningful data. By understanding how the process is speaking to us, we can stop overreacting and use the saved time to actually make meaningful improvements to the process. Graban has a great style of writing which makes a somewhat hard statistical subject easy to read. I enjoyed the narrative he gave of the CEO looking at the Bowling Chart and reacting to it in the third chapter. The CEO was following the red and green datapoints, and reacting by either pontificating as a means of encouragement or yelling “just do things right” at her team. And worse of all, she thinks that she is making a difference by doing it. Just try harder and get to the green datapoint! Graban also goes into detail on Deming’s Red Bean experiment that is a fun way of demonstrating the minimal impact a worker has on normal variation of the process through a fun exercise.

Similar to Deming’s line of questions regarding process improvementHow are you going to improve? By what method? And How will you know?, Graban also provides three insightful core questions:

  1. Are we achieving our target or goal?
  2. Are we improving?
  3. How do we improve?

We should be asking these questions when we are looking at a Process Behavior Chart. These questions will guide in our continual improvement initiatives. Graban has identified 10 key points that help us reflect on our learning of PBCs. They are available at his website. They help us focus on truly understanding what the process is saying – where are we and should we make a change?

Graban provides numerous examples of current events depicted as PBCs. Some of the examples include San Antonio homicide rates and Oscar Ratings. Did the homicide rate significantly go down recently? Did the Oscar ratings significantly go down in the recent years? These are refreshing because they help solidify our understanding. This also provides a framework for us to do our own analysis of current events we see in the news. Graban also provides an in-depth analysis of his blog data. In addition, there are several workplace cases and examples included.

The list of Chapters are as follows:

  • Chapter 1: Improving the Way We Improve
  • Chapter 2: Using Process Behavior Charts for Metrics
  • Chapter 3: Action Metrics, not Overreaction Metrics
  • Chapter 4: Linking Charts to Improvement
  • Chapter 5: Learning From “The Red Bead Game”
  • Chapter 6: Looking Beyond the Headlines
  • Chapter 7: Linear Trend Lines and Other Cautionary Tales
  • Chapter 8: Workplace Cases and Examples
  • Chapter 9: Getting Started With Process Behavior Charts

The process of improvement can be summarized by the following points identified in the book:

  • If we have an unpredictable system, then we work to eliminate the causes of signals, with the aim of creating a predictable system.
  • If we have a predictable system that is not always capable of meeting the target, then we work to improve the system in a systematic way, aiming to create a new a system whose results now fluctuate around a better average.
  • When the range of predictable performance is always better than the target, then there’s less of a need for improvement. We could, however, choose to change the target and then continue improving in a systematic way.

It is clear that Graban has written this book with the reader in mind. There are lots of examples and additional resources provided by Graban to start digging into PBCs and make it interesting. The book is not at all dry and has managed to retain the main technical concepts in SPC.

The next time you see a Metric dashboard either at the Gemba or in the news, you will definitely know to ask the right questions. Graban also provides a list of resources to further improve our learning of PBCs. I encourage the readers to check out Mark Graban’s Blog at LeanBlog.org and also buy the book, Measures of Success.

Always keep on learning…

In case you missed it, my last post was Ubuntu At the Gemba:

Aim for System Optimization with Kaizen:

Local

Kaizen is often translated as “Continuous Improvement” in Japanese and is identified as one of the core themes in lean. In today’s post I am looking at the question – can kaizen ever be bad for an organization?

In order to go deeper on this question, first we have to define kaizen as a focused improvement activity. The question at this point is whether we are optimizing the process. Merriam-Webster defines Optimization as;

Optimization – an act, process, or methodology of making something (as a design, system, or decision) as fully perfect, functional, or effective as possible.

In my opinion, kaizen does not mean to optimize the process to 100% perfection. My point of contention on this is that kaizen should not be about local optimization. Local optimization means to optimize a process so that it is fully optimized without taking the whole system into consideration. This leads to tremendous waste. The local improvement should not cause a problem to an upstream or downstream activity. My best analogy is to work out the upper body without taking the lower body into consideration. This leads to a disproportionately developed body. In a similar vein, Prof. Emiliani views kaizen as a non-zero-sum activity – “everybody wins’!

Let’s look at an example. As part of a kaizen event at a hospital, the intake staff was able to make the client intake process very efficient. They were able to show that their improvement activities resulted in a much shorter time for client intake and they were able to get more clients in through the door. However, this caused more problems at the downstream processes. The staff at these processes were not able to serve the higher number of clients adequately which resulted in higher customer dissatisfaction and staff burn-outs.

Kaizen is a gradual and small incremental change towards the ideal state. The key point here is “ideal state”. How would you define “ideal state”? The “ideal state” means the ideal situation for the organization as a whole. Taiichi Ohno, the creator of Toyota Production System, said that “No standard = no kaizen.” The standard defines the process at its current goal and has three elements;

  1. Takt time – the defined rate of production to meet customer demand
  2. Sequence of work – the defined sequence of work to ensure safety, quality and efficiency
  3. Standard Work in Process – the defined inventory required to ensure that the takt time goal is met

Toyota’s goal is to improve overall efficiency and not local efficiency. This defines the goal of kaizen. Break the current state and create the new standard – while keeping the overall efficiency in mind. Ohno’s favorite way to challenge the current standard is by asking to use fewer operators to achieve the same required output.

Management’s Role:

What is Management’s role in all of this? Management has to lay the framework for everything to function properly. Dr. Deming, the pioneer of continuous improvement activities, says the following;

It is management’s job to direct the efforts of all components toward the aim of the system. The first step is clarification: everyone in the organization must understand the aim of the system, and how to direct his efforts toward it. Everyone must understand the danger and loss to whole organization from a team that seeks to become a selfish, independent, profit center.

Source: The New Economics, Dr. Deming.

Final Words:

It is important to view the improvement activities from a big picture standpoint. Viewing kaizen from a system standpoint is essential. I have always been curious about how the small incremental improvement activities would make a big difference in the end.  I will finish this post talking about the 800 year old Bronze statue of St. Peter holding the keys to Heaven in St. Peter’s Basilica in Rome.

St Peter

It looks like St. Peter is wearing shoes on his right foot and sandals on the left foot. Over eight centuries, pilgrims have been touching his right foot that is more accessible (it sticks out more) and asking for blessings. No one has been rubbing on the foot or sanding it down.  There has been no complaint of vandalism or apparent damage to the statue. The simple act of touching and kissing over time worn the bronze statue down – that St. Peter lost all his toes on his right foot. It is said that the Church started requesting visitors to start touching the left foot more. It appears that the left foot has got a lot of catching up to do.

StPeter-feet

Always keep on learning…

If you enjoyed this post, you can read more here.

In case you missed it, my last post was Seneca’s “On Shortness of Life”.

Dr. Deming and Value Stream Mapping:

deming_2

Value Stream Mapping (VSM) has become an essential part of Lean. There have been several books written specifically on this topic. VSMs are not widely spread at Toyota. VSM is a creation of Mike Rothers and John Shook. This was based on the “Material and Information Flow Maps” at Toyota. The VSM was created as a means to systematically roll out lean implementation, and looked at current and ideal states from a system standpoint. The intent was to give the “big picture view” that was missing from lean implementations. The Material and Information Flow maps were used by a few specialists at Toyota as part of line conversions, and these later were used to help suppliers view the production system as an end to end pull system ultimately ending with material delivery to Toyota.

Dr. Deming’s Flow Diagram:

Dr. Deming was invited to Japan by the Union of Japanese Scientists and Engineers (JUSE) on July 15, 1950 to teach them about Quality Control. His teachings paved the way for a great change in regards to Quality in Japan. Dr. Deming taught the Japanese that production should be viewed as a system. The diagram below was taught first in August 1950 at a conference with top management at the Hotel de Yama on Mount Hakone in Japan.

deming_flow

Dr. Deming felt that his flow diagram was the spark in 1950 and onward that turned Japan around. It displayed production as a system to top management and engineers. He also viewed this as a type of diagram that showed the flow of materials and information. In his words;

To the make the flow diagram work, the flow of material and information from any part of the system must match the input requirements of the next stages. Thus, the aim in the flow diagram is for the material to come in at the front, and to emerge at the end as usable product or service. The flow diagram describes not only the flow of material, but also the flow of information needed to manage the system.

Source – “The New Economics For Industry, Government, Education” by Dr. Deming.

Dr. Deming described the diagram as a map for viewing the production system. He identified a feedback loop for continual improvement of products, services and continual learning, by keeping the consumer a part of the system.

Final Words:

It may be argued that Dr. Deming’s flow diagram is not similar to a Value Stream Map. However, I am positing that his lesson of seeing the system as a whole (end to end) laid the framework for the Material and Information Flow Maps. The first step of any implementation activity is to have a model of the system so that the cause and effect links in the system can be understood, first by theory and then by experiments. I will finish off with a funny Dr. Deming story;

One of Dr. Deming’s clients called him and said that he was having too many fires at his plant. Dr. Deming plotted the occurrences of fires on a control chart and determined that it was indeed a stable process.

“No, you are having just the right amount of fires,” he said, and then proceeded to explain the control chart to the client.

Source: Deming’s Profound Knowledge and Leadership, Carder and Monda.

Always keep on learning…

In case you missed it, my last post was Eight Lessons from Programming – At the Gemba.

The Opposite of Kaizen:

opposite_kaizen

“Kaizen” is the Japanese word that means “change for good”. Kaizen is probably the most used word in lean today. Kaizen has come to mean many things including “Kaizen events” – a week long group activity to improve a process. In today’s post, I am going to look at kaizen – the simple idea of “change for better” and look at what could be the opposite of kaizen. Kaizen is also translated as “continuous improvement”.

What is the Opposite of Kaizen? – Kaiaku

This is an interesting philosophical question. From a Japanese language standpoint, the opposite of kai-zen is kai-aku. In Japanese, Kai means “to change”, zen means “better” or “good” and aku means “bad” or “evil”. Thus kaizen literally means “change to be better” and kaiaku means “change to be worse”.

From a philosophical viewpoint, I do not agree that kaiaku is the opposite of kaizen. A person engaged in the kaizen mindset learns from failures as well. The fear of failure does not stop him from trial and error. Sometimes this activity can result in terrible failures – kaiaku. However, the mindset of kaizen is still alive. In fact, it is said that one learns the most from failures. Thus, kaiaku cannot be the opposite of kaizen. Failures which result in worse-of scenarios act as an impetus to make things even better.

What is the Opposite of Kaizen? – Kaikaku

Kaikaku translates from Japanese to English as “revolutionary change”. This is generally a large scale transformation. The intent behind kaikaku is that it is not a simple small scale change like kaizen. Toyota achieves improvement through both small scale and large scale improvements. They embrace both types. Toyota also embraces innovation (kakushin). From Toyota’s perspective any change that ultimately makes things better is always good! In this regard, the opposite of kaizen cannot be kaikaku either.

What would Ohno Say?

Taiichi Ohno, father of Toyota Production System, said the following about how to begin kaizen:

“You’ve got to assume that things are a mess.”

Ohno’s point behind this is that if you are happy and satisfied with where you are and what you are doing; you will never want to change. You have to be dissatisfied with what you are doing to motivate yourself in order to improve your process. The opposite of kaizen is complacency!

Being complacent means that you are happy where you are, and your goal is to maintain status-quo. In fact, Merriam Webster defines complacency as follows;

Complacency = self-satisfaction especially when accompanied by unawareness of actual dangers or deficiencies

This is the exact opposite of the intent behind kaizen – to improve/make things better. Complacency leads to stagnation, and ultimately this hinders survival. Dr. Deming is often misquoted with “It is not necessary to change. Survival is not mandatory.” His actual quote is;

“Learning is not compulsory; it’s voluntary. Improvement is not compulsory; it’s voluntary. But to survive, we must learn.”(Source: The Age of Stagnation, Satyajit Das)

There is an interesting anecdote in the Harvard Business Review article “What Working for a Japanese Company Taught Me by John E Rehfeld. John talked about his friend who was in charge of two factories, one in America and one in Japan. The Japanese factory always outperformed the American factory. His friend’s rationale was as follows:

“They both set the same target, and they both may hit it. But when the Japanese hit it, they keep going, whereas the Americans tend to stop and rest on their laurels before pursuing the next goal. So in the end, the Japanese achieve more.” They continuously strive for perfection with the goal of achieving excellence.

Final Words:

It is my view that although the opposite of kaizen from a linguistic standpoint is kaiaku, from a philosophical standpoint it is complacency. Complacency leads to stagnation, and makes one ignorant of the perils around. I talked about Leonardo da Vinci last time. I will finish off with a quote from him, and an anecdote involving Henry Kissinger and Winston Lord.

davinci

Iron rusts from disuse; stagnant water loses its purity and in cold weather becomes frozen; even so does inaction sap the vigor of the mind. (Source:The Notebooks of Leonardo da Vinci, Richte, 1888)

Winston Lord was working under the then National Security Advisor, Henry Kissinger. There were a lot of high priority national security projects going on at that time. Winston Lord was writing a special report for Henry Kissinger. He worked on it for days knowing how picky and critical Kissinger can be. Lord submitted the report to Kissinger. The report was immediately returned to Lord with a notation by Kissinger – “Is this the best you can do?”

Lord rewrote the report and polished it a little more. The report was again submitted, and almost immediately the report was sent back by Kissinger, again with the same question – “Is this the best you can do?”

Lord rewrote the report one more time and the report was again sent back with the same question. This time Lord snapped at Kissinger, “Damn it, yes, it’s the best I can do.”

“Fine. Then I guess I will read it this time”, Kissinger replied back.

Always keep on learning…

In case you missed it, my last post was Qualities of a Lean Leader.

It’s Complicated

Cynefin final

It’s Complicated:

PDCA, the four letter acronym made famous by Dr. Deming stands for Plan – Do – Check – Act. It is a continuous cycle.

PDCA is said to be the framework for scientific thinking and continuous improvement. I have always thought of PDCA to have something missing in it. It looked so simplistic. Can it really be that simple?

I have come to realize that what was missing was context; the context behind PDCA. It cannot be that everything you see is a nail, if you only have a hammer. What happens before PDCA? The moment before you decided, “Hey, let’s do PDCA.” What makes you decide the “scope” for PDCA? How do you know if PDCA is even appropriate?

This post is an ode to the Cynefin framework. For those who do not know the Cynefin framework, it is a brainchild of Dave Snowden, and it is a sense making framework. Dave Snowden has stated that in the Cynefin framework, data precedes framework and it is valid to understand. The Cynefin framework is not a categorization framework, where framework precedes data.

The idea behind the Cynefin framework is that when you encounter a problem or a new project, your first step is to understand what domain you are in. This provides us a framework to proceed. As a learning organization, it is essential that our efforts and our methodologies match the type of change that we are planning. The Cynefin framework lays the groundwork for this exact intent.

The Cynefin framework has 5 domains and is dynamic. No problem with high complexity or chaos ever stays in the same domain at all times. The problem we had last year may have appeared to be complex, but now it may be in the complicated domain, or even the simple domain. Even a situation from the Simple domain can collapse into the Chaotic domain if there is complacency.

Screen shot 2010-07-07 at 23.33.02

The following definitions are taken from Cognitive Edge website;

The Cynefin framework has five domains. The first four domains are:

Simple (also called as Obvious), in which the relationship between cause and effect is obvious to all. The approach is to Sense – Categorize – Respond and we can apply best practice.


Complicated, in which the relationship between cause and effect requires analysis or some other form of investigation and/or the application of expert knowledge. The approach is to Sense – Analyze – Respond and we can apply good practice.


Complex, in which the relationship between cause and effect can only be perceived in retrospect, but not in advance. The approach is to Probe – Sense – Respond and we can sense emergent practice.


Chaotic, in which there is no relationship between cause and effect at systems level. The approach is to Act – Sense – Respond and we can discover novel practice.


The fifth domain is Disorder, which is the state of not knowing what type of causality exists, in which state people will revert to their own comfort zone in making a decision. In full use, the Cynefin framework has sub-domains, and the boundary between simple and chaotic is seen as a catastrophic one: complacency leads to failure. In conclusion, chaos is always transitionary and dynamics are a key aspect.

This is summarized in the following figure.

Cynefin final

The need for the Cynefin Framework:

Most of the methodologies, including PDCA, assume some form of order. Sometimes this leads to the misapplication of methodology that leads to failures. Only Simple and Complicated domains assume some form of order. The Cynefin framework helps us in being efficient and at the same time effective.

There are minimal resources needed for a situation in the Simple domain. The answer is fairly obvious, and best practice is already known in the form of SOPs (Standard Operating Procedures) or work instructions. For example, the light bulb burned out – replace the light bulb. Project management is certainly not needed for this domain. There is no true need for a PDCA methodology in this domain. The Cynefin framework recommends sense-categorize-respond for this domain. The assumption is that there is a known best practice available or that the best practice is fairly straightforward.

The Complicated domain needs guidance from experts. Multiple solutions can exist, and we need experts’ help to identify the optimal solution. For example, if the light bulb keeps going out, it may not be as easy as replacing a light bulb. This is a domain that works well with PDCA. One should not imitate and apply the best-practice in this domain. Dave Snowden refers to a phenomenon called “premature convergence” where we stop exploring how to make ideas better, thinking that we have found the answer. Cynefin framework recommends sense-analyze-respond. This is similar to a PDCA approach.

The Complex domain does not have order. It is an unordered domain. We need patience for patterns to emerge in this domain. Cause and effect relations are not directly visible in this domain. The recommended practice is probe-sense-respond. Multiple and different PDCA loops might be required for this domain to let the patterns emerge. Think of any root cause projects that you completed, where you did not see the solution in the beginning, but on hindsight it made sense. Dave Snowden gives the example of “Houston, we have a problem” scene from the movie “Apollo 13”.

As the name suggests, the chaos domain is indeed full of turbulence and chaos. This is not a domain where you search for answers. This is a domain for rapid decisions to regain control and stabilize the turbulence. The recommended approach is act-sense-respond. The act phase can be an attempt to stabilize the turbulence. As you can see, this is not an ideal candidate for the PDCA approach. If PDCA is used, the Plan phase will need to be quite short. The goal of this domain is to quickly move to the complex domain as soon as possible. Dave Snowden’s example for this domain is the unfortunate 9/11 incident.

Final words:

In the business world, there is no solution that is one-size-fits-all. Context is everything! Each domain of the Cynefin framework comes with its own burden. Being too complacent in the Simple domain can push you into the Chaotic domain. Trying to imitate what worked for one company can cause you to fail (the Complicated domain). Not waiting for patterns to emerge in the Complex domain, and trying to push for best practices can push you over to the Chaotic domain. The Cynefin framework provides you a thinking framework to understand the scope of your situation and helps you in being efficient and effective with your PDCA approach. This post was written based on my thoughts on my learning with the Cynefin framework. I encourage the reader to read upon the Cynefin framework more at Cognitive-Edge.com. The HBR article “A Leader’s Framework for Decision Making” is also an excellent place to start.

Always keep on learning…

Is Inspection Value Added?

pass fail

In popular Lean circles, the idea of value-added is represented by the following two criteria;

  • Is your customer willing to pay for the activity?
  • Is the activity physically changing the shape or character of the product so that it increases the product’s value in the eyes of the customer?

In lieu of these criteria, is inspection value added? Before answering, please be aware that this is a loaded question. Also understand that the question is not “should we inspect product?”

Inspection generally does not alter the physical attributes of a product. Inspection in the traditional sense accepts or rejects the product. In this aspect, inspection should prevent a bad product from reaching the hands of the customer. Does this mean that then the inspection activity is value added?

As a customer, I would love it if the product is inspected, and reinspected ten times. But I would not want to pay for such an activity. Are we as a society of consumers wrongfully trained to think that inspection somehow increases the quality of the product?

Deming’s view:

Dr. Deming’s view of inspection is as follows;

Cease dependence on inspection to achieve quality. Eliminate the need for inspection on a mass basis by building quality into the product in the first place.

In fact, this is the third principle of his 14 key principles for management to follow for significantly improving the effectiveness of a business or organization. Deming’s view is clearly stated in his “Out of Crisis” book. “Inspection does not improve the quality, nor guarantee quality. Inspection is too late. The quality, good or bad, is already in the product.”

Shigeo Shingo’s View:

Shigeo Shingo is considered by many a powerful force behind Toyota Production System. He trained Toyota employees with his “P-courses”. Shingo was the person behind Poka-yoke (Error proof) and SMED (Single Minute Exchange of Dies). In his views, there were three types of inspection:

  • Judgment Inspection – inspections that discover defects
  • Informative Inspection – inspections that reduce defects
  • Source Inspection – inspections that eliminate defects

Judgment inspection is an inspection that is performed after the fact. The lot is produced, and then inspection is performed to determine if the lot is acceptable or not. In Shingo’s words “It (Judgment Inspection) remains inherently a kind of postmortem inspection, however, for no matter how accurately and thoroughly it is performed, it can in no way contribute to lowering the defect rate in the plant itself.” Shingo continues to state that the Judgment Inspection method is consequently of no value, if one wants to bring down defect rates within plants.

Informative Inspection is an inspection that helps in reducing defects. This method feedbacks information to the work process involved, thus allowing actions to take place to correct the process. Shingo describes three types of Informative Inspections.

  1. Statistical Quality Control Systems – This is the system with control charts where one can identify trends or out of control processes, aiding in getting the process back to stability.
  2. Successive Check Systems – This is the system where the component gets inspected by the next operator in the line. Any defect is identified and corrected almost immediately by letting the previous operator know. Please note that ideally this system uses 100% inspection.
  3. Self-check systems – This is the system where the operator can inspect the work that he/she did, and fix the problem immediately. Please note that ideally this system uses 100% inspection.

The final category is Source Inspection. In this category, the feedback loop is so short that as soon as the error occurs, the feedback kicks in preventing the error from becoming a defect.

Feedback Loop – The Key:

The key in determining value in the inspection process is the length of the feedback loop. Judgmental Inspection is the least value adding in this regards because the product lot is already built and completed. Informative Inspection is value adding, since the feedback loop is considerably shorter. Finally, the source inspection is the most value adding since the feedback loop is the shortest.

The feedback loop is shown below.

feedback loop

Thus, the shorter the feedback loop, the higher the inspection method’s value.

Final Words:

This post started with a question, Is inspection value added? Errors are inevitable. Drifts in processes are inevitable. Learning from errors is also becoming inevitable. Inspection activities that increase the system’s value are definitely value added. I used to wonder, whether kaizen is value added. Is a customer willing to pay for an organization to be a learning organization? I came to the realization that kaizen is based on a long term principle. The real value is in cultivating the long term trustful relationship with the customer.

Inspection activities that allow the organization to grow and learn are definitely value added. The table below summarizes this post.

table

Always keep on learning…

Continuous Improvement Inhibitors and ‘Respect for People’:

respect

I recently reread Deming’s Out of Crisis book. I came across a list that caught my eye – perhaps I overread it last time, or did not pay enough attention to it. This list is based on a conversation with 45 production workers. According to them, these inhibitors stood in their way to improvement of quality and productivity. Bear in mind that this book came out first in 1982. After more than thirty years, how many of the items in the list are still valid today? How many of these inhibitors do you have at your workplace?

  • Inadequate training
  • Delays and shortages of components
  • Inadequate documentation on how to do the job
  • Rush jobs (bad planning)
  • Outdated drawings
  • Inadequate design
  • Foremen do not have sufficient knowledge to give leadership
  • Inadequate and wrong tools and instruments
  • No lines of communication between production and management
  • Poor working environment
  • Poor performance measurements
  • Defective components at incoming
  • Struggle to get technical help from Engineers

It is said that Deming helped complete Toyota Production System with the introduction of the PDCA cycle as part of Kaizen. If I look at the list above, I realize that majority of the items are to do with Respect for People.

Maybe it is not by accident that the Toyota Way consists of ‘Continuous Improvement’ and ‘Respect for People’.

respect2

The Toyota Global website states the following;

The Toyota Way is supported by two main pillars: ‘Continuous Improvement’ and ‘Respect for People’. We are never satisfied with where we are and always work to improve our business by putting forward new ideas and working to the best of our abilities. We respect all Toyota stakeholders, and believe the success of our business is created by individual effort and good teamwork.

http://www.toyota-global.com/company/history_of_toyota/75years/data/conditions/philosophy/toyotaway2001.html

There is a saying from Toyota “Monozukuri wa hitozukuri,” which roughly translates to “making things is about making people.”

Deming did not talk specifically about ‘Respect for People’. However, his fourteen key principles to managers for transforming business effectiveness were very much about ‘Respect for People’. I have highlighted the sections that I believe applies to ‘Respect for People’.

  1. Create constancy of purpose toward improvement of product and service.
  2. Adopt the new philosophy. We are in a new economic age.
  3. Cease dependence on mass inspection.
  4. End the practice of awarding business on the basis of a price tag alone(This is about long-term relationship of loyalty and trust with your supplier base).
  5. Improve constantly and forever the system of production and service.
  6. Institute training.
  7. Adopt and institute leadership.
  8. Drive out fear.
  9. Break down barriers between staff areas
  10. Eliminate slogans, exhortations, and targets for the work force.
  11. Eliminate numerical quotas for the work force. Eliminate numerical goals for people in management.
  12. Remove barriers that rob people of pride of workmanship.
  13. Encourage education and self-improvement for everyone.
  14. Take action to accomplish the transformation. The transformation is everybody’s job.

Final Thoughts:

A lot of people before me have tried to define what ‘Respect for People’ mean to them. Jon Miller at GembaPantarei has further clarified that a better translation is Respect for Humanness or Humanity.

http://gembapantarei.com/2008/02/exploring_the_respect_for_people_principle_of_the/

To me, ‘Respect for People’ determines why I come to work today and tomorrow. My view is that by creating the equation making things is making people, Toyota has placed people development as a value added activity.

My view is that by creating the equation making things is making people, Toyota has placed people development as a value added activity.

If you agreed with the list of continuous improvement inhibitors, and if you believe that all, if not some, of the inhibitors are applicable to your organization, you may need to look at ‘Respect for People’.

Always keep on learning…