The Mystery of Missing Advent Calendar Chocolates:

It is Christmas time, which means it is advent calendar time for the kids and for those of us who are kids at heart. My wife bought our kids chocolate advent calendars from Trader Joe’s. For those who do not know advent calendars, these are countdown calendars to Christmas starting on December 1^st. Each day has a window which you can open to reveal a chocolate. Each day has a uniquely shaped chocolate, a Christmas tree, a stocking etc. The kids love this.

We keep the advent calendars on the top of our refrigerator to ensure they are not tempted to eat all of the chocolate at once. This morning, I found the advent calendars on the table and a crying Annie. Annie is our youngest daughter. She was very upset.

“I did not get any chocolate today from my calendar”, she said while crying.

“You must have eaten it already”, was my response. Of course, the kids eat chocolate and sometimes they are impatient and eat more than one day’s worth. In my mind, it was a reasonable assumption to make.

Annie explained that she opened the window with 6 on it and did not find any chocolate. I looked at the calendar, and sure enough, the window for day 6 on it was open. My initial hypothesis stayed the same – Annie ate the chocolate, and she is not telling me the entire truth.

My wife suggested she open the window for day 7 and eat that chocolate. Annie then proceeded to open the window with 7 on it, in front of me. Lo and behold, it did not have any chocolate. Annie looked at me with sad eyes. I realized, I was wrong to have assumed that Annie had eaten the chocolate!

“This is a mystery”, said Audrey, her twin sister.

Now I had a second hypothesis – those darn calendar makers; they do not know what they are doing. They obviously missed filling all the spots with chocolate. As a Quality Engineer, I have seen operator errors. I have now jumped to my second hypothesis.

Having thought about for a bit, I looked at the available information. Based on what Annie told me, the chocolate was not in its spot for two consecutive days. These calendars did not have the numbers in the consecutive order. They were placed in random order. It did not strike to me that two candies at different locations would be missing candy. She had opened a spot between 6 and 7 on an earlier day, and it had the candy.

I had a reasonable hypothesis – the operator/equipment missed the spots in the calendar. I have seen it happen before in different environments. But still, something was not right.

I proceeded to put the advent calendar back onto the top of the refrigerator. Then I thought of something. I wanted to test the calendar more. I carefully opened the calendar from the base. It was a card board box with a plastic tray inside.

Just then I found out what happened! On multiple places, the chocolate was missing. The chocolate were misplaced from its cavities. They were all gathered at the bottom of the box. It could be from the transportation. It could be the end user i.e. my excited young daughter who shook the calendar. It could be the design of the calendar that allows extra space between the tray and the cardboard.

The most important thing was that Annie was now happy that she got her candies. Audrey was happy that we indeed had a mystery that we could solve. My wife and I were happy that our kids were happy.

Final Words:

This personal story has made me realize again that we should not jump to conclusions. Listen to that tiny little voice that says “there is something more to this”…

Always keep on learning…

In case you missed it, my last post was about “Lady Tasting Tea”.

Let’s Talk About Tea:

This week, I was talking to one of my colleagues and going off on a tangent we began discussing tea. His parents are from UK. Today’s post is inspired by that conversation.

Milk First or Tea First:

The question of whether to add milk first or tea first is an interesting one. As part of writing this post, I did some research on this one. The first documented account of milk being added to tea is from Johan Nieuhof (1618-1672), a steward of the then Dutch ambassador to China. He wrote about adding one fourth of warm milk to tea with salt. The idea of using milk with tea was made popular in Europe by social critic Marie de Rabutin Chantal, the Marquise de Seven in 1680.

The socially correct protocol, according to Douglas Adams (author of Hitchhiker’s Guide to the Galaxy) and many others is to add milk in after tea. There are many anecdotes on why this is the case. The most popular version is about the quality of tea cups back in the day. Pouring hot tea first broke the low quality cups. The upper class of the society showed off their high quality cups by pouring hot tea first and then milk. The people who could not afford high quality tea cups poured milk first and then tea. Another reason could be also the way the process of making tea was documented. As noted above, the documented process was to add milk to tea.

George Orwell even wrote an essay on making tea called “A Nice Cup of Tea”. His preference was to add tea first and then milk. His logic was as follows;

One should pour tea into the cup first. This is one of the most controversial points of all; indeed in every family in Britain there are probably two schools of thought on the subject. The milk-first school can bring forward some fairly strong arguments, but I maintain that my own argument is unanswerable. This is that, by putting the tea in first and stirring as one pours, one can exactly regulate the amount of milk whereas one is liable to put in too much milk if one does it the other way round.

Douglas Adams on the other hand liked to add milk first even though it was not the socially correct protocol. Today, scientists will tell you that the proper way of making tea is to add the milk first and then tea. Milk proteins when exposed to a temperature above 75 degrees C (167 degrees F) will start to degrade through the process of denaturation. This is more prone to happen when milk is added to tea rather than when tea is added to milk.

The Lady Tasting Tea:

The story of the lady tasting tea is perhaps the most fantastic story in the field of statistics. There are a few different versions as to where the incident took place. The story goes that in an English afternoon in 1920’s, a statistician, a chemist and an algologist were sitting together. The statistician offered to make tea, and proceeded to pour tea and then milk. The algologist, a lady (hence the name a lady tasting tea) objected to the process. She told the statistician that she preferred to have the milk poured before tea. She claimed that she could tell the difference. The chemist who was the fiancée of the algologist immediately wanted to test her claim, as any warm blooded scientist would do. The statistician proceeded to create an impromptu test for the lady. He created four cups of tea with milk first, and then four cups of tea with tea first. He randomized the cups using a published collection of random sampling numbers. The lady was informed of the test protocol and then she tasted each cup and identified all the cups accurately, thus standing by her claim.

The statistician was Sir Ronald Fisher, the chemist was Dr. William A Roach and the lady algologist was Dr. Blanche Muriel Bristol. The story was documented by Sir Fisher in the groundbreaking book “The Design of Experiments” and in his paper “The Mathematics of a Lady Tasting Tea”. The probability of the lady getting all the results correct was 1/70 = 0.014. This value is less than the magical 0.05. Interestingly, Sir Fisher wrote the following about the 0.05 value in the paper;

“It is usual and convenient for experimenters to take 5 percent, as a standard level of significance…”

If the lady had gotten one result incorrect, the p-value would had been 0.243, and the testers would have failed to reject the null hypothesis that the lady has no ability to tell the difference between the two styles of making tea. Thus, one can say the test is not fair since if the lady failed once, it would not help justify her claim. In the paper, Sir Fisher advised that to improve the test, one should use 6 cups each of tea. The p-value of getting one incorrect is only 0.04, which is still less than 0.05. Thus, the lady has a little more leeway.

This story helped explain the idea of randomization and significance testing. The test’s efficacy is improved further if the total number of particular styles were kept secret. Dr. Bristol was told about the exact number of each style of tea beforehand.

The Answer to the Ultimate Question of Life, the Universe, and Everything:

In Hitchhiker’s Guide to Galaxy, the answer to the Ultimate Question of life, the universe and everything is given as 42! I came across a possible explanation during my research for this post based on Douglas Adam’s passion for tea.

42 = fortytwo

For tea two.

Two for tea!

Always keep on learning…

In case you missed it, my last post was about Respect for Humanity.

Respect for Humanity (An early theme in Toyota Production System):

This post is mainly about Respect for People in Toyota Production System, to show that this idea has been an integral part of Toyota Production System from the early days on.

“Toyota Production System – The foundation is respect for human rights”.

This is a quotation from a Toyota Production System book. Respect for people got famous with the famous Toyota Way document in 2001. This principle has been included within Toyota Production System for a long a time. In fact, the quotation I have cited is from “Toyota Production System First Textbook”. This is one of the early documents created within Toyota in the early 1970’s. An interesting trivia is that some of the text was written by Fujio Cho, the very person behind the Toyota Way 2001 document.

Section 5 of this document is titled “Man-Hour Reduction and Human Relations”. This section calls out TWI (Training Within Industry) Job Relations manual and cites “People must be treated as individuals” phrase from the Job Relations manual.

I have written about TWI previously here. TWI was an emergency service by US to help nation’s war contractors and essential production. There was a need to produce a lot in a short amount of time, and this required training operators to be better within a short amount of time. C R Dooley, the Director of TWI, stated the following; “TWI’s objectives were to help contractors to get out better war production faster, so that the war might be shortened, and to help industry to lower the cost of war materials.” Job Relations is one of the manuals of the TWI program. In the foreword of this manual, C R Dooley stated that “Giving workers technical skill alone is not enough. Supervisors must give every man and woman at work the leadership that enlists cooperation and teamwork.” Toyota added Job Relations to its internal training curriculum in 1951.

The “Toyota Way 2001” document made “Respect for People” the new thing in lean. This theme was not at all present in any of the western books on Lean or Toyota Production System. Naturally, this theme got a lot of attention fast.

Yasuhiro Monden’s Write-up on Respect for Humanity:

Respect for people also goes by Respect for Humanity. In my opinion, this was best described by Prof. Yasuhiro Monden in his 1983 book “Toyota Production System”. He wrote;

“At Toyota, respect for humanity is a matter of allying human energy with meaningful, effective operations by abolishing wasteful operations. If a worker feels that his job is important and his work significant, his morale will be high; if he sees that his time is wasted on insignificant jobs, his morale will suffer as well as his work.”

Prof. Monden also links to the ability of the worker to stop the line in case of a problem with Respect for Humanity. He states “Since quality control based on autonomation (Jidoka) calls immediate attention to defects or problems in the production process, it stimulates improvement activities and thus increases respect for humanity.”

Prof. Monden also gives two rules to follow when making job improvements;

Give the worker valuable jobs – the job performed by the worker (operator) should not be riddled with non-value added activities.
Keep the lines of communication within the organization open – A relationship of trust and credibility needed for improvement activities need open lines of communication.

One of the mantras at Toyota is “Monozukuri wa hitozukuri” or “Developing products is about developing people”. This idea is underlined by Prof Monden. He has identified three sub-goals for Toyota Production System. They are:

Quantity Control – The ability of the system to adapt to daily and monthly fluctuations in demand in terms of quantity and variety.
Quality Assurance – Assurance that each process will supply only good units to subsequent processes.
Respect for Humanity – Cultivating Respect for Humanity while the system utilizes the human resources to attain its cost objectives.

Final Thoughts:

I will finish with an amusing article on Walmart. In this year’s shareholder meeting, Wal-Mart announced that they will stop playing Celine Dion and Justin Beiber in an effort to boost employee morale. Apparently, the stores were playing a CD with Celine Dion and Justin Beiber non-stop driving the employees crazy. This notorious playlist has been replaced with Wal-Mart Radio. This announcement received the most cheers from the crowd! This was based on multiple feedback from the employees.

Always keep on learning…

Innovation and Kaizen are not oil and water:

Kaizen is likely the most used Japanese word in the world of manufacturing. Kaizen simply means to improve for better. The “zen” part of Kaizen is not the same as the commonly known word “Zen”.

zen = virtue or good

Zen = literal meaning is “meditation”. This Japanese word has roots in the Sanskrit word “dhyan”.

It is quite possible to achieve Zen through kaizen. 🙂

This post is about the relationship between kaizen and innovation. Kaizen is often described as continuous improvement through small changes while innovation is described as a big change, or a complete breakthrough. The current paradigm is completely destroyed. From afar, these two concepts seem different – almost like oil and water.

What is the relationship between innovation and kaizen?

The book Kaizen Teian III (later published as The Improvement Engine) by the Japan Human Relations Association, states that kaizen cannot thrive without innovation.

This is an interesting concept – if kaizen is continuous improvement through small changes, how can a company stay competitive just by making minor improvements? The book describes innovation as a bulldozer cutting a path through a rough terrain. Improvement activities are described as a means to leveling the path cut by the bulldozer, and paving it into a smooth road!

Figure taken from Kaizen Teian III.

The book further states that there is nothing more wasteful than trying to activate a kaizen system in a company that is not engaged in innovation! Trying to clear a path in a rough terrain with a pick and shovel will not do when you competitors are using a bulldozer.

My thoughts:

What is the purpose of kaizen?

There are two objectives for kaizen;

Continuous improvement to make work better
Development of employees so they can be better at problem solving and developing others.

If kaizen can be described as a bottom-up process, then innovation can be described as a top-down process. You cannot move towards your ideal state (true north) without one or the other. You need both bottom-up and top-down forces to move towards your ideal state. They both create the perfect recipe for kaizen culture.

Always keep on learning…

In case you missed it, my previous post was about ESP and statistics.

Extra Sensory Perception Statistics:

In today’s post, I am going to combine two of my favorite topics – mindreading and statistics.

I should confess upfront that I do not read minds, at least not literally. I do have a passion for magic and mentalism. I would like to introduce the readers to Joseph Banks Rhine. He is the creator of ESP (Extra Sensory Perception) cards. These are a set of 5 cards with 5 shapes (circle, cross, waves, square and a star). These cards were used for testing ESP. The readers might remember the Bill Murray scene in the movie Ghostbusters. The ESP cards are a common tool for a mentalist.

In 1937, Zenith Radio Corporation carried out multiple experiments under the guidance of Rhine. A selected group of psychics chose a “random” sequence and transmitted it out during the radio show. The listeners were asked to “receive” the transmitted sequence, write it down and send it back to the radio station. The sequence had 5 values and each value was binary in nature. This could be heads and tails, light and dark, black and white, or a group of symbols. The two values were represented as 0 and 1. Thus, a possible sequence could be 00101.

The hypothesis was that human beings are sensitive to psychic transmissions. It is reported that over a million data points were collected as part of these experiments. From a statistics viewpoint, this is a statistician’s dream come true!

The results of the study implied strongly about the existence of ESP. The number of correct guesses was significantly high, if the calculations were based on assumption of randomness.

A million data points is a statistically valid sample size. The studies were blind in nature. The “psychics” in the radio station did not cheat. The responding listeners did not have a way to know the sequence before-hand. So did they prove that ESP is real?

Enter Louis Goodfellow:

Goodfellow (an apt name) was a psychologist involved in the study. He realized something was fundamentally wrong with the study. The data that was transmitted was not truly random. The data was “randomly” chosen by the psychics. Unfortunately, being random is not something that we, human beings, are good at. We will try really hard to create a random sequence, and in the process create a completely non-random sequence. Certain sequences are chosen more than the others, across the board. With over a million data points, there should have been close to 3% occurrence of 11111 or 00000. The data showed this was actually less than 1%. Additionally, with such a large sample size, we would expect uniform data, meaning all sequences should show up with nearly equal proportions. This was not the case either.

In other words, the study revealed that the psychics were indeed human beings. Goodfellow repeated the study without involving the psychics. The study group was required to create a “random” sequence. The resulting data was very much similar to the Zenith radio data. Goodfellow also repeated studies with truly random sequences, and the study group failed to “receive” the sequences. (A psychological interpretation of the results of the Zenith radio experiments in telepathy)

The basic assumptions of independence and randomness were not followed for the original study. Thus, we still do not have evidence that ESP is real.

Always keep on learning…

Machine – Let your imagination run wild!

On June 17, 2015, Mordvintsev, Tyka et al posted a blog on the Google Research Blog. The title of the blog was interestingly “Inceptionism: Going Deeper into Neural Networks”. This post discussed using the Machine Learning algorithm, Neural Networks to identify objects in any picture. This was referred to as “Deep Dreaming”. Since then, the concept of machines dreaming has gone wild!

Neural Network is a Machine Learning algorithm used for fields like speech recognition, character recognition etc. The algorithm requires supervised learning at first. At this stage, the algorithm is trained using known samples. The strength of the internal networks gets stronger as the algorithm gets more answers correct. This weakens the pathways leading to incorrect answers, and strengthens the pathways leading to correct answers. Once the “training session” is over, the algorithm will be used on new samples, where the answer is not provided to the algorithm.

Google has created a program aptly named “DeepDream”. In their own words;

Google has spent the last few years teaching computers how to see, understand, and appreciate our world. It’s an important goal that the search giant hopes will allow programs to classify images just by “looking” at them.

And this is where Google’s deep dream ideas originate. With simple words you give to an AI program a couple of images and let it know what those images contain ( what objects – dogs, cats, mountains, bicycles, … ) and give it a random image and ask it what objects it can find in this image. Then the program start transforming the image till it can find something similar to what it already knows and thus you see strange artifacts morphing in the dreamed image (like eyes or human faces morphing in image of a pyramid).

In other words, we are encouraging the machine to run their imaginations wild with pareidolia, the phenomenon where one sees a shape or a form on an unrelated object, like seeing a face on the surface of the moon. The algorithm will try to find objects in a picture, and any resemblance is made stronger and stronger until it creates a familiar object. Of course, I wanted to test this out. You can see the deep dreaming effects on my current LinkedIn profile picture.

You can see that my black hair was used to weave in imaginary eyes. Interestingly, my beard became the face of a dog. The algorithm uses the pictures database it knows (over 1 million pictures). Thus most of the dreamed pictures are common animals and objects.

I thought I would then test the algorithm with Card III from Rorschach Inkblot test. This card is seen to have two humans or ape like shapes interacting with each other.

The algorithm was able to identify two dogs with weird limbs, another dog in the middle, and two containers of some sort.

Practical Uses:

This program is very entertaining and provides insights into how the machine actually sees or dreams, as Google likes to put it. Other than the entertaining and deep philosophical aspects of a machine dreaming, there are practical uses for this.

What if the machine can imagine a picture through the eyes of a famous artist? Using a similar algorithm, we can have a “new” painting from Vincent van Gogh of the Eiffel Tower. I have taken the following example from here. You can see that the clouds were enhanced with the strong Van Gogh style strokes.

More examples were found here. The following is the Starry Night version of San Francisco Golden Bridge.

We can apparently go one step further, and imagine what a painting of Golden Bridge would look like if it was part Vincent Van Gogh’s Starry Night and part Edvard Munch’s The Scream.

I am blown away by this!

Can we go from Stills to Animation?

Apparently, this does not stop here. Mbartoli has created an algorithm that converted a gif file of a scene from 2001: A Space Odyssey in the artistic style of Edvard Munch’s The Scream.

My Favorite Example of Machine Learning in a Movie or a TV Show:

I have been enjoying learning Machine Learning. I always keep my eyes open for movies or TV shows that feature some form of Machine Learning. Recently, I came across “Be Right Back“, the first episode in season 2 of the British TV show, Black Mirror. If you have not seen the show, I highly recommend it. I will be revealing the main plot of the episode here, so if you have not seen it, you may want to stop reading now.

The premise of the episode is that the main character, Martha, loses her boyfriend, Ash, after he dies tragically in a car accident. Ash was a heavy social media addict. The brilliant notion that the episode put forth is this – can a computer be trained with all the social media left behind by the boyfriend in the form of tweets, blog posts, videos etc. and create an AI (Artificial Intelligence) boyfriend? The AI boyfriend looks and sounds like Ash. It also makes jokes as Ash would. Maybe, this is the final frontier? Man living on through Artificial Intelligence!

What the future holds – DeepDreamFlix?

In the future, you can turn the DeepDreamFlix app on, and type in “Alfred Hitchcock + Jackie Chan + Western” and the app will create a brand new Western movie directed in the style of Hitchcock starring Jackie Chan. 🙂

This post was written to introduce the cool concept of Neural Network, and to encourage the reader to get interested in Machine Learning. There are many free and cost effective courses online. I highly recommend Udacity, and Coursera.

Edit on 9-12-2017 – Another good resource that was brought to my attention is Springboard.

Always keep on learning…

Process Capability – Are You Really Capable?

I have always been interested in process capabilities. I will not be going into how you can calculate the process capability in this post. In this post, I will try to look into the feasibility of a process capability study. The goal of this post is to encourage the reader to understand the process capability and sigma value correlation. The most popular value for a process capability index seems to be 1.33. This is applicable for Cpk or Ppk.

There are two ways you can look at your process capability. The first way is Cpk, commonly referred to as Short Term Capability. This assumes that your process is stable, and uses the R/d2 estimate for standard deviation.

The second way is Ppk, commonly referred to as Long Term capability. This is for used looking at the potential capability and uses the sample estimate of standard deviation in the formula. There is no need of assumption of stability for using this.

Relation between Sigma value and process capability index:

The formula for calculating the sigma value from the process capability index value is show below.

Sigma = 3 * (Process capability index value)

Thus if your Ppk value is 1.33, this equates to a sigma value of 4.

A ppk value of 2.00 refers to the ever famous 6 sigma value.

Why 6 sigma values are hard to conceive:

I came across a cool table that correlates the sigma values to approximated frequency of daily events (if the event was to occur on a daily basis). Please note that this does not include the 1.5 sigma shift and assumes normality.

Please note that the column in the right is based on the assumption that the event is occurring on a daily basis only.

So next time you achieve a process capability value of 3.00 or more, understand that this indicates a sigma value of 9.00 or more. In other words, it is quite impossible to achieve such a feat. This could be a strong indication of autocorrelation. Autocorrelation indicates that the data you collected are extremely homogeneous and that this may not include any possible process variations. Of course, the other possibility is that the specifications are very wide. More on this on a later post.

Always keep on learning…

Is Murphy’s Law alive and well?

In today’s post, I will be trying to look into Murphy’s Law.

There are multiple versions existing for this law, the most common version being – “whatever can go wrong will go wrong”. Some other variations are as follows;

If there is a possibility of several things going wrong, the one that will cause the most damage will be the one to go wrong.
If everything seems to be going well, you have obviously overlooked something.
Nature always sides with the hidden flaw.

Murphy’s Law makes a pessimist out of the most optimistic man. Is it true that the universe has a tendency for causing things to fail? Does Murphy make a rational man go “Why me?”, when something unexpected happens?

A common version of Murphy’s law is the case of buttered toast. The buttered toast always fall on the buttered side. Let’s look into this deeper.

Does buttered toast listen to Murphy?

The following section is taken from “The Australian Journal: A Weekly Record of Literature, Science, and Art, Volume 23”, from 1888. The highlighted section shows that the idea of buttered toast/bread falling on its buttered side is common, even in the 1800’s.

Interestingly, studies have shown that buttered toasts fall on their buttered side almost 62% of the time. This would mean that it is not a fifty-fifty chance like flipping a coin. Why? This seemingly curious “bad luck” can be explained with science. Delving deep into the case of buttered toast, it becomes clear that the following factors always remain the same;

The toast always starts with the buttered side face-up
The height of the fall is similar (2-3 feet). This is because the toast is held at waist height generally, and in the case of falling from a table, the standard table height is between 2-3 feet.

These two factors increase the chances for the toast to fall on the buttered side. In fact, studies have shown that when toasts are thrown up in the air, the likelihood decreases to fifty-fifty. Alternately, when the toast is dropped from a height of 7-8 feet, the likelihood of buttered toast falling on the unbuttered side goes up back to about 62%. The reader can find more about this here and here.

Does Murphy still seem threatening?

Factors which cause Murphy to visit:

I have compiled a list that explains why Murphy is prevailing.

Nature of humans: Humans always remember when something bad happens to them. Do you remember the last time your car broke down and you had to call for it to be towed away? Do you remember the other 99.9% of time, where you did not have any problems with the car? Since your brain likes to avoid making mistakes, it likes to recall the bad times more so that you do not repeat the same mistakes. The downside of this is that it can make you start noticing only the bad events. Think of a large white paper with a small black dot. Our attention is on the black dot, and not at the remaining 99.9% of the white space.
Law of large numbers: The bad thing about events with relatively small probabilities is that they will still happen. No matter how small the probability, with enough chances the event will happen. The probability of winning the powerball lottery is 1 in 292,201,338. Even with such a small probability, people still win the lottery on a regular basis. The probability of somebody winning a lottery goes up when the prize gets really high (>$300 Million). This is because, a larger amount of tickets are sold during that time. As Law of large numbers dictates, with enough chances even the low probability event of winning a lottery happens.

Combining the Nature of Humans, and the Law of large numbers, you have the perfect storm that allows Murphy to rule the world. The egocentric view of humans tends to make events about them, when from a probability standpoint, it could have happened to anybody. There is a profound difference between asking “What are the chances of it happening” and “What are the chances of it happening to me?”

Law of Nature: It is the law of nature that everything degrades over time. Eventually, all products will fail. A good example is when you move into a new house, and after about 7 years, more than one appliance starts to breakdown. First it was the refrigerator, and now it is your washer as well. The fact that the two appliances were bought together might escape your mind, and you will blame Murphy.
Poor Processes: In relation to item 3 discussed above, if you have poor processes, the chance of multiple things to fail goes up. A good example is poor preventive maintenance procedures. Multiple equipments can break down at the same time, if they are not maintained properly. If one equipment can go bad, there is a good likelihood for another to go bad at the same time, if the same poor preventive maintenance program was being used. A poorly designed system can become a playground for Murphy.
Special Causes: Sometimes the unlikely event(s) happens due to special causes. Sometimes this special cause can be an enabling condition that allows multiple things to breakdown. The special cause at times is people. People are inherently inconsistent, and they can add inadvertent variation to the process that makes thing go wrong.
Complexity and Chaos: Murphy’s law is very much relevant in the presence of complexity and chaos. In the presence of disorder and uncertainty, the reliability of a system can breakdown easily. Any order from constraints is disrupted and this allows more things to go wrong. I welcome the reader to visit Cognitive Edge website to learn more about this.

Final Words and the story of Arthur Ashe:

As detailed in the buttered toast section, it is imperative that one tries to understand why something went wrong. What are the factors affecting the process? What are the chances of the event to happen? Is there indeed a pattern or is the pattern created by the perception? The buttered toast is a rigged game where there is high likelihood of the toast to fall on its buttered side when dropped from a height of 2-4 feet.

WHY ME?

Arthur Ashe, the legendary Wimbledon player was dying of AIDS which he got due to infected blood he received during a heart surgery in 1983.

From the world over, he received letters from his fan, one of them conveyed: “Why does God have to select you for such a bad disease?”

To this Arthur Ashe replied: The world over–50,000,000 children start playing tennis, 5,000,000 learn to play tennis, 500,000 learn professional tennis, 50,000 come to the circuit, 5000 reach the grand slam, 50 reach Wimbledon, 4 to semi finals, 2 to finals. When I was the one holding the cup, I never asked god “Why me?”

And today in pain, I should not be asking GOD “why me?”

Always keep on learning…

100% Visual Inspection – Being Human

The FAA (Federal Aviation Administration) defines visual inspection as “the process of using the unaided eye, alone or in conjunction with various aids, as the sensing mechanism from which judgments may be made about the condition of a unit to be inspected.”

Visual inspection, especially 100% visual inspection, is used by many industries as part of their Quality or Risk Management programs. Sometimes, as the last resort, companies implement 100% inspection if the residual risk is deemed to be high. I had previously written about inspection here and here.

In this post, I will discuss visual inspection in detail, value of 100%, 200% or 300% inspection (100% inspection followed by 100% inspection by one or two inspectors), factors impacting visual inspection, and end with a story from the field.

Flow chart for visual inspection:

The figure below is taken from the book “Human Reliability in Quality Control”, edited by C G Drury and J G Fox.

Figure 1: Flow chart for visual inspection

In “Visual Inspection: A Review of the Literature” by Judi E See (a Sandia National Laboratories report), visual inspection is detailed in a five step process:

In general, there are five basic steps that must be completed during an inspection task:

Set up: obtain procedures or work instructions, items to be inspected, and equipment needed for inspection
Present: prepare item for inspection (e.g., by installing in holding fixture or microscope)
Search: examine item for possible defects
Decide: compare potential defect against standards to determine if it exceed the standards
Respond: accept or reject the item based on the decision made in Step 4, mark the item as needed, and complete required paperwork

Effectiveness of 100% Inspection:

Juran, one of the giants of Quality, has written that, based on his studies performed on Inspector accuracy, 100% inspection is about 87% effective. Juran gave the following formula to calculate the Accuracy of an Inspector.

Accuracy of inspector = percent of defects correctly identified = (d-k)/(d-k+b)

where d = defects reported by the inspector

k = number of good units rejected by the inspector

d-k = true defects found by the inspector

b = defects missed by the inspector, as determined by check inspection

d-k+b = true defects originally in the product

This brings us to the next question, is 200% or 300% inspection effective?

From a pure probability standpoint, multiple inspections are indeed effective. For example, if we determine that 100% inspection is 85% effective (as an example), we can then calculate the probability of rejects being identified by at least one inspector.

P(rejects being identified by at least one inspector) = 1 – (1- e)^n

where e = inspection effectiveness (85% in the example)

n = number of 100% inspections (3 in the example)

Thus, we can determine that the 300% inspection with an inspection effectiveness value of 85% is 99.7% effective. In other words, 300% inspection will still result in 0.3% of the bad product to be missed.

The number of rejects caught by 100% inspection can be found by the following calculation.

# of rejects caught = N * p * e

where N = Lot size

p = % non-conforming product

e = inspection effectiveness

Please note that all this math and probability may not hold water in real world. This is because we were treating the three events of inspection as independent events. In real life, Inspector 3 might assume that Inspector 1 and 2 are extremely good, and that he does not need to pay full attention. Unfortunately, this bad logic may be used by Inspector 1 or 2, and in the worst case by all three inspectors. This type of “leaning on the other inspector” thinking breaks the independent event assumption we used in our probability calculation.

Factors impacting visual inspection:

In the wonderful report from Judi E See, we can also find a detailed list of factors that can impact inspection performance. One can find that any Attribute Agreement Analysis that is performed on visual inspection can be quickly challenged due to the high number of factors that need to be considered. On the contrary, this also explains why 100% inspection still produces rejects.

Table 1 – Factors impacting visual inspection performance

I will discuss some of these factors that I found to be interesting below.

Defect Rate:

Interestingly, as the defect rate goes down the inspection accuracy suffers. Alternatively, as the defect rate goes up, the inspection accuracy improves. In other words, if your lot has low number of defects, there is a really small likelihood of finding any rejects.

Multiple 100% Inspections:

I discussed briefly about multiple inspections above. The report however brings interesting “twists” to multiple inspections. First off, the report does find that multiple 100% inspections, especially for critical rejects, are better than just one 100% inspection. The report then goes on to discuss five possible methods to incorporate multiple 100% inspections.

 Each inspector inspects only half the batch in parallel

 Two accepts: both inspectors inspect every item and both must accept an item for the system to accept it

 Two rejects: both inspectors inspect every item and both must reject an item for the system to reject it

 Re-inspect accepts: Inspector 2 inspects only those items accepted by Inspector 1

 Re-inspect rejects: Inspector 2 inspects only those items rejected by Inspector 1

Gender:

There has been a misconception in the manufacturing industries that women are better at visual inspection than men. The corollary is that men are better at assembly operations than women. The report found that such notions are indeed false. There is no proven correlation between gender and inspection effectiveness.

Workplace design:

In “Sound Practices for Consistent Human Visual Inspection” by James A Melchore, the author states the following.

Recent studies by Knapp (9) and Budd (8) indicate that inspection with a light intensity of 550 foot candles will increase detection of particulates. Knapp’s data indicates that the use of 550 foot candles light intensity, combined with an 18% gray background, enabled 95% detection of a 150-μm particle 95% of the time.

Final Thoughts and the Story of the Box of Rejects:

Harold F. Dodge has stated that “You cannot inspect quality into a product.” The feedback loop should be as small as possible for an inspection activity to be value adding. This feedback also has the benefit of improving the process since the operator is made aware of the issue almost immediately. The best case is to have poke-yoke or mistake proofing such that mistakes do not happen at all.

I do not have a source for the story below. I heard this story below that contrasts the western and eastern thinking.

A famous American company thought that they would try to have a component manufactured in Japan on a trial basis. As part of the qualification activities, the American company clearly wrote that they will accept only three rejects per 10,000 parts.

When the delivery came from Japan, they found two boxes accompanied by a letter from the Japanese firm.

“Dear Sir,

We are having a hard time meeting your requirements. We had to manufacture the three defective parts separately to meet your requirements. Please reconsider your position.”

The second box that was much smaller in size was clearly labeled as noted in the letter “THREE REJECTS AS REQUIRED. DO NOT USE FOR PRODUCTION.”

Always keep on learning…

The Mysterious No Fault Found:

As a Quality Engineer working in the Medical Device field, there is nothing more frustrating than a “no-fault-found” condition on a product complaint. The product is returned by the customer due to a problem while in use, and the manufacturer cannot replicate the problem. This is commonly referred to as no-fault-found (NFF). I could not find a definite rate on NFF for medical devices. However, I did find that for the avionics industry it is 40-60% of all the complaints.

The NFF can be also described as “cannot duplicate”, “trouble not identified”, “met all specifications”, “no trouble found”, or “retest ok”. This menacing condition can be quite bothersome for the customer as well as the manufacturer. In this post, I will try to define some red flags that one should watch out for, and a list of root causes that might explain the reasons behind the NFF condition. I will finish off with a great story from the field.

Red flags:

The following list contains some of the red flags that one should watch out for, if no-fault was found with the product that was returned. This list is of course by no means meant to be an exhaustive list, but might provide some guidance.

Major incident associated with the complaint – If the return was associated with a major incident such as a serious injury or even worse, death, one should test the unit exhaustively to identify the root cause.
Unit was returned more than once – If the unit was returned for the same problem, it is an indicator of an inherent root cause creating the problem. Sometimes, an existing condition can act as an enabling condition and can create more than one effect. In this case, the problem may not be the same for the second or third return. Alternatively, the enabling condition can be present at the customer’s site.
Nonstandard Rework(s) performed on the unit during production – I am a skeptic of reworks. A rework is deviation from the normal production. And sometimes, fixing one thing can cause another thing to fail.
The product is part of the first lots produced after a major design change – If the product validation process is not adequate or if proper stress tests were not performed, the unit can be produced with latent issues/bugs.
More than one customer reporting the same problem – If there is more than one source reporting the problem, it is a clear indication of an inherent issue.

Potential root causes for NFF condition:

The following list contains some of the root causes that might be associated with a no-fault condition. This list is of course by no means meant to be an exhaustive list.

Adequacy of test methods – If the test method is susceptible to variations, it may not catch failures. This cause is self-explanatory.
Excess stress during use – Reliability Engineering will tell you that if the stress during use exceeds the inherent strength of the product, the product will fail. This stress can be environmental or can be due to use beyond the intended use of the product. An example is if the product is used at a wrong voltage.
New user or lack of training – If the end user is not familiar with the product, he/she can induce the failure that might not occur otherwise. This is not an easy root cause to figure out. Sometimes this is evident by the appearance of the product in the form of visible damages (dents, burn marks etc.)
High number of features – Sometimes, the higher the number of features, the more the complexity of the product and worse the ease of use of the product. If the product is not easy to use, it can create double or triple fault conditions more easily. A double or triple fault condition occurs when two or three conditions are met for the fault to happen. This is considered to be isolated in nature.
Latent bugs/issues – No matter how much a new product design is tested, all the issues cannot be identified. Some of the issues are left unidentified and thus unknown. These are referred to as latent issues/bugs. This is the reason why your mobile phone or your computer requires updates or why some cars are recalled. These bugs will result in failures that are truly random and not easy to replicate.
Problem caused by an external accessory or another component – The product is sometimes used as part of a system of devices. Sometimes, the fault may lie with another component, and when the product is returned, it may not accompany all the accessories, and it will be quite hard to replicate the complaint.
Lack of proper validation methods – Not all of the problems may be caught if the validation methods are not adequate. This cause is similar but not the same as latent bugs/issues. Here, if there was no stress testing performed like transportation or environmental, obvious failures may not be caught.
Customer performed repairs – Sometimes, the failure was induced by something that the customer did on the product. This may not always be evident unless revealed by the customer.
Customer bias – This is most likely the hardest cause to identify on this list. Sometimes, the customer may “feel” that the product is not functioning as intended. This could be because they experienced a failure of the same brand at another time, and the customer feels that the entire product brand is defective.
Other unknown isolated event – Murphy’s Law states that “whatever can go wrong will go wrong.” Law of Large Numbers loosely states that “with enough number of samples, even the most isolated events can happen.” Combined together, you can have an isolated incident that happened at the customer site and may never happen at the manufacturer site.

The mystery of diathermy burns:

I got this story from the great book “Medical Devices: Use and Safety” by Bertil Jacobson MD PhD and Alan Murray PhD. Sometimes, a surgery that uses a device like an RF Generator can cause burns on the patient from the heat induced by the device. This is referred to as “diathermy burns”.

A famous neurosurgeon retired and started working at a private hospital. Curiously, after a certain date, five of his patients reported that they have contracted ugly, non-healing ulcers. These were interpreted as diathermy burns. These burns were present on the cheek bones of the patients who were placed face-down for the surgery and on the neck region of the patient who were operated in the supine position (face-upward). The surgeon has had a long uneventful and successful career with no similar injuries ever reported.

No issues were found with the generator used for the surgery. A new generator was purchased, and the problem persisted. The manufacturer of the generator advised replacing the wall outlet. The problem still persisted. The surgery routines were updated and rigorous routines involving specific placement of electrodes were put in place. The problem still persisted.

A clinical engineer was consulted. He also could not find any fault with any of the equipment. At that point he requested witnessing the next operation. During this, it was discovered that the new assistant surgeon was placing his hands heavily on the patient’s head during the operation. Thus, the diathermy burns were actually pressure necroses caused by the assistant surgeon. These apparently can be misinterpreted as diathermy burns!

This story, in a curious way, implies the need to go to the gemba as well! Always keep on learning…

Harish's Notebook – My notes… Lean, Cybernetics, Quality & Data Science.

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Author Archives: Harish

The Mystery of Missing Advent Calendar Chocolates:

Let’s Talk About Tea:

Respect for Humanity (An early theme in Toyota Production System):

Innovation and Kaizen are not oil and water:

Extra Sensory Perception Statistics:

Machine – Let your imagination run wild!

Process Capability – Are You Really Capable?

Is Murphy’s Law alive and well?

100% Visual Inspection – Being Human

The Mysterious No Fault Found: