Originally posted on Andrea Gabor:
When I returned from speaking at the annual conference of the Deming Institute in Los Angeles last month, the education sites were abuzz about a new Time magazine cover trumpeting “Bad Apples”, the latest example of what has become a new national sport–knee-jerk teacher bashing.
It was a sad reminder of how much our quick-fix, here-today-gone-tomorrow society has forgotten about what our leading institutions learned, less than four decades ago, about the best approach to improving quality—whether at companies, schools or other institutions. These were hard fought lessons learned during a period of deep economic malaise—during the late 1970s and early 1980s—from the man who may have been the most important, and most misunderstood, management thinker of the 20th century.
As I pondered the Time magazine cover and the national narrative of education failure, which scapegoats classroom teachers as the…
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To be clear, I am not a believer, but I do want to understand the role religion has played in our history. Why does it have such a strong grip on so many of us regardless of culture? I don’t want to displace religion without having something else to take its place and serve the needs it has done up to now. The good news is many civic institutions are rising up to the task. I expect that trend to continue into the future freeing us from the need to believe in something supernatural.
I enjoyed the book. I agree with de Waal’s message. But, I didn’t think it was as fun to read as Robert Wright’s “The Moral Animal“. Still, I would recommend it for you to check out. I would love to hear what you think :)
“Eliminate numerical goals, posters, and slogans for the work force, asking for new levels of productivity without providing methods.”
– Point No. 10 in Dr. W. E. Deming’s 14 points for management as written in “Quality, Productivity, and Competitive Position.”
A few weeks ago I had an excellent exchange on Twitter with UK Police Inspector Simon Guilfoyle on the topic of setting numerical targets. He asked “How do you set a numerical target without it being arbitrary? By what method?” Unfortunately, Twitter’s 140 character limit isn’t sufficient for adequately answering his question. I promised him I would write a post that explained my thinking.
When I was working for Samsung Austin Semiconductor (SAS) as a quality assurance engineer, one of my assigned responsibilities was to manage the factory’s overall nonconforming material rate. Over the course of my second year, the factory averaged a four percent* nonconforming material rate. The run chart for the monthly nonconforming material rate showed a stable system of variation.
As the year drew to a close, I began thinking about my goals for the following year. I knew I would continue to be responsible for managing the factory’s overall nonconforming material rate. What should I set as my target for it? Not knowing any better, I set it to be the rate we achieved for the current year: four percent. If nothing else, it was based on data. But my manager at the time, a Korean professional on assignment to the factory, mockingly asked me if I wasn’t motivated to do better. He set my target at two percent*; a fifty percent reduction.
What was the two percent number based on? How did he come about it? I had no insight and he didn’t bother to explain it either. From my perspective, it was an arbitrary numerical target; plucked out of thin air. I remember how incredibly nervous I felt about it. How was I going to achieve it? I had no clue nor guidance. I also remember how anxiety filled and frustrating the following year turned out for me. I watched the rate with a hawk eye. I hounded process engineers to do something whenever their process created a nonconforming lot. It was not a pleasant time for anyone.
Since then I’ve worked at several other companies in different industries. Nevertheless, my experience at SAS seems to be the norm when it comes to setting targets. This is regardless of the role, the industry or the culture. And, as far as I’ve been able to figure out, this approach to setting targets is driven more by tradition and arrogance than any objective thoughtful method. “Improve performance by 50% over last year!”, so the mantra goes. Worse still, no method is provided for achieving such arbitrary improvement targets. I’ve been told “You’re smart. You’ll figure out how to do it.”
So it’s not a surprise for me that folks like the good Inspector have become convinced all numerical targets are inherently arbitrary; that there is no objective and justifiable way to set them. Having been on the receiving end of such targets many times, I used to think the same, too. But just because people don’t know of a different way to set a target, one that is objective and can be justified, doesn’t mean there isn’t one. I believe numerical targets can be set in an objective fashion. It, however, requires thoughtfulness, great effort and understanding on the part of the person setting the target.
One way to set a target is to use the performance of a reference for comparison. In my case, the SAS factory I worked at had a sister facility in Korea. It would have been reasonable, albeit crude, to set my target for the nonconforming material rate to that achieved by the sister facility (if it was better.**) An argument could have been made that the target was achieved elsewhere, so it can be reached.
As part of our Twitter exchange, the Inspector made the point that regardless of whether these factories were defined to be sisters, there would still be differences between them. Therefore, they will generate a nonconforming material rate that is a function of their present system architecture. He is absolutely right! Setting a target for my factory based on the performance achieved by its sister facility alone will do nothing to improve the performance of my factory. It’s already doing the best it can.
But that’s not the point of setting the target: to operate the same system and expect an improved performance. The point of setting the target is to trigger a change in the system, a redesign in such a way as to achieve a level of performance that, in this case, has been achieved elsewhere. The sister system can be treated as a reference and studied. Differences between systems may be identified and eliminated. Along the way we may find out that some differences cannot be eliminated. Nevertheless, by eliminating the differences where possible the two systems are made more similar to one another and we will have improved the performance.
In the absence of a reference, simulations may be used to objectively define a target. The factory’s overall nonconforming material rate is the combined result of the nonconforming material rates of its individual processes. Investigating the performance of these inputs can help identify opportunities for improvement for each: stabilizing unstable processes, running stable processes on target, reducing the variability of stable on-target processes. All of this can be simulated to determine what is ideally possible. A justifiable target for the nonconforming material rate can then be set with the results. Best of all, the method by which it can be achieved gets defined as part of the exercise.
Finally, targets may be set by the state of the greater environment within which a system operates. All systems operate in a greater environment (e.g. national or global economy); one that is continuously changing in unpredictable ways. City populations grow or shrink. Markets grow or shrink. Polities combine or fragment. What we once produced to meet a demand will in a new environment prove to be too little or too much. A change in state of the external environment should trigger a change in the target of the system. A change in the target of the system should trigger a redesign of the system to achieve it. In Systems lingo, this is a tracking problem.
Targets are essential. They help guide the design or redesign of the system. They can be defined objectively in several different ways. I’ve outlined three above. They do not have to be set in the arbitrary way they currently are. But setting targets isn’t enough. Methods by which to achieve them must be defined. Targets, even objective ones, are meaningless and destructive without the means of achieving them. Failure to achieve targets should trigger an analysis into why the system failed. They should not be used to judge and blame workers within the system.
Sadly, people are like water, finding and using the path of least resistance. Setting arbitrary improvement targets is easier than doing all the work required to set objective ones. They have been successfully justified on the grounds of mindless ambition. No one questions the approach out of fear or ignorance. Positional authority is often used to mock or belittle the worker for not being motivated enough when the truth is something else: managerial ignorance and laziness to do their job.
* I’ve changed the numbers for obvious reasons. However, the message remains the same.
** As it turned out, the nonconforming material rate achieved at my factory was the best ever in all of Samsung!
Every religion I’ve been exposed to is steeped in rituals and traditions that reach deep into history. I have no doubt that the various beliefs came to be with purpose. They solved a particular problem of the time. They were useful and brought tangible benefits. We carry them on now because we believe they worked in the past and that they will continue to work now and into the future.
What we fail to recognize is that the world is not static. The context for a given ritual or tradition has changed. Reality is like a slow boiling cauldron. Looking in, you think you have identified the surface of the liquid. It looks about the same from moment to moment but it is perpetually bubbling, always shifting. You need to be aware of its shifts and match them to stay on top. The Buddha had this insight 2500+ years ago: all things are conditionally arisen. Our actions need to meet the present reality.
The irony is that while the Buddha’s teachings questioned the validity of rituals and traditions of other religions of his time, Buddhism itself has became steeped in rituals and traditions over the ages. In “Confession of a Buddhist Atheist” Stephen Batchelor shares his experience of them with Tibetan and Zen Buddhism; his disillusionment with both, and his personal journey to find the historical man that came to be called the Buddha. Along the way he identifies what he believes were the Buddha’s core teachings.
I found the book very readable. I was sympathetic to Batchelor’s story and I gained from his insights.
Last month I asked “How are you, as a Quality professional, perceived?” in several LinkedIn discussion groups. I hoped to understand what we thought others thought of us. I wanted a qualitative measure of our awareness.
I parsed 108 comments from 55 people. Of them, 30 felt they were perceived poorly, 17 were ambivalent, and 8 felt that others viewed them favorably. The comments fell into one of the following categories:
(-) Necessary Evil/Imposed Cost
(-) Hard to Understand
It appears we, Quality professionals, are very aware. We are sensitive to what others think of us. That is the good news. The bad news, however, and it is really bad news, is that we seem to think others consider us a serious drag on business.
I wondered if such harsh self-criticism was just an issue of poor self-esteem, but I don’t think it is. Based on my observations and experience, I find it to be a fair assessment of how others view us. Even we hold such views of other fellow Quality professionals.
But hold on second. That is not what our profession is about. We are not supposed to be drags on business. We are supposed to be the people that help the makers make things better, faster, stronger.
So where are we going wrong?
If the definition of quality has to do with meeting or exceeding the expectations of the consumer, first we need to understand who is the consumer of the services that Quality professionals offer. Isn’t it our employer? The end user isn’t paying for what we do. Next we need to understand what are the consumer’s expectations. How many of us really understand our employer’s wants? (Try not to substitute in what you think the employer should want with what the employer actually wants. Also, let’s get real, most companies’ Quality Policy is just a set of platitudes.) Finally, we need to evaluate our efforts in the context of what our employer wants.
In this light, do the results our actions as Quality professionals conform to the requirements of our employer? If not, aren’t we imposing a loss on our employer, to use Taguchi’s term? And, from the looks of the categories above, it is not an insignificant loss. Contrary to our purpose, we are generating suffering through our actions!
It is not the role of the Quality professional to set the objectives for the company. It is our role in the service of our employer to provide options on how best to meet those objectives. It is not the role of the Quality professional to choose the ‘best’ option. It is our role to help execute our employer’s choice in the most effective way. I think it would serve us well to get off of our high horses and stop thinking of ourselves as saviors. The sooner we start cooperating with others – being of service to them instead of demanding actions from them – the better we will all be.
In general we look for a new law by the following process. First we guess it. Then we…Now don’t laugh. That’s really true.
Then we compute the consequences of the guess to see what…if this is right…if this law that we guessed is right we see what it would imply.
And then we compare those computation results to Nature. Or we say compare to experiment or experience; compared directly with observation to see if it works.
If it disagrees with experiment, it’s wrong. In that simple statement is the key to Science. It doesn’t make a difference how beautiful your guess is; it doesn’t make a difference how smart you are who made the guess, or what his name is. If it disagrees with experiment, it’s wrong. That’s all there is to it.
– Richard Feynman
Drop a pebble into a pond. Its effects ripple out. But the ripples don’t radiate infinitely across the surface. Nor do they last forever. At sufficient distance from their center they are hardly distinguishable from the surrounding water. A point beyond the reach of the ripples wouldn’t know that a pebble was ever dropped into the pond.
Drop two pebbles into a pond. Each generates ripples that radiate out. If the pebbles were dropped far from each other, their ripples die out before reaching one another. Each unaware the other happened just as before. But if the pebbles were dropped close to each other, their ripples interfere with one another. Some reach through to the centers themselves. Thus making their presence known. “Here I am! I exist!”
We are sources of ripples in this expanse of existence. I cause ripples at every point and instant I am. So do you. But until our ripples interact with one another we cannot know of each other. In ancestral times we were separated far enough from one another for our ripples to ever interact. We were independent. Alone. That space has shrunk to almost nothing in our time. Our ripples constantly collide with one another. Sometimes constructively, sometimes destructively. We are painfully aware of each other without announcement.
We absorb some of the energy from the ripples that bombard us. Not enough to damp them completely. They reflect off of us. We react to counteract their impact on us. And so no ripple ever settles out. Each seems to get an invisible kick and be periodically rejuvenated. By what and from when seems shrouded in mystery. The water’s surface is forever unsettled. This is the chaos that is life. No peace. No quiet.
As we grow in number, as the space between us continues to shrink, the closer we get to one another, the more we are bombarded with original ripples and ripples from interacting ripples. They come at us from all directions. They come at us faster. There is no way to predict and prepare for the next collision with the here and now. There isn’t time to process what it means. There is no thing to thank or to blame. We only experience it. Rich. Momentary. Unique.
A particular process makes parts of diameter D. There are 10 parts produced per batch. The batches are sampled periodically and the diameter of all the parts from the sampled batch is measured. Data, representing deviation from the target, for the first 6 sampled batches is shown in Table 1. The graph of the data is shown in Figure 1. Positive numbers indicate the measured diameter was larger than the target while negative numbers indicate the measured diameter was smaller than the target. The upper and lower specification limits for acceptable deviation are given as +/- 3.
The most recent batch, sample batch number six, shows one of the 10 parts having a diameter smaller than the lower specification limit. As such, it is a nonconforming part.
The discovery of a nonconforming product triggers two parallel activities: i) figuring out what to do with the nonconforming product, and ii) addressing the cause of the nonconforming product to inhibit the nonconformance from occurring again.
Nonconforming product may be repaired or reworked when possible, but it can always be scrapped. Each one of these three options has its own set of complications and cost.
Repairing a nonconforming product involves additional steps beyond what are usually needed to make the product. This additional processing has the potential to create previously unknown weaknesses in the product e.g. stress concentrations. So repaired product will need to be subjected to testing that verifies it still satisfies its intended use. For this particular case, repairing is not possible. The diameter is smaller than the target. Repair would have been possible if the diameter had been larger than the target.
Reworking a nonconforming product involves undoing the results of the previous process steps, then sending the product through the standard process steps a second time. Undoing the results of the previous process steps involves additional process steps just as were required to repair a nonconforming product. This additional processing has the potential to create previously unknown weaknesses in the product. Reworked product will need to be subjected to testing that verifies it still satisfies its intended use. For this particular case, reworking is not possible.
Scrapping a nonconforming product means to destroy it so that it cannot be accidentally used. For this particular case, scrapping the nonconforming part is the only option available.
ADDRESSING THE CAUSE
In order to determine the cause of the nonconformity we have to first determine the state of the process i.e. whether the process is stable or not. The type of action we take depends on it.
A control chart provides a straightforward way to answer this question. Figure 2. shows an Xbar-R chart for this process. Neither the Xbar chart (top), nor the R chart (bottom) show uncontrolled variation. There is no indication of a special cause affecting the process. This is a stable process in the threshold state. While it is operating on target i.e. its mean is approximately the same as the target, its within-batch variation is more than we would like. Therefore, there is no point trying to hunt down a specific cause for the nonconforming part identified above. It is most likely the product of chance variation that affects this process; a result of the process’s present design.
In fact, the process was left alone to collect more data (Figure 3.). The Xbar-R charts do not show any unusual variation that would indicate external disturbances affecting the process. Its behavior is predictable.
But, even though the process is stable, it does produce nonconforming parts from time to time. Figure 4. shows that a nonconforming part was produced in sampled batch number 22 and one in sampled batch number 23. Still, it would be wasted effort to hunt down specific causes for the creation of these nonconforming parts. They are the result of chance variation that is a property of the present process design.
Because this process is stable, we can estimate the mean and standard deviation of the distribution of individual parts. They were calculated to be -0.0114 and 0.9281. Assuming that the individual parts are normally distributed, we can estimate that this process will produce about 0.12% nonconforming product if left to run as is. Some of these parts will be smaller than the lower specification limit for the diameter. Others will be larger than the upper specification limit for the diameter. That is, about 12 nonconforming pieces will be created per 10,000 parts produced. Is this acceptable?
If the calculated nonconforming rate is not acceptable, then this process must be modified in some fundamental way. This would involves some sort of structured experimentation using methods from design of experiments to reduce variation. New settings for factors like RPM or blade type among others will need to be determined.
Through the books I’ve recently read I’ve come to see culture as an output, a result or an emergent property of a system. Furthermore, just like all outputs, it cannot be managed directly, as Matthew E. May points out in his post “To Change A Culture, Change The System.” The only way to manage outputs is to change the inputs to the system and/or change the system.
I’ve come to believe that we’re wired to focus on outputs and sort good from bad. And, why not? For most of our evolution we’ve never had any control of our environment. We’ve been a part of the system. In that context it’s perfectly natural for us to comment on culture and sort it into good or bad. However, just as you can’t inspect quality into a product as Harold S. Dodge pointed out, you can’t improve culture by calling out its positive or negative attributes.
In the case of the modern organization, perhaps you can select the type of people to minimize cultural diversity. (Cultural diversity here refers to mindset, drive, focus, etc.) But in my experience, with the way that process (i.e. interviews) works right now, it amounts to shots in the dark. Better to setup a system that is robust to the variation in its human resource to yield a cohesive culture.
System design requires designers (leaders) who have a vision for the system. They must understand the context for their system and then design their system to produce the desired result. These are all skills that can be learned, but so few bother. It’s hard work. There’s no instant pudding. But who’s got the time?