We've been talking lately about how formal processes can avoid catastrophic mistakes and I've got more to say on the subject. But this is a timely interruption.
A while ago, I sat down with Michelle Bangert of Quality Magazine, when they published my article about the Seven Quality Management Principles. Originally, we were just going to talk about the article itself, and maybe to recap it for people who prefer podcasts to blog posts. But the conversation unwound itself according to its own internal rules, the way any good conversation does. After forty minutes we had discussed at least a dozen topics; in some ways it felt like we had been talking all day, and in other ways it felt like we were just beginning to scratch the surface. Among other things, our conversation touched on topics like the following:
How I came to write the article on the Seven Quality Management Principles.
When to expect the upcoming changes to ISO 9000 and ISO 9001.
How blogging is different from kvetching.
How to use blogging as a branding tool.
Why I am delighted when people argue with things I've written.
(As a bonus, I describe two different times I've had to retract something I'd written because feedback from readers showed me I was wrong: I mean here and here.)
How lessons from parenting also apply to Quality.
How my career in Quality started, and why you shouldn't imitate me.
Career highlights and stories for audit nerds.
Comparing Stuttgart with Santa Barbara as wine countries.
The hidden message of German architecture.
Why do I anonymize my stories, and when do I not?
Where is the other column that I write, and what is it about?
What is the difference between rules in young/small organizations, and rules in old/large ones?
I've discussed Quality Policies in earlier posts—especially here and here, for example—but I've never explained how to write one. Honestly, I'd never thought about it before now. I guess I thought that somehow the writing would take care of itself. But Kyle Chambers and Caleb Adcock over at Texas Quality Assurance have done me one better on this point. In a recent episode of their #QualityMatters podcast (episode 181, to be exact) they talk through the theory behind Quality Policies and give step-by-step instructions for writing one. And mostly I agree with them.
You can play the episode as a podcast by clicking here, or you can play it on YouTube by clicking the video image below.
Kyle and Caleb start their episode with a general discussion of whether Quality Policies have to be inspiring (2:30) and of what the formal requirements for a Quality Policy actually say (5:00).* (Kyle quotes the requirements from ISO 9001, clause 5.2, but makes the point that the analogous clauses in other comparable standards all say basically the same thing.) But soon they move to the central question. Kyle asks (at about 8:50), Is there a formula for writing a Quality Policy? And then he proposes one: Simply describe what your business does, and lay it out like this:
We provide <these goods or services> …
… to <these customers>.
We meet all requirements and we improve continually <by using these and those methods.>
Absolutely straightforward.
Then Kyle shows how this formula works in practice by using it (from about 11:00 to 18:00) to generate a Quality Policy for his own business, Texas Quality Assurance. He first states it at about 16:45, and then goes back to revise it a little later.
The next part of the conversation (from 19:45 to about 24:20 or so) is on how to derive major Quality objectives from your Quality Policy, and the method is equally direct.
Look at your Policy.
See the things it says you do?
Those are your major Quality objectives. Do them.
More exactly, use your Policy statement to make a concrete list of what you have to achieve.
Naturally you might need to track department-level KPIs as well, just to make sure you are on track. But the business-level objectives are to do the things you say you do in your Policy.
After that, Kyle and Caleb discuss some pointers on things-NOT-to-do and on how to brainstorm effectively, they bring up a few incidental clarifications, and the talk is over.
I said at the outset of this post that mostly I agree with everything Kyle and Caleb say here. My one caution is a point of emphasis, rather than anything stronger. If you follow Kyle's formula, your output will be perfectly serviceable. But will it be a policy, or just a statement of the scope of your management system? ISO 9000:2015, clause 3.5.8, defines a policy as:
intentions and direction of an organization as formally expressed by its top management.
In other words, a policy should answer certain kinds of broad questions before they are ever asked. Do you allow a customer to return a product if it is defective because the customer himself broke it? Yes or No, that's a policy (though not exactly a Quality Policy). On the other hand, "We sell lawn furniture" is less obviously a policy. Now, I can imagine circumstances where it might function as a policy,** and that's why I qualify my disagreement as no more than a point of emphasis. But if you can think of something stronger to say about your attitude or strategic direction with respect to Quality, then say it.
All the same, Kyle's formula generates results that are a lot better than many of the Quality policies currently out there. Kyle talks (6:30) about one company he knows whose "Quality Policy" expresses an aspiration about the kind of company they want to grow into twenty years from now. But that's a Vision, not a Quality Policy. Your Policy has to describe what you are doing today. If it doesn't, and if you hope to be certified to ISO 9001, your auditor will write you up for any discrepancy. Don't give him findings that are so obvious.
There is one topic on which Kyle admits a little disappointment with his own formula: it is not reliably flashy or inspiring. That is, if you apply this formula, the output might sound a little dull (2:30, 3:30, 32:50). I think if that's the biggest risk you have to worry about then you are doing pretty well. There are a lot of dreadful Quality Policies in the world, and this formula will give you one that works just fine. If you really want one that's flashy and inspiring, well, Quality Policies have to express a commitment to continual improvement, so maybe "Writing a flashier Policy" is a good target for future improvement.
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* When I make reference to the podcast, I will give approximate time markers. These are not exact.
** For example, if there were a debate within top management whether to start a second product line that was totally unrelated to the primary business, "We sell lawn furniture" might be a useful policy statement to shut down the proposal.
This week I had another chance to sit down with Kyle Chambers of Texas Quality Assurance, this time to talk about Boeing. Like me, Kyle has had a series of episodes dealing with Boeing's troubles in the last year, and he always brings a refreshing and very practical energy to all Quality topics. I start off talking about the FAA report that I discussed here last week; but in the course of the discussion we also cover why Boeing should hire TQA to revamp their training programs, and how to make safety classes matter to people who don't want to be there.
Back in April I wrote about how the current Russo-Ukrainian war could lead to the unraveling of the system of global certification. Just recently I got the chance to discuss this very issue live, with Kyle Chambers of Texas Quality Assurance and Christopher Paris of Oxebridge International. It was a lot of fun. Kyle brings an infectious energy to any conversation he's a part of, and Chris has a wealth of knowledge about the international certification scheme; so the two of them together fleshed out my original thesis in ways I hadn't expected. (The fun quote in the title is an adaptation of something Chris says, but you have to listen to the podcast to find out who he's describing and what his actual words are!)
This post is a little bit outside my normal range of topics, but it illustrates some important weaknesses in the use of mathematical models. Since we are all asked to create quantitative models from time to time, maybe the example is not out of place. I will ask your indulgence for the first couple of paragraphs, because it will take me a few minutes to get to the point where the models become important.
A number of days ago, Dawn Ringrose of Organizational Excellence Specialists mentioned on LinkedIn that she had discovered a recent podcast, and she encouraged those of us who follow her to listen to it. The podcast contains a deep and wide-ranging interview conducted by psychologist and public intellectual Jordan Peterson, where he talks with the authors Marian Tupy and Gale Pooley about their recent book Superabundance: The Age of Plenty. You can find the full recording here:
The basic thesis of the book is that abundance is increasing at a faster rate than the population, so over time we are all getting richer and richer—"so rich," in fact, that "we don't know how rich we are." A secondary thesis is that this abundance is driven by the application of human creative ingenuity, and therefore that we all benefit from (1) a large population of other people (more minds to create more new ideas) and also from (2) political and social forms that allow enough personal liberty that these ideas can be brought to market, where the price mechanism sorts out the good ones from the bad ones.
It is important for me to clarify right at the beginning that the authors carry on two different kinds of arguments during their long discussion with Dr. Peterson, a negative one and a positive one. The negative argument is against Thomas Malthus, Paul Ehrlich, and a group that they characterize collectively as "radical environmentalists," "romantics," and "vicious anti-humanists." The positive argument is where they talk about the phenomenon of "superabundance" in the past, present, and future.
The negative argument is largely successful; its main drawback is that Tupy and Pooley spend so long at it that after a while I found it kind of exhausting. But even as I got tired of hearing them make the same points yet again, I had to agree that the points themselves were largely valid. The positive argument ... well, I'll talk about that in a minute.
What Malthus and Ehrlich and the others have in common is that they are alarmed at the growth of human population. And they express this alarm in clear predictions about the future—namely, that the continued growth of the worldwide human population will lead to widespread famine, malnutrition, disease, and warfare over an ever-decreasing pool of finite resources. The numerical calculations behind these predictions have always looked convincing, and so the arguments themselves have sounded plausible. Over the years, these arguments have been accepted by quite a lot of people. And they have led some agitated writers to describe humanity as "a cancer on the face of the earth."
The problem is that the apocalyptic predictions of Malthus and Ehrlich have never come to pass. Over the last hundred years (for example), worldwide levels of disease and malnutrition have dropped even as worldwide population has sharply increased. The prices of many resources have fallen. In short, results in the real world have consistently contradicted the results predicted by the mathematical models used by Malthus and Ehrlich. This proves that those models were wrong.
One drawback of repeating the negative arguments so often is that sometimes it is easy to confuse the question whether Tupy and Pooley are refuting their adversaries or making a positive case in their own name. But the distinction is important. Even though Malthus and Ehrlich were certainly wrong, that doesn't ipso facto prove that Tupy and Pooley are right. Still, as I say, the negative argument is largely successful.
The positive argument
The positive argument uses extensive historical research and careful mathematical modeling to show that prices have gotten cheaper over the long haul, with the result that individuals have gotten (in terms of buying power) correspondingly richer. Specifically, Tupy and Pooley identify fifty commodities spanning a wide range of use-cases, and show that over the long term the prices for all of them have dropped steadily and dramatically.
Comparing prices across a century or more can be a challenge, because the value of one dollar in 1822 has more or less no relation at all to the value of a dollar in 1922 or 2022. Tupy and Pooley have an ingenious solution for this problem. Instead of looking at prices as marked on a price tag—this is called "prices in nominal dollars"—they ask, "How long would a person in that year have had to work in order to earn enough money to buy this or that? And how long would a person today have to work to buy the same thing?" They call this the time-price of a good, and so the foundation of their research is a study of time-prices across the years for fifty different commodities. Naturally one risk in the calculation of a time-price is that you have to use a standard rate of pay and different jobs pay different wages. The standard they choose is the average rate of pay for blue-collar or unskilled labor. Their goal is not to show that life has gotten better for millionaires, after all, but that it has gotten better for everyone.
So, as I say, the foundation of their research is a tabulation of the time-prices of a wide range of commodities across the years. What they show is that these prices have fallen consistently over time, and keep falling. Their conclusion is to reject the narrative that says humanity is confronted with finite resources. Finite resources, they say, are just a myth. Based on their data, they argue, all commodities are getting cheaper and more abundant.
Real-world results
But in fact not all commodities can be made infinitely abundant. Consider the commodity "Real estate within 50 miles of downtown Los Angeles." There is a theoretical maximum of Ï€ x 50 x 50 = about 7854 square miles of such real estate—the actual number is a little over half that because the rest is covered by the Pacific Ocean. Call it maybe about 4000 square miles, of which some is too mountainous to build on. But in any event, when it is gone it is gone. The price mechanism cannot create more land within 50 miles of downtown Los Angeles, once it has all been built on. The classic answer from economists is that when one resource becomes unavailable, a high price will encourage substitution of a similar good instead. And yes, I'm sure that when all the land within 50 miles of downtown Los Angeles is built up, there will still be land available within 50 miles of downtown Bozeman, Montana. Whether that constitutes an adequate substitute is perhaps a matter of taste; I know some people who would actively prefer Bozeman to LA. But moving to southern Montana instead of Southern California will at any rate make it hard to drive into town to catch an evening performance at the Ahmanson.
These theoretical limits have real-world consequences. My parents bought their house in 1973. Even after you adjust for inflation—in other words, measuring in constant dollars—that house now costs 3.5 times what it cost when they bought it. What about the time-price? Remember that Tupy and Pooley express the price of commodities in terms of the number of hours of unskilled labor it costs to acquire them, so let's do that. In 1973 a laborer or helper in the building trades made $6.06 per hour. The equivalent pay in 2022 is $15.00 per hour. Looking at my parents' house, then, in 1973 it cost 5776 hours of unskilled labor to buy it in cash. The same house in 2022 costs 55,260 hours of unskilled labor to buy it in cash. In other words, measured in time-price of unskilled labor, the price of that same house today is nearly ten times what it was in 1973! So much for the claim that everything is getting cheaper.
Or what about medical care? I recently found an article on the Internet reporting a hospital bill from 1961 to deliver a baby: total cost before insurance was $419. Today that number is $18,865. Measuring in time-price instead of nominal dollars, we calculate that the hospital stay for delivering a baby cost just over 69 hours of unskilled labor in 1961. In 2022, the same delivery costs over 1257 hours of unskilled labor, or more than 18 times as much.
In other words, when Tupy and Pooley chose what to measure, that choice affected the outcome of their calculations. Does it matter, or am I just cherry-picking examples to make them look bad? It's a fair question, so let's step back from the numbers for a minute to take an overall look at the big picture.
In 1960, a man earning one full-time blue-collar salary could buy a house and raise a family. Detroit was full of these men; so were countless towns spread across the industrial Northeast.
Today a man earning one full-time blue-collar salary can do neither of those things, if he can find a job at all. This is part of why the once-thriving Northeast is now called "the Rust Belt."
These real-life consequences are the direct opposite of what the "superabundance" model predicts. And when results in the real world contradict the results predicted by a model, that proves that the model is wrong. It fails to match reality. The Tupy and Pooley "superabundance" model is, in its way, just as wrong—and thereby just as flawed and just as pernicious—as the models used by Malthus and Ehrlich.
Why does their argument sound so plausible? Well, Malthus's argument sounded plausible too. So did Ehrlich's. These things are complicated, they have a lot of different aspects, and getting them right is really hard. Numerical models can make the picture simpler, so we have a better chance to understand it. But numerical models can distort as much as they illuminate, even if unintentionally. You can find a quick summary of some of the traps that threaten the unwary in, for example, Darrell Huff's delightful little book How to Lie With Statistics. Or consider the remark in an earlier post in this blog that "There is no metric in the world that cannot be gamed." If you're not careful, there is always the risk that your numbers will prove anything.
To bring this back to the Quality profession, the critical point is that you can never trust the numbers in any model without checking them both quantitatively and qualitatively against the real world. Or more memorably, "However beautiful the strategy, you should occasionally look at the results."*
To summarize my assessment of the initial podcast, their negative argument is fine but they spend too much time on it. Their positive argument goes astray, and ends up wrong. A lot of the people who logged into YouTube to leave comments on this conversation were really impressed by it, but in the end I'm not one of them.
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* This phrase has been attributed to Winston Churchill, though the attribution has also been disputed.
Once again I had a chance to sit down with the exuberant Kyle Chambers of Texas Quality Assurance for a wide-ranging conversation about Quality Policies and what's wrong with so many of them. Over the course of the discussion we explore what policies are really for, how the ISO 9001 standard makes the situation worse, and why you might write your policies differently in Texas and California.
Last week I sat down with Kyle Chambers of Texas Quality Assurance to record my first-ever podcast. We had a wide-ranging discussion: the original idea was that we would talk about Context of the Organization and how your COTO analysis can benefit you. But while we were at it, we also talked about how to design your QMS, why policy matters, how to avoid common pitfalls in auditing ... and how the world is secretly run by intergalactic space lizards!
About a month ago, I listened to a podcast that I really enjoyed. It was an interview with Steve Gompertz, a partner with QRx Partners and a long-time expert in Quality for the medical device industry. And what I loved about the discussion was how much of Gompertz's experience resonated with my own.
The medical device industry is—for obvious reasons—one of the most heavily-regulated industries in the country or the world. My own work has been in less-heavily regulated areas like telecommunications and automotive diagnostics. So I expected to hear about an environment much stricter than any of the ones I'm used to.
But no! What Gompertz learned about working under FDA regulation was exactly what I learned about working under a regimen of central directives in global companies: the structure of rules looks burdensome from the outside, but inside there is actually a fair bit of room to breathe.
After all, it's not in the interest of the FDA for medical device companies to go out of business. They just want the devices to be provably safe before they are used on human beings. Therefore, Gompertz points out, the FDA largely regulates what you have to do, but leaves it up to you to figure out how to do it. This gives you a lot more leeway than many people realize, leeway that you can use to make regular improvements in your processes and methods. He tells stories of specific changes he made one time or another, which his colleagues were sure the FDA would reject—and the FDA auditor loved them. "Why don't other companies do it this way, when it's so much simpler?"
There's more, but I'd rather let him speak for himself. This, though, is exactly how Quality ought to be done.
