7 ms·

Poor Deming never stood a chance

195 points by todsacerdoti 8mo ago

baxtr 8mo ago
Maybe it’s my limited intellect but I found Drucker to be a lot easier to understand. Where Deming reads like a science paper, Drucker reads like an installation guide.
- zem 8mo ago
  that kind of ties in with the article's thesis; deming's approach is more scientific in the classic sense of taking observations and using those to build up your mental models, whereas drucker proposes a one size fits all recipe for managing roadmaps.
- friendzis 8mo ago
  Not necessarily limited "intellect", but rather limited background knowledge. Deming requires quite a bit of knowledge and understanding in failure/success modes. The core tenet of Deming is that every output is a result of some process and, therefore, output is controlled by controlling* the process itself. Look at your process and tackle failure modes in this priority list. Drucker, on the other hand, puts the process under the fog of war and basically says deploy pressure on process outputs and let the process adjust itself. It requires much less understanding behind the processes to make sense. * - Process control in Deming is mostly about variability.
  - bryanrasmussen 8mo ago
    So following Drucker would be the cause of a lot of "every metric becomes a target" in management?
    - asplake 8mo ago
      Obviously the truth is messier than that, and it's worth noting that Drucker later recognised the toxicity of Management by Objectives and disavowed it. Quite a bit of OKR literature is devoted to avoiding it becoming its progenitor, MBO. Worth adding that Deming (after Shewhart) recognised two kinds of variation: special cause (specific the work item in question) and common cause (an artifact of the process). That knowledge work involves a lot more of the former than does manufacturing does not excuse inattention to the latter.
      - ChrisMarshallNY 8mo ago
        > Drucker later recognised the toxicity of Management by Objectives and disavowed it. Reminds me of a seminal treatise for Waterfall by Royce[0], where he basically says it’s fraught with issues, but can be coerced into something semi-usable. Not exactly a ringing endorsement. I think that paper is used as the template for all Waterfall work. [0] https://dl.acm.org/doi/abs/10.5555/41765.41801 https://dl.acm.org/doi/abs/10.5555/41765.41801
  - baxtr 8mo ago
    What’s the most basic way to get into the works of Deming?
    - kqr 8mo ago
      Read his books.
      - baxtr 8mo ago
        Which one first?
        kqr 8mo ago
        The New Economics! Then Out of the Crisis. Then if you're really hardcore, Some Theory of Sampling.
    - esafak 8mo ago
      Get a book on applied statistics, such as Think Stats: Probability and Statistics for Programmers (https://allendowney.github.io/ThinkStats/ https://allendowney.github.io/ThinkStats/) Computer Age Statistical Inference (https://hastie.su.domains/CASI/ https://hastie.su.domains/CASI/) Statistical Rethinking (https://xcelab.net/rm/ https://xcelab.net/rm/) An Introduction to Statistical Learning (https://www.statlearning.com/ https://www.statlearning.com/) Obviously maths is going to be involved to do the subject justice. These recommendations are more about applied statistics, but that's the foundation. From there it is a small transition to statistical process control.
- jacquesm 8mo ago
  Deming -> Strategy Drucker -> Tactics
- adolph 8mo ago
  > Where Deming reads like a science paper, Drucker reads like an installation guide. If you are looking for "do this" then Deming is not your person. If you seek understanding that drives transformation (knowing what to do in your context based on a broadly applicable value system), then Deming's system can deliver. Lessons from the Red Bead Experiment include the fallacy of rating people and ranking them in order of performance for next year (based on previous performance), as well as attributing the performance of the system to the performance of the “willing workers” in this simulation of an organization governed by what Dr. Deming referred to as the “prevailing system of management.” https://deming.org/explore/red-bead-experiment/ https://deming.org/explore/red-bead-experiment/
black_13 8mo ago
[dead]
roenxi 8mo ago
It is also worth noting that US management is notoriously bad at the actual management. Toyota v. US car manufacturers did not look like a fair fight when Deming was in the ascendant, and it is hard to tell given the scales involved but it looks a lot like the US has been outmanoeuvred in all aspects of industry by the Asians. US companies are generally a better bet though, because despite the handicap of being run by Americans, they are hosted in a country that generally believes in freedom and rule of law which means they have an unfair advantage even if they do a sub-par job of making the most of what they have. Exceptions abound in the details.
- arkh 8mo ago
  The thing is, the Toyota methods relies on people on every level to work to improve processes. If you're an employee and know you'll be there 10 years down the line or even until you retire, you have an incentive to improve said processes. Now check most Western companies: since the 70 / 80, everything is about reducing headcount. Lay-offs, outsourcing, offshoring, now the concept of spending your whole working life at the same company feels like a fever dream. So why would an employee try to improve things for the company when they know there is no future for them there? Better improve their own career and future prospect. So yeah, things like Kaizen are doomed to fail until things change.
  - ghosty141 8mo ago
    > Lay-offs, outsourcing, offshoring, now the concept of spending your whole working life at the same company feels like a fever dream You are missing something here imo, very few companies actually increase pay (or to be more clear, show a clear way to get there) enough to make it attractive enough to stay there for long periods of time. From my experience here in Germany the people staying at companies for a long time are those who don't focus on their career.
    - esafak 8mo ago
      Moving around distributes knowledge making for a healthier economy overall. The alternative looks like Korean chaebols.
hbarka 8mo ago
This is a very trivial treatment of Deming and I’m surprised how it makes its way to the top of HN. The arc from Walter Shewhart to W.E. Deming is a bedrock foundation in an Industrial Engineering curriculum. These men paved the manufacturing process quality principles of modern industrialization. Drucker was about management science, truly an apples to oranges comparison.
- kranke155 8mo ago
  Management Science? Only management science I read so far (with actual measured outputs and ideas) was Peopleware. Everything else was more like philosophy. Has anyone ever measured, long term results from multiple management methods? What I saw when I looked into it was simple - the Toyota Way was the model for a lot of successful companies, including Pixar.
  - bsenftner 8mo ago
    Peopleware is extremely old, and if you were to crack open a modern MBA text you'd find statistics and statistical process control type of thinking integrated everywhere, in all the MBA subjects. Management being soft and opinionated ended a long time ago, but then again, "the future is unevenly distributed" so who knows what conceptual envelope you find yourself.
    - bubblewand 8mo ago
      What I’ve seen in the wild is that this is entirely a veneer. It’s important to have numbers. It doesn’t matter if they mean anything. In fact, management is full of numbers that a slightly-clever high school sophomore who’d paid attention in science classes could tell you are totally useless, because they were gathered all wrong. They mean nothing whatsoever. They’re just noise. But nobody wants to hear stuff like “well first we’re going to need a baseline, and if you want it to be any good we’ll probably need two years or so before we can start trying to measure the effects of changes”. They just want something convincing enough that everyone can nod along to a story in a PowerPoint in four months. Two years out? Lol you’ll be measuring something totally different by then anyway. Your boss may be in a different role. You’ve asked something the company is literally incapable of. Meanwhile, last I checked, measuring management effectiveness isn’t something we can do in practice for most roles, except bad ways that only pretend to tell us something useful (see above). Good scientists, excellent and large dataset, just the right sector, just one layer of management under scrutiny, maybe you get lucky and can draw some conclusions, but that’s about it, and it’s rare to see it happen in an actual company. Any companies that do achieve it aren’t sharing their datasets. This kind of thing has been consistent everywhere my wife or I have worked. Similar things reported by many friends. Companies want to pretend to be “scientific” and “data-driven” but instead of applying it to only a couple things where they might do it well (enough data, cheap to gather metrics, clear relevant business outcome) they try it everywhere, but don’t want to spend what it would take to be serious about it, with the result that most of their figures are garbage. This trend has become just another “soft”, as you put it, tool.
whatever1 8mo ago
Fundamentally stock markets won the world of business, so everything has a horizon of a financial quarter. Hence, every action of a company needs to be measured against the upcoming quarterly results. OKRs et al are great at that. Who cares about quality/sustainabily. We just want the stock go wheeeeee and get our bonuses.
- derriz 8mo ago
  Not sure about your opening thesis. The vast majority of employees work for privately held businesses and from personal experience of working in such companies, “management” by OKRs and the like is common in companies who are not listed on stock markets also.
  - regularfry 8mo ago
    There'll inevitably be cargo culting driven by MBA curriculums and "they're making a lot of money, let's do what they did" without examining the specifics of the situation to distinguish luck from judgement.
- ghywertelling 8mo ago
  We need to appoint people who really CARE. When CEOs hop from company to company, the culture of CARING takes a backseat. Everything becomes transient with no one with deep technical or cultural knowledge at the driving seat. This is the reason people who have been at the company for long time should get a chance to transform it from within like Satya Nadella.
- esafak 8mo ago
  OKRs took off after a VC named John Doerr introduced them to Google, and the industry followed their lead. https://www.whatmatters.com/ https://www.whatmatters.com/
anonymousiam 8mo ago
The main point that I did not see mentioned in this piece is that Deming should only be applied to MANUFACTURING environments, because things like engineering are too chaotic to identify processes or trends in the engineering itself, and trying to control those engineering processes with SPC doesn't really improve the quality of the engineering, it just adds stress, makes things take longer, and probably lowers the quality of the thing that is being engineered. Obviously, if a quality issue is detected in manufacturing, there may be some steps that engineering could take to improve the manufacturing process and make things stable enough to obtain meaningful statistics. This is part of the Deming feedback process, and part of the System Engineering Life Cycle.
- ignoramous 8mo ago
  > trying to control those engineering processes with SPC doesn't really improve the quality of the engineering, it just adds stress, makes things take longer, and probably lowers the quality of the thing that is being engineered Totally depends on the scale. For pizza-sized times with a neighbourhood pizza shop sized impact, sure. Large scale projects without controls & feedback loops in place will fall apart; see: Scaling teams: https://archive.is/FQKJH https://archive.is/FQKJH If you'd follow some medium to large scale projects (like Go / Chromium), the value of processes & quality control, even if it may seem at the expense of velocity, becomes clear. The great insight of Deming's methods is that you can (mostly) identify the difference between common and special causes mathematically, and that you should not attempt to fix common causes directly - it's a waste of time, because all real-life processes have random variation. Instead, what you want to do is identify your mean and standard deviation, plot the distribution, and try to clean it up. Rather than poking around at the weird edges of the distribution, can we adjust the mean left or right to get more output closer to what we want? Can we reduce the overall standard deviation - not any one outlier - by changing something fundamental about the process? As part of that, you might find out that your process is actually not in control at all, and most of your problems are "special" causes. This means you're overdriving your process. For example, software developers working super long hours to meet a deadline might produce bursts of high producitivity followed by indeterminate periods of collapse (or they quit and the whole thing shuts down, or whatever). Running them at a reasonable rate might give worse short-term results, but more predictable results over time, and predictable results are where quality comes from. https://apenwarr.ca/log/20161226 https://apenwarr.ca/log/20161226 Distributed systems is also a way to be throughly humbled by complexity: https://fly.io/blog/corrosion/ https://fly.io/blog/corrosion/
jongjong 8mo ago
Or you can just abolish the fiat soft money system, let the corporations go out of business, let efficient small companies take their place and then you'd have founders who actually care about long term results in charge and they could manage the company however they want. If they do a bad job, they'd go out of business. If they do a good job, they'd stay afloat or maybe even grow a little. And over time, all the companies with incompetent leaders would be wiped out, everyone would get a fair shot at being a leader and everyone would end up in their rightful place and capitalism would function as it was designed.
- ninalanyon 8mo ago
  > abolish the fiat soft money system, And replace it with what?
  - pjc50 8mo ago
    These people always mean gold. It's not a great thing to tie the size of your economy to the size of world mining.
    - jongjong 8mo ago
      Anything scarce really. Anything which slows down or stops the government printing of money is beneficial.
      - mr_toad 8mo ago
        Fiscal prudence could accomplish that. But if you look at the history of the United States, fiscal prudence is an exception to the rule.
  - deleted 8mo ago
    [deleted]
  - radiator 8mo ago
    Whatever existed before it
iamflimflam1 8mo ago
This made me spit out my coffee… > One of the virtues of OKRs is that they are straightforward for managers to apply.
- laserlight 8mo ago
  Talking about “virtues” of OKRs is a long stretch.
- estearum 8mo ago
  Well it didn't say successfully. But truly there's nothing easier than putting a couple bullets in a document and saying, "Now go forth, underlings, and make these bullets ring true! If you don't, you're fired and without health insurance."
kqr 8mo ago
> for thermostat B there are many more outliers. We’d say that [...] thermostat B is not [under statistical process control]. (In practice, you’d draw a control chart to identify whether the system is under statistical control). I did draw the control chart, and thermostat B is definitely under statistical process control: https://xkqr.org/info/xmr.html?baseline=33,97,41,65,72,71,64,93,62,71,64,70,100,71,64,71,46,64,38,70,63,64,71,61,70,101,64,48&values=38 https://xkqr.org/info/xmr.html?baseline=33,97,41,65,72,71,64...
- kqr 8mo ago
  To be clear, this is not a diss of the article. It is hard to create fake data that look realistic at first glance but are not under statistical process control. That's just how good control charts are at separating signal from noise. If someone wants to learn more, this is how I put the introduction: https://entropicthoughts.com/statistical-process-control-a-practitioners-guide https://entropicthoughts.com/statistical-process-control-a-p...
  - jacquesm 8mo ago
    Fantastic stuff, thank you.
- contravariant 8mo ago
  Eh depends what you mean I suppose, but a small dense cluster with enormous outliers is not a great sign usually. Almost nothing has (effectively) unbounded variance, so most things are under statistical control in a sense. With some notable exceptions (earthquakes, any other event with exponentially decreasing frequency and exponentially increasing damage). For the sake of argument I assumed the author meant that the variance of the thermostat was too high to be practical.
  - hyperpape 8mo ago
    Statistical control is a very specific term: https://entropicthoughts.com/statistical-process-control-a-practitioners-guide https://entropicthoughts.com/statistical-process-control-a-p..., and one which you'd expect anyone with a significant interest in Deming to understand. My expectation is that Lorin would read the parent comment and say some variant of "oh, whoops, I didn't check." As the parent noted, it's not really that important to the overall point.
ontouchstart 8mo ago
This topic is very relevant in the age of agentic AI when every decision is a statistical next token prediction “trained” on some loss function. AGENT.md, SOUL.md etc are just smoke and mirrors of The Wizard of the Oz. Eventually manager as a profession will be replaced by tools, just like computer as a profession, editor as a profession. The evolution of computer science will be manager science. There is more than loss function and KPI.
a_c 8mo ago
The analogy I used with the team was that, set the goal, present the map, and figure how to make a better map. Drucker was about the goal with a given map. It is not uncommon for people receiving the OKR not resonating with it. Sometimes they actually have insight into making a better map, but if OKR is OKR, one just have to follow, people swallow their thoughts
analog31 8mo ago
The big squiggly mess in the article is filled with people. I think Deming’s deepest concept was giving workers on the production line simple tools to improve processes on their own. His books are filled with exhortations to trust the workers. This is what American managers could never bring themselves to do. Even in manufacturing, the application of statistical process control was never entrusted to the workers, but became a department of its own, with bureaucracy, OKRs, and elaborate software.
- bluGill 8mo ago
  >Even in manufacturing, the application of statistical process control was never entrusted to the workers, but became a department of its own, with bureaucracy, OKRs, and elaborate software That is wrong thinking. While you can go overboard with bureaucracy, the line worker doesn't have the the background (or time) to evaluate statistics. You need an expert in statistics at times to see if what looks like a pattern really is. Mean while the line worker needs to spend their time on what they are good at. Trust the line worker is important, it just isn't a shortcut to people who really know specialized domains.
  - woleium 8mo ago
    The line worker has a canny instinct for the right answer long before the statistics are significant though. Kinda what Gladwell talks about in Blink
    - bluGill 8mo ago
      Sometimes, but a few times those instincts are very very wrong. Blink is interesting, be sure to read the criticism though https://en.wikipedia.org/wiki/Blink:_The_Power_of_Thinking_Without_Thinking#Reception https://en.wikipedia.org/wiki/Blink:_The_Power_of_Thinking_W... - it isn't clear that the book is correct.
    - throwway120385 8mo ago
      In my experience, the line worker's instincts are to be trusted but verified. If we blindly follow every crisis from the line we'd quickly find ourselves in a pit. These crises need to be backed up with context and a sense of criticality as there are finite resources to work through problems, and solving one person's immediate problem on the line may have a crushing impact elsewhere.
rglover 8mo ago
Optimize for mediocrity, you get mediocrity. Optimize for quality, you get quality. Choose wisely.
lowbloodsugar 8mo ago
“ Drucker makes a manager’s life easier, Deming makes it harder” This is why companies can’t do Agile, especially Scrum: Scrum requires the most of the people in power, who typically can’t be bothered and who get to dictate process.
- esafak 8mo ago
  Statistical proficiency should be a requirement for managers.
Creator71 8mo ago
[dead]
foobarbecue 8mo ago
For the millionth time, would it kill ya to spell out the abbreviation the first time you use it? My googling suggests we're talking about https://en.wikipedia.org/wiki/Objectives_and_key_results https://en.wikipedia.org/wiki/Objectives_and_key_results , but my googling isn't always right.
- photios 8mo ago
  I envy people that haven't had to deal with the OKR crap in "modern" IT orgs. :D
- rawgabbit 8mo ago
  Yes, that's correct.
rawgabbit 8mo ago
OKRs attempts to impose top down centralized command and control. What ends up happening is an executive who is accountable notices an OKR trends the wrong way and when he asks why, the best bullshitters deflect blame. And nothing is resolved. The Toyota Way attempts bottom up process improvement. Teams generally organize themselves. Team leads take responsibility for quality and report it up the chain. Instead of deflecting blame, they often work themselves to exhaustion. Which is not an ideal result either.
ineedasername 8mo ago
* Eliminate management by numbers, numerical goals. Substitute leadership.* That’s a lot harder, and more expensive in the short term. It’s also something that benefits from having long term employees that both understand the organization and don’t view the organization as hostile to their employee interests whenever some other metric of short term profitability is a mutually exclusive choice. Without this, you’re limited to people who may have more natural or intuitive leadership capabilities instead of those who can learn, given the opportunity and example. In short, corporate practices have systematically eliminated the circumstances under which there would grow a sufficient number of people with leadership skills.
azalemeth 8mo ago
I think this article is a great opportunity to mention two under-used statistical techniques: Deming regression [0] and the Theil-Sen estimator [1]. They both fit straight lines to noisy data. Deming regression is an errors-in-variables model that tries to fit the line of best fit when you have errors in both x and y and they are both known and in general _different_; the Theil-Sen estimator is based on medians and is particularly robust if you have an error process that fails more "one way" than the other. Simple linear regression is everywhere in our lives and yet remarkably not robust to errors that are not IID normal, particularly with a small number of data points: a process that can only fail in one direction if it breaks is likely to completely and utterly bugger up the line that you fit. Both approaches have their place and I wish were more widely used, particularly by people who like fitting linear models to complex phenomena because they are easily understood. [0] https://en.wikipedia.org/wiki/Deming_regression https://en.wikipedia.org/wiki/Deming_regression [1] https://en.wikipedia.org/wiki/Theil%E2%80%93Sen_estimator https://en.wikipedia.org/wiki/Theil%E2%80%93Sen_estimator
an-allen 8mo ago
yea that system was a lot more than he was prepared for :)

195 points by todsacerdoti 8mo ago

baxtr 8mo ago

Maybe it’s my limited intellect but I found Drucker to be a lot easier to understand. Where Deming reads like a science paper, Drucker reads like an installation guide.

zem 8mo ago
that kind of ties in with the article's thesis; deming's approach is more scientific in the classic sense of taking observations and using those to build up your mental models, whereas drucker proposes a one size fits all recipe for managing roadmaps.
friendzis 8mo ago
Not necessarily limited "intellect", but rather limited background knowledge. Deming requires quite a bit of knowledge and understanding in failure/success modes. The core tenet of Deming is that every output is a result of some process and, therefore, output is controlled by controlling* the process itself. Look at your process and tackle failure modes in this priority list. Drucker, on the other hand, puts the process under the fog of war and basically says deploy pressure on process outputs and let the process adjust itself. It requires much less understanding behind the processes to make sense. * - Process control in Deming is mostly about variability.
- bryanrasmussen 8mo ago
  So following Drucker would be the cause of a lot of "every metric becomes a target" in management?
  - asplake 8mo ago
    Obviously the truth is messier than that, and it's worth noting that Drucker later recognised the toxicity of Management by Objectives and disavowed it. Quite a bit of OKR literature is devoted to avoiding it becoming its progenitor, MBO. Worth adding that Deming (after Shewhart) recognised two kinds of variation: special cause (specific the work item in question) and common cause (an artifact of the process). That knowledge work involves a lot more of the former than does manufacturing does not excuse inattention to the latter.
    - ChrisMarshallNY 8mo ago
      > Drucker later recognised the toxicity of Management by Objectives and disavowed it. Reminds me of a seminal treatise for Waterfall by Royce[0], where he basically says it’s fraught with issues, but can be coerced into something semi-usable. Not exactly a ringing endorsement. I think that paper is used as the template for all Waterfall work. [0] https://dl.acm.org/doi/abs/10.5555/41765.41801 https://dl.acm.org/doi/abs/10.5555/41765.41801
- baxtr 8mo ago
  What’s the most basic way to get into the works of Deming?
  - kqr 8mo ago
    Read his books.
    - baxtr 8mo ago
      Which one first?
      - kqr 8mo ago
        The New Economics! Then Out of the Crisis. Then if you're really hardcore, Some Theory of Sampling.
  - esafak 8mo ago
    Get a book on applied statistics, such as Think Stats: Probability and Statistics for Programmers (https://allendowney.github.io/ThinkStats/ https://allendowney.github.io/ThinkStats/) Computer Age Statistical Inference (https://hastie.su.domains/CASI/ https://hastie.su.domains/CASI/) Statistical Rethinking (https://xcelab.net/rm/ https://xcelab.net/rm/) An Introduction to Statistical Learning (https://www.statlearning.com/ https://www.statlearning.com/) Obviously maths is going to be involved to do the subject justice. These recommendations are more about applied statistics, but that's the foundation. From there it is a small transition to statistical process control.
jacquesm 8mo ago
Deming -> Strategy Drucker -> Tactics
adolph 8mo ago
> Where Deming reads like a science paper, Drucker reads like an installation guide. If you are looking for "do this" then Deming is not your person. If you seek understanding that drives transformation (knowing what to do in your context based on a broadly applicable value system), then Deming's system can deliver. Lessons from the Red Bead Experiment include the fallacy of rating people and ranking them in order of performance for next year (based on previous performance), as well as attributing the performance of the system to the performance of the “willing workers” in this simulation of an organization governed by what Dr. Deming referred to as the “prevailing system of management.” https://deming.org/explore/red-bead-experiment/ https://deming.org/explore/red-bead-experiment/

black_13 8mo ago

[dead]

roenxi 8mo ago

It is also worth noting that US management is notoriously bad at the actual management. Toyota v. US car manufacturers did not look like a fair fight when Deming was in the ascendant, and it is hard to tell given the scales involved but it looks a lot like the US has been outmanoeuvred in all aspects of industry by the Asians. US companies are generally a better bet though, because despite the handicap of being run by Americans, they are hosted in a country that generally believes in freedom and rule of law which means they have an unfair advantage even if they do a sub-par job of making the most of what they have. Exceptions abound in the details.

arkh 8mo ago
The thing is, the Toyota methods relies on people on every level to work to improve processes. If you're an employee and know you'll be there 10 years down the line or even until you retire, you have an incentive to improve said processes. Now check most Western companies: since the 70 / 80, everything is about reducing headcount. Lay-offs, outsourcing, offshoring, now the concept of spending your whole working life at the same company feels like a fever dream. So why would an employee try to improve things for the company when they know there is no future for them there? Better improve their own career and future prospect. So yeah, things like Kaizen are doomed to fail until things change.
- ghosty141 8mo ago
  > Lay-offs, outsourcing, offshoring, now the concept of spending your whole working life at the same company feels like a fever dream You are missing something here imo, very few companies actually increase pay (or to be more clear, show a clear way to get there) enough to make it attractive enough to stay there for long periods of time. From my experience here in Germany the people staying at companies for a long time are those who don't focus on their career.
  - esafak 8mo ago
    Moving around distributes knowledge making for a healthier economy overall. The alternative looks like Korean chaebols.

hbarka 8mo ago

This is a very trivial treatment of Deming and I’m surprised how it makes its way to the top of HN. The arc from Walter Shewhart to W.E. Deming is a bedrock foundation in an Industrial Engineering curriculum. These men paved the manufacturing process quality principles of modern industrialization. Drucker was about management science, truly an apples to oranges comparison.

kranke155 8mo ago
Management Science? Only management science I read so far (with actual measured outputs and ideas) was Peopleware. Everything else was more like philosophy. Has anyone ever measured, long term results from multiple management methods? What I saw when I looked into it was simple - the Toyota Way was the model for a lot of successful companies, including Pixar.
- bsenftner 8mo ago
  Peopleware is extremely old, and if you were to crack open a modern MBA text you'd find statistics and statistical process control type of thinking integrated everywhere, in all the MBA subjects. Management being soft and opinionated ended a long time ago, but then again, "the future is unevenly distributed" so who knows what conceptual envelope you find yourself.
  - bubblewand 8mo ago
    What I’ve seen in the wild is that this is entirely a veneer. It’s important to have numbers. It doesn’t matter if they mean anything. In fact, management is full of numbers that a slightly-clever high school sophomore who’d paid attention in science classes could tell you are totally useless, because they were gathered all wrong. They mean nothing whatsoever. They’re just noise. But nobody wants to hear stuff like “well first we’re going to need a baseline, and if you want it to be any good we’ll probably need two years or so before we can start trying to measure the effects of changes”. They just want something convincing enough that everyone can nod along to a story in a PowerPoint in four months. Two years out? Lol you’ll be measuring something totally different by then anyway. Your boss may be in a different role. You’ve asked something the company is literally incapable of. Meanwhile, last I checked, measuring management effectiveness isn’t something we can do in practice for most roles, except bad ways that only pretend to tell us something useful (see above). Good scientists, excellent and large dataset, just the right sector, just one layer of management under scrutiny, maybe you get lucky and can draw some conclusions, but that’s about it, and it’s rare to see it happen in an actual company. Any companies that do achieve it aren’t sharing their datasets. This kind of thing has been consistent everywhere my wife or I have worked. Similar things reported by many friends. Companies want to pretend to be “scientific” and “data-driven” but instead of applying it to only a couple things where they might do it well (enough data, cheap to gather metrics, clear relevant business outcome) they try it everywhere, but don’t want to spend what it would take to be serious about it, with the result that most of their figures are garbage. This trend has become just another “soft”, as you put it, tool.

whatever1 8mo ago

Fundamentally stock markets won the world of business, so everything has a horizon of a financial quarter. Hence, every action of a company needs to be measured against the upcoming quarterly results. OKRs et al are great at that. Who cares about quality/sustainabily. We just want the stock go wheeeeee and get our bonuses.

derriz 8mo ago
Not sure about your opening thesis. The vast majority of employees work for privately held businesses and from personal experience of working in such companies, “management” by OKRs and the like is common in companies who are not listed on stock markets also.
- regularfry 8mo ago
  There'll inevitably be cargo culting driven by MBA curriculums and "they're making a lot of money, let's do what they did" without examining the specifics of the situation to distinguish luck from judgement.
ghywertelling 8mo ago
We need to appoint people who really CARE. When CEOs hop from company to company, the culture of CARING takes a backseat. Everything becomes transient with no one with deep technical or cultural knowledge at the driving seat. This is the reason people who have been at the company for long time should get a chance to transform it from within like Satya Nadella.
esafak 8mo ago
OKRs took off after a VC named John Doerr introduced them to Google, and the industry followed their lead. https://www.whatmatters.com/ https://www.whatmatters.com/

anonymousiam 8mo ago

The main point that I did not see mentioned in this piece is that Deming should only be applied to MANUFACTURING environments, because things like engineering are too chaotic to identify processes or trends in the engineering itself, and trying to control those engineering processes with SPC doesn't really improve the quality of the engineering, it just adds stress, makes things take longer, and probably lowers the quality of the thing that is being engineered. Obviously, if a quality issue is detected in manufacturing, there may be some steps that engineering could take to improve the manufacturing process and make things stable enough to obtain meaningful statistics. This is part of the Deming feedback process, and part of the System Engineering Life Cycle.

ignoramous 8mo ago
> trying to control those engineering processes with SPC doesn't really improve the quality of the engineering, it just adds stress, makes things take longer, and probably lowers the quality of the thing that is being engineered Totally depends on the scale. For pizza-sized times with a neighbourhood pizza shop sized impact, sure. Large scale projects without controls & feedback loops in place will fall apart; see: Scaling teams: https://archive.is/FQKJH https://archive.is/FQKJH If you'd follow some medium to large scale projects (like Go / Chromium), the value of processes & quality control, even if it may seem at the expense of velocity, becomes clear. The great insight of Deming's methods is that you can (mostly) identify the difference between common and special causes mathematically, and that you should not attempt to fix common causes directly - it's a waste of time, because all real-life processes have random variation. Instead, what you want to do is identify your mean and standard deviation, plot the distribution, and try to clean it up. Rather than poking around at the weird edges of the distribution, can we adjust the mean left or right to get more output closer to what we want? Can we reduce the overall standard deviation - not any one outlier - by changing something fundamental about the process? As part of that, you might find out that your process is actually not in control at all, and most of your problems are "special" causes. This means you're overdriving your process. For example, software developers working super long hours to meet a deadline might produce bursts of high producitivity followed by indeterminate periods of collapse (or they quit and the whole thing shuts down, or whatever). Running them at a reasonable rate might give worse short-term results, but more predictable results over time, and predictable results are where quality comes from. https://apenwarr.ca/log/20161226 https://apenwarr.ca/log/20161226 Distributed systems is also a way to be throughly humbled by complexity: https://fly.io/blog/corrosion/ https://fly.io/blog/corrosion/

jongjong 8mo ago

Or you can just abolish the fiat soft money system, let the corporations go out of business, let efficient small companies take their place and then you'd have founders who actually care about long term results in charge and they could manage the company however they want. If they do a bad job, they'd go out of business. If they do a good job, they'd stay afloat or maybe even grow a little. And over time, all the companies with incompetent leaders would be wiped out, everyone would get a fair shot at being a leader and everyone would end up in their rightful place and capitalism would function as it was designed.

ninalanyon 8mo ago
> abolish the fiat soft money system, And replace it with what?
- pjc50 8mo ago
  These people always mean gold. It's not a great thing to tie the size of your economy to the size of world mining.
  - jongjong 8mo ago
    Anything scarce really. Anything which slows down or stops the government printing of money is beneficial.
    - mr_toad 8mo ago
      Fiscal prudence could accomplish that. But if you look at the history of the United States, fiscal prudence is an exception to the rule.
- deleted 8mo ago
  [deleted]
- radiator 8mo ago
  Whatever existed before it

iamflimflam1 8mo ago

This made me spit out my coffee… > One of the virtues of OKRs is that they are straightforward for managers to apply.

laserlight 8mo ago
Talking about “virtues” of OKRs is a long stretch.
estearum 8mo ago
Well it didn't say successfully. But truly there's nothing easier than putting a couple bullets in a document and saying, "Now go forth, underlings, and make these bullets ring true! If you don't, you're fired and without health insurance."

kqr 8mo ago

> for thermostat B there are many more outliers. We’d say that [...] thermostat B is not [under statistical process control]. (In practice, you’d draw a control chart to identify whether the system is under statistical control). I did draw the control chart, and thermostat B is definitely under statistical process control: https://xkqr.org/info/xmr.html?baseline=33,97,41,65,72,71,64,93,62,71,64,70,100,71,64,71,46,64,38,70,63,64,71,61,70,101,64,48&values=38 https://xkqr.org/info/xmr.html?baseline=33,97,41,65,72,71,64...

kqr 8mo ago
To be clear, this is not a diss of the article. It is hard to create fake data that look realistic at first glance but are not under statistical process control. That's just how good control charts are at separating signal from noise. If someone wants to learn more, this is how I put the introduction: https://entropicthoughts.com/statistical-process-control-a-practitioners-guide https://entropicthoughts.com/statistical-process-control-a-p...
- jacquesm 8mo ago
  Fantastic stuff, thank you.
contravariant 8mo ago
Eh depends what you mean I suppose, but a small dense cluster with enormous outliers is not a great sign usually. Almost nothing has (effectively) unbounded variance, so most things are under statistical control in a sense. With some notable exceptions (earthquakes, any other event with exponentially decreasing frequency and exponentially increasing damage). For the sake of argument I assumed the author meant that the variance of the thermostat was too high to be practical.
- hyperpape 8mo ago
  Statistical control is a very specific term: https://entropicthoughts.com/statistical-process-control-a-practitioners-guide https://entropicthoughts.com/statistical-process-control-a-p..., and one which you'd expect anyone with a significant interest in Deming to understand. My expectation is that Lorin would read the parent comment and say some variant of "oh, whoops, I didn't check." As the parent noted, it's not really that important to the overall point.

ontouchstart 8mo ago

This topic is very relevant in the age of agentic AI when every decision is a statistical next token prediction “trained” on some loss function. AGENT.md, SOUL.md etc are just smoke and mirrors of The Wizard of the Oz. Eventually manager as a profession will be replaced by tools, just like computer as a profession, editor as a profession. The evolution of computer science will be manager science. There is more than loss function and KPI.

a_c 8mo ago

The analogy I used with the team was that, set the goal, present the map, and figure how to make a better map. Drucker was about the goal with a given map. It is not uncommon for people receiving the OKR not resonating with it. Sometimes they actually have insight into making a better map, but if OKR is OKR, one just have to follow, people swallow their thoughts

analog31 8mo ago

The big squiggly mess in the article is filled with people. I think Deming’s deepest concept was giving workers on the production line simple tools to improve processes on their own. His books are filled with exhortations to trust the workers. This is what American managers could never bring themselves to do. Even in manufacturing, the application of statistical process control was never entrusted to the workers, but became a department of its own, with bureaucracy, OKRs, and elaborate software.

bluGill 8mo ago
>Even in manufacturing, the application of statistical process control was never entrusted to the workers, but became a department of its own, with bureaucracy, OKRs, and elaborate software That is wrong thinking. While you can go overboard with bureaucracy, the line worker doesn't have the the background (or time) to evaluate statistics. You need an expert in statistics at times to see if what looks like a pattern really is. Mean while the line worker needs to spend their time on what they are good at. Trust the line worker is important, it just isn't a shortcut to people who really know specialized domains.
- woleium 8mo ago
  The line worker has a canny instinct for the right answer long before the statistics are significant though. Kinda what Gladwell talks about in Blink
  - bluGill 8mo ago
    Sometimes, but a few times those instincts are very very wrong. Blink is interesting, be sure to read the criticism though https://en.wikipedia.org/wiki/Blink:_The_Power_of_Thinking_Without_Thinking#Reception https://en.wikipedia.org/wiki/Blink:_The_Power_of_Thinking_W... - it isn't clear that the book is correct.
  - throwway120385 8mo ago
    In my experience, the line worker's instincts are to be trusted but verified. If we blindly follow every crisis from the line we'd quickly find ourselves in a pit. These crises need to be backed up with context and a sense of criticality as there are finite resources to work through problems, and solving one person's immediate problem on the line may have a crushing impact elsewhere.

rglover 8mo ago

Optimize for mediocrity, you get mediocrity. Optimize for quality, you get quality. Choose wisely.

lowbloodsugar 8mo ago

“ Drucker makes a manager’s life easier, Deming makes it harder” This is why companies can’t do Agile, especially Scrum: Scrum requires the most of the people in power, who typically can’t be bothered and who get to dictate process.

esafak 8mo ago
Statistical proficiency should be a requirement for managers.

Creator71 8mo ago

[dead]

foobarbecue 8mo ago

For the millionth time, would it kill ya to spell out the abbreviation the first time you use it? My googling suggests we're talking about https://en.wikipedia.org/wiki/Objectives_and_key_results https://en.wikipedia.org/wiki/Objectives_and_key_results , but my googling isn't always right.

photios 8mo ago
I envy people that haven't had to deal with the OKR crap in "modern" IT orgs. :D
rawgabbit 8mo ago
Yes, that's correct.

rawgabbit 8mo ago

OKRs attempts to impose top down centralized command and control. What ends up happening is an executive who is accountable notices an OKR trends the wrong way and when he asks why, the best bullshitters deflect blame. And nothing is resolved. The Toyota Way attempts bottom up process improvement. Teams generally organize themselves. Team leads take responsibility for quality and report it up the chain. Instead of deflecting blame, they often work themselves to exhaustion. Which is not an ideal result either.

ineedasername 8mo ago

* Eliminate management by numbers, numerical goals. Substitute leadership.* That’s a lot harder, and more expensive in the short term. It’s also something that benefits from having long term employees that both understand the organization and don’t view the organization as hostile to their employee interests whenever some other metric of short term profitability is a mutually exclusive choice. Without this, you’re limited to people who may have more natural or intuitive leadership capabilities instead of those who can learn, given the opportunity and example. In short, corporate practices have systematically eliminated the circumstances under which there would grow a sufficient number of people with leadership skills.

azalemeth 8mo ago

I think this article is a great opportunity to mention two under-used statistical techniques: Deming regression [0] and the Theil-Sen estimator [1]. They both fit straight lines to noisy data. Deming regression is an errors-in-variables model that tries to fit the line of best fit when you have errors in both x and y and they are both known and in general _different_; the Theil-Sen estimator is based on medians and is particularly robust if you have an error process that fails more "one way" than the other. Simple linear regression is everywhere in our lives and yet remarkably not robust to errors that are not IID normal, particularly with a small number of data points: a process that can only fail in one direction if it breaks is likely to completely and utterly bugger up the line that you fit. Both approaches have their place and I wish were more widely used, particularly by people who like fitting linear models to complex phenomena because they are easily understood. [0] https://en.wikipedia.org/wiki/Deming_regression https://en.wikipedia.org/wiki/Deming_regression [1] https://en.wikipedia.org/wiki/Theil%E2%80%93Sen_estimator https://en.wikipedia.org/wiki/Theil%E2%80%93Sen_estimator

an-allen 8mo ago

yea that system was a lot more than he was prepared for :)