IT BEGAN LIKE ALL EXPERIMENTS, by asking a question.

Actually two.

I wanted to know if it was possible to semi-automate fetching and formatting StatsCan tabular data with a Claude Code skill. It turns out that yes, this is quite possible and actually trivial when you use the Web Data Services endpoints StatsCan provides and marry that up to some python code to format the results into a two column, tab-delimited list that’s put into the clipboard, ready to paste into PBC Analyzer PRO. Mischief managed.

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I was well-pleased with the experiment and set about refining the skill so it could be queried to fetch almost any dataset. I’d initially started with Table 18-10-0004-03, Food Purchased in Stores, as part of a course I’m developing to teach journalists and media types how to analyze data with a PBC so as to avoid taking in a show at the Two Data Point Comparison Theatre.

This made me ask another question: is it possible to marry the core components of PBC Analyzer PRO with this StatsCan querying skill to fetch, format, and plot tabular data into a process behaviour chart in one go? In about 15m with Fable 5 (Anthropic’s latest model, de-fanged for consumers) I got my answer: totally, and then some.

The Core Skill: statscan-tables

This is the tiny skill I created first in Claude Code to hit the StatsCan Web Data Service API endpoints, get the data I wanted, and format it so you just CTRL-V to paste it into PBC Analyzer PRO.

So, to fetch data from table 18-10-0004 we’d just enter this query.

/statscan-tables table 18-10-0004 tab-delimited past 24 months as Monthly YoY % Change

After a brief moment, Claude Code churns and gives us this, which I’ve truncated to keep it brief:

Date StatsCan: YoY % Change Food (2024-06-2026-05)<br>Table: 18-10-0004
2024-06 0.028
2024-07 0.027
2024-08 0.027
2024-09 0.028
2024-10 0.03
…
2026-01 0.073
2026-02 0.054
2026-03 0.04
2026-04 0.035
2026-05 0.038

This is automagically added to the clipboard for us, so all we need to do is fire up PBC Analyzer PRO, hit shortcut key ‘P’ to bring up the paste window, and CTRL-V our results into it:

… then click Paste to Chart and presto, I have a PBC — shifted limits are mine:

Total time invested? A couple of minutes. And it will go faster on subsequent runs thanks to some smart updating and caching of query parameters and vectors that remember where to find specific data sets.

Previously, my workflow for an analysis was:

1. Go to the web page with the tables, select my date range, filter to the rows I want

2. Download the .csv,

3. Reformat the .csv in Excel, do some math, highlight, copy-paste.

Now it’s a single, repeatable query in natural language that gets me to the fun part of analyzing the data FASTER. No more 3-step shuffle.

Skill Evolved: statscan-to-pbc

Naturally, I wondered what would happen if I cut myself out as the middle-man and just left it up to Claude to fetch the data, format it, then present it as a PBC using a stripped-down bare-bones version of PBCPRO’s engine and display. I asked Fable 5 to plan an extension of statscan-tables that integrated the core components to do this and in about 10m we had a working version, totally one-shotting myself in the process.

Now, you can use the evolved skill, statscan-to-pbc to query the same data table and present it as an HTML file with this:

/statscan-to-pbc pull last 12 months of food purchased in stores inflation figures, Monthly % Change YoY, as a pbc

Total time invested: ~30s. This is a quantum improvement with the only caveat being the chart doesn’t have all the features of the app - it’s just a chart control. This said, I’ve added features to the skill to allow for shifting limits and even separating time series to exclude a data point and render each adjacent to each other in the same chart. Below is a time series showing estimated annual emigration figures with 2020 removed because it was a legitimate special cause:

Total time invested: ~30s. No downloading, no cutting and pasting. Just me and Claude having a chat about what to fetch and analyze.

Chat and Chart Analyses

As fun as this is, the real party is in the comments Claude makes after running a query. For example, when I ran the query for pulling a PBC for the inflation figures on all food, which includes restaurants — I didn’t this, so I fixed the query. It then provided this post-run analysis of BOTH charts without my prompting:

See the highlighted part about the “GST/HST base effect” ? In a prior chat long ago I raised this as a confounder brought about by government policy, which was a fantastic example of tampering with a system. In December 2024 our Prime Minister decided to institute a broad tax holiday on food that ran until February 15, 2025. It was a largely political move designed to curry favour with an electorate that had become increasingly disenchanted with the PM — everyone knew it would result in a decrease in inflation as retail prices dropped by 10%-13%:

Claude totally picked up on this and called out the Rule 1 signal in the YoY total food inflation figures for being so elevated, which makes sense as the policy took hold in January 2025 to drop the inflation rate — 12 mos later, the usual variation in the rate looks ginormous as we returned to usual inflation rates. This confirms the effect of tampering presenting a signal of SPECIAL CAUSES of variation!

This takes time series data analysis into a whole new direction: not only can you do an analysis of a particular data series, but get an intelligent comparison with others as you “chat and chart”…

Great. How Do I Use These Claude Skills?

A few pre-requisites are needed:

• First, you need a Claude subscription - the Pro plan is fine, runs $24 CAD/$16 USD per month. This also gets you the second pre-requisite:

• Claude Code. Fear it not, even if you’re not a developer, this is required because the skill requires permissions that the standard web-based Claude client doesn’t have for running the Web Data Service queries — it’s on a not-allowed list for some reason…

• Python standard library, version 3.10 or better.

statscan-tables

Head over to my github page for the statscan-tables skill that you can freely download and install on your local machine either with .zip or using cloning the repo directly — full directions in the README on the page, along with some sample queries to run.

You can get the .zip HERE.

statscan-to-pbc

If you are a paid-tier subscriber (THANK YOU FOR YOUR SUPPORT!) you can download the complete end-to-end Claude Code skill for automatically fetching and presenting StatsCan data as a PBC via our brand-spanking-new files repository here:

https://auth.digestibledeming.com/files

Sign-in with your subscription email to get the magic link that will take you to the page where you can download it:

Full instructions in the README, which is effectively the same as what you would do with statscan-tables, ie. unzip to your ~/.claude/skills folder and you’re good-to-go. Launch Claude Code and begin your queries and chart-making. If you get stuck, just ask Claude what to do - it will help you out!

Feedback!

If you are able, give the Claude Code skill a spin and tell me how it worked for you. For now, it’s tied to StatsCan, but ostensibly it could be pointed anywhere there’s a reliable API endpoint for querying and extracting data into a Process Behaviour Chart. That will come in later revisions…

TODAY IS CANADA DAY, the day we Canucks celebrate the birth of our nation at a conference in Charlottetown, Prince Edward Island in 1867. It’s also a time worthy of recalling a short story from Out of the Crisis where Dr. Deming applauded the management at Reimer Express Lines, Ltd., for their observations and thinking about their operations and how to improve them. It’s also a good example of how Deming’s theory of management could be applied to the service industry.

Based out of Winnipeg, Manitoba Reimer’s tractor trailers were a mainstay on Canadian highways that many road-trippers will no doubt remember seeing. Sadly, after 67 years in business, they went defunct in 2019 after merging with a larger company, YRC Freight, which took over the brand and liquidated assets. While no longer with us, they provide a Deming-approved example of good management.

You Are Looking at the Only Person in Your Company that Our Customers Ever See

Our story begins part-way in to Chapter 7 of Out of the Crisis where Dr. Deming opens with a reminder (above) that was given to drivers at Reimer that business relationships are fragile and business can be easily lost with “gnarly service or looking like a vagabond”. Deming notes this isn’t a useless exhortation that he’d warn about - these are things directly within the driver’s ability to control. However, what happens when it becomes difficult to maintain that relationship because of problems the driver cannot control?

A Letter from Top Management:
What Business Are We In?

Dr. Deming describes a letter he received from Donald S. Reimer and John W. Perry, CEO and a top manager of Reimer, retelling some of their early experiences and successes improving their management. It began with a survey they gave to each of their 35 city drivers that asked:

How, in one sentence, would you describe the business we are in?

They got 32 different responses and none seemed to address what management expected. Two were highlighted:

The truck business. (This could mean that we are in the business of buying and selling trucks. No indication of requirements or standards that apply to our business, which is service.)

Transport business. (This could mean that we transport people by train, bus, or air, or that we sell buses.)

Deming, W. Edwards. Out of the Crisis (The MIT Press) (p. 194). The MIT Press. Kindle Edition.

Pause here for a moment: what would happen if you asked a similar question in your organization? What answers would you expect from colleagues and employees? Why?

This is a brilliantly simple and effective way to diagnose how well you have communicated the AIM and PURPOSE of your business to everyone. The deviations from your expectations are sources of COMMON CAUSES of variation and can be the source of all kinds of trouble in processes.

Improving Services by Understanding Drivers’ Frustrations

Reimer and Perry’s letter goes on to explain what leadership did next to explain their business as a service, and by working to understand the plight of drivers providing that service, including an example of management doing what for all intents was a Genba exercise with a driver. I’m reproducing this verbatim from the book, emphasis mine:

When we started to dig deeper, in a question-and-answer session with the men, I began to describe service as a process. To complete a service we must also complete various segments of our activities to ensure that the process is successful. For example, to suggest that we are in the freight business without any exposure to the services that we can provide between Eastern Canada and Western Canada is really missing the point.

It is up to the management to help the employees to understand that they are an important part of the overall business activity, the activity being service. So we worked for a few weeks with our drivers in Eastern Canada in reference to linking their pick-ups to our line-haul operations and to delivery in cities 1500 or 4000 miles to the west.

We have found that frustrations over various problems of drivers have disappeared as we improve the process, in contrast with acceptance of frustrations from trying to solve effects and not to get rid of causes. I should like to give you one small example.

Recently in Vancouver our terminal manager travelled with a different driver each day. He called me up one day excited about one driver, who, for a number of months, both he and the despatcher had deemed to be hopeless in productivity. Travelling with this driver, the manager noted that the radio was not working well. Vancouver is very hilly, and this driver served a number of our customers in the valleys, in which the reception was poor or non-existent. When the manager looked into it, he discovered that the driver had complained a few times to our local supervisors about radio reception, but we had not listened. The driver often had to drive miles out of his route to get out of the shadows of the hills, to seek audible reception in order to report a problem in collection or delivery, and to keep in touch with the despatcher.

Deming, W. Edwards. Out of the Crisis (The MIT Press) (pp. 194-195). The MIT Press. Kindle Edition.

In this example, management had formulated an opinion about a driver’s productivity without first understanding the system constraints they operated under, which was tied to the physical geography of his routes and radio technology of the day. Management held responsibility for the outcomes of the system they had designed for this driver, and by riding along they discovered firsthand the COMMON CAUSES of variation that were at play affecting the reliability of his schedules, things that were outside of his control to change — a good reminder about Deming’s observation, again from Out of the Crisis:

No amount of care or skill in workmanship can overcome fundamental faults in the system.

(3rd ed., p. 315)

In this case, it’s pretty difficult to overcome Canadian geography near the Rockies, even with modern cell towers and satellite communications as the “shadows” can still persist, as anyone with Sirius XM in their car can attest.

Summing it Up

This was a refreshing read for me as I recalled Reimer and the role they played in building the Canadian economy by providing a vital service for moving goods large distances. Much of our history is founded on transportation from railways to shipping and trucking, each in a lattice of interconnections and interactions like intermodal freight. What was most inspiring was to see how Canadian business leadership took a keen interest in improvement of their management practices, even in a short anecdote about asking a simple question to learn a great deal about making their business work reliably. That the Good Dr. Deming gave them an approving nod is something to take pride in and remember on this Canada Day.

Reflection Questions

Consider Deming’s recounting of Reimer’s letter and how they began to improve their leadership by asking their drivers a basic question, and what that uncovered.

• What type of responses would you get from frontline workers in your own organization or business if asked that same question?

• How would you answer this question?

• Consider a time when you observed someone labeled a poor performer or problem employee. Was anyone in management “riding along” with them to understand their challenges, or just offering judgements? What was done to help them? What were the outcomes? What could have been done differently?

• Consider the driver who was unfairly labeled a poor performer and whose complaints went unheard until their manager rode along with them to witness the COMMON CAUSES of variation they had to contend with (mountains, radio) to make deliveries and keep in touch with dispatch. What similar circumstances have you observed where COMMON CAUSES are blamed on employees who can’t influence them? Has any manager “rode along” to learn?

As always, sound off in the comments below, and HAPPY CANADA DAY to all my Canadian subscribers: your continued support is really appreciated!

THE AIM for this post is to propose a hypothesis: that an organization led by leadership who think and act with a Deming perspective, one that is actively working to improve their systems and processes according to his teachings, is in the best possible position to sensibly adopt and benefit from AI than any of their Old Economics peers.

Sensible adoption means something specific: deploying AI solutions where scaled process automation genuinely improves the quality of products and services, while freeing people to apply their talents and creativity to problems that benefit customers over the long run, rather than as a means to eliminate headcount and optimize for costs.

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In a recent post, The Four Horsemen of the Coming Layoffs Apocalypse, I wrote about how AI is supercharging the worst instincts of management that Deming identifies in The New Economics. Here I ask: What would a sensible AI adoption look like inside a Deming-led organization? How would it differ? Why?

Grab a cup of your best joe and let’s dive in...

tl;dr

For the impatient, we look at how AI adoption goes wrong when guided by the theory of the prevailing style of management, and how it can lead to expensive failures like those experienced by Klarna and Microsoft Copilot. I frame this using three key stages of AI adoption that industry commentator Nate B. Jones relates in a recent video and how poor outcomes are almost guaranteed given inconsistencies in how information is structured and captured.

What is the AIM for Adopting AI?

Deming teaches us that “management in any form is prediction,” and in most AI adoptions under the prevailing style of management we see leadership predicting lower costs, higher customer satisfaction, and profits to match. Reality? Not so much.

In a video from earlier this year, AI commentator Nate B. Jones tells the story of two failed AI rollouts, payment processor Klarna and Microsoft Copilot, to illustrate the readiness problem organizations are facing, and how a naive adoption driven by poor assumptions can lead to expensive disasters.

For example, Klarna’s CEO found out the hard way when he replaced ~700 customer service reps with AI agents to improve resolution times. What followed was MBO on steroids: turnarounds dropped from 11 minutes to 2 while simultaneously increasing complaints. Suddenly, a projected $40-$60M in savings turned into a loss of unknown and unknowable proportions as loyal customers were alienated and future prospects turned off. By mid-year, the CEO admitted “cost was a predominant evaluation factor, but the result was lower quality.” Perhaps too late, Klarna scrambled to re-hire the laid-off humans.

Jones suggests that the bots didn’t fail, but dutifully met their goal. What was missing from the agent’s knowledge was Klarna’s intent to build lasting customer relationships to last a lifetime in a highly competitive market. Ironically, it might have tipped leadership’s hand on what they actually valued more: saving money.

When unguided by good theory, it’s easy to see how the CEO could be misled into believing short service calls across-the-board lead to lifelong customer relationships: the wrong things get prioritized and translated into errors of omission for the agents. Deming warned that people will do almost anything to meet a target or goal, even at the expense of the system: AI makes achieving this kind of destruction at scale extremely easy.

Why Most Organizations Aren’t Ready

Jones frames the evolution of AI engineering into three stages: prompt engineering (”how do I talk to AI?”), context engineering (”what does AI need to know?”), and intent engineering (”what does AI want?”). Most organizations stall at the second stage because their knowledge is locked in silos, scattered across systems, and living in people’s heads rather than documented in a way agents can consume. Without that foundation, intent engineering isn’t a next step; it’s a pipe dream.

Microsoft Copilot is the proof of concept. Despite billions invested and 85% Fortune 500 adoption, only a small fraction of organizations moved from pilot to meaningful deployment. Jones rejects the standard explanation (poor UX, weak model quality) and instead names it for what it is: deploying AI without organizational intent alignment is like hiring tens of thousands of new employees and never telling them what the company does, what it values, or how to make decisions. You get activity. You don’t get productivity.

Jones names three root causes underneath all of this:

• Organizations don’t have consistent, explicitly documented values (incoherence);

• The people who build agents and the people who set strategy don’t talk to each other (two cultures)

• Most leadership teams have never had to make their intent explicit and structured before (fundamental difficulty).

None of this is a technology problem. It’s a management problem.

Which is where the good Dr. Deming comes in.

Let me explain…

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The route to transformation is to understand and apply profound knowledge.

It will not suffice to learn all about the present style of management. One could learn all there is to know about ice, yet know very little about water. [Contributed by Dr. Edward M. Baker]

- Deming. The New Economics. (2nd ed. pp. 123-124, 3rd ed. p. 85)

Transformation produces qualitative differences from the previous state. Relationships that exist in one phase do not exist in other phases. For example, the behavior of a solid can’t be predicted from the behavior of a liquid. Therefore, knowledge of the behavior in one state cannot help to understand and predict behavior in another. A lesson for managers is that the knowledge, skills, and practices that worked in the past will not work as well, and may even be counterproductive, in a qualitatively different future state.

- Edward Martin Baker. The Symphony of Profound Knowledge: W. Edwards Deming’s Score for Leading, Performing, and Living in Concert . iUniverse. Kindle Edition. (loc 709)

THE AIM for this post is to challenge a popular misconception I’ve noticed in articles and blogs presenting systems thinking as a defining skill or capacity that’s missing from the modern manager’s playbook, and this alone will unlock superior capabilities in today’s AI-driven, uncertain world. While learning to see organizations as systems of causal relationships is valuable, it’s insufficient on its own and can lead to frustration when gaps emerge that it cannot adequately explain beyond “it’s the system” — a better theory is required.

Dr. Deming helps us to fill in these gaps with the System of Profound Knowledge, which is what we’re going to revisit today. This will be of particular interest to new subscribers and those just starting their Deming journey.

Operational Definition

Before we get carried away, let’s nail down an operational definition of systems thinking, which I will define very broadly as the perspective of seeing and thinking in terms of the interdependence and interactions of components for a particular purpose. I will bring in Dr. Russel Ackoff here as well to say that in so doing we cannot understand a system through its parts but in how they are put together to accomplish its purpose.

Interestingly, Deming did not make "systems thinking" one of the four domains of profound knowledge. He instead chose the phrase "appreciation for a system," a distinction we'll return to shortly.

Systems Thinking, AI, and Systems Doing

Over the weekend I read two Forbes articles: Today’s Most Crucial Leadership Skill Is Systems Thinking and AI Can’t Fix What Leaders Still See As Tasks: Why Systems Thinking Must Return To Leadership. The former argues that leaders need to adopt systems thinking to cope in a VUCA world (volatility, uncertainty, complexity, and ambiguity) and offers a starter kit of five tools to adopt this newfangled mindset. The latter frames the urgency to become a systems thinker to avoid the pitfalls of naively automating and optimizing system processes with AI and expecting good things to happen.

Each doesn’t offer advice that is wrong per se, just incomplete. Why?

First, because they present a theory that suggests by holding all else constant (ie. the prevailing style of management used almost everywhere), this change in mindset alone will result in better adaptability and successful AI adoptions.

Second, because it fails (ironically) to address the systemic causes driving adoption of a systems thinking view in the first place, ie. the shortcomings of the prevailing style of management.

Third, because buried within the advice is a belief that we can a la carte our way to a better state. Just change your mindset, don’t worry about performance appraisals. On this, Deming was clear that this isn’t going to change much:

Management is in a stable state. Transformation is required to move out of the present state, not mere patchwork on the present style of management.

- The New Economics (2nd ed. p. 123, 3rd ed. p. 85)

So, what are we missing out on when we only look to systems thinking?

Coach Vader and the 97% Untouched Gains

Regular followers and readers will recall this meme I’ve been using for a while of an unnamed person in a Darth Vader costume standing in the surf vainly trying to desalinate the ocean into a 2l bottle with a Brita filter jug. He’s a stand-in for the role I’ve played in my career as an agile software dev team coach trying to teach teams new ways of delivery while holding back the dominant culture of the organization. About as successful.

I based this on a table Dr. Deming uses in The New Economics (2nd ed. p. 37, 3rd ed. p. 27) called The Theory for Leadership of the Transformation for illustrating “where we are and what needs to be done”: 3% has been accomplished by improving processes for individuals and teams that produce figures while the 97% high-leverage, big-gains system changes have yet to even be considered.

What Deming’s table doesn’t do that my meme does is show the unceasing futility at changing what people do without changing why they do it in the first place: the overwhelming effects of the prevailing style of management. As Vader diligently and vainly works to filter out the cultural seawater to help the team with new ways to work, the source of the seawater goes unaddressed. This is principally why most transformations face-plant after the coach or executive sponsor departs: the sea of opposing troubles eventually swallows them whole.

Transforming Ice to Water

In my opening quotes I refer to a favorite Deming passage (borrowed from his colleague Dr. Ed Baker, who also contributed the table behind Coach Vader…) that sums up Deming’s view on transformation as a change of state akin to ice and water. It’s insufficient to make better ice when you need to use water: the properties of one do not transfer to the other when a state shift has occurred.

However, Deming was speaking to us as one who had already transformed himself. He used words that while sounding familiar carried totally different meaning because they are used in a different context. What we require is a map or route to get from where we are now to where he is, to shift our thinking state from ice to water…

The System of Profound Knowledge

Dr. Deming provides this to us using a peculiar map to get our thinking aligned with his comprised of four parts:

1. Appreciation for a System

2. Knowledge about Variation

3. Theory of Knowledge

4. Psychology

Each represents a domain of knowledge that interacts with the others as a system that work together to transform how an individual sees, thinks, acts, and leads within an organization: from ice to water. Here’s another way to visualize them as a diagram from Peter Scholtes’ The Leader’s Handbook — I’ve added color and illustrations, along with Scholtes’ Six New Leadership Competencies that relate to each domain:

Deming teaches us that you don’t need to be an expert in one or all domains to benefit from applying it - just making an effort to learn and build understanding of the model is sufficient to get started. In this regard, note how the first quadrant is called Appreciation for a System and not Systems Thinking. Deming always chose his words carefully, and here he is lowering the bar to entry: you don’t need to go full Donella Meadows or Russ Ackoff to begin your transformation, you can just start with a simple definition as he provided in Chapter 3: Introduction to a System:

A system is a network of interdependent components that work together to try to accomplish the aim of the system.

A system must have an aim. Without an aim, there is no system.

- The New Economics. (2nd ed. p. 50, 3rd ed. pp. 35-36)

NB: Nothing is preventing you from deepening your expertise in systems if you want, it just isn’t a hard requirement. It’s enough to begin understanding how managing a system differs from managing parts: it’s the interactions between them that matter most.

Your Organization as a System

What the Forbes articles and blogs I’ve read lack is some visual that aspiring managers and leaders could use to help see their organization as a system, to better appreciate how the components are put together and interact with one another. Dr. Deming used a flow diagram to do just that, recommending that you label it with the names of people who do various jobs and how they are accountable to one another for the quality of work they pass along.

This can be easily adapted to describe any organization’s workflow and to begin surfacing the processes in each stage that govern how something is made for a subsequent process to consume and transform or add value for the customer.

Knowledge About Variation

We’ve covered this in depth here in the newsletter as it’s my favourite domain, but for those new to SoPK, this is understanding that all systems vary in their operation, sometimes by some amount normally (common-cause), and sometimes by some amount extraordinarily (special-cause): good management depends on knowing one from the other. We typically do this with the aid of a Process Behaviour Chart that allows us to sift signals from noisy data. You can create your own with my member-exclusive tool, PBC Analyzer PRO, which teaches you about variation as you use it.

Theory of Knowledge

This domain is all about how we learn through testing our expectations against reality using a theory. Deming teaches:

Knowledge is built on theory. The theory of knowledge teaches us that a statement, if it conveys knowledge, predicts future outcome, with risk of being wrong, and that it fits without failure observations of the past. Rational prediction requires theory and builds knowledge edge through systematic revision and extension of theory based on comparison of prediction with observation.

Deming, The New Economics. (2nd ed. p. 102, 3rd ed p. 69 )

It is rooted in the philosophy of conceptual pragmatism that American philosophy C.I. Lewis developed in the early part of the 20th century, and influenced Dr. Deming’s mentor, Dr. Walter Shewhart. Deming synthesized and refined this philosophy into the Shewhart Cycle that is referred to as a PDSA or Plan-Do-Study-Act/Adjust loop. It is a structured method for making a change to a system guided by theory rather than just shooting from the hip.

An example: Deming teaches us that management in any form is prediction. If a manager decides to introduce an incentive system to improve productivity with “On-the-Spot” awards, they are revealing a theory of management that people are best-motivated by rewards and that the system’s performance can be improved this way. As the program is introduced, the manager gets feedback on whether the prediction is true, including any detrimental downstream effects.

Psychology

This could well be rephrased to “Appreciation for People” or to borrow from lean/TPS: “Respect for People” for the reasons Dr. Deming included it in SoPK. He explains:

Psychology helps us to understand people, interaction between people and circumstances… People are different from one another. A manager of people must be aware of these differences, and use them for the optimization of everybody’s abilities and inclinations. This is not ranking people.

People are born with a need for relationships with other people, and need for love and esteem by others. One is born with a natural inclination to learn. Learning is a source of innovation. One inherits a right to enjoy his work. Good management helps us to nurture and preserve these positive innate attributes of people.

- Deming, The New Economics. (2nd ed. pp. 107-108 , 3rd ed. pp. 73-74)

Incidentally: how many management books have you read that use language like this to describe the necessary behaviours and thinking of managers? I digress…

Knowledge of psychology in the SoPK helps us to appreciate how the common practices of management today destroy self esteem, joy in learning, pride in work, connection between employees and leadership, and ultimately innovation and quality. Effective managers often recognize this intuitively, but SoPK helps to explain why they matter and how organizational systems nurture or destroy them.

Summary and Reflection Questions

I opened this post by observing systems thinking isn’t enough to improve our fortunes because it leaves unaddressed gaps that can frustrate leadership as new circumstances arise. I’ve explained how the System of Profound Knowledge helps to fill those gaps, to help us shift from knowing all there is to know about ice to understanding how to navigate the waters of organizational systems by building knowledge of variation, learning through theory, psychology, and yes, systems. Our challenge is to develop our own sense of mastery in using the model to develop “lenses” to see the world through to transform our thinking and behaviours toward greater abilities to manage for quality and innovation.

Consider my observations about the prevalence of systems thinking as the solution to our present management troubles and my opinion that it doesn’t go far enough to remedy the gaps in our knowledge. Consider further Dr. Deming’s System of Profound Knowledge and its four constituent domains.

• Where have you seen systems thinking alone presented as a solution to organizational challenges?

• What benefits can systems thinking provide without the additional domains of knowledge in Deming’s SoPK?

• How could developing knowledge in systems thinking lead to something SoPK-like?

• If you currently use SoPK, which domains do you rely on the most? Least? Why?

• What situation have you observed or experienced where knowledge of SoPK domains might have helped prevent a mistake?

• If you agree with my thesis that systems thinking is necessary but insufficient, why does SoPK present a more complete model for improving how we lead and manage?

As always, let me know your thoughts in the comments below.

Related Reading

We all have a visceral, negative reaction to layoffs. I have been laid off twice myself and despise them just as much as you do. I’m not saying this is right. It’s happening no matter how we feel about it. I want you to be in the best position possible, and that’s why this article is free for everyone.

- Vin Vashishta, Cloudflare And Meta Just Told You Which Jobs Get Cut Next. Most People Aren’t Listening.

It is no longer socially acceptable to dump employees on to the heap of unemployed. Loss of market, and resulting unemployment, are not foreordained. They are not inevitable. They are man-made.

Deming, W. Edwards. Out of the Crisis (The MIT Press) . The MIT Press. Kindle Edition.

IN CHAPTER 6 OF THE BIBLICAL BOOK OF REVELATION, written by “John”, the end-times of civilization as we know it is heralded by four horsemen who are released when Christ breaks the first four seals of seven prophetic scrolls, each representing a catastrophic force that will affect humanity: Conquest, War, Famine, and Death. In sum, they are a progressive, inexorable series of events that cannot be stopped, designed to eliminate people and bring about judgment. I was reminded of this as I read a recent Substack post by Vin Vashishta about how AI-inspired mass-layoffs have already begun and are similarly heralded by three “horsemen”, each targeting a specific strata of workers, and is in motion at tech giants Cloudflare and Meta.

High ROI AI

Cloudflare And Meta Just Told You Which Jobs Get Cut Next. Most People Aren't Listening.

Everything we’re seeing with layoff cycles aligns with the early innings of what AI and agents are scaling in the business. Too many are focused on AI and agents replacing people, but that is not wha…

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2 months ago · 93 likes · 2 comments · Vin Vashishta

Vashishta’s premise here isn’t to introspect on this, however: as unfair as it is, he accepts this is the way things are unfolding and instead offers strategic countermeasures that the soon-to-be-culled can do to buy some time. It’s well-intended realpolitik rather than richly-deserved reproach. It’s also a perfect illustration of how seeing and thinking with an Old Economics view both creates and solves problems in a never-ending cycle.

Let’s dive in…

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Dramatis Persona: Cloudflare’s CEO

Before the first seal is even broken, however, we meet our Fourth Horseman: the CEO of Cloudflare himself. Like Conquest in Revelation, he always rides out first, armed not with a bow but a dashboard instead. His two proclamations are written in the prevailing style of management’s virtual textbook of faulty practices, setting the conditions for everything that follows:

AI systems can now measure an organization with a level of objective detail and precision that was previously impossible, even for the best employees…

If an engineer on my team can now be 10 times as productive, I'm going to hire as many as I can find.

Thus, we have an understanding of two AI-induced hallucinations that inform the CEO’s theory of management.

• You cannot manage what you cannot measure. (“a costly myth”, Deming tells us in Ch. 2 of The New Economics…)

• Individual productivity is isolated to their performance. (the myth of performance appraisal — Deming explained this in Ch. 2 as well using the equation with two unknowns)

These are strong tells that they do not think in terms of systems of interactions, but in managing cost centres, visible figures, fear, and optimizing wholes through their parts. Not surprising as most do, but it’s the coupling and harnessing of AI that supercharges the assumptions in the theory, and this is very problematic when exercised through the power of a system owner who has the highest leverage to effect change. AI becomes not only a force multiplier for scaling effort of employees, but also potentially damaging management interventions. More on that in a future post…

Vashishta’s Three Cycles of Layoffs

In the post, we learn who will be affected by each cycle as the AI-augmented prevailing theory of management takes over and scales the work beyond human capacity, resulting in three waves of layoffs — our three remaining horsemen, released by top-management who foolishly break the seal on three respective scrolls:

• In the First Cycle, the seal on the Scroll of Measuring & Reporting is broken: those who measure and create reports are the first to fall: the auditors, legal, compliance, revenue recognition, HR, and middle-management. Anyone who measures and produces reports.

• In the Second Cycle, the seal on the Scroll of Information Owners & Movers is then broken: those who move information around the organization, like HR, compliance, revenue ops, marketing ops, chiefs of staff and internal comms are next. Productivity metrics and quotas will be assigned to roles that never had them before, with emphasis on making the charts go up and to the right at ever-increasing speed. But there’s a hidden trap: leadership is going to pivot away from volume to value…

• Finally, in the Third Cycle, the seal on the Scroll of Optimization & Metrics-Driven Performance Management is broken: those who bought into the myth of being productive as a job-protection strategy will fall. Management’s worst instincts will become activated as they realize maybe they got the exhortations to be more productive all wrong: it’s not about volume, but value generated per employee. Invasive surveillance technology, like keystroke loggers, will be used to sift out the 10x over-performers from the “mid-to-low” under-performers - hyperscaled rating and ranking as it were.

Augmented by AI, these cycles will happen rapidly: HR will become a centre of compliance excellence using scaled computing to sort through gigabytes and terabytes of employee rating and ranking data to determine who is pulling three red beads or less per shift. What they believe they will gain in productivity, they will lose in institutional knowledge, not least because most organizations are nowhere near ready to go full-AI because their systems and knowledge aren’t discoverable or in a form that could be used effectively.

The Countermeasures: Good Luck.

Vashishta suggests that each of these cycles has a countermeasure, which I think amounts to a coping strategy to delay the inevitable: something to make you feel like you’re doing something other than accepting your fate — and that’s if you’re lucky enough to work in an Old Economics organization that will tolerate you coloring outside the lines, straying into other lanes, and telling management what they should be doing.

You’re bound to step on some landmines, so your mileage may vary:

• To ward off the horseman of Measuring & Reporting, he suggests shifting from creating deliverables to interpreting signals in data and automating low-value parts of your job before someone else does it for you. Practical, but it presumes leadership wants interpretation rather than conformance. In most organizations running on the prevailing style, the report is the deliverable, a compliance artifact, not a decision tool. The analyst who shows up with unsolicited judgment and advice is as likely to be seen as overstepping as adding value. And automating the mechanical parts of your own role is a fine idea right up until it makes you look redundant faster, not safer.

• To avoid the horseman of Information Owners & Movers, he advises to stop being the information bridge or gatekeeper and become a system improver instead: build self-serve systems to relieve you from this burden and replace your recurring status meetings with AI-generated briefs to make your value visible to the C-Suite. The problem is structural: redesigning systems requires commensurate authority that’s usually a management responsibility, not an individual contributor's. In the prevailing style, the org chart isn't up for discussion by the people who live inside it. And you can't redesign what you can't see: siloing and information hoarding are features of this management style, not bugs. The person most positioned to become a 'system improver' is the one most likely to be told to stay in their lane.

• Finally, to escape the scythe of the third horseman of Optimization & Metrics-Driven Performance Management, Vashishta recommends connecting your work to visible business outcomes and optimize for value per artefact delivered. Keep your decisions and judgment focused on prioritization, tradeoffs, and strategic decisions. But this advice has a deeper problem that Dr. Deming would spot immediately: it assumes the connection between individual work and business outcomes is knowable and attributable at the individual level. If your feature improves retention, is that you, the product strategy, the sales motion, the market timing? In the prevailing style, attribution is political, not statistical. The people who successfully 'prove their value' are often the ones closest to visible metrics or most skilled at self-promotion — not necessarily the ones whose work actually moved the needle.

Let’s pause here and ask ourselves: what do we notice about these proposals from the perspective of a student of Dr. Deming’s theory of management? Consider the Nine Faulty Practices and Seven Deadly Diseases.

The Analysis

After some consideration, I think a few things will come to mind:

• First, they put the onus on the affected to build their own lifeboats to avoid being the next to go. The posture of fixing problems in the system is correct, but they are operating from the position of least leverage to do so. The job of effecting the three countermeasures belongs to management. We might well ask why they haven’t been helping their employees to do these things in the first place.

• Second, they leave the systems of management completely untouched, and in fact reward the worst instincts they enable. This leads to superstitious learning or the belief that any apparent success is attributable to their genius to move toward AI while holding all else constant.

• Third, there’s no mention of benefit to the customer from all this activity, just an assumption. Will quality improve by laying off people and using AI? And what is the customer experiencing while all three cycles are running? Vashishta's frame is entirely about what happens to workers, which is understandable — but Deming's frame always starts with the customer and benefits to society, and that question is conspicuously absent from every C-suite workforce planning session being described here.

Dr. Deming would often rebuke leadership who’d go on about their lofty plans with a three word question: By what method? Artificial Intelligence is the response he’d get today, but AI is just smart, scaled automation, not a method. So far, it looks like leadership once again ducking their responsibilities to lead, as Deming would say.

Summary and Reflection Questions

AI isn’t going anywhere: it will increasingly come to be used for smart and stupid reasons and there will be advances and setbacks, particularly wherever there is naive management that just sees a magical bromide for all the troubles of managing people. AI chatbots are deliberately designed to be agreeable and to only dispense hard facts when you go out of your way to configure them so. Deming’s warning that fear invites wrong figures could be inadvertently baked-in if there’s no attention paid to it. In this respect, it’s worth recalling the climb-down Elon Musk had to make in his pursuit of a 24x7 lights-off factory run entirely by robots: Humans are underrated. The institutional knowledge that walks out the door in each of Vashishta's three cycles is exactly the kind of thing that isn't in any system, isn't discoverable by any AI, and isn't recoverable once it's gone.

Consider the three cycles of layoffs that Vashishta describes in his post and Cloudflare’s CEO’s disposition on AI.

• Does AI invalidate Deming’s theory of management?

• If an organization can be measured with a precision no human team could match, does that precision translate into better results for customers? How?

• Will the people who survive the layoffs find joy and purpose in their work? Why or why not?

• Vashishta's countermeasures put the burden of survival entirely on the individual worker: is that the right place to put it?

• What would you recommend to a colleague who just realized their role fits one of the three cycles?

As always, share your thoughts in the comments below — tell me how AI is affecting your job or those of your clients, and how they are coping.

We cover a wide range of topics from lessons his mother taught him to manage his dyslexia, to quizzing Dr. Deming with 52 questions in 22 minutes which landed him an internship that would open doors to working with Dr. Genichi Taguchi and Dr. Willie Hobbs Moore, and of course the late, great Clay Christensen.

Set your reminders now and come back on Monday to learn about Bobby Moesta’s Deming journey!

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THE AIM for this short note is to let you know about a PDSA I’ve been running for a couple of weeks with my paid-tier subscribers that I’m now making available to all: The Digestible Deming Shop. I’ve partnered with fourthwall to provide wearable and usable creations of my own design for showing off your Deming cred while subtly inviting a conversation about his teachings and philosophy with friends, family, colleagues, and classmates. My aim is to create unique products that function as passive evangelizing by deliberately piquing the curiosity of those who see them.

The shop goes live May 30, 2026!

Samples?

Being Gen-X, putting things on t-shirts and caps (and coveting them) is in my DNA, so I’ve applied these sensibilities toward creating graphics inspired by my own appreciation for Deming. For example, take this tee that re-imagines The New Economics as an 80s Atari-style throwback:

Or this one that is a simple expression of the three reasons Deming would give to attendees of his seminars about why they were there — a good reminder about what we should all be striving for…

I’ve also released a limited Gold Edition Embroidered Digestible Deming Cap that will be retired after Father’s Day and maybe brought back for a special occasion in the future. Maybe!

There are several other items that you can find in the shop, and I’ve got a pipeline of news designs that I’ll be adding over the coming weeks. My inspiration is the esoterica of Deming, the forgotten or obscure “if you know, you know” tidbits of his wisdom, along with his “greatest hits”. Things that were once lost, but now are found.

Do let me know what you think of the shop and what items you’d like to see added in the future to further the aim of evangelizing Deming through wearable and usable products you’re proud to show off to friends and colleagues.

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I was inspired to reach out to Bobby when I read his LinkedIn post (see link below to post I wrote) thanking Dr. Deming for all he taught him and how his principles live on in his own original work today, in a very Shoichiro Toyoda way. He is as passionate as about sharing his knowledge as he is humble and grateful for who taught him.

In this episode we cover:

• How his mom helped him learn to see patterns in the whitespaces between words that would help him overcome his difficulties reading and teach him about the interactions in systems

• How he started his career as an intern with Dr. Deming

• His later work with Dr. Genichi Taguchi and learning about Design of Experiments

• Assisting Dr. Willie Moore in conducting tours of Ford suppliers and teaching fundamentals of quality

• Meeting Clay Christensen and how their 27-year partnership began

• The development of Jobs-to-be-Done theory

• The Four Forces that govern how customers decide (or do not) to buy

• How they apply to “hiring” a new job

• The thread of Dr. Deming that runs through them all

I guarantee you will come away from this interview inspired by Bobby’s passion and enthusiasm for teaching others how to use Deming’s principles in ways that they may never have considered before.

Where to Get Bobby’s Books

If you want to learn more about how JTBD theory works and is applied across multiple disciplines, check his books out here:

• Demand Side Sales 101: Stop Selling and Help Your Customers Make Progress

• Learning to Build: The 5 Bedrock Skills of Innovators and Entrepreneurs

• Job Moves: 9 Steps for Making Progress in Your Career

Contacting Bobby Directly

Also as mentioned, if you want to reach Bobby with a question directly, do so via his LinkedIn account here.

Upcoming Q&A Session

As mentioned at the end of the interview, Bobby has graciously offered to return for a follow-up Q&A podcast where he will answer your questions about what you heard in this interview or indeed anything about himself or his books, theory, etc. Pop your best ones into the comments below.

PS: Web Store is Now OPEN

A quick reminder that the web-store is open and you can order your Digestible Deming wearables and usables here. Use promo code REDBEADS to save 20% off your first order and pick something up for the Deming-aligned dad or dad-to-be in your life just in time for Father’s Day.

NB: This is still open only to paid-tier members who should all have received the password to get into the store - DM me if you didn’t receive it.

Related Reading

THE AIM for this post is to give you a heads-up that this Monday, May 25th my one hour conversation with the mind behind Jobs-to-be-Done Theory, Bobby Moesta, will be dropping as an exclusive to paid-tier subscribers to the newsletter. This is an interview you won’t want to miss as Bobby takes us on a whirlwind tour of his life from learning to manage his dyslexia and turn it into heightened powers of observation, to meeting Dr. Deming and interning for him, to working with Dr. Genichi Taguchi, Dr. Willie Hobbs Moore, and his 27-year partnership with the legendary Harvard Business School professor, Clay Christensen, that would help bring JTBD to fruition.

What I have found inspiring about Moesta’s work and thinking is that he fills in a gap Deming left behind (perhaps deliberately!) on how to bridge his theory and principles into sales — we get plenty of advice about what makes a product or service appealing, why zero defects isn’t a solution, why innovation is essential, why a satisfied customer may still switch, and how commissions destroy systems, but not a lot on the psychology behind a system that motivates or demotivates a sale. In this interview, Moesta enthusiastically shares his discoveries over forty years on how to lead and sell with a Deming view that anyone can apply with some thought. It’s a lot of fun, too…!

If you haven’t upgraded your subscription yet, consider doing so to take advantage of this exclusive. We will also be taking questions from paid-tier subscribers for a follow-up Q&A with Bobby in the future!

Learn More About Bobby

THE AIM for this brief post is to share a brief essay that Demand-Side Sales author and ReWired Group founder, Bobby Moesta, posted on LinkedIn earlier today. Within he explains the impact Dr. Deming had on him, first interning, then later through Deming’s System of Profound Knowledge, and how it all rewired his thinking and influenced his life’s work.

To quote him, Deming’s fingerprints are on all of it — small wonder why he named his consultancy “ReWired”! With his permission, I’ve reproduced his post below.

Brief Bio:

I’ve written here before about Bobby Moesta in my review of his book where he applies what he learned from Deming to selling — see this link to read up on it and why you should get a copy, even if you’re not in sales:

From that post, here is his bio:

How Deming Rewired My Thinking

In 1985, I met W. Edwards Deming. I was a very young and inexperienced engineer. He was 84 and still trying to teach the world how to think. I interned for him over two summers — and it rewired how I see everything.

Most people know Deming for quality. But late in his career, he distilled a lifetime of work into something deeper: his System of Profound Knowledge. Four interconnected lenses for understanding reality clearly enough to actually improve it.

Appreciation for a System. You can't optimize a piece and expect the whole to get better. Most companies optimize departments and destroy the system.

Knowledge of Variation. Most managers react to noise as if it's signal. They reward and punish people for variation the system produced. Tampering with a stable system makes it worse, not better. Once you see this, you can't unsee it.

Theory of Knowledge. There is no knowledge without theory. You can't just "look at the data." You need a prediction — a basis for learning. This taught me to ask better questions before jumping to solutions.

Psychology. Deming cared deeply about people. About intrinsic motivation. About how ranking and rating systems crush the human spirit. Management's job is to create conditions where people take pride in their work.

What struck me most wasn't any single idea. It was how he thought. He saw connections across statistics, philosophy, psychology, and systems that nobody else was drawing. He didn't think in silos. He thought in systems.

That way of seeing the world became the foundation for everything I've done since — Jobs-to-Be-Done, the Four Forces of Progress, demand-side innovation. Deming's fingerprints are on all of it.

He passed in 1993. But in a world drowning in data and starving for understanding, his lesson still stands: without theory, there is no learning. And without understanding the whole system — including the people in it — there is no improvement.

I owe more to those two summers than I can ever fully articulate.

Thank you, Dr. Deming.

Upcoming Podcast

I’ll be recording a new episode for The Digestible Deming podcast with Bobby Moesta next week. If you have any questions you’d like me to ask him, post them in the comments below before next Wednesday (May 20). Reminder that when it drops it will be available to paid tier subscribers first, then free tier two weeks later.

Oh, and if you haven’t subscribed to Moesta’s ReWired Group newsletter, do so! Here’s the link - bottom right of the page in the footer.

The first step in any organization is to draw a flow diagram to show how each component depends on others. Then everyone may understand what his job is. If people do not see the process, they can not improve prove it. Anyone needs to see the process as a catwalk, a flow diagram-Paul Batalden, M.D., 13 November 1990.

W. Edwards Deming. The New Economics for Industry, Government, Education (Kindle Locations 287-289). Kindle Edition.

Joy in work. Suppose that we post names on Figure 6: you work here; John works there; I work here. Then everybody body may see straightaway what his job is-whom do I depend on, who depends on me. Anyone may now understand stand how his work fits in with the work of other people. He may now engage his mind as well as his labor. He understands now what is meant by doing a good job. He may now take joy in his work.

W. Edwards Deming. The New Economics for Industry, Government, Education (Kindle Locations 513-516). Kindle Edition.

THE AIM for this post is to share a technique I picked up for seeing proposed system improvements as a network of hypotheses called a Driver Diagram. I learned about it from the book that inspired my latest podcast guest, John Dues, on his journey toward improving schools with a Deming view, Learning to Improve: How America’s Schools Can Get Better at Getting Better, and I think it’s a fantastic way to learn about how to lead with a systems view while planning actionable improvements.

Below, I’ll explain what a Driver Diagram is and how it is constructed, leveraging the example the authors provide in the book. Let’s begin…

What is a Driver Diagram?

At its simplest, a map of proposed improvements and changes to a system and the expected outcomes they will contribute to realizing.

It’s comprised of four key elements.

1. The Improvement AIM: a definable and measurable goal for the proposed change.

2. The Primary Drivers: The hypotheses about the main areas of influence that will effect the change in the system, the “big bets”, or the “what”.

3. Selected Secondary Drivers: Specific system components that are predicted to “activate” the Primary Drivers, ie. the “how”.

4. Supporting Change Ideas: PDSA experiments to test changes in processes, tools, etc. to see if they result in an improvement.

1. Start with the AIM

As with anything to do with systems, you need to first begin by understanding the AIM for a proposed improvement with a clear and measurable outcome. In effect, an operational definition for the change. Don’t skimp on this step: bring people together, share ideas for what your goal is, whether you think it’s within the capability of everyone involved, and the methods and means you’ll use to evaluate success, including how long the changes will need to run and who will be impacted.

In the book, the authors illustrate a goal of increasing the number of students who achieve a college math credit in their first year from 5% of cohort to 50% of cohort. Other examples could be:

• Reducing lost time to rework from 30% per week to 20%

• Increasing survey response rates from 15% to 50%

• Reducing the time to promote a feature into production from two weeks to two days

• Improving patient recovery times by 25% for a specific procedure

Note that this may feel like we’re about to violate Dr. Deming’s Sixth Faulty Practice of Management, setting numerical goals without methods to achieve them, but this isn’t the case. As we progress through the subsequent steps, we’ll build-out the supporting components that will contribute toward achieving the goal.

2. Develop the Supporting Primary Drivers

Next, we need to think about the best initial bets to effect the AIM of the proposed improvement, the primary drivers. Think of these as predictions for what the team (you are working as a team, right?) strongly believes will contribute toward realizing the AIM.

Following the example in the book, the authors provide four primary drivers that they bet would contribute toward the aim of boosting the percentage of first year students achieving a math credit from 5% to 50%:

• Change the instructional system to incorporate learning goals that actually matter to students’ personal, civic, and work lives;

• Support development of “persistence” skills in students to meet math challenges and see them through to solution;

• Ensure students literacy levels are sufficient to understand math problems, reason mathematically, and communicate results;

• Manage variation in teaching by providing consistent teaching resources and support to lead instruction in the classroom.

NB: observe how the arrows point from the primary driver nodes toward the AIM.

3. Select Secondary Drivers

Once we have agreed-upon “big bets” that are going to achieve our improvement AIM, we can detail the smaller bets or predictions or secondary drivers that will act as “levers” to activate them. The objective here isn’t volume but quality. Select a few secondary drivers that will effect the most productive change in corresponding primary drivers.

Again, following the example in the book, the authors identify five levers that will help to activate the second primary driver, Productive Persistence:

• Reduce transitions and assure sustained enrollment across semesters

• Deliberate focus on “starting strong”

• Promote students’ ties to peers, faculty, and program

• Create math that matters with course material students find interesting and intrinsically-motivating

• Enhance faculty’s skills and beliefs regarding student engagement and success

NB: I’ve highlighted the nodes for the second secondary driver and its antecedents in the diagram above to show the linkages back to the AIM. We’ll detail the supporting PDSA experiments next.

4. Create PDSAs to Test Changes for Improvement

Finally, we round out our Driver Diagram with the change experiments we plan to run to test our ideas against reality. These can be new or enhanced processes, tools, materials, or norms that we require to sustain productive change. Critically, this also the engine room for creating new knowledge using PDSA or Plan-Do-Study-Act loops.

Once again, the book’s authors outline six PDSAs that support the secondary driver for ensuring a “strong start”, which in turn is an activating lever for the Productive Persistence primary driver.

• Create engaging opening lessons to capture attention and interest early and ensure success

• Institute direct interventions with students to keep them engaged

• Implement changes in the informational infrastructure to enable improvement efforts and provide timely access to emerging evidence for students at risk of failure

• Create activities to establish supportive classroom connections between students, faculty, and program

• Develop supportive classroom norms for productive struggle with math

• Institute professional development for faculty to promote “strong starts”

And so we have a relatively simple flow diagram that at-a-glance provides infromation about the improvement theory we’re going to test. I’ve made mine a little fancier than the one in Learning to Improve with color-coding and highlights to make reading and following the flow from PDSA to AIM easier to see and process — anything to relieve cognitive load is good!

Bringing it All Together: Visualizing Your Theory of Improvement

Much like Deming’s Production Viewed as a System, a Driver Diagram organizes and presents the discrete, interrelated components that describe a theory of improvement as a system. In fact, it’s in building the map together with peers that everyone begins to learn about the first domain of Deming’s System of Profound Knowledge: Appreciation for a System. Some benefits it provides:

• A map that shows participants how their ideas, interests, and expertise fit into the improvement effort;

• A visualization of the complex relationships that comprise an improvement

• A means for professionals to find where they can best contribute or help the larger improvement effort

• A knowledge artefact that can be referenced for future improvement

• A concrete illustration of where individual boundaries of understanding are

• A means for tracking which PDSAs are currently in flight

Moreover, as the book authors point out, it is by design, “possibly wrong and definitely incomplete”. As the map is used gaps in understanding, oversights, errors, and new evidence will inevitably surface, and it will become refined as these feedbacks are incorporated.

My preference for building these kinds of diagrams is on large, highly visible walls or whiteboards with post-its (Super-Sticky, thank-you-very-much) because it engages the mind better when writing and arranging the nodes. Of course, you can use tools like Miro to build them virtually, however, you need to have regular sessions where everyone gets together to review it otherwise it can become lost and invisible.

Reflection Questions

Consider the example of how to build a Driver Diagram that we’ve just walked through: how could you design a new improvement with it? What would be the AIM of the change you’d want to make? Who could you involve in detailing the primary and secondary driver nodes? What PDSAs would you try to test your change ideas?

What would make it easy for everyone to participate? What sources of resistance do you predict? How could they be overcome?

As always, drop your thoughts into the comments below and tell me how you prefer to visualize and organize system change work…

Subscriber Exclusive

If you are a paid-tier subscriber, you’ll get first access to a custom tutorial app I put together for learning about the Driver Diagram using the example from Learning to Improve that includes a tool for creating your own:

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If you have questions or comments on the episode, feel free to leave them in the comments below and where appropriate I will forward to John for answers. Alternatively, you can reach out to him directly via the links below.

Who is David Langford?

In the podcast, we discuss John’s coaching relationship with world-renowned Deming in education expert, David Langford, and learn about his early career at Mt. Edgcumbe High School in Sitka, Alaska where he began to pioneer Deming-inspired techniques in his classroom. The video below tells his story and early successes:

John’s Inspirational Reading List

• Learning to Improve: How America’s Schools Can Get Better at Getting Better, Anthony Bryk

• Measures of Success, Mark Graban

• Punished by Rewards, Alfie Kohn

• The Symphony of Profound Knowledge, Dr. Edward Baker

• Thinking in Systems, Donella Meadows

• Understanding Variation, Dr. Donald Wheeler

Where Can I Find Out More?

You can contact John Dues via LinkedIn or email at improve@johnadues.me; you can also learn more about United Schools here. John’s book, Win-Win, can be ordered via Amazon here.

Related Reading

HEADS-UP, there’s a new podcast arriving on Monday, May 4 where I will be speaking with the Chief Learning Officer and Chief Operating Officer for United Schools, John Dues, about his Deming journey, experiences applying the philosophy in education, and his excellent book, Win-Win: W. Edwards Deming, the System of Profound Knowledge, and the Science of Improving Schools. If you are in education or are passionate about learning new ways to improve education with a Deming view, this is an episode you’re going to want to watch. And by the end I guarantee you’re going to want to get a copy of his book to learn more!

NB: This episode will be available to paid-tier subscribers first, with a general release two weeks later on May 18. If you haven’t yet, do consider upgrading your subscription to get early access to exclusives like this — at $8/mo it is a bargain!

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THE AIM for this post is to share what I learned and observed from a post I came across recently by , MLA for Strathcona-Sherwood Park and Minister of Technology and Innovation for the Alberta Government here in Canada, about how two senior leaders in his ministry collaborated to upgrade two failing legacy IT systems for 5% of the estimated cost and years ahead of schedule for comparable projects. And they did so while increasing staff morale, joy in work, and trust.

While remarkable on its own, it’s even more remarkable in how they achieved this with a change in their thinking that is very Deming-aligned and I think a template for how we can not only improve the effectiveness of government IT initiatives, but also for how we can reverse the trend of Canada’s flagging productivity.

Let’s take a quick tour.

The Situation: Old-and-Busted IT Systems

At issue were two legacy systems internal staff used to track the inventory of buildings the government owned, and the budgets and timelines of new constructions like schools and hospitals. Both were woefully out-of-date and had survived three upgrade attempts over the past ten years. They also were serial violators of Taiichi Ohno’s principle of “respect for people” as ministry staff had to manually cut-and-paste spreadsheet data from one part of the system to another.

This made processing slow, error-prone, and frustrating for everyone involved. Something had to be done.

The Estimates: $19M Now, $54M Over 4 Years

As is SOP almost everywhere, a competitive bidding process was initiated to get estimates on what it would take to fix the systems. Because everyone knows you get the best results from competition, it’s the universal management solvent used almost everywhere!

Thirteen bids came in, four were shortlisted. Costs?

$19M in up-front (analysis, architecture, new hardware, etc.)
$54M in estimated total delivery costs over FOUR YEARS.

Of course, these are all estimates, and in the prevailing way IT systems are designed and deployed you may as well double that estimate. And be prepared for it to not work the way it was intended. Oh, and this was just for ONE of the systems.

The Solution: Ditch the RFP, Go In-House with AI-Enhanced Developer Teams, Release Frequently

Given the estimates and three dismal failures beforehand, leadership decided to try something radically different and use what I’d call a systems view of the situation rather than following the standard prevailing style of management playbook. They decided to reject the RFPs and bring the project in-house, staffing it with product teams for each legacy system whose members were recruited for their strong IT skills and aptitudes. They then equipped the them with the latest AI-enhanced coding tools like Claude, Copilot, and Gemini, then structured their project management to deliver in two week iterative planning/release cycles.

$5M was budgeted for the initiative, codenamed PRISM, and was enthusiastically supported by the two senior managers who would partner in a vendor/client relationship between their departments. They were beginning to work together as a system with the aim for everyone to win.

The Results: In Production Today for <$2.6M

I won’t bury the lede any further: in less than a year they have both solutions in production and being used by over 600 ministry staff every day, and for about half the cost they budgeted ($2.6M). Their bi-weekly reviews (what some may call Sprint Reviews…) are attended by over 170 internal staff who are lining up to see the next features, fixes, and improvements, along with providing feedback. This builds trust.

A big contributing factor to making this work was augmenting the teams’ technical skills with AI-enhanced tools to collapse the thinking/coding/testing cycles into minutes rather than hours or days, while maintaining quality. The aim was to mitigate the usual post-deployment rework that tends to emerge as the new system grows in scope and complexity, since the tools can take the entire codebase into consideration.

In layman’s terms they eased the cognitive overload on the team so they could focus on getting desired, tested features in front of their users quickly to elicit feedback on how well they were meeting their requirements, feeding back changes or improvements iteratively.

NB: they didn’t just hand over dev to AI, humans were involved in checking the features as they were developed, and in some instances coded entirely by hand where the tools couldn’t work. Attention was given to good security practices throughout.

Unexpected Developments

Three interesting side-effects resulted from PRISM:

1. AI tools (Gemini) were used to rapidly convert 50h of recorded walkthroughs of the legacy systems into detailed build specifications the product teams could implement with their tooling within minutes. What would have cost hundreds of thousands in staff or consultant’s fees was reduced to pennies.

2. The Alberta AI Academy was created to help government staff build critical AI skills and capabilities that they could use in their own departments to meet the Minister Glubish’s mandate to engage 25% of the public service to improve productivity by 25%.

3. Non-technical staff began to use AI-enhanced apps like Lovable to create feature prototypes to share with the product teams.

Deming Analysis: Well-Aligned

I don’t know whether the Minister or the senior leaders and staff involved in this project have ever read Deming, but they did exemplify some key traits that are well-aligned with what he would recommend.

• First and foremost, leadership made the changes to the system only they could make: eliminating the RFP bid process, moving development in-house, recruiting skilled staff, equipping them appropriately, then designing and implementing a project management process (which I would bet is Scrum) with feedback loops to plan, deliver, and adapt in two week iterations. This required what Deming would call win/win cooperation as a system between two previously siloed departments.

• AI-augmented tooling allowed the developers to rapidly implement, test, and deploy features for real users, collapsing the traditional big-consultancy dev/rework delivery cycles. This increased the quality of their features because they got feedback sooner, which naturally boosted productivity and created trust as they were better-able to give users what they wanted and to a high degree of fidelity. NB: leadership provided ways and means to become more productive, not just directives or targets.

  

• In turn, curiosity and joy in learning and joy in work followed as developers gained new skills and could see their solutions used by their peers and colleagues to meet real needs. And this joy was infectious, encouraging non-technical staff to seek out AI development tools and training — all with the encouragement of management and leadership. This is how you create and capture the bottled lightning of intrinsic motivation.

• Following on this, the teams and their leadership created knowledge about what it takes to use new AI tooling to upgrade legacy IT systems, which they then used to create a free training program. This captures aspects of Deming’s Theory of Knowledge domain in his System of Profound Knowledge, and Point 13 of his 14 Points: encourage education and self-improvement for everyone.

• Finally, and most importantly, the productivity gains made in this project meant that the staff assigned to manual spreadsheet copy-and-paste detail could be assigned to projects more deserving of their talents, leading to further knock-on productivity gains.

In sum, Minister Glubish and his senior leaders and staff demonstrated how a Deming-aligned view to leading, organizing, and working together as a system with a view to improving quality can yield impressive results. And all without the big consultancy price tag.

My Thoughts

For many years I’ve held a dim-view of the RFP/competitive bidding process because of the underlying assumption that investment is protected by competition instead of intelligent management. Experience has shown the opposite with the RFP process gamed by politics, insider influence, and low-ball bids with the expectation on both sides of the equation for cost and schedule overruns. Minister Glubish and the PRISM project demonstrate that this model can be successfully challenged, delivering a solution well under budget, ahead of schedule, all while boosting staff morale and stakeholder trust.

To be sure, this was always the promise of Agile software development, but the key difference here is that leadership was engaged to make the required supporting system changes. This means the early successes are nurtured, improved, and shared so they don’t die on the vine or are left up to staff to hold together in spite of everything else. My expectation is that through this PRISM, leadership is seeing a new way to lead.

What Do You Think?

Consider the PRISM project and how Minister Glubish and his team approached designing a new way to deliver a legacy system upgrade well-under budget and ahead of schedule. Consider further the dividends paid to the staff who developed the solution, along with their non-technical “customers” who learned new AI skills along the way. How could this be used as a model for improving similar operations in government and the private sector? What challenges do you foresee?

Consider further the training program that was developed to share foundational knowledge with other staff and the public? How could you adapt this to your organization? Could something similar be done to teach leadership how to shift their thinking toward a systems view? How so?

Let me know in the comments below!

Related Reading

It is no longer socially acceptable to dump employees on to the heap of unemployed. Loss of market, and resulting unemployment, are not foreordained. They are not inevitable. They are man-made.

Deming, W. Edwards. Out of the Crisis (The MIT Press) . The MIT Press. Kindle Edition.

THE AIM for today’s post is to inform readers, especially those who are bursting with renewed energy from their study of Dr. Deming’s theory, what they are up against in the corporate world and why transformation, if it is to succeed and be long-lasting, needs to begin at the top. We’re going to do this by reviewing a video by Brian Creely, a former recruiter (who, ironically, was laid off himself…) about the processes behind how you end up on a list to be targeted for layoffs.

To be frank, it’s a depressing window into the corporate world where individuals are measured, rated, ranked, and put into a spreadsheet for “execution”, but it is necessary to really appreciate and understand the enemy, which is the prevailing style of management, and how to articulate better alternatives.

With that out of the way, let’s begin…

Rationalizing Layoffs

In the video, Creely lays out the unsurprising reasons that management reach for layoffs as a corrective measure when profitability and viability are on the line. These seem logical, but are short-term fixes that leave underlying causes untouched:

• Cost reduction — nothing works faster than getting labour off the books, even with severance.

• Strategic pivots — think: we need AI specialists and don’t want to invest in training or upskilling.

• New senior leadership — out with the old and untrustworthy, in with the new who are loyal.

• Signaling discipline to investors — aka managing to the next quarter, a classic faulty practice of management.

• Funding new intiatives — this comports with a strategic pivot, eg. let’s AI all the things and sort out the mess later, when we’re better able to do so. (Which never really happens…)

Common to all measures here is looking first to eliminating jobs as control lever and not considering the downstream consequences which can be costly to repair, eg. losing expertise only to buy it back at a premium with consultant’s fees. I have some empathy for a change in senior leadership, but I’d only go along with it if there was sincere outreach to existing staff to join the new teams, first.

Selecting Targets 🎯

With a rationale in-place, Creely next describes how the wheels of prevailing style of management now grind on to compiling a “hit-list” of targets for layoffs:

1. HR spins up an Excel spreadsheet detailing every employee with their salary, tenure, performance ratings, and project assignments in the areas selected for layoffs.

2. A headcount or dollar target is then set, eg. eliminate 15%-20% of headcount or annual salary payouts, with allowances for severance.

3. Assessments may be done in-tandem, eg. putting employees on impossible Performance Improvement Plans to encourage them to quit so severance is saved.

4. Managers may use the Nine Box Grid, originally developed in the 1970s by McKinsey for General Electric, to rate and rank their direct reports according to performance vs. potential, sometimes in conjunction with other managers and leaders to reduce bias.

5. Targets are then selected by senior leadership and HR who reconcile the names against cost targets and other sentiments, often without the direct manager’s knowledge.

6. Legal is then brought in to advise on risks and bless the list before the trigger is pulled and HR is engaged into “Layoff Mode”.

Deming Analysis

As quoted in the opening of this newsletter, Dr. Deming believed that contrary to the prevailing ideologies of management, layoffs are not some market-driven weather system that leadership are helpless to avoid: they are man-made, a result of bad management decisions. Rx? Learning new theory.

Specifically, Deming teaches us:

• Leadership goes first: In The New Economics, he explains how Japanese management deal with flagging fortunes starting at the TOP, as opposed to the North American model that starts with the victim of bad management:

  1. Cut the dividend. Maybe cut it out.

  2. Reduce the salaries and bonuses of top-management.

  3. Further reduction for top-management.

  4. Last of all, the rank and file are asked to help out. People that do not need to work may take a furlough. People that can take early retirement may do so now.

  5. Finally, if necessary, a cut in pay for those that stay, but no one loses a job.

  
  (2nd Ed p. 27, 3rd Ed pp. 20-21)

• Leadership is responsible for leading improvements to the system: In Out of the Crisis, Deming explains how leadership’s aim exceeds mere oversight, but direct participation in improving the system:

  Aim of leadership. The aim of leadership should be to improve the performance of man and machine, to improve quality, to increase output, and simultaneously to bring pride of workmanship to people. Put in a negative way, the aim of leadership is not merely to find and record failures of men, but to remove the causes of failure: to help people to do a better job with less effort.

  Deming, W. Edwards. Out of the Crisis (The MIT Press) (p. 248). The MIT Press. Kindle Edition.

  NB: the emphasis on helping people become more productive, which is a direct route to improving competitive standing in the market and by extension the nation’s competitive standing in the world.

• The majority of performance (and opportunities for improvement) belongs to the system: This is Deming’s famous 94%/6% distribution heuristic which teaches us to look first to how we have organized things to produce a certain outcome rather or extenuating circumstances rather than blaming the employee as it has a predominant effect on apparent “performance”. We viscerally learn and experience this in the Red Bead Experiment:

  No amount of care nor skill in workmanship can overcome the fundamental faults of the system.

  W. Edwards Deming. The New Economics for Industry, Government, Education (2nd Ed p. 34 , 3rd Ed. p. 24).

  This reinforces improvements starting with the system.

• Thus, the performance of the individual cannot be isolated from the system: How management assembles, equips, and instructs staff determines their apparent performance, despite how much HR pretends we can. Deming likened this to trying to solve an equation with two unknowns.

  Let x be the contribution of some individual, (yx) the effect of the system on his performance. The suppose we have some number for his apparent performance, such as eight mistakes for the year, or sales of $8M. Then:
  
  x + (yx) = 8
  
  We need x. Unfortunately, there are two unknowns and only one equation. Johnny in the sixth grade knows no-one can solve this equation for x… They ignore the other term (yx) which is predominant.
  
  Ibid. (2nd Ed pp. 25-26, 3rd Ed p. 19)

• Consequently, we set ourselves up for expensive failures when we rate and rank individuals:

  Ranking creates competition between people, salesmen, men, teams, divisions. It demoralizes employees. Ranking comes from failure to understand variation from common causes.

  
  W. Edwards Deming. The New Economics for Industry, Government, Education (Kindle Locations 258-259). Kindle Edition.

  Again, we learn the futility of rating and ranking in the Red Bead Experiment on Day 3 where we use the willing workers’ sums of red beads to justify laying off the worst-performing half of the team.

This brings up another important conclusion that I distilled from Dr. Deming’s advice a while ago…

Chapman’s Law of Performance Appraisals

Regular readers will recall my mini-clinic post about The Case of the Lost Bonuses where we learned how the CEO of high-end furniture designer and manufacturer, MillerKnoll, said the quiet part out loud during a town-hall when sales staff fretted about their performance bonuses while the company faced a $26M shortfall. To wit:

Don't ask about what are we going to do if you don't get a bonus! Get the damn twenty-six million dollars! Spend your time and your effort thinking about the twenty-six million dollars we need and not thinking about what you're going to do if we don't get a bonus, all right? Can I get some commitment for that? I would appreciate it!

Did you catch the irony? If, as we’re led to believe, bonuses incentivize top-performance out of people, why on earth would the CEO not promise to honour their bonuses and then some to get out of a $26M fix? Because of what I call Chapman’s Law of Performance Appraisals: it was never about your performance in the first place, it was about retaining you for as long as you were useful.

There are countless examples of this every day, and you’ve probably witnessed or experienced some yourself. Even the most hardened practitioner of the deluded arts of the prevailing style of management will dump bonuses and forget your past glowing appraisals when their mood shifts. Then it’s all “just business”. For another perspective, see my post about a McKinsey consultant who found herself fired for her performance despite having a good 3-month performance appraisal.

Rx? Rate and Rank Systems, Not People

Given Deming’s admonitions and guidance, this should be obvious but we rarely see it play out in workplaces across the country. If we take a people-first approach to layoffs, we lose more than just the individuals, but their institutional knowledge and skills that are not easy to make up. In one example I’ve mentioned here before, a bank executive came into work one day after a “vertical slice” of the organization was laid off, discovering that in the cull were several people vital to managing a critical line of business project with visibility right to the top. They had to hire the staff back as consultants at a significantly higher rate of pay.

In a Deming-led organization, leadership has a fundamentally different view of their staff and does everything to understand and protect them because they know they are not fungible resources that can be ramped up and down at will without cost. They choose to invest in their education, skills, equipment, and in how they organize them to meet challenges. Often they are invaluable sources of inspiration and innovation who can see opportunities for improvement well ahead of leadership because they are closest to the work. As Deming once said, no one should lose their job for improving the system.

Related Reading

THE AIM for this post is to share a short video by Marcus Buckingham about a common practice in the prevailing style of managment: providing subordinates (and sometimes peers) with feedback about their performance. We do this because of a conceit we hold that people cannot improve or excel unless we tell them how they are doing in our expert opinion. I think Buckingham’s criticism of this widely-held misconception is well over the target from a Deming perspective that’s worth investigating.

Oh, and a hat-tip goes to by colleague Mara Svenne who pointed me to the video and inspired this post. Thanks Mara!

The (False) Theory of Giving Feedback

In the video, Buckingham explains that the theory of giving feedback is supported by three (false) underlying beliefs:

1. The giver is the source of truth about the receiver: “I need to tell you how you’re falling short because if I didn’t, you wouldn’t know.”

2. Learning is a process of filling an empty space: People learn through the knowledge of others telling them.

3. Excellence can be defined in advance: It exists independent and apart from the individual being excellent.

He then explains the falsehoods:

1. The giver is not the source of truth about the receiver: in fact, they’re unreliable because they are only the source of truth about themselves and their experiences.

2. Learning is not filling someone up: it’s insight that comes from recognizing patterns within themselves, revealing something that is to them.

3. Excellence is not independently uniform or homogenous: it is integral to the individual, it cannot be defined ahead of time or apart from you.

This, he concludes, is why feedback never helps: it impairs true learning by moving you away from your optimal way of integrating new knowledge, skills, and behaviours. As an example, he cites Rick Barry, one of the top free-throw shooters in the NBA, who didn’t throw overhand like the majority of players, but under-hand granny style. If he were coerced to throw overhand, he’d likely never have achieved his record.

Deming Analysis

I think Buckingham is mostly over the target with the lie of feedback in that it is the predominant way performance appraisals are handled under the prevailing style of management we all put up with, ie. infrequently and according to a list where the receiver is judged on a “meets specifications” basis against a set of arbitrary criteria. Feedback can be useful if it is given in the moment it occurs such as coaching guidance, eg. “try this way…”, “avoid this…”, “have you considered…?”, “what’s holding you back?”, “can this be improved? how?”, etc.

There is a lot to consider about Buckingham’s Lie about Feedback from a Deming perspective:

1. An individual’s performance is tied to the system they are within: they cannot overcome the design of the system regardless of their skill or care. We see this absurdly demonstrated in the Red Bead Experiment where the foreman constantly provides feedback on the willing workers’ performance, but doesn’t understand he’s actually reacting to the system’s performance, of which the workers are a component.

2. Variation, variation, variation. Deming teaches us that there is variation in all things: between people, in materials, machinery, processes. Our struggle toward “excellence” in anything lies in how well we manage our variation around an optimal target, not how well we conform to the target itself. As I’ve mentioned in prior newsletters, this is the difference between compliance excellence and contextual excellence. Rick Barry innately understood this in spades, and you probably do as well.

3. Rating and Ranking. In the second chapter of The New Economics, Dr. Deming calls out the typical practices of rating and ranking individuals, teams, and departments (a form of feedback) as a faulty practice that causes heavy losses. Rx: manage the company as a whole system, seek optimizations for how everyone performs together instead of optimizing individual parts with feedback on their apparent “excellence” apart from others they interact and work with.

4. Joy in Learning. Dr. Deming lamented how our culture of hyper-competitiveness worked to extinguish the intrinsic joy of learning and discovery we’re all born with, replacing it with humiliation, fear, self-defense, and competition for grades, awards, and top-ratings on the job. He called this process The Forces of Destruction, and offered instead that grades and ratings be abolished in favour of more guided, coached instruction. As a business professor at NYU he practiced what he preached, never grading papers, but commenting on them. His aim was on the quality of the paper and whether the student demonstrated mastery of the concepts.

5. Manager as Coach and Counsel. In The New Economics, Dr. Deming outlines the role of a manager of people in fourteen points (no, not those 14 Points…), #5 being: “He is coach and counsel, not a judge.” (2nd Ed, p. 125) This echoes what he says in Chapter 4 about the transformation of an individual through learning profound knowledge who will: “Set an example; Be a good listener, but will not compromise; Continually teach other people; and, help people pull away from their current practice and beliefs and move into the new philosophy without a feeling of guilt about the past.” (2nd Ed, p. 93)

Feedback Losses

I think that while Dr. Deming would agree with Buckingham’s basic assertion that feedback (as commonly practiced) impairs learning, he’d go much, much further to call out its negative effects on individuals when they are rated and held to account for their performance in a system that management has designed. This kind of feedback does not encourage better performance in the future:

It nourishes short-term performance, annihilates long-term planning, builds fear, demolishes teamwork, nourishes rivalry and politics.

It leaves people bitter, crushed, bruised, battered, desolate, despondent, dejected, feeling inferior, some even depressed, unfit for work for weeks after receipt of rating, unable to comprehend why they are inferior. It is unfair, as it ascribes to the people in a group differences that may be caused totally by the system that they work in.

Basically, what is wrong is that the performance appraisal or merit rating focuses on the end product, at the end of the stream, not on leadership to help people. This is a way to avoid the problems of people. A manager becomes, in effect, manager of defects.

Deming, W. Edwards. Out of the Crisis (The MIT Press) (p. 102). The MIT Press. Kindle Edition.

The last point is the most striking that many manager and givers of feedback don’t often consider: that by agreeing to “fix” someone by reacting to their apparent performance, they are managing defects. Some might find no problem with this, but it is not lost on the recipient that they are being criticized for meeting someone else’s specifications.

On this point, and in alignment with Deming, I coincidentally spied this comment below Buckingham’s video:

I’ve witnessed this effect among my manager coachees as well, including some who full well understand the arbitrariness of the performance management game. Some fell into despondence for long periods just like their direct reports…

Rx? Understand Your Employees, Improve the System

There is a lot a manager can do besides providing tone-deaf feedback, starting with understanding their business as a system of components that produce behaviours and outcomes as they work together. If an employee seems to be struggling in certain areas, start by asking yourself:

1. How much do I know about them? What are their strengths and weaknesses? Are they in a role that best leverages their innate skills and abilities? Where might they find more enjoyment?

2. What aptitudes in learning are they demonstrating? Are we supporting or squelching this? How can we address this?

3. How could the system be contributing to their struggles? Is it in an upstream or adjacent dependency? Are they actively compensating for the system’s flaws? Are they reacting to bad management?

4. How could we better design the work so they can improve?

5. How could we better support them in their work? In-situ coaching? Better tools and materials?

Discuss with the employee, see what suggestions they have and what system management solutions you can bring to bear. Always be open to suggestions from them and their peers.

Reflection Questions

What do you think about Buckingham’s video from a Deming perspective? Is it indeed a lie that people need feedback?

What has your experience been receiving feedback, either from a superior, a colleague, friend, or maybe your team? Was it delivered to improve your compliance excellence with a specification, or your contextual excellence at achieving a goal that worked with your abilities instead of against them? What was the outcome?

How do you give feedback? In-situ as a coach? Once a year through a performance appraisal? Quarterly as a reminder? What have you tried? What works best?

Let me know in the comments below!

Related Reading

EDIT: I’ve added a link to a paper below that Brian and his colleagues wrote in 2018 about addressing Canada’s productivity challenge. Worth a read!

EDIT 2: This post is now available to all subscribers, along with Part 1.

THE AIM for this post is to share the second part of our interview with Brian Watson, former (retired) Director for the Magna Centre for Supply Chain Excellence at Conestoga College in Kitchener-Waterloo, Ontario on addressing Canada’s productivity challenge. In Part 1 we learned about Brian’s 21-year career in industry and how he incrementally learned and applied the teachings of Dr. W.E. Deming, Dr. Eli Goldratt, and Dr. Taguchi to improving operations at four different manufacturing plants. In Part 2 we learn how he translated this experience into the creation of an innovative curriculum at Conestoga College that would eventually attract the attention of Magna International who would fund the creation of a new department dedicated to improving supply chains. Throughout, we tie the lessons learned to how Canada’s productivity could benefit from a program like Brian’s now.

Bonus Material: Rethinking Productivity Paper

After publishing this post, I had a conversation with Brian and he was kind enough to share a paper with me that he wrote in 2018 with his colleagues to describe their vision for addressing Canada’s productivity challenges, especially in manufacturing and supply chain management scenarios. Within, they lay out the case for teaching new competencies at the high school and post-secondary levels based on the ideas and philosophies of Dr. W.E. Deming, Dr. Eli Goldratt, Dr. Genichi Taguchi, and many others. Brian also weaves an actual case study of a firm that engaged with his program and students that led to improvement of some of their lagging production lines.

For me, this was the most impactful part of the paper, where Brian describes the capstone projects his students would work on with industry partners. This is, in my humble opinion, the root cause for Canada’s productivity challenge:

Many of the projects identify tens to hundreds of thousands of dollars, and in some cases millions of dollars in improvement opportunities while increasing delivered customer value! That said, far too often student teams run into the frozen preferences of traditional management. Some traditional managers have gone so far as to say “The data contained in the Current State Map is wrong and it needs to be changed to show……” Despite the fact that the data contained in the CSM is theirs, what these (embarrassed) managers are really saying is that they are not willing to change. (p. 31)

You can download a copy of the paper here.

Related Reading

Edit: Given the importance of our talk, I’ve decided to make this post available to all subscribers, along with Part 2.

THE AIM for today’s post is to share with you a video podcast interview I recorded earlier this week with Brian Watson, former (retired) Director for the Magna Centre for Supply Chain Excellence at Conestoga College in Kitchener-Waterloo, Ontario. I got to know Brian in 2018 after meeting him on LinkedIn and we became fast-friends, with him inviting me to tour his department and me inviting him to speak at my meetup, SystemsThinkingTO, later that year.

In this conversation, we tackle a problem near and dear to my heart, the challenge of improving Canada’s lagging productivity, which Brian explains through the retelling of how he improved the operations of four manufacturing businesses through the application of the philosophy and teachings of Dr. W.E. Deming, Dr. Eli Goldratt, and Dr. Genichi Taguchi. Along the way he tells us about the challenges he encountered with managers and employees, and how he won them over, and what happened to them after he moved on. If you have ever wondered how Deming’s ideas could work in a Canadian context and how you could get started doing the same, you’re going to want to hear what he has to say!

In Part 2, we’ll learn how Brian’s career transitioned from industry to education and how the Magna Centre for Supply Chain Excellence was created and what he did in his role as Director to bring his prior two decades’ of experience to students. We’ll also talk about strategies for contributing to improving Canada’s productivity no matter what industry you are in.

Related Reading

THE AIM for this quick post is to give you a little teaser from an upcoming episode of The Digestible Deming Podcast where I interview Brian Watson, the former (retired) Director for the Magna Centre for Supply Chain Excellence at Conestoga College in Kitchener-Waterloo, Ontario. In Part 1, we talk about his early career transforming manufacturing businesses using the philosophy and teachings of Dr. W.E. Deming, Dr. Eli Goldratt, and Dr. Genichi Taguchi, and how these lessons could be applied to reversing Canada’s lagging productivity challenges.

If you’ve ever wondered how Deming could work in a Canadian context, you’re going to want to subscribe and see this podcast. We’ll be recording Part 2 next week, and publishing the following Monday.

NB: This episode will be for paid-tier subscribers only.