The Opposite of Satisfaction is NOT Disatisfaction

Motivational theory is separated into two camps, the Agency Theory (Incentive Theory) camp and the Two Factor Theory camp.  Every performance incentive or bonus program is an example of the Agency Theory at work. Agency theory was popularized by two economists (Jensen & Meckling) in 1976 who concluded that managers do not always act in the best interest of shareholders (OMG!). Jensen and Meckling argued if you want managers to behave differently, compensate them according to the behavior you are trying to elicit. This theory effectively delegated management to a formula (Source: Clayton).

Steven Levitt, co-creator of Freakonomics, famously put Jensen & Meckling’s theory to work when trying to potty train his three year old daughter, Amanda. After struggling to achieve the desired behavior, Levitt provided Amanda  with a bag of her favorite treat (M&M’s) every time she successfully used the potty. After 3 days Levitt’s daughter was able to tinkle on command to collect her favorite treat at will (see Youtube video below).

The example of a three old exploiting her father’s incentive system to rob him of her favorite  treats is cute, but is an example of what occurs everyday in the corporate world. Clever individual’s will exploit incentive systems to maximize their personal gain (see this post). I like to refer to this as the “Ship it! We’ll worry about it when it comes back” attitude, where quality is often tossed aside in an effort to maximize short term gains. When the incentive system is applied to safety goals, injuries will go unreported and the game can become increasingly dangerous.

Herzberg’s Two Factor Theory takes a more complex approach to motivation. Herzberg concluded that satisfaction and dissatisfaction are not on the same continuum. Herzberg found that there are motivating factors which lead to satisfaction and Hygiene Factors which lead can lead to dissatisfaction. The best case scenario for hygiene factors is “no dissatisfaction”. Thus, the opposite of satisfaction is not dissatisfaction.

Examples of Motivating  factors include:

  • Challenging Work
  • Recognition
  • Personal Growth
  • Responsibility

Examples of Hygiene factors include:

  • Compensation
  • Job Security
  • Work Conditions

What we can learn from studying Herzberg’s theory is that motivation is more complex than simply incentivizing the right behavior.  Though the hygiene factors must be “right” to prevent dissatisfaction,  motivation requires individuals to have a mission and purpose. My favorite example of this lesson is the story of John F. Kennedy and the NASA janitor. When asked by the President what he “did for NASA”, the janitor’s response, “I am putting a man on the moon” (link).

Click the Link Above to Check out Clayton Christensen's, "How Will You Measure Your Life" 

How Nate Silver made me a better Metallurgist

Nate Silver is the founder of, creator of the PECOTA baseball forecasting system used by Baseball Prospectus, and a renowned political forecaster. In his book, The Signal and the Noise, Nate outlines the creation of the PECOTA system and lessons learned from Bill James (founder of Sabermetrics), along with taking a look at other forecasting problems opportunities. Silver’s PECOTA system relies on a metric resembling the similarity index  proposed by Bill James in his 1986 Baseball Abstract. James developed the similarity index as a tool for comparing any two major league players. In James system the index starts with a 1000 points and detects points based on a set of guidelines. Highly similar players will have indexes as high as 950 or 975. Similarly the PECOTA system uses an index to evaluate a player against a multitude of former major and minor leaguers to project a players performance.

For a young metallurgist whose livelihood depends on projecting the results of varying parameters of an assortment of  metallurgical processes to achieve a desired result, how could the lessons of a Sabermetrician help? The opportunity presented itself with the need to develop a high strength product in Alloy 825, an austenitic iron-nickel-chromium alloy commonly used in environments where enhanced corrosion performance is required. The product was to be cold-worked (i.e. deformed at room temperature) to a desired size and strength level. The challenge is none of this data was readily available!

After performing a simple Google search, data for other austenitic alloys such as Alloy 625 (a Ni based alloy) and 316 stainless steel (Fe based) could readily be obtained from sources like ATI and Special Metals. Thus, a simple curve could be fitted to the results for these two alloys. Following Silver’s first principle, Think Probabilistically, a Monte Carlo simulation was developed using several distributions fed into the model to generate a distribution of results at each cold working level. The Monte Carlo simulation was formulated feeding a similarity index varying uniformly (0.5-0.9), a normal distribution of fully annealed Alloy 825 yield strengths, and a normal distribution of residuals from the fitted cold working curves for Alloy 625 and 316. An outline of the model is provided in the figure below.

Alloy 825 Model

The Monte Carlo simulation results are provided in the graph below with the blue line representing the mean result with respect to degree of deformation (i.e. percent cold work / area reduction), the redline representing the 99% probability and the bottom line representing the 1% probability. The customer upper and lower specification limits (USL & LSL) are also plotted for reference. The work hardening curve below shows that at a cold working percent of about 30 the product is nearly assured to meet the tensile strength requirements. These results were subsequently validated with actual experiments with a percent error of less than 3%. Eureka!

825 Model Results


3 Books for Leading the Fight against the Illusion of Management

#1 The Drunkard’s Walk

The Drunkard’s Walk… How Randomness Rules our Lives by Leonard Mlodinow is essential  reading for crusaders against the Illusion of Management. Mlodinow provides readers with an entertaining look at the probability of Roger Maris breaking Ruth’s homerun record in 1961 (3.1%), an introduction to Bayes’ theorem, the bias of statistics in the courtroom, and much more.

#2 The Black Swan

The Black Swan… The Impact of the Highly Improbable by Nassim Nicholas Taleb challenges portfolio theory and the normal distribution and introduces his readers to the concept of asymmetrical risk (high probability of small loss and low probability of tremendous reward).

#3 The Signal and the Noise

The Signal and the Noise… Why so many predictions fail but some don’t by Nate Silver walks readers through the art of forecasting through a look at Moneyball, Global Warming, and the accuracy or inaccuracy of television pundits. Silver provides a fantastic introduction to Bayes’ theorem, power-law distributions, and overfitting.

Are You Living in a Red Bead Experiment?

The red bead experiment was created as a gift for W. E. Deming as a demonstration of the Illusion of Management. The Red Bead Experiment consists of a paddle with 50 slots and a group of “willing workers”. The workers are instructed to dip the paddle into the pan of beads (white & red) which contains 20% red beads & 80% white beads. The goal of is to produce white beads, as the customer will not accept red beads. The workers are required to read the detailed work instructions and extract the beads from the pan using the paddle. A trained quality inspector counts the beads and records the results. “Management” provides encouragement and administers discipline to “poor performers”. An example of the kit is provided below (source).


To illustrate the experiment I’ve typed names in an excel spreadsheet selected 6 “willing workers” who have graciously applied. Each worker randomly generates carefully scoops 50 beads each week and the total number of red beads is diligently recorded by the inspector. After 4 weeks the average number of defects (red beads) is calculated for each “willing worker”. The quality manager presents the results to the staff at the monthly management review with the following chart.


The conversation likely goes as follows:

Quality Manager: “Here is the average weekly scrap performance for each operator. As you can see they all missed the target of 4 red beads per week; however, Brad was quite close.

Human Resources Manager:”Tom was the worst performer. We need to consider putting him on a performance improvement plan“.

Production Manager: “We need to create a quality mindset and start doing things right the first time

Engineering Manager: “I’ll have the process engineers watch Brad and try to glean any Best Practices

Next Management Review:


Quality Manager: ” The results from the previous month have not improved. We need to make quality a priority

Human Resources: “Tom’s performance has clearly not improved. I recommend we move to terminate him. Brad’s performance has also been impacted by everyone else’s. And Tessa is only getting worse!

Production Manager: “We must drive accountability down to the operators

Engineering Manager: “Clearly they [operators] are not following the standard work”

Final Management Review:


Quality Manager: “The results are not improving…. We need to hold the supervisors accountable”

Human Resources: “The New Girl is an improvement over Tessa and Brad is heading in the right direction now. I don’t know what else you want me to do!

Production Manager: “I keep emphasizing quality in the kick off meetings… these people are just not getting it!

If the fictitious scenario created using a random number generator in excel hits close to home, you have my condolences. This example and Deming’s Red Bead experiment are intended to help managers think about the system and processes which generate the results. They also illustrate how “data driven” thinking with a touch of confirmation bias can get out of hand quickly as managers perpetuate the Illusion of Management.


For more info on the Red Bead Experiment check out this Youtube video here . Also for an additional resource on Dr. Deming, please check out the book linked below.

Donald Trump, Control Charts, & Lesson’s in Psychology from my Favorite Cartoonist

Note: The Illusion of Management is not a political blog; however, those struggling to create  change within their organizations and build a culture of responsible data analysis can learn a tremendous amount from Scott’s new book. Thus, I explore the concepts of Adams’ book and how they pertain to the Illusion of Management.

Win Bigly is Dilbert cartoonist (and trained hypnotist), Scott Adams, newest book where he reviews his prediction that Donald Trump would win the 2016 election from the viewpoint of Trump using the techniques of what Adam’s describes as a Master Persuader. Other examples of Master Persuaders are Steve Jobs and Tony Robbins.  Adams walks readers through his explanation of why facts don’t matter, tactics of Master Persuader’s, and explores the psychological concepts of confirmation bias and cognitive dissonance through the lens of the 2016 election. Adams explains a Master Persuader is an individual who recognizes people are irrational 90% of the time (recall behavioral finance advocates from the last post argued innate irrationality invalidated the efficient market hypothesis) and uses this observation, along with confirmation bias to “pace and lead” the victims subjects. Adams draws  upon examples such as Trumps extreme immigration stance early during the Republican primaries as a way to match his supporters on an emotional level and then lead  them later in the race as he transitions to a less extreme position. Adams also notes Trumps ridiculous behavior and visualizations such as the infamous “Wall” are a tool to prompt discussion which elevates the issue in importance as a result of the “energy” consumed from the ensuing discussions and ridicule. Enlightened individuals such as Illusion of Management readers have likely seen this tactic deployed in the office as a skilled individual can successfully “spin up” a seemingly benign issue and suck the life out of an organization.

Persuasion Tip #4: The things that you think about the most will irrationally rise in importance in your mind.

Another popular tactic outlined by Adams is the High Ground Maneuver where a persuader elevates the discussion to a level where everyone agrees. Adams example is Steve Jobs’ handling of “Antennae gate”  where Jobs famously stated all smart phones have problems  in response to issues with the iPhone 4. In the corporate world you’ve likely heard ambiguous statements such as  “We need to focus on quality” or “create a quality mindset”(Note: Strategic Ambiguity is also a tool of the Master Persuader). Inevitably if the High Ground Maneuver goes unchecked the organization will likely kick of projects such as standard work deployment projects where the resulting  work instructions are pulled out for external audits and collect dust for the other 360 days a year.

Persuasion Tip #13: Use the High Ground Maneuver to frame yourself as the wise adult in the room. It forces others to join you or be framed as the small thinkers.

Though Win Bigly aids readers in identifying tactics of the Master Persuader, the continued discussion of confirmation bias is vital for ensuring meaningful performance improvement. From the perspective of the Illusion of Management, confirmation bias is crucial in perpetuating an environment where “noise” in performance data can be translated into “evidence” of progress or poor performance based on the bias of the observer. Adams uses evolution to explain this as understanding reality isn’t essential for people to live long enough to procreate. Evolution & confirmation bias also explains why people are skilled at pattern recognition enabling humans to circumnavigate the globe with the stars; however, prompts us to see patters where information is purely random (Source). The most fundamental tool in the war against confirmation bias & motivated persuaders is the control chart. Control charts can be used with upper and lower control limits which bound individual results within a range which is considered “noise” or common cause variation. Other rules such as those developed by Western Electric (link) can be used further enable filtering of signal from noise. Armed with tools such as control charts and the lessons of Master Persuaders outlined by Adams’ provide enlightened and motivated individuals with a fighting chance against the Illusion of Management.

Persuasion Tip #7: Its easy to fit completely different explanations to the observed facts. Don’t trust any interpretation of reality that isn’t able to predict.

Driving Out Fear: The Hidden Risk

W. Edward Deming was an American statistician, management consultant, and professor credited  with influencing the Toyota Production System (TPS) and lean manufacturing (subjects which will be explored on this blog at some point). Along with the Plan-Do-Check-Act cycle and advocacy for statistical quality control (e.g. control charts), Deming developed his 14 key management principles. In this post we will explore #8, “Drive out Fear”.


“Drive out fear, so that everyone may work effectively for the company”

– W.Edward Deming

I found myself pondering this concept most recently while reading “A Random Walk Down Wall Street” by B.G. Malkiel.  A book first published ~40 years ago which explores the efficient market hypothesis, wisdom or lack thereof of investment gurus, and the madness of crowds in great detail.  In his book, Malkiel discussed the work of Kahnemann & Tversky (critics of the efficient market hypothesis) whose behavioral experiments discovered that people are extremely loss averse and in situations where loss was assured, individuals exhibit risk-seeking behavior. One such example is provided below:

Consider the below options for yourself:

Option 1: Guaranteed loss of $750

Option 2: 75% chance of a $1000 loss & 25% change of no loss

When faced with the two options provided above, Kahnemann & Tversky found that  90% of subjects selected option #2, despite the fact that the expected value of both decisions is the same. How could this concept apply to organizations?

In organizations where individuals are held “accountable”, often a euphemism for a perform or “you’re fired” management philosophy, individuals are put in situations where loss may be assured. Based on the work of Kahnemann & Tversky it is the tendency of the vast majority of people to exhibit risk seeking behavior. In the world of business this may result in shipment of nonconforming product, hiding injuries, violation of environmental laws & policies, etc.

In my own experience I’ve witnessed individuals “manipulating” on-time delivery numbers, supervisors aborting lengthy processes early to meet delivery commitments,  and overriding safety features to reduce cycle times. One organization had a philosophy uttered jokingly by employees on the production floor, “Ship it! We’ll worry about it when it comes back”. As a young engineer I found myself chastising the individuals while seemingly oblivious to the fact that the behavior was human nature and a response to the management system.

For years, quality professionals have been focused on developing detailed “processes”  to the point of annoying everyone within the organization. The switch of ISO 9001(Quality Management System used across many countries & industries as the minimum requirements of a quality management system) from process thinking to risk based thinking has quality professionals around the world preparing SIPOC’s (Supplier-Input-Process-Output-Customer) diagrams and performing FMEA (failure modes and effects analysis) activities throughout their organizations in an attempt to map out organizational risk. I find myself wondering how we eliminate the greatest risk, FEAR in our organizations?


“Well, 99% of things done in the world good or bad is to pay a mortgage”

-Nick Naylor, Thank You for Smoking


W. Edward Deming:

14 Points for Total Quality Management:

“A Random Walk Down Wall Street”:

ISO 9000:

Performance Improvement Lessons from a 6 Year Old

One of the pivotal experiences which provided the catalyst for starting this blog occurred in Fall of 2012 when I was living with my soon to be first wife  and helping to raise her son.  He was in his 1st grade and was struggling with a behavior to a degree where the school put him on a daily behavior report card system.

The report card consisted of him receiving a score card every day with each school subject and recess. Under each subject he would receive either a smiley face or frown face for behavior and work and a single face for recess.   The report card allowed for a maximum of 13 Smiley faces though he typically received around 3-4.

After begging, bribing, scolding, pleading,  and any other “Hail Mary” attempt to try to get him to behave I had a Eureka moment. After each day we attempted a different reward (bribe) or punishment in desperation to try and correct the behavior. Being an experienced process engineer I figured there would be no way to determine which input variable would lead to the desired output if we were consistently turning the proverbial knobs on a daily basis. I recognized the school had gifted us with a measurement system allowing us to trial a single incentive system and evaluate its effectiveness over a period of time and use statistics to measure the difference. This would prevent us from chasing “noise” and ensuring we would be able to identify real progress. Thus, we set a goal of 10 Smiley Faces per day and worked out an incentive program with his after school day care teacher.

Lo and behold the number of smiley faces increased (see the example graph below) and he drastically reached a point where he was averaging nearly 10 smile faces per day. Being the obnoxious data savvy engineer, I performed a t-test and the resulting p-value was significantly below 0.05 indicating the change in performance was statistically significant. For the next several days I practiced my interview with Oprah and Dr. Oz while brushing my teeth as I had successfully deployed process engineering and management fundamentals to solve the fundamental challenge in parenting.

A few days (or maybe a couple weeks), I ended up picking up the boy from school and ran into his teacher. Proud of my accomplishments I had to ask, “How has his behavior been?”. Waiting in anticipation for an “Atta Boy” moment his teacher paused and said, “he is receiving a lot more smiley faces; however, by the end of the day he is a real terror”. Note: She likely put it more politically correct, but you get the gist.

Defeated, I had my first real world lesson in Goodhart’s law: “When a measure becomes a target, it ceases to be a good measure”. Children 1, Engineer 0.

Smiley Faces



Goodhart’s Law:


the Illusion of Management

The Illusion of Management is a term I initially coined to describe regression towards the mean and the subsequent political grandstanding by “savvy” managers. As a data enthusiast I found myself constantly amazed observing managers taking credit for regression towards the mean with no fundamental change to the process generating the results.

I illustrate this point below with an actual quote:

“Scrap improved because I have been emphasizing quality in the weekly safety meetings”- Anonymous Plant Manager

Best case I find these scenarios annoying and an impediment to real continuous improvement efforts, while worst case ensuring over confident managers with little to no knowledge of causal relationships are rising to the top of American institutions.

In this blog we will explore this and other concepts to help the fellow “Enlightened” enjoy some laughs and blow off steam!


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