Forget SMART. Be Unreasonable!

“The reasonable man adapts himself to the world; the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man”

-George Bernard Shaw, Maxims for Revolutionists

SMART is an acronym for SPECIFIC, MEASURABLE, ATTAINABLE, RELEVANT, TIME-BOUND which is “preached” as gospel in business school textbooks. Though Specific, Measurable, Relevant and Time-bound are essential, “Attainable” leaves the door open for excuses and under performance. SMART leads organizations and individuals to set small goals (e.g. 10% improvement in insert random KPI) and neglects to do things that can lead to 50% or 100% or more improvement. The key to rapid and meaningful improvement is being UNREASONABLE.


Being UNREASONABLE forces you and your team to think differently and discontinue the status quo. One of my favorite examples of  this is the setup reduction results obtained by Wiremold, the company who is the subject of James Womack’s book Lean Thinking. In each of the examples below setup time was previously measured in hours and ended up being measured in minutes with the smallest reduction being 88%! Setting large goals when it comes to operational excellence enables your organization to truly change the process vs. “just working harder”.

Setup Reduction

Source: Art Byrne, The Lean Turnaround

As Tim Ferriss puts it in his book The 4-Hour Workweek, setting unrealistic goals provides you with an “adrenaline infusion” which will motivate you to be successful. Ferriss also asserts that the level of competition is fiercest for “realistic” goals, paradoxically making them the most time and energy-consuming. So take the road less traveled and set unreasonable goals with these tips:

  1. Select goals that are relevant to you or strategically important
  2. Aim big! 50 to 100% improvement minimum
  3. Break up goals into defined steps with the maximum timeline of 6 months
  4. Do things that get you out of your comfort zone


If the set-up time reduction didn’t do it for you, check out other examples below:

  • Losing 20lbs. of fat in one month (Link)
  • Retire at the age of 30 (Link)


If you enjoyed this post and are curious to learn more about the books referenced check out Tim Ferriss’, The 4-Hour Workweek and Art Bryne’s, The Lean Turnaround using the links below.


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


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