Operational Excellence by Design eNewsletter – October 2019
-October 2019 Monthly Musing This is a generalization, but the real reason why most engineers rarely make good business people is because they are fixated on the perfect. They are terrified of what they do not know or understand – and even more terrified to release to those who do. Their idea of too much risk is that there exists any risk at all. Ironically, almost all of their perceived risk comes from their lack of understanding that which is outside their purview of expertise; marketing, sales, finance, supply chain, and other topics which might vary from engineer to engineer. …
The sad reality of “quality” today: How innovation and growth creates waste and inefficiencies
-In the late 1970s, “quality” began to evolve from its historically “Neanderthal” passive inspection approach to its current “Cro-Magnon,” more pro-active, project-based approach “bolted-on” to the current operational status quo. Joseph Juran was a pioneer in such efforts. Various subsequent adaptations such as Six Sigma and Lean evolved it further; but, over time, it has become comfortably stuck in a misguided focus on tactical improvements at the expense of strategic improvements—doing things right as opposed to doing the right things right. Jeff Liker, professor of industrial and operations engineering at the University of Michigan, writes (from a 2011 private correspondence with…
All the Ways Hiring Algorithms Can Introduce Bias
-In today’s age, the question comes to fore: Do hiring algorithms prevent bias, or amplify it? This fundamental question has emerged as a point of tension between the technology’s proponents and its skeptics, but arriving at the answer is more complicated than it appears. HBR means that hiring is rarely a single decision, but rather the culmination of a series of smaller, sequential decisions. Algorithms play different roles throughout this process: Some steer job ads toward certain candidates, while others flag passive candidates for recruitment. Predictive tools parse and score resumes, and help hiring managers assess candidate competencies in new…
Using Algorithms to Understand the Biases in Your Organization
-Algorithms have taken a lot of heat recently for producing biased decisions. People are outraged over a recruiting algorithm Amazon developed that overlooked female job applicants. Likewise, they are outraged over predictive policing and predictive sentencing that disproportionately penalize people of color. Importantly, race and gender were not included as inputs into any of these algorithms. Should we be outraged by bias reflected in algorithmic output? Read on to find out. Read more
The promise and challenge of the age of artificial intelligence
-McKinsey & Co means that embracing AI promises considerable benefits for businesses and economies through its contributions to productivity growth and innovation. At the same time, AI’s impact on work is likely to be profound. Some occupations as well as demand for some skills will decline, while others grow and many change as people work alongside ever-evolving and increasingly capable machines. The briefing concludes with a set of issues that policy makers and business leaders will need to address to soften the disruptive transitions likely to accompany its adoption. Let’s take a closer look at the promises and problems of…
Game Theory challenge: Can you predict human behavior?
-Given a range of integers from 0 to 100, what would the whole number closest to 2/3 of the average of all numbers guessed be? For example, if the average of all guesses is 60, the correct guess will be 40. The game is played under conditions known to game theorists as “common knowledge:” every player has the same information— they also know that everyone else does too. Lucas Husted explains the Game Theory challenge for predicting humn behavior in this thought-provoking video. Watch the video
