The decade of the 2020s has given businesses—and the consumers who count on them—quite a ride. Neither tea leaves nor the best efforts of analysts could have predicted the impact that the one-two punch of a pandemic and roller-coaster economy would have on both markets and marketplaces.

Consumers have found their pockets filled and emptied. Employers have had trouble staying open and then difficulty hiring enough staff for swelling demand. Moreover, companies have struggled to get the raw materials and transportation network needed to deliver their products to a wanting public.

In this decade, consumers, already emboldened and savvier due to a digital revolution, have continued to evolve in buying habits and expectations. However, even considering recent events, these “evolved” consumers didn’t emerge overnight. Instead, they have been evolving for the last two decades, maybe even more.

‘You see, but you do not observe,” Sherlock Holmes told Dr. Watson in Sir Arthur Conan Doyle’s (1891) A Scandal in Bohemia. Taiichi Ohno, who developed Henry Ford’s lean production system into the Toyota Production System, told managers to stand in a circle on the shop floor and observe everything that happened there. If people who try this exercise see but do not observe, neither they nor their organizations will gain from it. History shows that countless people, possibly tens of millions, have overlooked breakthrough improvement opportunities that are obvious to anybody who knows what to look for.

Do bosses trust employees to be productive when working out of the office? Microsoft released a new study in which it found that 85 percent of leaders say the “shift to hybrid work has made it challenging to have confidence that employees are being productive.” More concretely, 49 percent of managers of hybrid workers “struggle to trust their employees to do their best work.”

This lack of trust in worker productivity has led to what Microsoft researchers termed productivity paranoia, “where leaders fear that lost productivity is due to employees not working, even though hours worked, number of meetings, and other activity metrics have increased.”

We live in a rural area, and many of our nonconsumables are purchased online. We’re deceivingly spoiled living near an Amazon fulfillment center because we can order an item on Saturday, and it arrives on Sunday. To me, this is a masterful logistical feat of filling an order, getting it into the delivery system, and into our community mailbox the next day, especially on a Sunday.

Unfortunately, the glaring downfall in creating a satisfied customer comes at the final leg of the journey: getting the product into our hands. Amazon’s logistical prowess loses its patina when a desktop computer is shipped via the U.S. Postal Service, and we must make a 20-mile round trip and stand in line at the post office. If the package won’t fit in a rural parcel locker, UPS or FedEx are the logical choices. The question is who makes those choices—or any one of the many choices that affect customer satisfaction.

It’s a conundrum that faces everyone who operates a manufacturing or service business: Most are unaware of the dire consequences of a defect reaching a customer until a process server hands them a lawsuit. By then it’s too late. Regardless of the outcome, the people and businesses will be permanently scarred and damaged.

Most of the more than 700 companies I’ve worked with during the last 50 years have robust customer service and warranty processes that never address the question: What might be the dire consequences of a defect reaching one of our customers?

During the last two decades, I’ve expanded my quality management consulting work to include consulting and testifying in products liability and organizational negligence cases. In more than 40 lawsuits, I’ve gathered empirical evidence which concludes that our fundamental tenets of quality management are seriously flawed. Our charter has been to minimize process variability and reduce defects to an acceptable level. Both canards are folly.

Responding to disasters is one of the most important activities that employees can be asked to grapple with. From natural disasters like hurricanes and earthquakes to technological situations such as power outages, chemical spills, and transportation accidents, as well as security emergencies like acts of terrorism and mass shootings, a property should be prepared for any of these events.

The metric for success in a disaster response isn’t the detail of the plans or the usefulness of the equipment; it’s the level of employee empowerment that makes all the difference. Employees must go beyond being just bystanders who are told what to do. They must be transformed into emergency responders capable of activating themselves and leading in the instant a disaster strikes.

I am 100 percent a millennial (lol), which—according to the Merriam-Webster dictionary—means I’m a “person born in the 1980s or 1990s.” To me, being a millennial means belonging to a cultlike group within a large population of present-day wannabe-adults for whom seemingly arbitrary words like Rugrats, Dunkaroos, Charizard, Spice Girls, and It’s Morphin’ Time!!! can spark extreme nostalgia and reboot our brains back to the golden days of real pop culture.

Like many other ’90s kids, I grew up raising my Tamagotchis, scraping my knees because I refused to wear lame knee pads when rollerblading, transforming my Transformers, and terrorizing my sister with Ninja Turtles action figures.

Last month we found that capability and performance indexes have no inherent preference for one probability model over another. However, whenever we seek to convert these indexes into fractions of nonconforming product, we have to make use of some probability model. Here, we’ll look at the role played by probability models when making these conversions.

Many people have been taught that the first step in the statistical inquisition of their data is to fit some probability model to the histogram. Here we’ll begin with the 250 values shown in figure 1. These values have an average of 7.4 and a standard deviation statistic of 5.2.

Figure 1: 250 observations from one process

I responded recently to a LinkedIn post regarding AI-assisted robotic recycling. The sorting speed is so fast, we almost miss each sort in the blink of an eye. Having observed this same activity attempted by humans—and overlooking the upstream potential to avoid this kind of recycling mess at the source (the wasteful consumer)—I’m all for the potential to pass off these kinds of tasks to machines. 

Humans doing this work must operate at a much slower pace, risk injury, and aren’t as precise as the AI robot. And, of course, humans must also deal with the stench of garbage; these robots, at least, have no sense of smell to distract them from their work. 

Following the pandemic, the Great Resignation marked a trend with large numbers of people resigning from their jobs. However, quitting isn’t an option for everyone; many people have obligations, such as loans to repay or medical insurance that is linked to their jobs. Serving notice was simply not an option. Therefore, quiet quitting has become an alternative to resigning.

So what is quiet quitting? Why do so many employees see it as an option? Should employers worry about it?

What is quiet quitting?

Simply put, quiet quitting is taking a step back and evaluating your work effort. Essentially, you’re determining whether you’re going above and beyond for a company that isn’t reciprocating.

This trend originated on TikTok and quickly spread through news and social media. Commonly, it’s described as:
• Restricting work to work hours and not picking up tasks after this time
• Avoiding work calls and messages during vacations and time off
• Doing the designated work that was listed in the job description
• Unsubscribing from the hustle culture of going beyond any additional duties

Overall, it’s sticking to one’s job description and certain work hours, and not stressing with job-related tasks outside of office hours.