OM in the News: Amazon’s Way of Measuring Work

Amazon warehouse workers who can’t ‘make rate’ don’t last.

Austin Morreale worked at the Amazon fulfillment center in Edison, N.J.. “It was 10 hours of pretty much mind-numbingly boring work, pretty much standing in the same position for the whole shift,” he said. “But at the end of the shift, I was drenched in sweat and aching like I hadn’t ached since I was playing competitive soccer.”  Morreale was slow, he says, and kept messing up the patterns for efficiently putting items on robotic shelves—known as “stowing.” He couldn’t “make rate”: Amazonese for keeping up with the pace of work. In Amazon’s fulfillment centers, writes The Wall Street Journal (Sept. 11-12, 2021), human productivity is measured by an overall pick or stow rate calculated for each worker at a robot-fed pick-and-stow station.

On the job, no one ever stood behind Morreale and barked at him to work faster. But twice a day at a stand-up meeting, his shift managers told the group how everyone was doing. They knew because Amazon’s software, and an assortment of sensors in the warehouse, tracked workers’ every move. Knowing that if you don’t make rate you’ll get a warning, triggered by an algorithm, and if it happens often enough your job is in danger, can be a powerful psychological spur to work harder, and possibly to exceed your physical limits. (In 2019, Amazon reported 5.6 injuries per 100 workers. The average rate for warehouses in the U.S. that same year was 4.8 per 100).

More than a century ago, Henry Ford pioneered systems for speeding up work that we take for granted today (see Chapters 1 and 10). What Morreale experienced was Amazon’s 21st-century, algorithm-driven successor to Fordism. It’s a mix of surveillance, measurement, psychological tricks, targets, incentives, sloganeering, and an ever-growing array of technologies. This system of technologically supercharged management can be benevolent, or sinister, or both.

Imagine the delight of  Ford, if he could know, to the millisecond, how long it took every worker to complete a task, every day, in every facility he owned. Imagine what early time-and-motion experts Frank and Lillian Gilbreth could have accomplished had they been able to discard their film cameras and replace them with millions of hours of video captured from the digital cameras that watch every station at Amazon’s fulfillment centers.

 

Classroom discussion questions:
1. How would the use of time studies, detailed in Ch. 10, be impacted by the Amazon approach for setting standards?

2. What are the responsibilities of operations managers in dealing with productivity and safety at their warehouses?

 

 

Teaching Tip: Measurements That Mislead

Today’s Wall Street Journal (April 2-3,2011) has a wonderful article on the subject of work measurement (Ch.10). The question of how to deal with employees who know they are being observed for a time study arises in every class I teach.  About 30 years ago, Prof. Paul Sackett, at the U. of Minnesota, began timing the speed of supermarket cashiers ringing up a few dozen items. He found (unsurprisingly) that some were faster than others. All knew they were being observed. Today, with electronic scanners recording the pace of workers over long periods of time, it is once again clear that productivity varies greatly.

Sackett assumed these separate measures (short-time observed and longer-period with no observer) would generate similar rankings. But they yielded very weak correlations. This led him to believe that there are highly motivated, focused people who perform well  when they know they are being tested for “maximum performance”.  On the other hand, “typical performance” (how well one does over the long haul) is influenced by a whole different set of character traits.

Think of the SAT,LSAT, or GMAT. They each take just a few hours (under maximum performance) and are supposed to give a reading of an individual’s talent. Though the SAT does a modest job of predicting grades for freshmen (R-squared=.12), it doesn’t do well at predicting post-college achievement. (Same for the LSAT).

“Even the NFL Combine is a big waste of time”, says the Journal. There is no consistent statistical relationship between how well players do at the Combine and how they succeed in the NFL. Grit, on the other hand, which reflects a person’s commitment to a long-term goal, does predict levels of achievement. (It seems like one needs grit to get a PhD) . But workers, football players, and students do not reveal their levels of grit when taking a brief test. What really matters, as we all know, is what happens after the test is over.

Teaching Tip: Fun Class Exercise in Work Measurement (Ch.10)

I am always looking for a simple, fun exercise to break up class for a few minutes, and find that shuffling cards is perfect for Chapter 10’s topic of Time Studies.

The equations average observed time, normal time, and standard time are very straightforward (see Equations 10-1,10-2, and 10-3 in the text). But some bright students  usually inquire as to where the Performance Rating needed in Equation 10-2 comes from. So I bring as many decks of playing cards as I can find to class that day.

Explaining that there are some tasks for which the Normal time is already known and established, I recompute the equation as Performance rating factor=Normal time/Average observed time. Then I bring out the decks of cards and group the students into teams that measure how fast each person can deal the deck into a very neat bridge hands (ie, 4 piles of 13 cards per pile). Each student repeats the task 2-3 times, while team members time her, then compute the average. Each person takes a turn being timed.

The Standard time for this task turns out to be 30 seconds , and I point out that the dealing activity resembles other production tasks involving manual dexterity. Each person’s Performance rating is then computed (30 sec./average time) and recorded by the team on the board next to that student’s name. Students have fun seeing who has the highest and lowest Performance ratings for this type of job skill.