Wednesday, September 9, 2015

"Attention Managers, You Can Improve Corporate Efficiency by Randomly Promoting Employees"

Arbitrary and capricious, that's my motto.
The Trouble With Scientific Management
Employers are increasingly turning to workforce management software to get white-collar employees to operate at maximum productivity. Software can track where workers are at all times, and what, exactly, they’re working on.

This echoes the push for the scientific management of laborers around the turn of the twentieth century, led by Frederick Taylor. Taylor presented his work, particularly at the Bethlehem Steel plant in Pennsylvania, as an example of the way precise measurement and a well-calibrated incentive system could dramatically improve productivity. But the actual process of changing how people at the plant worked was not so simple, as a 1977 account by historian Daniel Nelson points out.

The aspect of the reorganization that Taylor later made famous in his book, The Principles of Scientific Management, actually affected only a small part of the plant’s operations—simple manual labor done in the plant yard. Nelson writes that, before Taylor’s intervention, the workers there did only a third to a fourth as much work as comparable employees at other plants, making any success Taylor achieved seem less impressive.

Taylor and his team set to work with laborers loading pig iron onto railroad cars. They chose 10 of the best workers and had them load a car as fast as possible. This rate turned out to be the equivalent of 75 tons per day, more than five times the typical rate of 13 tons per day. But, after one car, the workers were exhausted. The management team then set the target amount that “first-class” men should be able to load at 45 tons per day, deducting 40 percent for rests and necessary delays. As Nelson points out, the figure of 40 percent seems to have been completely arbitrary.

Taylor then set a piece rate for tons of steel loaded that would work out to $1.68 per day for a top worker, significantly more than the existing fixed rate of $1.15 per day. Managers told the 10 “best men” that they would begin getting the new rate, but the laborers refused, quit working, and were eventually fired.

The managers only got a significant number of recruits for the new payment system after they started treating workers in “a liberal way” and providing alternative duties when they were tired or hurt.

Among the few workers who qualified as “first-class” was Henry Noll, whom Taylor later wrote about, using the pseudonym Schmidt, as a vindication of his methods. But Noll was an anomaly. Of 40 men hired in the course of a few months, managers determined that only three were “first-class.” Ten other made a “fair day’s wages” under the piece work system, but they wrote that most others “break down after two or three days.”...MORE
And a repost of a repost:
Attention Managers, You Can Improve Corporate Efficiency by Randomly Promoting Employees
 repost from November 2010.

It sounds like a Dilbert strip but it's true.
From the abstract at Physics arXiv:
The Peter Principle Revisited: A Computational Study

Here is the paper presented at Econophysics Colloquium 2009 (55 page PDF)
Here is the layman's version in the NYT's 9th Annual Year in Ideas:
Random Promotions
...They also tried alternately promoting the absolute best and absolute worst performers. That, too, worked out better than promoting on merit. The scientists say these strategies work because they harness "Parrondo's Paradox," a piece of game theory in which you win by alternating between two losing strategies. "In physics or game theory, this isn't new," says Andrea Rapisarda, a physicist at the University of Catania in Italy and a co-author of the study, which was recently published in the journal Physica A.

As Rapisarda points out, if you could know for sure that the people being promoted would excel in their new jobs, that would be the best strategy of all. But if you aren't sure — and in the real world, we rarely are — then random works better. 
HT: Improbable Research who awarded the authors the 2010 Ig Nobel Prize in Management for this work.