In 1893, an American mechanical engineer named Frederick Winslow Taylor started a management consultancy in Philadelphia.

This consultancy was fairly unique at the time, specializing as it did in the systemization and

optimization of management and manufacturing processes. He focused, in other words, on helping businesses, especially those that made things,

figure out the optimal way to make more of those things faster, cheaper, and ideally of a higher

quality as well. And he did so through rigorous application of experimentation, data collection, and eventually the adjustment

of often minor facets of the requisite processes, so that the whole business would get stronger

when those tweaked pieces were then put back together and applied holistically.

Taylor's conception of what he called scientific management was primarily oriented around formalizing previously rule-of-thumb style metrics and approaches,

hiring and training employees more intentionally and uniformly, providing very specific instructions and expectations for each employee,

and making sure that those up top, the managers,

did as much work as the laborers, that work consisting mostly of collecting data,

crunching that data for insights, and then applying those insights in a more scientific way,

so as to continue improving these processes over time.

Taylor's overarching theory was that there was likely an optimal way to do everything,

and the closer a business or a worker within that business could get to that optimal state,

the better the business, the better the worker, and the better the people on the

other end of whatever good or service they produced would be. When applied appropriately

and consistently, these principles did tend to result in at times significant improvements

in output, and at times dramatically more efficiencies, especially efficiencies of scale.

And many of these new efficiency-derived improvements stemmed from Taylor's aforementioned

technique of breaking large tasks, or even the whole of a job, into discrete pieces, and then

optimizing each of those pieces. Unfortunately, this collection of

...