“Taylorism” may be a new term to many of you, but I’m sure the concept isn’t.

Frederick Taylor (1856-1915) is credited with the management theory of Scientific Management, also often called “Taylorism”, which basically boils down to the idea that business processes can be optimized through scientific observations, such as time and motion study, to the point that an untrained monkey can do the work with the proper guidance from the theoretically omniscient management.

In the last decades, Scientific Management/Taylorism has gotten a lot of flack, and rightly so. It views “front-line” workers as cogs in a machine, with no ability to think, reason, or imagine ways of improving their daily work. It’s seen as inhumane at worst, and ineffective at best—particularly for knowledge work.

Concepts like Agile and LEAN are largely rooted in a reaction against Taylorism. Of course, Taylorism is now deeply engrained in the management culture of the western world, and it will take many more decades to eliminate it completely, if that’s ever even possible (or desirable).

But there’s one area where we all thrive with a Taylorist mindset.

When is the last time you tried to optimize a function in your program?

Or you tried to speed up a CI/CD pipeline?

If these aren’t examples of systemic improvement through time and motion studies, I don’t know what is.

The important distinction from “classical” Taylorism is that the subject of our study is a computer (or network or other mechanical or electrical system), not human beings.

Taylorism is great when it’s applied to actual (or proverbial) cogs in actual machines.

A huge part of DevOps is applying the Taylorist approach to our computer systems.

So Taylorism is dead, as a management philosophy for humans. But long live Taylorism as an optimization philosophy for machines.