Deep dive: The 100-year-old manufacturing problem

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It’s been more than 100 years since Henry Ford initiated the era of mass production.

It’s been at least 30 years since lean and the Toyota Production System were popularized in the West.

And it’s been more than five years since the World Economic Forum declared that manufacturing has entered the Fourth Industrial Revolution.

Despite all of this innovation and change, manufacturers are running blind to the greatest sources of value and variability inside the factory: manual activities.

This page will help you understand why.

100-year-old Manufacturing Problem
100-year-old Manufacturing Problem

Part I: Why do we lack data on manual activities?

A 100-year-old data methodology

If you visit any assembly line today, you will see the same thing: A person observing the line and gathering data, by hand.

The time and motion study methodology was developed a century ago by Frederick Taylor and Frank and Lillian Gilbreth. And while people may now use phones instead of stopwatches, the principle remains the same.

Bad data, and not enough of it

Time-and-motion studies are a poor source of data. They’re performed manually, which means they produce minuscule sample sizes. And the very act of one person observing another introduces significant observation bias.

Part II: Why does this data gap matter?

People still matter — a lot

Humans are not being displaced inside of factories nearly as rapidly as the headlines would have you believe.

In fact, Drishti’s research with Kearney shows that a surprising amount of plant value — and variability — is still in the domain of people.

of factory tasks are still performed by people

of defects can be traced to human causes

of a quality engineer’s time is spent on root cause analysis

of an industrial engineer’s time is spent performing time studies

Source: Drishti + Kearney / The State of Human Factory Analytics

Bad data = bad decisions

Unfortunately, most Industry 4.0 innovation is focused on machine data streams. Factories face a data gap: they are unable to sufficiently measure the bulk of their activities.

As a result, time-and-motion study data form the basis of decisions on production, capacity, resource usage, hiring, and more… with predictably negative consequences.

Read a detailed report: The State of Human Factory Analytics

See how Drishti improves quality, productivity and training at scale.