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An Enigma Called Automation — Is There a Concealed Goldmine Behind The $160 Billion Glitter?

An Enigma Called Automation — Is There a Concealed Goldmine Behind The $160 Billion Glitter?

March 24, 2020

$160 billion! That is the market size of the Industrial Automation industry.

While this is impressive on its own, are we overlooking its true potential by relying on a popular urban legend in economics? It’s time for a disruption.

Photo by James Pond on Unsplash

It’s the American dream, you dig?

The 1950’s was known as the golden age of consumerism.

The global economy was largely dominated by the United States. World War II was over. It was a time for rebuilding and growth.

The general assumption about this era is that more government spending led to more jobs.

No longer diverted by war, veterans returned from the front lines and together with others back home, joined the workforce.

Many believe that more people in the workforce resulted in more productivity.

This in turn led to a greater amount of goods and services produced. Gainfully employed masses opened up their purse strings and spent more.

Photo by Clem Onojeghuo on Unsplash

The suburbs. White picket fences. Manicured lawns. A ’55 Chevy.

This is why the economy boomed in the 1950’s.

Economics 101, right? Except, not quite.

I didn’t change. I just see things differently.

Photo by Haley Lawrence on Unsplash

If the economy is strongly related to more people working, and therefore more spending in our societies, surely the data supports this theory.

Well, kind of.

But the real issue is, we’re not asking the right question.

As the world’s population grew, did we get richer, spend more, and fuel the economy?

The answer to this is no.

Let’s look at the growth of world GDP and GDP per capita (per person) through the ages.

[Newgeography, Madisson, IMF]

As you can see, from the 1800’s — 1950’s, both GDP and GDP per capita only grew marginally each year with population growth.

Curiously, around the 1950’s — 1960’s, world GDP and GDP-per-capita saw hockey stick-growth; not proportional to population growth.

So it’s not the size of the population that matters.

What is responsible for this incredible growth from the 1950’s-1960’s?

It’s ‘I, Robot’…not ‘We the People’.

Heightened productivity in the ’50s and ’60s is the reason for astronomical GDP and GDP-per-capita.

Critical drivers for growth are — industrial robots and large-scale automation. Not the number of people available to work.

In fact, more automation nurtures greater wealth and quality of life for a country.

This is applicable even today.

The appeal of lower-cost manual labor has lured many a manufacturer to other shores.

But does low-cost labor = more productivity?

Take a look at the chart depicting labor costs vs. manufacturing output per person (productivity) in India, China, Germany and the US.

If more people = better productivity were true, then India and China would have a higher manufacturing output per person.

However, the United States and Germany has much higher productivity — due to investments in automation.

As a result, their residents enjoy better infrastructure and a higher standard of living.

So, should we be satisfied with how far we’ve got? Or can we do better?

Can we increase manufacturing productivity across the board so everyone can reap more benefits of industrial automation?

Why settle? What’s our BHAG?

Our Big Hairy Audacious Goal.

$160 billion spent on industrial automation with $48 billion on industrial robots is great.

The global market for industrial robots alone is estimated to reach an impressive $5 billion by 2024 (Markets and Markets report).

With a healthy CAGR of 9.2%, we’re on a good path to progress.

But in contrast, another innovative market — the app and internet economy — is growing at 37% CAGR! (App Economy Report, App Annie).

How can we disrupt the industrial robot and automation industry to realize its true potential?

“All disruption starts with introspection” — Jay Samit.

The argument that low-cost labor is the key driver for manufacturers does not hold under scrutiny.

While $160 billion is spent on industrial automation, the world spends $15 trillion on manual labor wages.

A large portion of these tasks are repetitive and require only low-level skills.

The U.S. alone spends $1.5 trillion on manual labor wages.

In an ideal world, we could deploy robots to boost productivity to incredible heights as quickly as we would deploy an app.

If cost isn’t the main driver for manufacturers using manual labor for repetitive tasks, then what is?

As you sow, so you ROI.

Photo by Sylvia Zhou on Unsplash

Humans are the preferred choice for tasks requiring critical thinking and decision making.

However, for repetitive tasks, manual labor cannot compare to the productivity gained through automation.

Which is the foundation for successful mass production.

A manufacturer’s real hesitation about large investments in industrial automation is reduced clarity upfront about total costs involved.

In other words, their ROI or Return on Investment.

Let’s look at an example: millions of people pay hundreds of dollars for a smartphone. Why?

We know exactly what we’re getting — from the very start.

Once we buy our phone, there are no customization required, and future app purchase costs are insignificant.

But manufacturers currently do not have this luxury with respect to industrial robots.

Two-thirds of upfront costs in an automation implementation is due to customization.

Also, market and customer demands are constantly changing.

The days of Henry Ford’s “You can have any color as long as it’s black” are over. In an era of hyper-personalization, manufacturers must stay agile.

Any more changes may need more customization — which means recurring costs.

Currently, it is easier to estimate ROI with manual labor versus robotics due to many reasons —

  • Varying Object Sizes, Shapes, and Configurations: Manufacturing involves a variety of objects that need to be assembled together. Robot arms today do not “see” what they pick up and cannot adapt to even minor variations in the placement of objects or configurations. Only manual labor or expensive custom solutions can work in these situations.
  • Manual labor is easier and less expensive to train: Due to mass manufacturing, there are more SKUs to manufacture and product lifecycles are shorter. What’s trending today might not be in just a year’s time. Re-configuring and re-caliberating these large industrial robots for minor changes is very time-consuming, expensive, and can slow down production.
  • Even “Smart” robots require expensive customization: Picking objects from bins and assembling them together is a very high-level task for the industrial robots today — and this activity constitutes a majority of the tasks required in manufacturing. The solution is only expensive customizations — which affects the bottom line.

As you can see, while we’ve come a long way, we still have a long way to go.

If the industrial automation industry needs to turbo-boost market demand, we cannot continue to build a better mousetrap.

We need to think bigger and build disruptive technology to experience exponential growth like cloud computing or the app-based economy.

After all, “Arriving at one goal is the starting point to another” — John Dewey.

If you liked this article, you may appreciate our insights into the secret to healing manufacturing’s trillion dollar problem 

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