“Look at the world around you. It may seem like an immovable, implacable place. It is not. With the slightest push — in just the right place — it can be tipped.” — Malcolm Gladwell

Why is Henry Ford known as the “Father of the Auto World”?

Contrary to popular belief, he did not invent the automobile. In fact, the first automobile that could transport humans was created more than a century before the launch of Ford’s ‘Model T’ design.

Similarly, why is Johannes Gutenberg credited as the inventor of the Printing Press? Particularly when printing with blocks existed hundreds of years prior.

Revolutions are not borne from handcrafting one-of-a-kind luxuries for a few. They are wrought from gratifying the inborn needs of the many.

Ford and Gutenberg did not originate the concept of automobiles or printing. But their technologies heralded veritable ‘storming of the Bastille’ moments in their respective fields.

Their genius transformed the worlds of transportation and communication. Much like the discovery of flight and, decades later, the internet and smartphones did.

Their designs made advanced technological concepts reproducible, inexpensive, and therefore, accessible to the masses.

Another industry, the world of industrial automation, is now ripe for a shake-up. Incremental innovations in robotics ushered in progress. But we need more.

A roadblock in Robotics

In most factories today, robots are relegated to easy tasks like painting and welding. It often comes as quite a surprise to those unfamiliar with the world of manufacturing, due to the futuristic hype that surrounds robots.

They wonder, “Surely, factories today are lined with robots busy assembling entire cars, and more?”

Honestly, the answer is no.

Many repetitive and simple tasks, even today, are done by humans. And this is due to a limitation in the field of industrial robots.

As an industry, we haven’t figured out how to make robots “see” a bin of mixed items, identify what they need, pick it up, and perform simple jobs like fixing a cable, or positioning screws.

Machine vision exists. But machine vision combined with robot intelligence is not easily reproducible…yet.

It involves heavy and expensive customization. Traditional robot arms are usually retrofitted with newer technology and are programmed to “understand” one specific item or task. Changes often mean reprogramming and training.

For most manufacturers, this level of customization is not worth the effort, despite the trade-offs. Instead, they turn to humans to conduct these highly straightforward and reproducible tasks, simply because they are inexpensive and easy to train.

Overly relying on manual labor for extremely simple and repetitive tasks can lead to lower productivity levels, inconsistent quality, and higher overheads. And manufacturing systems are not resilient to withstand black swan events such as the COVID-19 pandemic.

How do we cross this deceptively simple hurdle with far-reaching impact?

A breakthrough in visual intelligence

Even renowned titans of industry acknowledge the significance of having robots with visual intelligence. Veterans such as Bill Gates and Jeff Bezos estimate about a decade to reach this breakthrough.

“I think robots that have vision and manipulation as good as humans is a huge milestone that will happen in the next decade and is being underestimated.” — Bill Gates

“I think grasping is going to be a solved problem in the next 10 years,” he said. “It’s turned out to be an incredibly difficult problem, probably in part because we’re starting to solve it with machine vision, so (that means) machine vision did have to come first.” — Jeff Bezos

At Cynlr, we believe we have the answer. And we look forward to sharing it with the world of manufacturing very soon.

Here’s a sneak peek at us crafting our pilot solution into a full-fledged product.

We are CynLr. Visit our website to learn more about our exciting world of vision intelligence. Follow us, Gokul NA and Nikhil Ramaswamy, to stay updated on our latest.

Is This ‘Small’ Step for Robots the Next Giant Leap for Manufacturing? was originally published in cynlr on Medium, where people are continuing the conversation by highlighting and responding to this story.

This is quite a dystopian present we’re living in.

In the blink of an eye, COVID-19 turned our collective health on its head; our global economy brought to its knees. The World Trade Organization estimates global trade to drop significantly in 2020 to as much as 13% to 32%.

Like all disasters, this pandemic laid bare the strengths and faults of our systems. This includes everything from our immune systems to the resilience of leading industries shouldering our economies.

Man has often looked to nature as a source of inspiration for innovation. Once again, it may be time to learn how to build robust systems to power through tough times.

Imitation Is the Best Form of Survival

The good news is, if infected with the COVID-19 virus, the majority of us can recover by relying on our own immune systems. How do we manage this without having been exposed to this type of virus before?

We have our adaptable internal defense processes to thank. Our bodies study and analyze; calibrate and re-calibrate efforts until the threat dissipates. Afterwards, there is intense rebuilding work to make use stronger than we were before.

Our bodies keep us safe by observing, adapting, and learning from experience.

Constant assessment and adaptation to the changing effects of an infection is at the crux of our immunity. After all, this is the essence of survival.

There are powerful parallels here to how industries can design more resilient systems to inoculate against adversity.

Particularly, the world of manufacturing.

Everybody Hurts

One of the earliest to feel the impact of the COVID-19 crisis, apart from healthcare, was the manufacturing industry.

Early on, as the virus raged across through Asia — the world’s manufacturing hub — one thing was clear. Traditional manufacturing systems were not strong enough to resist the effects of black swan events like this pandemic.

China’s industrial production dropped 13.5% in January and February this year compared to last year (World Economic Forum). This may not seem like a lot until you consider that no other disaster or recession has had such an impact.

As the virus invaded other corners of the world, there was no difference. Nations and industries with robust contingency processes were better positioned to mitigate the effects of the virus. Ones without, unfortunately, succumbed to the trappings of their fragile systems.

Global PMIs, or Purchasing Managers’ Indices, took a huge hit. In the United States, a National Association of Manufacturers’ survey revealed 53% of manufacturers anticipated a change to their businesses in the upcoming months. Production in the EU’s automotive industry has practically halted — it makes up 7% of the GDP.

One of the main issues is that the global pandemic raised demand for essential items while lowering the need for non-essentials. This is comparable to how our bodies when faced with danger. Our immune systems put all non-essential processes on pause and focus on making antibodies and other critical molecules.

To adapt to the change in demand, manufacturers intensified production of vital products. However, several stopped short, compromised on product functionality, or were not as responsive as they could have been — if their systems were built to offer stronger immunity against adverse events.

Manufacturing Compromises

Take the case of Tesla CEO, Elon Musk versus the Governor of California.

Tesla CEO, Elon offered to manufacture and send over 1,000 ventilators for Coronavirus patients. After underestimating the complexities of medical device manufacturing, Elon then offered design expertise. But the need of the hour was agility to mass-produce ventilators, not a new design. Tesla ended up buying machines that were not ventilators and supply them to the hospitals. The saga continues.

Other stalwarts of the American automobile industry have mobilized their factories to make medical equipment, masks, and other essentials. Ford and GE partnered with a small medical device manufacturer, Airon to produce ventilators. General Motors and Xerox are also re-configuring their assembly lines and processes to make medical equipment. In Britain, carmaker McLaren is redesigning simpler ventilators to produce them.

In India, Mahindra, Tata Motors, and others are also figuring out how to manufacture ventilators and meet patient demand.

All these ventilators are not the high-functioning versions available on the market pre-pandemic. They are simpler models re-designed for automobile manufacturers to manufacture with ease.

While efforts to step up and offer support during a global crisis are impressive, we do notice compromises that had to be made to overcome intrinsic systemic challenges.

Survival of the Fittest

“It is not the strongest of the species that survives, nor the most intelligent. It is the one that is most adaptable to change.”

- Unknown but attributed often with Charles Darwin

We observed 3 key obstacles that manufacturers had to overcome to manufacture essential items:

1. ‘Big Manufacturing’ could not immediately adapt processes to produce ventilators and other essentials. They had to partner up with medical device manufacturers. This delay cost us valuable time in the battle against the Coronavirus.
2. Ventilators had to be re-designed or simplified to their core elements before manufacture.
3. Producing essential goods in industries that bank on manual labor is like walking a tightrope. How do you mass-produce necessary goods while keeping employees safe? Coronavirus outbreaks have already affected the American meatpacking and food processing industry. All factories need to keep a tight grip on safety protocols to prevent infection.

While immediate danger calls for quick innovations, true strength-building lies in the aftermath.

This applies to our bodies fighting infection or industries trying to survive in a crisis. This pandemic lends significant opportunity to reflect upon renovating legacy systems for endurance.

How can we weave in threads of resiliency through our manufacturing systems to ensure long-term sustainability?

An Eye for Change

There are a few popular misconceptions that may be holding back the $160 billion industrial automation industry. Addressing these issues holds the key to building immunity against future calamities.

An outsider’s perception of a modern-day factory vs. real life

One may imagine a line of advanced and efficient robots employed in assembling cars or other products. But the ground reality involves much more manual labor for simple tasks. This is only because automation today, doesn’t allow robot arms to “see” and “learn” as they go.

Need to pick an item out of a bin of items? Manual labor.
Fix a screw in place and turn it? Manual labor.
Handle dynamic objects like wires or cables? Manual labor.

More people does not signify higher productivity or a better economy

This is another urban legend that adaptable automation can address while crisis-proofing the economy.

We need to design automation systems with resilience in mind to prevent a breakdown during the next crisis.

Several countries are already beginning to think along these lines. Mark Cuban, a prominent American billionaire, called for more automation and sophisticated robotics to safeguard American manufacturing. China is ramping up robot deployment in the wake of the COVID-19 crisis. India is working on robotics to help in the healthcare space.

In rushing to deploy robots, the main issue remains unaddressed.

Automation’s predicament is that even advanced robots can only do very simple tasks, such as:

• picking up very specific items and placing them in a particular pre-programmed location, and
• moving around in a pre-programmed fashion to disinfect or clean.

There is still a strong need for robots that can see, learn, and grow. Investing in them will build our economic immunity and boost our chances of survival.

“Government systems suffer from two weaknesses. They are complex. And they are slow. We need to change this. Our systems need to be made sharp, effective, fast, and flexible…”

— Prime Minister of India, Narendra Modi

The same can be said of manufacturing and global supply chain systems. It’s time to rebuild — for greater immunity against future crises.

After COVID-19: Manufacturing Needs to Boost Its Immunity was originally published in cynlr on Medium, where people are continuing the conversation by highlighting and responding to this story.

$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.

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.

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.

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.

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.

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 —

• Healing Manufacturing’s Trillion Dollar Migraine (Part 1)
• Healing Manufacturing’s Trillion Dollar Migraine (Part 2)

About Cynlr

I am Nikhil Ramaswamy, CEO and Co-Founder of CynLr, with my business partner, CTO and Co-Founder, Gokul N A. We are a deep-tech and venture-funded startup looking to advance the field of robotics through next-gen visual object intelligence.

Whether a curious investor, robot-aficionado, software and algorithm whiz or a combination of the above, discover more at www.cynlr.com.

An Enigma Called Automation — Is There a Concealed Goldmine Behind The $160 Billion Glitter? was originally published in cynlr on Medium, where people are continuing the conversation by highlighting and responding to this story.

Robots Have “Force”

Seek Jedi Master.

The industrial robot industry is a whopping $16 billion. The market is worth double that when factoring in all the components and services that feed into this industry (International Federation for Robotics).

Industrial robots can be re-programmed and used for various applications. The most versatile of industrial robots today are articulate robot arms — sophisticated electro-mechanical machines but with no “intelligence” associated. These robot arms can operate with three to even seven degrees of freedom. An significant evolution from the early days of rigid automation for every type of motion.

Robots today can repeat their movements with high precision. They can move away from a position and then return to it with as little as 0.01 mm difference from the previous time it occupied the place. This is known as position repeatability.

Additionally, they completely dwarf human ability by achieving speeds upto 3 m/s, and payload capacities of several hundred kilograms.

However, they are limited in situations where they need to adapt to dynamic conditions.

What are some complex jobs for robots?

· Bin Picking & Placing— Identifying items from a bin, picking the right one irrespective of orientation and accurately orienting the item as required

· Working with screws — Picking up screws, placing them in the right hole, and holding it right while making the first few turns. Think about it. Almost every single object manufactured needs screws!

· Dealing with wires,cables and flexible objects — changing geometries each time

Bin picking, orienting and placing is so simple for adults; we forget how complex it could be for a robot. It involves many steps –

Industrial robots cannot “look” and “figure out” that a specific part has been moved a few millimeters away, or the orientation is a little askew, or even recognize an object that may have a shadow cast on it. They then become incapacitated and unreliable.

The real issue is that having achieved the original goals of automation, we stopped asking the right questions.

Historically, we asked our robots to -

· Perform a specific task predictably and repeatedly.

· Complete their tasks without “seeing,” “thinking,” or “understanding” what they were doing.

Ultimately, a vast majority of tasks in any industry require flexibility in the face of changing parts, orientations, or locations, but our robots are not equipped to accommodate for that.

In an attempt to fix this, a large part of the robotics world is devoted to building costly customized solutions with very tiny tolerances into standard automation processes so their robots can pick items “blindly.”

However, products have gotten more sophisticated, greater productivity is the need of the hour, and flexibility in an ever-changing world is paramount.

It is no longer feasible for customized mechanisms to be the norm.

In other words…

Dear Robots,

It’s Not You, It’s Us.

In this age of hyper-consumerism, intense competition from across the globe, and rising customer demands for the next generation of products, sometimes within a few months of the original launch, the old automation model cannot be sustained.

It already costs large manufacturers trillions of dollars in wasted opportunities, high labor costs, and losses in productivity.

Take the example of Adidas recently shutting down its “Speedfactories.” Adidas had factories in Germany and the United States designed to decentralize its operations and bring products to customers in Europe and North America much faster.

Speedfactories were considered to be futuristic and highly automated with cutting-edge robotics. They were also meant to be an alternative to outsourcing to lower-cost labor markets like China. Alas, this too remained a damp squib.

We need a paradigm shift in how we envision robot automation.

Sophisticated robot arms need advanced robot eyes and brains to see better, think, understand what they are doing, and adapt to shifting products, orientations, and situations.

Robot system designers need to aim for a new goal and, in the words of Adidas’s arch nemesis, we are here to “Just Do It.”

I am Nikhil Ramaswamy, CEO and Co-Founder of Cynlr, with my business partner, CTO and Co-Founder, Gokul N A. We are a deep-tech and venture-funded startup looking to advance the field of robotics through next-gen visual object intelligence.

Whether a curious investor, robot-aficionado, software and algorithm whiz or a combination of the above, discover more at www.cynlr.com

Healing Manufacturing’s Trillion Dollar Migraine (Part 2) was originally published in cynlr on Medium, where people are continuing the conversation by highlighting and responding to this story.

325,000 cars reserved in just one week. Estimated worth? $14 billion.

Faced with this incredible demand, Elon Musk had an exciting problem to solve.

His solution? Ramping up production from zero to 20,000 cars/week. This had never been done before — but Tesla was up for the challenge.

Looking to achieve super-human production speeds, efficiency, and accuracy, Tesla invested in advanced robots with the goal to hyper-automate their factories.

“Robots will be able to do everything better than us”- Elon in 2017.

Within a year, Elon admitted that, “humans are underrated” as production was a mere 2,000 cars/week. While Tesla eventually reached 5,000 cars/week, it was still a far cry from the original target.

And not without bringing human factory workers back into the fold.

The challenge is that there are thousands of differently-shaped objects that need to be placed in various orientations to construct one car. While robots in assembly line can perform repetitive tasks with great efficiencies, they are still not as adaptable to minor changes as humans are.

Surely this issue is confined to the complex world of car manufacturing?

Not really.

A virtual tour of the logistics marvel that is Amazon’s warehouse in Seattle is proof.

Once an order is placed on Amazon’s website, ground robots detect the ordered item’s location and transports the specific shelving units across their vast facility. It sounds simple, but in reality, this is a tremendous accomplishment.

However, a significant part of the Amazon warehousing process needs human packers. Robots bring shelves right to a human packer — who is responsible for picking and packing products. Amazon sells an extensive range of products in every shape, size, color, and configuration. Items are also placed in various orientations on their shelves.

Only a human picker can identify the right object, pick it up, orient it accurately, and pack it.

Only manual labor is flexible enough and, therefore, handles a large part of the assembly or delivery process.

Indeed, most production line and logistics robots are proficient at:

· Detecting the presence of simple objects with a stable base in a specific orientation in one location

· Picking up and placing objects in a precise pre-programmed fashion

· Performing simple tasks like painting and welding non-complex parts

Manual labor is essential for all other tasks.

Robots today are highly capable, but do not operate well if there are minor manufacturing variations, orientation discrepancies, lighting fluctuations, and other such changes.

They cannot adapt to even minor complexities in shape or pick items out of a bin, or handle dynamic objects like cables.

So, why the push for greater automation on the manufacturing and warehousing floors?

Why not continue this partnership between man and machine?

To err is Human.

To forgive, Very Expensive.

23% of all unplanned downtime in manufacturing is due to human error (Vanson Bourne).

Employee Absenteeism and turnover is a high 37% in manufacturing and distribution plants across the U.S (Association for Manufacturing Excellence).

Manual labor costs over $15 Trillion in wages globally for low-skilled repetitive tasks. Even in cases where automation is ideal. These tasks account for 31% of the time spent performing physical tasks during work hours.

Automating these repetitive tasks could increase global productivity by 0.8 to 1.4% (McKinsey).

So, the appeal for automation is clear.

Robots don’t get bored, don’t make mistakes, don’t get tired, don’t take breaks, and provide an incredible level of sustained quality for repetitive tasks over a very long time.

Automating repetitive tasks can achieve -

· Significant cost-savings

· Higher productivity for mid- to high-skilled employees

· Superior quality control

· Security against high turnover, uncertainties in global trade, governments, and regulation changes

However, training robots involves intense planning, systems integration, testing, and stabilization of hundreds of components and their sub-components to achieve even a simple task.

…and when you need to make a small change or modify a product, you’re back to square one.

This is why, despite the astronomical costs and unpredictability, manual labor is still the primary go-to resource for simple, repetitive tasks on factory floors worldwide.

In the world of manufacturing, automating the random bin picking, orienting and placing processes will be the balm to many headaches.

But these activities, while quite simple for humans, are very daunting for robots.

So what’s missing in today’s robots? Read part 2 of this story.

I am Nikhil Ramaswamy, CEO and Co-Founder of Cynlr, with my business partner, CTO and Co-Founder, Gokul N A. We are a deep-tech and venture-funded startup looking to advance the field of robotics through next-gen visual object intelligence.

Whether a curious investor, robot-aficionado, software and algorithm whiz or a combination of the above, discover more at www.cynlr.com

Healing Manufacturing’s Trillion Dollar Migraine (Part 1) was originally published in cynlr on Medium, where people are continuing the conversation by highlighting and responding to this story.