Healing Manufacturing’s Trillion Dollar Migraine (Part 1)
It’s April, 2017. Tesla just announced the Model 3.
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
