In less than nine years from today, Arnold Schwarzenegger as ‘The Terminator’ will journey back in time. A sophisticated Artificial Intelligence network and its robot army will send him to stop their enemy from being born. According to the ‘The Matrix’ pictures, a brand new war between humans and machines has been currently underway.
Science fiction perpetuates the myth which we now reside at a hyper-automated universe filled with super-intelligent robots that can do it all. However, in the world of mass-manufacturing and creation, one obstacle appears to avoid this dream from becoming a reality.
Automation’s most unsolved issue
The holy grail in production is to attain hyper-automated factories in which each manual activity in the shop floor is done by robots. This enables greater speed, efficiency, and superior excellent control.
Nonetheless, in manufacturing today, only very simple and highly repetitive tasks can be automated using robots, and only after incurring gigantic investments in infrastructure to steer clear of any variabilities.
In ecommerce warehouses, robots are for the most part relegated to automating the substance transport and the sorting process. This is true in even the most advanced of warehouses and factories such as people of Tesla and Amazon. However, there’s an enormous untapped blue sea of chance in dynamic manipulation where tasks involve picking, orienting, and placing items from unpredictable problems.
To illustrate, let’so look at the procedure for assembling a screw. Screws and their companions – nuts, washers, washers, springs, and also more – are jointly called fasteners. At the moment, in factories around the world, only humans can reliably select a screw from a bin, orient it, and set it in place in the ideal angle via the screw hole without slipping a ribbon. This procedure is replicated to resolve any related nut, washer, and clamp.
After these components are at the perfect spots, automated power screwdrivers can screw them into place much faster than a person could. Nevertheless, the manual job of picking and putting the screws contributes to enormous inefficiencies in manufacturing, and may comprise around 50 percent of total labour costs involved in producing a product.
31 percent of the time spent by the world’s manual labour force is about doing manual tasks. Automatable actions are responsible for nearly $15.8 Trillion in wages (McKinsey Report). Apart from labour costs, automating repetitive, low-skilled activities can boost throughput and productivity, and improve safety and quality. The technical feasibility of accomplishing these tasks, however, is reduced below 50 percent, irrespective of the business, whenever there’s a unpredictability involved with the task.
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Unpredictability is pricey 
The job of fixing a screw introduces a great deal of unpredictability. Each pair of fasteners has to be put in a precise direction and angle before turning the screw. While performing the job a few times over may be simple for humans, it’s still a challenge when having to do it quickly and repeatedly over extended periods of time – such as at a factory.
It’s even more difficult to automate it. Analysts at Bernstein and UBS analyzed Tesla plants and said, “In final meeting, robots may apply torque —but they don’t discover and account for threads which aren&; #x2019;t straight, bolts which don’t quite match, fasteners that don’t align or seals which have a defect. Humans are excellent at this. ”
The majority of the goods within this world are kept together with attachments. A vehicle, typically, has 3,500 fasteners. Think about the labour needed to assemble just one car. In 2019, the world created 90 million cars. This implies humans were involved in picking and placing screws a whopping 300 billion occasions. Assuming a conservative estimate of 5 seconds to place and fasten one screw, it’s still 10,000 man-years of manual effort for cars within one year.
Their complexities aren’t going away; automation has to catch up. A jet plane has 2-3 million fasteners. And even a smartphone has been held with approximately 75 fasteners. This is a enormous number of manual labour for tasks which are too simple for humans but are still set up because of limitations within the field of automation. These exceptionally repetitive low-skilled activities are eventually detrimental to even the labour employed to do them. They often cause repetitive stress injuries that lead to a higher number of errors, which then leads to costly snore.
Trying to automate these tasks usually leads to heavy customisation, which is far from ideal.
Infographic: Manual Fastener Attachment Processes are much slower and more expensive than automated
The irony in habit mass manufacturing
Industrial robots placed in factories today demand expensive customisations. There is little uniformity. Robots cells today need customisation to work nicely with specific products and processes which takes up time and resources and may get pricey.
Manufacturers often end up paying more than twice the price of an industrial robot only to have the robot to operate, because of the additional gear, end-of-arm tooling, demand for robot specialists, re-calibration, and also excellent control evaluations involved with the customisation process during implementation.
In the past, these upfront capital costs can be justified only if there’s a reasonable return of investment (ROI) over a lengthier time period. However, in now ’s world, this is no longer a viable choice.
Heavy customisation attracts rigidity. Each robot at a factory performs one specific action on one specific item. At a moment when adaptability and efficacy define whether producers will flourish or not, this really is an issue. To compete in our global economy, distribution and production costs need to be low. This requires increased standardisation and enhanced assembly logistics and lines.
On the other hand, product life cycles are much shorter because of changing consumer demand. And introducing a brand new item, or perhaps a design tweak to an existing product ends up needing more customisation in the automation infrastructure. Apart from being a disruption to manufacturing, it involves another round of expenditures.
The irony of significant customisation within the field of mass manufacturing isn’t even dropped to industry insiders. After all, the ultimate goal of streamlining manufacturing building lines would be to save costs by making more of the same faster and with greater quality controllers. However, an alternative does not exist just yet. This implies manual labour still performs a huge majority of the pick-and-place tasks, while even the most advanced industrial robots struggle to scale.
Though many have attempted to automate pick-and-place tasks professionally without the demand for sophisticated customised structuring mechanisms, none have succeeded yet. This may be the largest barrier to cross automation. In 2016, when Bill Gates was asked what he thought the largest improvement in technology in the next 20 years would be, he responded,”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.”
Amazon Founder and CEO, Jeff Bezos echoed these thoughts three decades later saying, “I believe grasping is going to be a solved problem in the subsequent 10 decades. It’s turned out to be an incredibly difficult problem, likely in part because we’re starting to solve it using machine vision, therefore (so ) machine eyesight did have to come first. ”
The only option appears to be via progress in machine vision: robots which could actually view, “comprehend ”, grip, and manipulate objects such as humans can. This can help the field of automation reach its full potential in the form of streamlined, hyper-automated warehouses and factories of the future.
(Disclaimer: The views and opinions expressed in this essay are those of the writer and don’t necessarily reflect the opinions of YourStory.)
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