From advanced robotics in R&D labs to computer vision in warehouses, technology is making an impact on every step of the manufacturing process. Article by CBInsights.
Lights-out manufacturing refers to factories that operate autonomously and require no human presence. These robot-run settings often don’t even require lighting, and can consist of several machines functioning in the dark. While this may sound futuristic, these types of factories have been a reality for more than 15 years.
To imagine a world where robots do all the physical work, one simply needs to look at the most ambitious and technology-laden factories of today. For example, the Dongguan City, China-based phone part maker Changying Precision Technology Company has created an unmanned factory.
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Everything in the factory — from machining equipment to unmanned transport trucks to warehouse equipment — is operated by computer-controlled robots. The technical staff monitors activity of these machines through a central control system.
Where it once required about 650 workers to keep the factory running, robot arms have cut Changying’s human workforce to less than a tenth of that, down to just 60 workers. A general manager for the company said that it aims to reduce that number to 20 in the future.
As industrial technology grows increasingly pervasive, this wave of automation and digitisation is being labelled “Industry 4.0,” as in the fourth industrial revolution. So, what does the future of factories hold?
Manufacturers predict overall efficiency to grow annually over the next five years at 7x the rate of growth seen since 1990. Manufacturing is deeply changing with new technology, and nearly every manufacturing vertical — from cars, to electronics, to pharmaceuticals — is implicated. The timelines and technologies will vary by sector, but most steps in nearly every vertical will see improvement.
Product R&D
From drug production to industrial design, the planning stage is crucial for mass-production. Across industries, designers, chemists, and engineers are constantly hypothesis testing.
Will this design look right? Does this compound fit our needs? Testing and iterating is the essence of research and development. And the nature of mass-production makes last-minute redesigns costly.
Major corporations across drugs, technology, aerospace, and more pour billions of dollars each year into R&D. General Motors alone spent upwards of US$8 billion on new development last year.
In the highly scientific world of R&D, high-calibre talent is distributed across the globe. Now, software is helping companies tap into that pool. While R&D scientists may seem non-essential to the manufacturing process, they are increasingly critical for delivering the latest and greatest technology, especially in high-tech manufacturing.
Companies are exploring robotics, 3D printing, and artificial intelligence as avenues to improve the R&D process and reduce uncertainty when going into production. But the process of hypothesis testing has room for improvement, and tightening iteration time will translate to faster and better discoveries.
Robotics & 3D Printing Speed Up Product Development Across Verticals
Accelerating product development is the number one priority for firms using 3D printing, according to a recent industry survey. Moreover, 57 percent of all 3D printing work done is in the first phases for new product development (ie: proof of concept and prototyping).
3D printing is already a staple in any design studio. Before ordering thousands of physical parts, designers can us 3D printing to see what a future product looks like. Similarly, robotics is automating the physical process of trial-and-error across a wide array of verticals.
In R&D for synthetic biology, for example, robotics making a big impact for companies like Zymergen and Ginkgo Bioworks, which manufacture custom chemicals from yeast microbes. Finding the perfect microbe requires testing up to 4,000 different variants concurrently, which translates to lot of wet lab work.
Using automatic pipette systems and robotics arms, liquid handling robots permit high-throughput experimentation to arrive at a winning combination faster and with less human error.
Looking beyond biotech, material science has played a pivotal role in computing and electronics.
Notably, chip manufacturers like Intel and Samsung are among the largest R&D spenders in the world. As semiconductors get ever-smaller, working at nanoscale requires precision beyond human ability, making robotics the preferred option.
Tomorrow’s scientific tools will be increasingly more automated and precise to handle micro-scale precision.
AI Is Hastening Materials Science Discoveries
Thomas Edison is well-known for highlighting materials science as a process of elimination: “I have not failed 10,000 times. I have not failed once. I have succeeded in proving that those 10,000 ways will not work.”
The spirit of Edison persists in today’s R&D labs, although R&D is still less digitised and software-enabled than one might expect. Better digitisation of the scientific method will be crucial to developing new products and materials and then manufacturing them at scale.
Currently, the hottest area for deals to AI startups is healthcare, as companies employ AI for drug discovery pipelines. Pharma companies are pouring cash into startups tracing drug R&D such as Recursion Pharmaceuticals and twoXAR, and it’s only a matter of time until this takes off elsewhere.
Citrine Informatics runs AI on its massive materials database, and claims it helps organisations hit R&D and manufacturing milestones 50 percent of the time. Similarly, Deepchem develops a Python library for applying deep learning to chemistry.
In short, manufacturers across sectors — industrial biotech, drugs, cars, electronics, or other material goods — are relying on robotic automation and 3D printing to remain competitive and tighten the feedback loop in bringing a product to launch. In future manufacturing processes, augmented and virtual reality could play a greater role in R&D, and could effectively “abstract away” the desktop PC for industrial designers, possibly eliminating the need for 3D printed physical models.
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