Over the next decade, machine learning is poised to transform every industry.
Artificial intelligence (AI) signals the arrival of the 4th Industrial Revolution. Machine learning is at the base of this technology feeding, driving and providing solutions for the inevitable disruption that follows.
Machine learning and AI are much more than normal technological advances.
This new technology will link unrelated industries and forge new synergies that disrupt every existing industry. Along the way, it will create entirely new industries and previously unseen jobs.
Game Changing Numbers
Research shows that artificial intelligence can increase productivity 40 percent by 2035, driving profit improvement of up to 38 percent.
Wall Street analysts are projecting 15.7 trillion dollars in AI-driven GDP growth by the year 2030, an amount larger than the GDP of China and India together.
Machine learning has the potential to reduce supply system errors by 50 percent, reduce administration costs up to 40 percent and cut transportation costs 10 percent.
While most industries are open to incorporating AI, numbers like these force manufacturing to pay special attention.
The automotive industry has rapidly adopted AI into nearly every aspect of the business. From human resources to production lines, this adoption has been driven by lack of skilled labor, shortage of engineers and skyrocketing costs for elements like energy and lawsuits.
The Human Factor
Labor is one of the biggest costs for automakers and other manufacturers. These costs are not just in wages. Management, redesigning systems for worker safety and constant training are more expensive than paychecks.
Some of these costs can be reduced through an unusual form of deep machine learning called cognitive intelligence. This branch of machine learning replicates human reasoning within defined limits.
Manufacturers are using cognitive AI to replace live help desks. Other uses include pattern recognition in insurance or injury data to spot troublesome patterns before injury or lawsuit problems become costly.
The automakers were some of the first manufacturers to adopt artificial intelligence to manage robotic car building lines.
Machine learning is applicable to quality control, inspection and slowing or speeding the assembly line. Changes can be made to account for delays from parts suppliers or to adjust to increasing order rates.
Henry Ford’s original moving assembly line revolutionized entire industries, not just automobile manufacturing. Now manufacturers see a similar level of disruption from the effects of AI and machine learning.
Artificial intelligence, robotics, and automation are frequently seen as threatening human jobs. In reality, humans are freed to perform tasks that are less dangerous or more complex, resulting in higher pay.
New jobs are also created to install, maintain, program and monitor robotic or AI installations.
Combining elements of machine learning like asset management, rules compliance, diagnostics and forecasting, cognitive AI is streamlining logistics.
Train scheduling, ordering, load tracking, and dealer stock checks are all common uses for cognitive AI in the automotive industry today.
Monitoring for continuous improvement in both manufacturing and business processes can reduce delays and overhead.
This is making for leaner, demand-based car design, production, and marketing.
Software and Controls Development
In today’s manufacturing, the software is pervasive throughout the process. Hiring, office work, and forecasting are just a few of the mundane business-side operations that have required software for decades.
Nowadays there are tasks like operating the assembly line, powering robots at welding stations, parts retrievers and facility environments as well.
The automakers were the first corporations to use computer-aided design (CAD) and systems integration.
That’s never changed and continues with even deeper involvement and integration today.
In today’s factories, a change made in the accounting department to save money on a part will change 3D design drawings to reflect the parts change as well.
Revisions to CAD designs can be automatically checked for safety requirements, efficiency differences, cost changes and regulatory compliance within seconds.
Companies like IBM, Apple and Microsoft have used machine learning for years to replace bits of code or to simulate program changes.
Manufacturers use custom software for their assembly lines and design studios. They have the same opportunities for machine learning as software companies do.
Transportation affects manufacturing in several ways.
The most obvious is in bringing materials to the factory and delivering finished goods to distributors. Clearly, machine learning is ready to make huge contributions in the form of load scheduling and tracking.
However, recent trends in self-driving cars and trucks will also have huge impacts on factory operations.
Trucks can drive themselves to whichever dock is preferable and call automated unloading equipment to drop the load. Inside the factory, shuttles can pick up and deliver to and from the assembly line autonomously.
Self-driving forklifts make efficient use of expensive floor space. In the marketplace, self-driving cars are inching ever closer to the showroom.
Machine learning can deliver data from the real world as cars are operating, allowing informed decisions by factory software for cost, reliability and safety improvements.
Artificial intelligence, machine learning and automation have transformed not only the assembly line but the factory around it, the warehouse that feeds it and the office that makes decisions about it.
Soon, the outside world that delivers raw materials and buys the products will also be disrupted and things will never be the same.
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