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Is Artificial Intelligence the Future of Manufacturing?
Definitely. Artificial intelligence is able to provide solutions to some of the challenges glaring manufacturing. Just like the invention of the steam engine gave rise to the industrial revolution, we are witnessing a new revolution in manufacturing through the rise of artificial intelligence and the possibilities it unlocks.
The entire product life cycle; planning, production, and distribution can be enhanced by artificial intelligence. A survey done by Deloitte states that 93% of companies believe that AI will be a pivotal technology to drive growth and innovation in manufacturing.
Through innovative applications of artificial intelligence, manufacturers can leverage it to design products, detect defects and in machine maintenance. It’s impossible to deny the impact that machine learning and natural language has had in manufacturing in recent years.
In this article, we’re going to look at 5 innovative applications of artificial intelligence in manufacturing in 2021.
1. Quality Control
While the human eye can miss even the big flaws in products, it’s obvious that we can’t detect microscopic errors.
Poor quality products affect manufacturers in various ways. From low profits to productivity. It goes without saying that low quality products will not have great customer satisfaction. In addition, it will give a negative public perception about the manufacturer.
In 2014, Takaka lost 24 billion dollars after recalls of its airbags after they were found to be defective and a potential life hazard. About 287.5 million airbag inflators were recalled. It was estimated that expenses originating from the recall totaled to about 607.8 million.
Statistics show that 59% of respondents in the manufacturing sector agree that quality control is the most important use case for artificial intelligence.
Manufacturing equipment fitted with high resolution smart cameras can zoom and identify microscopic errors. The equipment uses machine learning algorithms from information it collects to capture errors and flag them immediately.
Early detection of errors will improve the quality of products as well as save time and money while keeping your customers happy.
BMW, a leading automotive manufacturer, has leveraged AI to create accurate and high-quality car parts. By using Autonomous Machine Vision, they can get automatic image-based inspection and analysis of parts. Therefore, it allows them to detect any defects and act on them promptly.
2. Digital Twins
Do you find yourself at a crossroads with product design?
Innovating new designs can be cumbersome, costly and time consuming to most manufacturers. With increased demand and the competitive nature of the manufacturing industry, manufacturers have to up their game and create cutting-edge products. This will definitely improve customer experience.
The ability of digital twins to collaborate the physical product with the virtual product has increased productivity by 17-20%.
By using historical data, it has the ability to forecast and prepare you for any eventualities. By doing this, the manufacturer is able to put in place preventive measures.
In fact, the use of digital twins is expected to grow by 38% annually to reach 16 billion by 2023.
Interestingly, digital twin enables manufacturers to collect data from the virtual twin for them to improve the original products while monitoring production processes. Moreover, the data can help the manufacturers assess and identify issues such as quality and product performance and promptly rectify it.
Volvo, a renown automobile manufacturer leveraged digital twins to accommodate configurations selected by its customers based on what they liked.
By initiating digital twins for product quality assurance across its production plants, they were able to save millions of dollars and improved operational efficiency.
3. Predictive Analytics
Wouldn’t we love it if we could predict the future?
As artificial intelligence evolves day in and day out, manufacturers can now use predictive analytics to get real-time information of production processes.
Machine malfunctions, maintenance demands and raw material shortages are some of the pain points of manufacturing. They result in huge loss of revenue, expensive maintenance cost and ultimately slows down production efficiency.
According to IIoT world, over 80% of manufacturers are unable to tell their true downtime correctly. Machine downtime can cost up to 22,000 per minute which is 1.4M per month.
Fortunately, with the application of machine learning, predictive analytics has come to the forefront of changing and improving the manufacturing industry.
For a better grasp of predictive analytics, some underlying AI knowledge is crucial. Get that knowledge via these artificial intelligence training courses, which also help production managers boost RPA effectiveness and minimize downtime.
Predictive analytics uses real-time data to predict a number of issues that might arise during the product cycle. These include but are not limited to; machine failure, material demand and production demand.
This takes the edge off assumptions and significantly reduces downtime, and maintenance costs.
Roll-Royce, one of the world’s largest manufacturers of aircraft engines has leveraged predictive analytics to limit their carbon footprint. Machine learning allows them to monitor how their engines work and maintain each engine.
4. Collaborative Robots
There’s no doubt that robots are fast and efficient, more than humans. As the world is striving towards green energy solutions, the manufacturing industry is aiming at reducing its carbon footprints.
Before artificial intelligence and machine learning bots, manufacturers largely depended on human labour. Production was slow, filled with errors and low revenues. Workers were subjected to gruelling work with long hours, poor working conditions and very little pay.
How have robots improved manufacturing?
The installation of Cobots has increased between 2017-2019, 2018 being the peak, reaching 46%
Machine learning feeds the robots with data that allows them to perform repetitive tasks accurately and with efficiency, on the go. Manufacturers use industrial robotics to fill the looming skill gap of material handling and work safety.
Collaborative otherwise called cobots are designed to safely work alongside humans. A report by Reportlinkers indicates that, by 2025 robots will grow the most due to their easy-to-use programmable softwares.
When Koyo Electronic was facing productivity and quality challenges, it leveraged Cobot to work on the issues. It significantly increased productivity by 31% while reducing daily work time from 10 hours to 8hours . Not only were they able to save time, and revenue but improve the quality of the products as well.
5. Supply Chain Management
Manufacturers have their fair share of challenges when it comes to supply-chain management. As a manufacturer, you will need to consider a few supply chain management techniques for a seamless production process.
You will need to get a hand in the following;
- Transport and logistics,
- Financial planning
- Supplier network.
Delays by suppliers can cause diverse set backs in the entire production process. It can be challenging keeping up with customer buying behavior as their consumer demands change from time to time.
Does artificial intelligence help mitigate supply chain challenges?
Yes it does. Gartner predicts that by 2023, AI techniques will be an embedded component across 25% of all supply chain technology solutions. For a deep dive into intelligent supply chain analytics and how it can improve your manufacturing processes, spare some time to start learning AI with this simple 7-step guide that’ll get you quickly up to speed.
Offering new insights into numerous aspects of the supply chain, ML has also made the management of inventories and team members super simple.
Due to supply delays and poor transport logistics, manufacturers using vehicle tracking devices can keep an eye on supply trucks to make sure that transportation of goods is smooth.
Echo Global Logistics uses artificial intelligence and machine learning to give real-time solutions in procurement of transportation; shipment execution and tracking.
By using AI they allow their customers to make better shipping decisions.
Conclusion
Innovative application of artificial intelligence has impacted the manufacturing industry in a tremendous way. There’s no telling where artificial intelligence will take manufacturing in the coming years.
However, one thing is certain. With evolving technology embedded in artificial intelligence, it will only get better. Since the innovation of the steam engine in the 1600s, artificial intelligence is the next big innovation yet.
Not only has AI revolutionized manufacturing, it has changed the way manufacturers think and work.
Introduction of artificial intelligence embedded robotics has restored work safety, as well as humane working conditions. 2021 is the time to embrace artificial intelligence as the future of manufacturing.
