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Different Ways Machine Learning is Leading to Smarter Manufacturing

Last updated: 10 Apr, 2020 By | 6 Minutes Read

In the current time, machine learning has become a vital part of the manufacturing industry, helping to reduce costs and production time. Many manufacturing businesses use (Machine Learning) ML for production as well as to improve product quality. Machine learning, when integrated with robots, can make your manufacturing processes smarter by producing products with detailed precision, getting you production insights and forthcoming issues in machines, etc.

According to Deloitte, “Machine learning improves product quality up to 35% in discrete manufacturing industries.” Source

From a reduction in labor costs and downtime to enhancements in productivity and speed, machine learning with the help of robots is taking the manufacturing industry to the new heights.

Let’s now go further to what experts have to say on this topic.

Victor Fredung

1. Victor Fredung

Victor Fredung CEO, Shufti Pro

AI and machine learning has revolutionized the way business is done around the world. Manufacturing is no exception. According to estimates by McKinsey, the new technology has the potential to add up to $2trillion in manufacturing and supply chain planning. This exhibits the potential of machine learning to boost economic activity for countries employing it across industries for revenue growth, as well as efficiency.

According to Gartner, by 2022, the business value created by AI will be close to 4 trillion. The main focus is to operationalize AI solutions for product design, quality, and yield. Intelligent software can supplement measures to boost productivity and improve quality through streamlined manufacturing scheduling.

Data analytics are being used to make more informed and more precise decisions regarding production outputs or gleaning other actionable insights. As operations become more digitized and heavily reliant on data, cybersecurity becomes a foremost concern for all industries.

AI-backed solutions for safeguarding sensitive information and ensuring smooth workflows are finding their way into long term business strategies. Surveys show that a large percentage of business leaders believe AI tools help them handle inconsistencies in digital infrastructure with much more ease.

Brian Mitchell

2. Brian Mitchell

Manager of Digitalization for SKF’s Lubrication Management Division. With 14 years of professional experience, Brian has worked for SKF, Schneider Electric, Eaton, and Emerson, managing various portfolios related to IT and network communications. Brian holds Bachelor’s degrees in economics and mechanical engineering and a Master’s degree in mechanical engineering from the Missouri University of Science and Technology, as well as an MBA in business from Baldwin Wallace College.

My experience is primarily as a provider of smart technology to OEM’s. Several heavy industries, including Pulp & Paper and Steel, have major challenges related to process interruptions from unexpected equipment failure. These industries are rapidly implementing sensors that detect the health of production assets. Through machine learning, this sensor information can be analyzed, and, either manually or automatically, the production process can be adjusted to extend the life of the equipment to the next planned shutdown. Machine learning is routinely applied to extend the life of bearings, electric motors, pumps, and other common components.

Read Also : How to Manage Accounting for Manufacturing Business?

Adeel Shabir

3. Adeel Shabir

Content Marketing Executive, GigWorker

Top 3 Ways ML is leading Smarter Manufacturing Machine learning has been on trending, and many businesses are working on implementing their way of machine learning in their business. In 2020, 57% of buyers are likely to depend on companies to figure out what they require prior asking for something specific. 80% of executives believe that AI technology boosts overall productivity. (Source)

*Following are the different ways machine learning is leading smarter manufacturing:*

1. The process has been improved with the introduction of machine learning to the business. There are many businesses which have introduced the ML to manufacturing. The machines are learning different ways to work on the same project day in and day out. This way, they can work much more than humans can do.

2. Product development has been increased because the bulk creation of products has been seen as downtime for humans. Whereas, the machines can do a much better job of creating the same product over and over. There is no inconsistency in the product.

3. The quality fo the product has been increased with the help of machine learning. There are many ways that businesses are using ML for their manufacturing. Machines can identify the flaws in the final product much faster than humans.

David Singletary

4. David Singletary

I am a Tech Advisor for Wiss and Company, an Accounting and Consulting firm. I’ve been in the technology field for over 25 years.

Manufacturers who adopt machine learning can predict the maintenance of their machines. This will help reduce the wear and tear of the machines and keep them running smoother longer. Today if a machine breaks down unexpectedly, your production can come to a screeching halt, and customer orders can be delayed. If the sensors can predict or learn when there are indications of a problem, you can prepare for when the machine gets repaired, thus eliminating any surprises. According to a survey by PwC, manufacturer’s adoption of machine learning and analytics to improve predictive maintenance is predicted to increase by 38% in the next five years.

Machine learning can help improve the supply chain by reducing forecasting errors. This makes the ordering process more precise and keeps inventory lean, by invoking just-in-time inventory. Automating inventory using machine learning can increase inventory turns by 25%.

Assisted machine learning, still involves a human, and can increase individuals production versus actually eliminating jobs. For example, a quality control process can be increased by only focusing on output that yields a certain percentage of quality. The worker only needs to inspect these items to see if they can be used or not.

Manually doing quotes, pricing, and proposals is no less than a disadvantage for sales in any business as more often, who provides the first quote generally closes the deal.

Machine learning can automate this process. No longer do you need to rely on spreadsheets and manual processes to process a quote and get a proposal out the door.

Smart manufacturing is here to stay. Those that adopt early will keep up with the instant gratification mentality people now have. Those that are slow to adopt will find playing the catch-up game to be more costly and run the risk of losing customers.

William Taylor

5. William Taylor

Career Development Manager, VelvetJobs

Reducing labor costs and increasing productivity

In reference to your query about Different ways in which machine learning is leading to smarter manufacturing, please find my response below:

Machine learning is already reducing labor costs and downtime and is increasing workforce productivity and overall production speed. AI, in collaboration with the industrial IoT, is taking the industry toward smart manufacturing. This is because machine learning can be a part of daily processes in the entire manufacturing cycle. With the use of this technology, manufacturers can detect most types of issues arise while performing their routine production methods, from issues responsible for production delay to unprofitable lines.

*Bottom Line:* Machine learning is reducing labor costs and downtime and increasing workforce productivity

Read Also: Why Most Manufacturing Firms Outsource Accounting Services to India?

Thomas Bradbury

6. Thomas Bradbury

Technical Director, GetSongkey

Manufacturing is nothing like it was a few years ago. While manual labor and work was the primary requirement in manufacturing plant, today, a significant number of the tasks involved in the production of goods – ranging from the basic toy to something more complex, such as a car, are handled by artificial intelligence, along with machine learning.

Machine learning has already made a huge impact on manufacturing. By equipping an AI system with machine learning capabilities, it is possible for a computerized system to not only learn how the production of specific items work, but also to identify problems before an item leaves the factor, as well as to suggest potential improvements that could yield a boost in productivity – something essential in the competitive world we live in today.

Bottom Line: Machine learning makes it easier for manufacturers to produce items with fewer chances of errors.

Kenny Trinh

7. Kenny Trinh

Managing Editor, Netbooknews

According to Trendforce, the global market for smart manufacturing solutions is predicted to grow by $320 billion in 2020. Here are specific ways that machine learning is driving smart manufacturing to that prediction.

Robots

Factories across the world will use more than 3 million industrial robots by 2020, according to International Federation of Robotics.

Robots are revolutionizing manufacturing. Through machine learning, they have been able to learn repetitive tasks that are sometimes too complex or too dangerous for humans and then complete it much more quickly and accurately. Because of machine learning, they are even becoming more proficient in making decisions and operating autonomously.

For example, Fanuc – a Japanese manufacturer of industrial robotics & automation technology. By using a type of machine learning solution called deep reinforcement learning, Fanuc enables its robots to teach themselves new skills quickly and efficiently, without the need for complex programming.

Quality Control

Machine Learning enhances the level of quality in manufacturing processes. Through intelligent data analyses, deep-learning neural networks can predict which facilities require maintenance as well as optimize the operation of the said systems and facilities.

A good example is with the German industrial conglomerate Siemens. Siemens, for decades, has been utilizing neural networks (a subset of ML) for monitoring its steel plants and improving their efficiencies.

This results in *significantly reduced turbine emissions *of toxic nitrogen oxides without affecting performance.

Conclusion

After considering the experts’ opinions, it is now fair enough to say that the manufacturing industry has become technically-advanced with the help of machine learning. Manufactures, for decades, have been one of the early adopters of all types of technological advancements, including robotic process automation, digital solutions, etc. Thus, there’s nothing new that manufacturers across the globe are investing heavily in machine learning to boost their processes.

Read Also : 5 Technological Innovations Accountants in Manufacturing Sector Can’t Ignore

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