Introduction

 

Manufacturing is one of the main industries that uses Artificial Intelligence and Machine Learning technologies to its fullest potential. Smart Factories, also known as Smart Factories 4.0 have major cuts in unexpected downtime, better design of the products, improved efficiency and transition times, the overall quality of the product and safety of the workers. Artificial Intelligence is the heart of Industry 4.0, delivering more productivity while staying environmental-friendly.

 

Siemens, GE, Fanuc, Kuka, Bosch, Microsoft and NVIDIA among other industry giants are already heavily investing in manufacturing AI with machine learning approaches to boost every part of manufacturing. TrendForce estimates that Smart Manufacturing (the blend of industrial AI and IoT) will expand massively in the period from three to five years, by 2020 the global smart manufacturing market will be valued over $320 billion, with the compound annual rate of growth at 12.5%. In 2015 the number of functioning industrial robots in factories was 1.6 million, in 2019 the number is expected to grow to 2.6 million, according to the International Federation of Robotics.

 

Industry 4.0

 

Let’s start with explaining how exactly AI/ML solutions bring value to the manufacturing Industry.

 

The Benefits of AI and ML in Manufacturing

 

Smart Maintenance

Being a very important part of every asset-reliant production operation, maintenance of equipment is one of the biggest expenses in the manufacturing — unplanned downtime cost nearly $50 billion to plants and factories worldwide, 42% of it is because of asset failure.

 

That’s why predictive maintenance became a vital solution that will help to save an enormous amount of money, complex AI algorithms like neural networks and machine learning are generating trustworthy predictions regarding the status of assets and machinery. Remaining Useful Life (RUL) of equipment becomes significantly longer. If something needs to be repaired or replaced, technicians will know beforehand and even will know which methods to use to fix the issue.

 

Better Product Development

Generative design is the method that allows putting detailed brief created by humans into an AI algorithm. The information in the brief can contain different parameters like available production resources, budget and time. The algorithm examines all possible variations and generates a few optimal solutions. This set of solutions can be evaluated by pre-trained deep learning models adding more insights and picking certain options. You can go through this process as many times as you want to settle on a perfect one. Artificial Intelligence is completely objective without any unproven assumptions unlike humans could have.

 

 

Read more on Machine Learning in Enterprise

 

 

Quality Improvement

In the modern world of short TTM deadlines and increased level of complexity of the products, it becomes even harder to meet the highest standards and regulation in terms of quality. Customers expect impeccable products and defects causes recalls, which massively damages the reputation of the company and its brand. AI can alert about the problems at the production line that can result in quality issues. These faults could be major or subtle, but they all influence the overall level of production and could be eliminated in the early stages.

 

Machine vision, for example, is an AI solution that uses high-resolution cameras to monitor defects way better than a human can. It could be combined with a cloud-based data processing framework which generates an automatic response. Also, manufacturers can obtain data on the performance of their products when they hit the market to make better strategic decisions in the future.

 

Market Adaptation

AI and ML are already an essential element of Factory 4.0, but they also can improve supply chains, making them interactive to changes on the market beforehand, managers can improve their strategic vision relying on AI suggestions. Estimates are generated by AI based on linking together a number of factors like political situation, weather, consumer behavior, the status of the economy, e.t.c. Staff, inventory, the supply of materials could be calculated according to predictions.

 

The biggest companies around the globe are already utilizing AI and ML in manufacturing and investing millions in its development. Here are some of the most prominent examples of companies using it.

 

Machine Learning Examples

 

The Real World Use Cases of AI and ML

 

Siemens

 

The German conglomerate claims that its practical experience in industrial AI for manufacturing already boosted the development and application of the technology. For decades, they leveraged neural networks for monitoring steel factories as well as improving their performance. Over the last ten years, they invested over 10 billion in US dollars in acquisition of software companies.

 

In 2016 Siemens presented Mindsphere, a smart cloud that enables manufacturers to monitor machine fleets around the globe. They added IBM’s Watson Analytics to the functions offered by the service the same year. The purpose of this solution is to grab every parameter in the manufacturing process from development to delivery and find issues and the ways to solve them.

 

Siemens uses neural network-based AI in their gas turbines. Over 500 sensors monitor various parameters, and the system is learning and making decisions on adjusting fuel values for the most efficient performance.

 

Siemens also offers Click2Make – a product that set a goal to make a mass customization a reality. When the companies have a complete understanding of their resources and have cutting-edge robots, it will be possible. To illustrate this, imagine a company that needs to deliver a limited edition of chairs. All the company has to do is upload the design, then the systems would provide this information to the factories that have all the necessary tools to build them. After the curtain factory starts the production, the management of the company can seek potential buyers in real time. This boosts the path from design to delivery significantly.

 

 

Read more on Machine Learning and AI in Food Industry

 

 

General Electric

 

One of the biggest companies on the planet is making everything from home appliances to massive industrial machinery. They have over 500 plants worldwide, but they have only just begun to make them smart.

 

Brilliant Manufacturing Suite is an attempt from GE to track and process everything in every aspect of manufacturing to find all possible problems and failures. Their first Brilliant Factory in India received $200 million in investments and raised the effectiveness rate of the facility by 18%, thanks to this solution. GE’s Brilliant Manufacturing Suite is aiming to connect all elements of manufacturing like design, engineering or distribution, into one global smart system that is scalable. It even has its own Industrial IoT platform Predix. This platform uses sensors to monitor all aspects of the manufacturing process and performance of sophisticated equipment. Predix has deep learning capabilities that can process all that information and come up with actionable insights. GE already invested over $1 billion in this system and by 2020 Predix is going to process over 1 million terabytes of information a day.

 

At the moment General Electric runs 7 Brilliant Factories with the Predix system showing positive improvements in production.

 

Fanuc

 

This company from Japan implements AI to make robots smarter. In fact, it is a leader in industrial robotics integrating deep learning to robots. Fanuc collaborated with Rockwell and Cisco to introduce FANUC Intelligent Edge Link and Drive (FIELD), an IoT platform for manufacturing industry. The partnership with NVIDIA resulted in using Fanuc’s AI chips for the factories of the future. The usage of deep reinforcement learning led to the ability of some industrial robots to train themselves. Fanuc and NVIDIA are aiming to enable multiple robots to learn simultaneously. If robots can learn together, it will be faster for each of them individually. In the future robots will be able to share their skills with each other saving overall time of the manufacturing processes in the Smart Factory.

 

Conclusion

 

For years robotics, advanced analytics, and automation has been a major part of the manufacturing industry. The increasing scale of adoption of AI in manufacturing seems more like an evolution, rather than industry disruption. Technology is already here, and the more massive implementation is a matter of time — according to McKinsey by 2025 smart factories will generate $37 trillion. Are you ready to step into the future? Feel free to contact us to learn more about this topic and discuss the possibilities!