A major driving force in the manufacturing industry for some time now is what is called Industry 4.0, i.e. the fourth industrial revolution. Industry 4.0 builds on the foundations laid by the three previous revolutions, namely:
Industry 4.0 can be described as an amalgamation of different technologies that together create drastic changes in almost all industries. An important driving force for Industry 4.0 is Artificial Intelligence, AI. AI has been talked about for a long time and different industries have come a long way in applying the new technology. You can read more about Industry 4.0 in the following article: Industry 4.0 – dare to trust technology.
Much of AI's development takes place in the industrial sector – according to a study conducted by consulting firm Accenture, the manufacturing industry is forecast to grow by $ 3.7 trillion (yes, you read that right, trillion) by 2035, thanks to AI alone. What opportunities are there with today's AI technology and what does the future hold for AI in the manufacturing industry? Let's take a deep dive into the world of AI technology.
We start with the most basic - what exactly is AI? AI means that human processes are simulated in machines, especially in computers. These processes include learning, reasoning and self-correction. AI is found in many places around us and has become a part of our lives, with everything from voice control on phones to self-driving cars. AI plays a crucial role in the production system of the future and is part of many of the technologies that together make up Industry 4.0, such as the Internet of Things (IoT), big data, cloud services, robotics and simulation.
AI is sometimes confused with machine learning, and although the concepts have much in common, there is a difference. Machine learning is a specific aspect of AI, where computers can learn themselves and develop based on the information and data available. AI aspires to mimic human intelligence and perform tasks on its own, and machine learning is one of many complex processes required to achieve this.
AI and automation are sometimes mentioned as a threat, as there is often a concern that people will be replaced by machines. Although some monotonous tasks will eventually be replaced, new jobs will be created, just as during previous industrial revolutions. More focus will instead be on creative, innovative, and value addition tasks - as these types of tasks are difficult for AI to emulate. AI offers fantastic opportunities in a variety of areas.
Some examples of application areas for AI that are already in use today are:
The possibilities with AI are many and are constantly expanding. It is clear that the use of AI is leading to radical changes and will result in new business models and ways of working in most industries.
AI is not an end in itself but a way to achieve other goals linked to production, such as better reliability and quality, new services and better production plans. AI can be applied in many different business processes of a manufacturing company, not just in the manufacturing process itself. Here we take a closer look at some examples of how AI can be applied in the manufacturing industry:
Instead of working with research and analysis manually, AI can help identify long-term trends, opportunities and threats in terms of business and technology. AI can also create ideal customer profiles and forecasts on demand for your products.
Instead of manual planning and constraint optimization, AI can create causal modelling, use "if-so reasoning" and plan based on probability (so-called probabilistic planning).
Instead of manual and experience-based planning, AI can perform dynamic network analysis by modelling external dependencies and anticipating disruptions.
In other words, AI can be applied in many different areas and at many different levels in a manufacturing company. Other areas of application can be maintenance and quality.
Development is fast and AI will affect the manufacturing industry in ways we cannot predict today. Some trends and future prospects that we and our industry colleagues can see in front of us are:
Computer vision – Camera-based quality control via image analysis will grow strongly and become standard with most manufacturing companies. The technology connected to computer vision has developed rapidly and there is no reason to let a human do quality checks when AI can perform the same tasks faster and with better results.
Internet of Things (IoT) – IoT enables the regular collection of data from devices that are constantly connected and integrated with other important systems within the organisation. Thus, needs can be both predicted and met without a person having to lift a finger. For example, the system can see that a certain product is starting to run out of stock, calculate when a new delivery is needed and in what quantities, with information from, among other things, the order system. It can on its own place a new order with your supplier. In other words, IoT can optimise production planning and free up time for employees who have previously handled this manually.
The pace of technology development is increasing – The various technologies that have enabled AI are continuously being developed, and at an ever faster pace. The more technology develops, the better and more sophisticated AI technology becomes, which can thus solve increasingly complex tasks. This in the long run leads to more efficient processes, where more output can be created with less input. As a result, the manufacturing industry will become increasingly competitive and therefore it is extremely important to utilise the new technology to remain competitive as a company.
AI is here to stay. Given the scale of its attributes it is important to keep up to take advantage of all the benefits of the new technology. For companies that are going to start applying AI technology, it is important to understand that the decisions that are made now will have consequences in the future. Avoid locking yourself into a particular AI technology. How much your AI solutions will cost and how long they take to implement depends on how digitised you are otherwise.
It can also be good to start small, and not try to redo everything at once. Starting to work with AI does not have to mean replacing existing systems. Instead, start with your existing systems and think about how they could work even better with AI. Then try to find an AI solution that can be connected to your existing systems.
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