25 January 2021
Artificial Intelligence, the future for companies?
Value creation with Artificial Intelligence (AI) within the industry is becoming increasingly important. But what is AI and for what purpose can you use AI? Why should you get started with AI, and why is standardization so important in this process? Let’s take a closer look at it.
“It may feel big and far away. Yet big things start small. ”
Let’s start with the definition of AI: literally translated as “Artificial Intelligence”. But what exactly does that mean? If we look at the industry, we can refer to it as Algorithms that can make predictions for the future based on historical data. Little by little, those algorithms are getting smarter. They can take decisions faster than human beings and process large datasets with many possibilities, to draw conclusions from them quickly and efficiently. Human beings, on the other hand, can make better decisions based on certain expressions or emotions. But when you are talking about industrial decisions, AI is often better.
“AI can make decisions faster, more efficiently and on a larger scale. If something has never happened before, the algorithm just doesn’t know what to do. So a lot of training data and observations are needed to properly set up AI. With the use of AI, it is also important to consider what it costs and what it benefits. ”
A special AI method is “Machine Learning”, a collective term for mathematical models. Let’s clarify it a bit more with an example: a ship is equipped with a large engine with a certain pattern and fuel consumption. If the consumption is too high, you quickly want to know what is wrong. You can see deviations by using data. Algorithms recognize these quickly and you can immediately roll out that knowledge over other engines. A wonderful form of scalable technology!
There are several reasons to get started with AI. With AI you can:
Standardization is important to be future-proof, but why is a good standard so important for AI? Which design data do you need, which maintenance data and which data do you want to make available to AI as an organization? To retrieve data from installations, a good and future-proof software architecture must be in place. You need to know how and which data you want to get “up” from the installation. Names of attributes must be coordinated between the disciplines. It is also important to store and unambiguously process data, which makes capturing in modules essential. In short, just like for generative design and generating project documentation, it also applies to AI: bad input is bad output.
Covid-19 has accelerated developments such as AI. Companies are investing more and more in digitalization and we all contribute to it. How? Think of how Google sometimes asks you to provide your confirmation through selecting specific images with, for instance, all images with a traffic light. You help Google with imaging in this way. These kinds of processes are also emerging in the industry, where we also have to use our knowledge to teach algorithms what is ‘right’ and ‘wrong’.
AI is already greatly used in retail, yet AI may still sound a long way off for many companies in the industry. As an organization, you want to get started with data. Finding patterns, providing feedback, but above all asking the question: where can we create value? Is there a lack of urgency or a lack of data? Or we may still be wondering where to start. We currently see the greatest need for AI among OEMs and Asset Owners. They want to monitor an asset properly so that they can stay on top of it. By providing good service, the OEMs can distinguish themselves better and better. Asset Owners often have specific goals in mind, such as reducing costs and increasing safety. Hence, for these goals, it is ideally suited to apply AI!
But what does AI deliver? That is different for every company. Both quantitative and qualitative should be taken into account, such as the amount of data that is available, the quality of this data and the people within the organization. Maybe that’s why it’s good to ask ourselves: what if we don’t …?
Predictive Maintenance or Condition Based Maintenance is on the agenda for 90% of the industry parties. But how can you execute predictive maintenance if your design data is not even in order? A great topic for a new blog. Keep an eye on our website!
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