The intelligent product life cycle: How AI will affect PLM
Artificial intelligence (AI) is going to affect every aspect of our working life in the coming years within innovation, product development and Product Lifecycle Management (PLM).
You may have already seen or heard how Microsoft has marked itself with great success as a front runner for the integration of AI. With Copilot, Microsoft has set the stage for how AI i.a. can help to automate routine tasks, predict product failures and optimize design processes.
Microsoft's approach illustrates how AI can significantly enhance diverse processes and automate manual tasks such as document management and data collection. If we look at how Microsoft has approached the task with AI, it is a good guess that the AI wave will also hit PLM within the coming years. It is therefore crucial that companies are proactive and open to new technology, which can greatly help to streamline processes, pin point weaknesses, reduce wasted time and the risk of errors, automate tasks and much more when the big AI wave sooner than later hits PLM.
This does not mean that we leave the really big decisions and decisive choices to the artificial intelligence. There will still be a need to be able to attribute responsibility to a person or body – e.g. when it comes to absolute requirements or a specific compliance. But to automate manual everyday tasks and improve company results, AI can be decisive.
We have identified some of the key areas where we expect AI to influence the PLM landscape and contribute to an even more intelligent approach to product lifecycle management:
1. Requirements management and traceability
Requirements management is crucial in the product development process as it involves gathering, analyzing and defining the needs and expectations of a product's end users. AI, including natural language processing and the ability to create summary overviews of large amounts of information in the form of documents, videos and other data sources, can contribute to even better input into the specifications of new products. With better specifications, the possibility of formulating more stringent requirements for new products increases.
2. Faster product development
Data reuse is essential for efficient product development as it helps companies minimize duplication of work and reduce costs. AI-powered systems can analyze large amounts of data from various sources to identify patterns and dependencies that can optimize the process and help companies make better informed decisions based on historical data.
3. Automation and efficiency
AI's ability to automate monotonous tasks will allow companies to free up valuable employee time that can instead be used for more complex and strategic tasks and decisions. This will create a more efficient work process and free up resources to focus on innovation and creativity in product development.
4. Predictions that can help reduce errors
One of the most compelling benefits of AI is the ability to make low predictions. By analyzing large amounts of historical data, AI can identify patterns and potential failure opportunities, which can result in a significant reduction in costs and improvement in product quality. This proactive approach will be able to address errors before they spread, thus strengthening the company's reputation and customer trust.
5. Optimizing the design process
By analyzing data, AI can provide insights into how designs can be improved based on past experiences and market trends. This will be able to position companies to meet changing customer needs with a speed and precision previously unattainable.
Be a pioneer in the future of PLM and feel the difference
Of course, AI's presence in PLM is not just about automating tasks, but also about creating opportunities for improved decision-making, data utilization and user experience. The virtual fusion between human expertise and AI technology paves the way for a more intelligent, proactive and competitive approach to product development.
As we look to the future, it is critical for companies to embrace this transformation, incorporate AI into their PLM strategies and make informed decisions based on the intelligence that technology brings. Those who manage to leverage AI to refine their product lifecycle management will not only experience a faster and more efficient process, but also create products that are more in line with market needs and expectations.
In order for AI to be able to make a significant contribution, however, it is necessary that the material it is based on has a certain quality. AI does not create order in chaos, but can create more order and value out of something that is already good. We also believe that this is the case with PLM – the basics must be in order before technologies such as AI can step in and make the final positive differences.
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The CFO guide: Why your manufacturing company needs PLM
In some companies, CFOs and other managers do not take interest in which CAD and PLM solutions are chosen for the company. And some may even opt out of a PLM solution altogether. But it is far from only the development department that gains when a company invests in PLM.
That is why we have created a guide for CFOs in manufacturing companies. This guide will tell you, why you should be aware of possibilities with PLM.