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Digitisation is a key first step in using AI. AI is dependent on data. When it comes to machine learning (ML), a subset of AI, the program is learning from the data available to it. If that data is inaccurate, incomplete, or out of date, the conclusions from the ML will be faulty. In the case of manufacturing, this can represent millions of dollars in loss. Providing a solid data foundation for the AI means data must be connected and free from human error.
Manufacturers may have a digital system on their shop floor. The question here isn’t necessarily if you’re using a digital system. Perhaps a better, more critical question is if you’re using paper anywhere. For example, a state-of-the-art manufacturing execution system (MES) may control one line, but does another, lower-volume line rely on paper for its device history records? If so, this is a gap that needs to be closed in some way.
There’s no magic system that can take care of everything for a medical device manufacturer. Fortunately, we live in a world full of integration. Systems can exchange information in such a way that employees no longer have to enter the same data multiple times in different places. Now an MES can pull or push information directly to or from an enterprise resource planning (ERP) system, a material requirement planning (MRP) system, and more.
As previously said, AI is all the rage. That can lead to executives being determined to use AI for the sake of using AI. AI is only useful to an organisation if you have an idea of how to use it. This will largely be dependent on the data that your manufacturing sites collect and areas that you want to improve. Connected data is more accurate and plantwide digitisation means medical device companies can have a complete view of how they perform in key metrics. Establishing this baseline gives companies an idea of areas where they can improve and how to track that improvement.
AI can potentially be used to help a medical device company have the fewest defects and the highest yield using the most efficient employees. This level of sophistication in AI takes time because it requires more data and time for an AI to learn. A starting point could be understanding common factors in batches of varying levels of quality. For example, AI can determine that batches with a particular defect tend to include materials from a particular supplier. If the larger goal is to reduce defects by a certain percentage, companies need to identify what metrics can be measured and better understood to achieve that goal. Once you know what you need to measure, you need to start tracking and trending that data.
Ideally, an AI can do much of this work for you. But that depends on how much of the data is automatically collected in a centralised location. Some programs do offer a “set it and forget it” level of sophistication. If you’re still in the middle of your digital transformation, that kind of AI might not be possible yet. You can still do this step, but it’ll require more effort. Using a business intelligence tool will let you bring in information from multiple systems and analyse it so you can see whether you’re making progress.
If you can use AI, there are different levels of sophistication. Over time, an advanced program goes far beyond telling you if you’re moving toward or away from your goal. The AI will be able to tell you if you’ll hit the goal given certain variables and what changes to make to ensure you do reach it. This lets medical device companies improve product quality, decrease defects, and ensure patient safety.
Medical device manufacturing has much to gain from AI. While it isn’t essential at this point, competitively, it will be hard for manufacturers to compete with firms that do use AI. The benefits from producing better products faster with fewer errors gives manufacturers that do use AI an advantage over those that don’t. The best way to begin an AI journey is by digitising all systems and connecting your data so that an AI can begin being trained on it.
