Artificial intelligence in a production line does not have to mean that everything is automated and no people are involved. In contrast, people in AI remain important to create. This is especially true when using artificial intelligence, because the benefits can only be reaped if the right foundations are laid.

Step 1: Analyze the digital gap

Digitization is an important first step in using AI. AI relies on data. In terms of machine learning (ML), a subset of AI, the program learns from the available data. If these data are inaccurate, incomplete or outdated, the ML conclusions may be incorrect. In the case of production, this could represent losses in the millions of dollars. Providing a solid database for AI means that data must be interconnected and free of human error.

Manufacturers may have a digital system in their store. This question is not necessary if you are using a digital system. Maybe a better, more critical question if you use paper everywhere. For example, a state-of-the-art production execution system (MES) can manage one line, but does it trust the other low-volume line on paper for equipment history records? If so, it's a hole that needs to be closed somehow.

There is no magic system that can take care of everything on behalf of a medical device manufacturer. Fortunately, we live in a world full of unity. The systems can exchange information, so employees no longer have to enter the same data multiple times in different places. Now the MES can directly draw or pass information to or from the enterprise resource planning (ERP) system, the material requirements planning system (MRP), and so on.

Step 2: Set goals

As I said before, AI is in vogue. This can lead to drivers' decision to use AI to use AI. Artificial intelligence is only useful in an organization if you have an idea of how you can use it.

This will largely depend on the data collected by your manufacturing plants and the areas you want to improve. Associated data is more accurate, and nationwide digitization means that medical device companies have a complete view of how they are doing in key metrics. Establishing this baseline gives companies an idea of the areas where they can improve and how they can monitor this development.

Artificial intelligence can be used to help medical device companies with the least defects and the highest returns with the most efficient employees. This level of artificial intelligence improvement takes time because learning artificial intelligence requires a lot of data and time.

Understanding the common causes of batches of different quality levels can be a starting point. For example, artificial intelligence can determine that batches with a particular defect are likely to contain materials from a particular supplier. If a larger goal is to reduce defects by a certain percentage, companies need to know 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 tracking that data.

Step 3: Watch and watch

In fact, AI can do most of this work for you. However, it depends on how much data is automatically collected in one central location. Some programs offer a "set it and forget it" level of goodness. If you are still in the middle of your digital transformation, this kind of artificial intelligence may not be possible yet.

You can still take this step, but it will take more effort. With business intelligence, you will be able to take your information from multiple systems and analyze it to see when you are making progress.

If you can use AI, there are different levels of efficiency. Over time, the advanced program can do more than tell you when you are on or off target. Artificial intelligence will be able to tell you when you will reach a goal, with respect to certain variables and what changes will be made to ensure that you achieve it. This enables companies to improve the quality of medical device products, reduce defects and guarantee patient safety.

Conclusion

The manufacture of medical equipment has many advantages over artificial intelligence. Although it does not stand out at this point, it is difficult for manufacturers to compete with companies that use AI. The benefits of faster production of better products with fewer errors give manufacturers who use AI an advantage over none. The best way to start the AI journey is to digitize all systems and connect your data so that artificial intelligence can start training them.