Three Practical Steps to Using AI in Medical Device Manufacturing

ByLance T. Lee

May 31, 2022

Undoubtedly, there is a lot of hype around artificial intelligence (AI). He seems to be in all the other titles. There is no doubt that AI has the potential to transform medical device manufacturing. Rather than focusing on a complete transformation, manufacturers can see benefits by simply focusing on improving it.

AI on a production line does not necessarily mean everything is automated and no humans are involved. On the contrary, humans continue to be essential in manufacturing, even with AI. This is especially true in the early stages of using AI, as its benefits can only be reaped when the proper foundation is laid.

Step 1: Numerical Gap Analysis

Digitization is a key first step in using AI. AI depends on data. When it comes to machine learning (ML), a subset of AI, the program learns from the data it has. If this data is inaccurate, incomplete or out of date, the conclusions of the ML will be wrong. In the case of manufacturing, this can represent millions of dollars of loss. Providing a strong database for AI means data must be connected and free from human error.

Manufacturers can have a digital system in their shop. The question here isn’t necessarily whether you’re using a number system. Perhaps a better, more critical question is whether you’re using paper anywhere. For example, a state-of-the-art manufacturing execution system (MES) might control one line, but does another, lower-volume line use paper for its device history records? If so, this is a gap that needs to be closed one way or another.

There is 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 so 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, material requirements planning (MRP) system, and more.

Step 2: Set goals

As said before, AI is all the rage. This can cause leaders to be determined to use AI for the sake of using AI. AI is only useful to an organization if you know how to use it. This will largely depend on the data your manufacturing sites collect and the areas you want to improve. Connected data is more accurate and factory-wide digitalization means medical device companies can have a complete view of their performance in key metrics. Establishing this baseline gives companies an idea of ​​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 output by using the most efficient employees. This level of AI sophistication is time consuming because it requires more data and time for an AI to learn. A starting point could be to understand the common factors in batches of different quality levels. For example, the AI ​​can determine that batches with a particular defect tend to include materials from a particular vendor. If the larger goal is to reduce defects by a certain percentage, companies need to identify metrics that 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.

Step 3: Tracking and trending

Ideally, an AI can do much of this work for you. But that depends on how much data is automatically collected in a centralized location. Some programs offer a “set it and forget it” level of sophistication. If you are still in the middle of your digital transformation, this type of AI may not yet be possible. You can still do this step, but it will take more effort. Using a business intelligence tool will allow you to import information from multiple systems and analyze it to see if you are making progress.

If you can use AI, there are different levels of sophistication. Over time, an advanced program goes far beyond telling you whether you are getting closer or farther from your goal. The AI ​​will be able to tell you if you will achieve the goal given certain variables and what changes to make to ensure you achieve it. This enables medical device companies to improve product quality, reduce defects and ensure patient safety.

Conclusion

Medical device manufacturing has a lot to gain from AI. Although not essential at this stage, competitively it will be difficult for manufacturers to compete with companies that use AI. The benefits of producing better products faster with fewer errors give manufacturers who use AI an edge over those who don’t. The best way to start an AI journey is to digitize all systems and connect your data so that an AI can begin to be trained there.

About Master Control

MasterControl creates software solutions that enable life sciences and other regulated companies to deliver life-enhancing products to more people faster. MasterControl’s integrated solutions accelerate return on investment and increase efficiency by automating and securely managing critical business processes throughout the product lifecycle. More than 1,000 companies worldwide, ranging in size from five employees to tens of thousands, trust MasterControl’s cloud solutions to automate new product development, clinical, regulatory, quality management, supplier management, manufacturing and post-market surveillance. MasterControl solutions are well known for being scalable, easy to implement, easy to validate and easy to use. For more information, visit mastercontrol.com.

Industry Brief

Debunking Common Myths About Manufacturing Execution Software (MES)

The introduction of modern manufacturing solutions means that many of the common concerns about adopting manufacturing execution software today are more myth than fact. This industry brief dispels six common misconceptions about adopting an MES in life science manufacturing.

YOU’RE GOING TO LEARN:

  • Critical differences between a traditional MES and a modern MES solution.
  • Why overcoming common barriers to MES adoption will simplify digitalization in manufacturing.
  • How new workshop technology offers smarter, faster and more affordable alternatives to existing MES.

Learn more here.


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