Quality is of utmost concern to manufacturers everywhere, but perhaps nowhere does it hold greater importance than in the plant where medical devices are being made and defects can have life or death consequences.
Think for a second about the implications of faulty X-ray or ultrasound machines, surgical devices, or pacemakers. Until now, the way the quality of these devices has been controlled is through manual inspections performed by quality analytics (QA) teams. They often randomly inspect products coming off the assembly line, and if a product seems to contain a defect, it is eliminated. But what happens if the defects were not noticed until the product has been in use for a while, possibly even implanted into a patient?
Now consider if you could predict which devices might be defective and get to the root cause of the problem. Then you could have a chance to reduce defects and prevent them from happening.
By applying predictive analytics, companies can make a shift to a proactive mode to avoid potential problems before they happen, and look at the variables and data to know with a high degree of probability which products will sail and which will fail. It also frees up manufacturers from spending 90% of their time focusing on quality assurance and addressing problems so they can spend more time on strategic approaches and innovation. It's certainly a win-win situation for everyone, companies and consumers alike.
AI, more specifically machine learning is already being used among leading medical device manufacturers to help ensure that products are defect-free before they leave the plant. For example, a medical device manufacturer in Puerto Rico is using machine learning software to conduct predictive analytics using a combination of historical and current data to identify discrepancies, variances, and the smallest weakness that could cause a specific product to fail well before the product leaves the factory floor.
In addition, AI-driven solutions can identify patterns to proactively help manufacturers identify the root cause of failures that can lead to safer and more effective re-engineering and design of all products. In addition to saving lives, the solution saves costs in terms of fines, lawsuits, and FDA violations. It also helps companies save resources and raw materials that are not wasted when a product must go to the scrap heap.
3D Printing Requires New Approach to Quality Control
The explosion of 3D printing has enabled rapid prototyping of parts in medical device markets, becoming a game-changer in how parts and products are produced. Yet the challenge of such mass production is increasingly centered on quality control. AI can help manufacturers detect defects in real time, something that would be almost physically impossible using manual methods. However, big data is critical to this process. It can be collected during the manufacturing process and used to train the machine learning tool to spot anomalies and provide automatic self-correction.
The Possibilities of AI are Endless
In addition to product quality applications in the medical device market, predictive analytics, which is driven by machine learning, offers promising possibilities in all areas of medical device engineering and design. For example, predictive analytics can help inform an engineer about how upgrades or changes to specific parts or products could impact their operation or safety.
Companies are increasingly evaluating Industrial Internet of Things (IIoT) systems, which use data gathered from sensors on devices and provide valuable feedback and insights on ways manufacturers can improve or alter their products. When combined with machine learning tools, data driven by IIoT can harvest much greater insights and drive better decision making.
Taking the First Step Toward AI Evolution
During a time when medical device manufacturers are focused on a competitive, regulatory-controlled market, where they struggle to deliver innovative solutions free of defects, and to do so cost-effectively, how can they move toward AI-driven transformation on the manufacturing line? Below are three key tips.
- Understand that AI is a long-term commitment. Unlike other digital transformation initiatives, AI could never be a one-and-done project. The key to AI success is constant training. AI solutions need to be continuously fed new data in order to become smarter and more relevant, so it's important to retain or partner with firms that can provide this ongoing expertise to continuously improve the algorithm.
- Make sure your data house is in order. Effective machine learning solutions are only as good as the data provided. Medical device manufacturers should determine where all their data resides, what data may be lacking and work to centralize and augment it so they have ample fuel to drive more relevant solutions.
- Ask the right questions. Before you even think about building a predictive model, you need to know what you are trying to solve and work backward. Which parts or products are you evaluating? Are you trying to determine the likelihood of a device failure, or how to improve usability? You should conduct a pilot program to determine the ROI, time savings and other benefits you could achieve before moving forward with such a transformative project.
Innovation in the medical device technology market is having a major impact on improved patient outcomes, transforming the way providers treat patients and how diseases and conditions are managed. The use of AI to improve the quality of devices and ensure they are defect free before they leave the factory floor is fast becoming a game changer, improving safety, reducing costs and instilling greater confidence in an industry that is vital to the well being of all of us.