The World of Quality Control Has Changed

Due to persistent supply-chain challenges, medical device manufacturers should use every advantage they can get to edge out the competition. The last thing they need is to get in their own way, weighed down by issues that slow or even stop production.

One of the most exciting developments we’ve seen is the application of Artificial Intelligence (AI) and machine learning to quality systems in the manufacturing production process.

Backed by statistical algorithms, predictive quality analytics helps you predict production outcomes based on data pulled from across your operations.

What does this mean? Instead of reacting to quality issues such as defects as they happen, you can forecast their likelihood and act before they cost you in time, resources, and money.

Reactive versus Proactive

In heavily regulated markets such as medical device, quality drives success. And defects drive up costs.

Unfortunately, many manufacturers still take a reactive approach to quality systems in the production process, using historical data collected in an ERP and frequent testing in a live-production environment to ensure products meet quality standards. This approach is slower, less agile, and less reliable.

In contrast, predictive quality analytics leverage machine learning to forecast quality issues based on a dynamic set of real-time data from across the organization. That reduces the need for frequent starts and stops for testing or reconfiguring a line after an issue is detected.

The use of AI is increasingly common in manufacturing and has grown in its use in 2021. Here’s an example of how it works when applied to quality:

A machine-learning model identifies which batch could have potential issues based on factors such as the material supplier, workers, machinery, and raw material types. This allows the manufacturer to optimize material usage, identify and isolate defects, and predict scrap rates. In addition to predictive data, the model can provide real-time alerts based on live-production conditions.

For example, a manufacturer may identify that batches utilizing a chip from a specific supplier are more likely to have quality issues than batches without.

Or, production orders have a higher failure rate on Line No. 2 when operated by Joe Smith.

The data does not provide causation. But it does point to something to investigate so plans can be adjusted before production starts and quality issues emerge.

The ROI of Predictive Quality Analytics for Medical Device Manufacturing

You may have just chalked quality issues up to being a part of doing business, writing off the additional costs because they are expected in any production environment. But today every little thing matters. And advanced analytics can drive improvements where you may not even realize opportunities exist.

Find ROI in:

  • Reduced scrap, rework, sampling, and testing
  • More efficient use of equipment
  • More reliable delivery timing
  • Improved turnaround time for new products
  • Fewer recalls
  • Better and more efficient tracking for compliance

This technology is still in the early-adopter stage, which means that there’s a big opportunity for companies that already have a foundation for machine-learning initiatives. If you aren’t there yet in your data journey, there are steps you can take to build that foundation.

Find a partner that knows how to help manufacturers scale up their data and analytics capabilities so that you can take advantage of machine-learning models that have transformed quality assurance and quality control.

Predictive quality analytics speaks to the core benefits of digital transformation, with measurable impact in many areas of your business, including lower costs, increased product turnaround speed, higher overall quality, increased and improved tracking and compliance, timely feedback loop, and increased customer satisfaction.

Originally posted on MDDI Online at –

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