The World of Quality Control Has Changed

Given the ongoing supply chain challenges, medical device manufacturers must use every advantage they can get to beat the competition. The last thing they need is to stand their own way, burdened with issues that slow or even stop production.

One of the most exciting developments we have seen is the application of artificial intelligence (AI) and machine learning to quality systems in the manufacturing production process.

Predictive quality analytics, powered by statistical algorithms, help you predict production outcomes based on data from your operations.

What does this mean? Instead of responding to quality issues like defects as they occur, you can anticipate the likelihood of them occurring and act before they cost you time, resources, and money.

Reactive vs. Proactive

In highly regulated markets such as medical devices, quality drives success. Defects lead to higher costs.

Unfortunately, many manufacturers still take a reactive approach to quality systems in the production process, using historical data collected in 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 makes use of machine learning to predict quality issues based on a dynamic set of real-time data from across the organization. This reduces the need for frequent starts and stops to test or reconfigure the line after a problem is detected.

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

A machine learning model identifies a batch that may encounter potential problems based on factors such as material supplier, workers, machines, and types of raw materials. This allows the manufacturer to improve material utilization, 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 specify that batches that use a chip from a particular supplier are more likely to have quality problems than batches that do not contain it.

Or, production orders have a higher failure rate on line 2 when run by Joe Smith.

The data does not provide causation. But it does indicate something to investigate so that plans can be modified before production begins and quality issues arise.

ROI for Predictive Quality Analytics for Medical Devices Manufacturing

You may have attributed quality issues to it being part of doing business, writing off the additional costs because they are to be expected in any production environment. But today every little thing matters. And advanced analytics can lead to improvements where you may not even be aware of opportunities.

Look for ROI in:

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

This technology is still in its early adoption stage, which means there is a huge opportunity for companies that already have a foundation for machine learning initiatives. If you’re not on your data journey, there are steps you can take to build that foundation.

Find a partner who knows how to help manufacturers extend their data and analytics capabilities so they 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 a measurable impact on many areas of your business, including lower costs, faster product transformation, higher overall quality, increased and improved tracking and compliance, a timely feedback loop, and increased customer satisfaction. .

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