With ongoing supply chain challenges, manufacturers take every advantage to set themselves apart from the competition. The last thing they need are problems that slow production.
One of the most exciting developments is the application of artificial intelligence (AI) and machine learning to quality systems in the manufacturing process. Supported by statistical algorithms, analytics can predict production outcomes based on data from across the plant. Consequently, instead of reacting to quality issues as they occur, medical device companies can predict their likelihood before they might end up costing them time, resources, and money.
In highly regulated markets, defects drive up costs. However, many manufacturers still take a slower, less agile, and less reliable reactive approach to ensure products meet quality standards. In contrast, predictive quality analytics employ machine learning to forecast quality issues based on a dynamic set of real-time data from across the enterprise. This reduces the need for real-life testing or reconfiguration.
For example, machine learning might identify which batch could have problems based on the supplier, employees, machines, and material types. Thus, the manufacturer can optimize material usage, identify defects, and predict scrap rates.
In addition to predictive data, the model can provide real-time alerts. For example, a manufacturer can determine that batches with a chip from a particular supplier are more likely to experience quality problems than those without it. The data does not indicate the cause but may highlight something that needs to be investigated, allowing plans to be adjusted before production begins and quality problems arise.
Predictive quality analytics aligns with the core benefits of digital transformation and has measurable impacts on many areas of your business. These include lower costs, faster product turnaround time, higher overall quality, a timely feedback loop, and higher customer satisfaction.