Predictive Quality

FAQ

What is the definition of Predictive Quality?

Predictive Quality is critical to any manufacturing operation because it helps ensure that products meet the desired quality and minimize waste.

Traditional quality assurance methods such as sample inspections or 100% inspections can be time-consuming, costly, and prone to human error. At the same time, they do little to identify causes of quality problems or even eliminate them in-process. For this reason, many companies are turning to Predictive Quality.

Predictive Quality uses machine learning algorithms to detect and prevent product defects before they occur. It uses data from a variety of sources, including material protocols, supplier documents, production equipment, and inspection process results, to identify patterns and predict the likelihood of defects or automatically optimize manufacturing parameters for the best possible product quality.

How does Predictive Quality work?

• Data collection: The first step in implementing Predictive Quality is to collect data from various sources, such as machines, sensors, inspection processes, or customer feedback documents.
• Data analysis: Once the data is collected, it is analyzed using machine learning. The algorithms look for patterns and relationships in the data.
• Predictive modeling: Predictive models are developed based on the results of the data analysis. These models use the data to predict the likelihood of product failures.
• Predictive monitoring: The predictive models are integrated into the manufacturing process, and production equipment is monitored in real time. If the predictive models indicate a high probability of product defects, this is indicated and appropriate action can be taken before scrap occurs or the product reaches the customer.
• Process optimization: The predictive models are used to derive and provide optimization models. In-process and real-time suggestions for improving process parameters can be displayed and rejects can be reduced sustainably.

What are the benefits of Predictive Quality?

• Improved product quality: Predictive Quality helps improve product quality. By predicting and preventing product defects, manufacturers can ensure that only high-quality products reach the market.
• Reduced inspection effort: Predictive Quality enables dynamic random inspections by inspecting mainly products at the end of production for which a critical quality has been predicted, thus reducing inspection effort.
• Lower process costs: Process optimization produces less scrap, which greatly reduces process costs through less rework, reduced material usage, and a higher yield rate.
• Data-driven insights: Predictive Quality provides manufacturers with valuable insights into their production processes and even enables them to plan and realize higher quality in their current or future products.
• Reduced recall risk: By predicting potentially defective products or even through realized process optimization, the likelihood of delivering defective products to customers, and thus recall risks and costs, decreases dramatically.
• Reduced waste: By reducing the number of defective products, Predictive Quality helps conserve valuable resources and establish sustainable manufacturing.

What is an example of a Predictive Quality use-case?

For example, a manufacturer could use Predictive Quality to analyze data about its welding processes. The model would analyze data on parameters such as welding speed, temperature and pressure to detect patterns and anomalies. Suppose the model finds that a particular welding process repeatedly produces defective welds. In this case, it can alert the production team to investigate and fix the problem before it leads to significant scrap.