August 17, 2023
By: Isabell Bücher
What is Predictive Quality Analytics in Manufacturing?
Predictive quality analytics is a form of predictive analytics that focuses on quality issues. Predictive quality in manufacturing uses artificial intelligence (AI) and machine learning (ML) models to detect, correct, and reduce the occurrence of poor quality goods.
Traditional quality control (QC) processes inspect finished products visually for flaws. More recently, plants might use digital tools like scanners, but these still require human employees. Manual inspection is time-consuming, expensive, and prone to errors, and if you only run quality control checks at the end of production, there’s a greater risk of having to scrap significant volumes of product.
At the same time, the cost of failing to spot quality issues is almost unlimited. Product recalls can significantly harm revenue and profits, both in terms of direct losses like lost product and sales, and indirect losses such as damage to the manufacturer’s reputation. For example, in 2022 Jif peanut butter recalled 49 SKUs, costing the company up to $125 million in direct losses, and an unquantifiable amount in lost trust.
Predictive quality analytics make post-production inspection more efficient and help identify and avoid quality issues before they arise. These tools take data from sensors, machine records, Industrial Internet of Things (IIoT) devices, and historical production data from the data historian and ERP, and then apply AI and ML to produce meaningful quality insights.
Predictive quality tools can spot patterns and anomalies within manufacturing processes which could indicate quality problems, so that process engineers can take steps to prevent them. Depending on which parameters you use, predictive quality analytics can also detect hidden flaws which could lead to a warranty claim or complaint in the long term, as well as immediate quality issues.
How can predictive quality analytics help process manufacturers?
Process manufacturing plants are constantly striving to increase productivity while ensuring and improving product quality. Low quality products are a waste of time, resources, and raw materials, plus they reduce customer satisfaction and can harm the company’s reputation.
When defects are noticed earlier, problematic goods can be removed and corrected sooner. This limits the total number of parts scrapped and helps prevent batch-related and serial number-related failures. Plant engineers can also isolate and investigate defects more easily by determining when and where they occurred within the production process.
With the help of predictive quality analytics, process manufacturers can:
- Speed up root cause analysis
- Save time on manual quality control procedures
- Proactively adjust process parameters to reduce the number of defective batches
- Cut costs on wasted time, resources, raw materials for poor quality goods
- Raise quality for the entire production line
- Make better predictions about deadlines and deliverables
- Lower the risk of warranty claims, complaints, and recalls
- Improve quoting and procurement
- Boost customer satisfaction
- Raise brand reputation
- Increase production efficiency by removing bottlenecks and refining processes
How can process plants implement predictive quality analytics in manufacturing?
Ensure clean datasets
As the data science saying goes, garbage in, garbage out. It’s important to set up data pipelines that deliver clean, trustworthy data to predictive analytics systems. The more data you can gather, the more reliable your quality analytics will be. Connect all your systems, including time series data, discrete event or action data, data from IIoT devices, and more.
Democratize access to analytics insights
Your data science teams have enough on their plates without spending time analyzing predictive quality reports and answering questions from plant workers. Implement user-friendly systems with self-serve portals, easy to read dashboards, and intuitive interfaces that plant engineers can use independently.
Deliver alerts as early as possible
The sooner plant managers know about an actual or possible drop in quality, the sooner they can act to correct the issue. This way, you’ll have fewer products to scrap, and are more likely to be able to salvage affected items before they complete production.
How does predictive quality analytics in manufacturing benefit process plants?
Predictive quality analytics help process plants to increase customer satisfaction, reduce costs, and improve brand reputation by delivering higher quality products on a more consistent basis. With predictive quality analytics, it’s possible to boost both the quality and quantity of your product, ultimately increasing revenue and lifting the bottom line.