Manufacturers are constantly looking for ways to implement innovative technologies in their own companies, yet many are intimidated by the enormity of the potential applications.
Download this newest resource to learn about the top use cases of artificial intelligence (AI) in manufacturing and find out how modern organizations can harness the predictive power of the industrial IoT.
Predicting defects isn’t as simple as measuring a specific data point. Instead, accurate predictions require measuring as many parameters as possible that have the power to cause defects. Robust yet agile machine learning (ML) models hold the key by using real-world operating data to forecast an asset’s condition based on a wide variety of parameters. This AI/ML-based forecast can help manufacturers identify when assets need attention or maintenance before they fail.
Quality control in manufacturing
Quality control in manufacturing is a top concern for companies because it directly impacts brand integrity and customer satisfaction. Poor quality also results in increased expenses in the form of product rework, recalls and increased warranty costs. New technologies powered by AI, however, can detect quality issues that may escape traditional checks and improve the quality process by spotting anomalies in operating conditions. Quality assurance engineers can then forecast potential quality issues and fix the underlying production conditions before any serious problems surface.
Manufacturing companies that can accurately forecast and plan production are in a much better position to optimize their resources, leading to better profits and heightened customer satisfaction. When AI/ML is applied to data in the production process, forecasting is no longer just guesswork. Manufacturers can rely on models that are more precise and dependable.
Read the latest ebook, “Integrating AI and ML with IIoT,” to discover how your organizations can turn challenges into opportunities and harness your data to make meaningful improvements across the manufacturing process.