Solving Quality and Maintenance Problems with AI

Executive Summary

As artificial intelligence (AI) enters the business mainstream, one of its most promising applications is anticipating quality and maintenance problems before they cause real damage. Called predictive quality and maintenance (PQM), these solutions are being deployed at an accelerating rate, especially in the manufacturing, aerospace, and software industries.

But not all PQM solutions are created equal. Those based on a combination of machine learning, deep learning, and—in particular—cognitive computing create a truly unique out-of-the-box AI-based PQM solution.

This report is organized into three chapters. In Chapter 1, we introduce AI-based PQM and show how today’s market for quality and maintenance applications is evolving. In Chapter 2, we show that because none of the various types of AI can solve all PQM problems alone, applying them simultaneously is the key to success. This has led to cognitive computing as a basis for what is called complementary learning. We also introduce Intel Saffron AI as the only solution applying complementary learning principles to today’s PQM challenges. Finally, in Chapter 3, we discuss using AI-based PQM solutions to solve issues in the manufacturing, software, and aerospace industries.