Research Scope

AI in Quality - An Introduction

Digital transformation is driven by advancements in technology, including artificial intelligence (AI). AI automates complex tasks, analyzes vast amounts of data, and makes predictions not possible for humans. It helps manufacturers improve efficiency and productivity, reduce downtime, and enhance the quality of their products. As AI continues to advance, its integration is revolutionizing many manufacturing operations and traditional business processes. Quality control and quality management are key areas where AI is having a revolutionary effect.

While AI-driven predictive maintenance systems can anticipate equipment failures, AI-driven predictive quality management solutions can predict defects and quality issues early in the production process, thereby reducing waste and improving overall product quality.

Predictive quality analytics identifies and avoids quality issues before they arise. It analyzes data from numerous sources, such as machine records, sensors, IoT, the Industrial Internet of Things (IIoT), and historical production data from different software, and then applies AI and ML to offer relevant insights and to take preventative action. Manufacturers are producing more data than ever, and predictive algorithms can grow more precise and efficient over time with AI and ML as they process more data. Therefore, predictive quality analytics uses cutting-edge AI technology, data, and analytics to find patterns, trends, and anomalies in data (which could indicate quality problems) within manufacturing processes and enables process engineers and operators to preempt quality issues. Predictive quality is currently starting to be recognized as a core need and has a lot of ROI attached to it.

Digital technologies such as AI, ML, natural language processing (NLP), and advanced analytics are also applied in enterprise quality management system (EQMS) solutions, such as deviation/non-conformance management, corrective and preventive action (CAPA), preventative control, and complaint management, to name a few, to move from reactive quality to proactive quality.

Another way to use AI is for quality control in machine vision, video inspection, sensors, and monitoring systems to detect defects in products.

Research Highlights

Serviceable Addressable Market: Advanced analytics for manufacturing and logistics, $23 billion, CAGR 20%

Total Addressable Market: IT software and services for manufacturing and logistics $650 billion, 13% growth rate in 2024

AI enhances operational efficiency and risk management and provides the right insights crucial for business strategies. It is poised to expand in the global market at a rapid pace.

Frost & Sullivan expects that AI will become increasingly vital as manufacturing processes continue to become more complex and data-driven and as the need to produce high-quality products increases.