A global industrial and automotive technology provider faced limited visibility into the root causes of paint defects despite large volumes of production data. This led to late defect detection, increased rework, and reduced operational efficiency.

We worked together with them to create and implement a production-ready quality analytics solution to detect defect patterns and identify root causes using plant and process data. The result is a 20–40% reduction in rework and scrap and improved processes that increase consistency across lines, contributing to higher Overall Equipment Effectiveness (OEE).

The solution delivered measurable business impact, including a 20–40% reduction in rework and scrap, contributing to higher first-pass yield, improved throughput and lower operational costs. OEE increased through reduced quality-related downtime, faster issue resolution (lower MTTR) and more stable process parameters. Deployed as a production-ready solution in automotive paint shop operations, it leverages plant, process, and quality data to reliably detect defect patterns and validate quality improvements across production environments.

Automotive paint shops generate vast amounts of production and process data, yet manufacturers often lack clear visibility into why quality defects occur, even when machines appear correctly configured and operating without faults. Quality issues are frequently documented inconsistently, making it difficult for quality and production teams to trace defects back to their root causes. As a result, failures in the painting process are often detected too late, leading to increased waste, costly rework, and reduced production efficiency across paint shop operations.

Addressing these challenges requires strong expertise in data-driven solutions and industrial processes, making Accesa a suitable partner to design and implement advanced quality analytics capabilities tailored to complex manufacturing environments.

The solution provides an AI-driven quality analytics platform tailored for automotive paint shops. It combines advanced analytics, machine learning, and high‑volume production data to analyse plant, process, and quality information and detect defect patterns and root causes. By correlating quality deviations with production and process parameters, the platform enables early detection of failures in the painting process and establishes a structured, reliable quality data foundation for analysis. Integrated with the client’s existing systems and solution portfolio, the solution supports scalable deployment across multiple plants and customer environments, enabling data-driven optimisation of paint shop quality and performance.

The project enabled automotive manufacturers to significantly improve quality management and operational efficiency in paint shop operations. By providing early detection of failures and clear visibility into defect patterns and root causes, the solution helped reduce rework and scrap by 20–40%, lowering production waste and associated costs. At the same time, the platform improved OEE by reducing quality-related stoppages and enabling faster, data-driven process optimisation. As a result, manufacturers achieved more consistent product quality, higher production efficiency, and better-informed decision-making across their paint shop operations.

The approach is repeatable across automotive paint shops with similar production environments, as it builds on standard plant, process, and quality data typically available in manufacturing operations. Replication requires access to reliable and integrated data sources, as well as alignment with existing systems and processes to ensure effective deployment and scaling. The solution is already at a production-ready stage, integrated into the client’s portfolio and deployed across multiple environments, with next steps focused on further scaling, continuous optimisation and extending its application to additional plants and use cases.

Our collaboration marks a shift from reactive quality management to a proactive, data-driven approach in paint shop operations. By enabling early detection of defects, improving transparency and supporting continuous process optimisation, the solution drives more efficient, consistent, and cost-effective production, aligning with a Quality Intelligence value proposition while also contributing to improved OEE.

If you are looking to enhance quality performance in your own production environment, discuss your plant use case with us or explore how this approach can be applied to your production line.