Integrated SPC and FMEA for process improvement and increased reliability

Challenge

Companies are investing heavily in operational excellence and are already using tools such as Statistical Process Control (SPC), pFMEA, HACCP and automated quality inspection to manage quality risks. Nevertheless, analysing unexpected faults, rejects or customer complaints often remains a time-consuming and complex process.

When quality issues cannot be directly linked to a single process step, the necessary direction for an efficient root cause analysis is often lacking. Existing IoT and monitoring systems primarily record machine data, whilst factors such as operator actions, material properties and environmental influences are only visible to a limited extent. Furthermore, quality controls often only detect problems late in the production line or via customer complaints.

Today, SPC systems, FMEA’s and quality management systems often operate in parallel, without an integrated view of the entire production system. As a result, hidden interactions between processes remain difficult to detect, and knowledge building across lines, sites and product families is largely manual. This leads to slow analyses, difficulty in prioritising CAPA actions and an increased risk of recurring quality issues, scrap, rework and production disruptions.

Project goals

This project (INSPIRE_SBO) aims to develop an integrated methodology that combines SPC and FMEA within a system-of-systems APQP approach for the continuous improvement of entire production systems.

The solution will bring together SPC data, quality information and human expertise from FMEA’s into a single integrated quality management system. By combining data-driven analyses with process knowledge, the project aims to detect inconsistencies, accelerate root cause analysis and dynamically update quality models based on current operational conditions.

In addition, the project focuses on system-level reasoning across interconnected processes. This takes into account shared production constraints such as environmental factors, procedures and process dependencies to generate robust insights even with limited data. The system will identify weaknesses in the control strategy, propose priorities for CAPA actions and develop efficient user interfaces to make the complexity of quality management across entire production systems manageable.

The project is expected to lead to faster risk mitigation, fewer recurring quality issues and better utilisation of existing SPC, FMEA and quality management systems.