"Achieving predictive quality: how to optimize your non-conformance reports with AI
Artificial intelligence (AI) can provide resource-saving support in quality management when processing deviations or non-conformities. In conjunction with a cloud-based document management and quality management system (DMS/QMS), companies can implement methods such as the 8D process more efficiently. As a result, they receive meaningful non-conformance reports that serve as the basis for successful predictive quality management.
In quality management, non-conformance reports (NCR) are valuable for learning from mistakes, proactively preventing them and bringing the company closer to the goal of predictive quality. An AI-supported DMS/QMS supports the 8D method, for example, and then links the learnings along the value chain.
Lukas Hengster, QM expert and Head of Business Development at Fabasoft Approve GmbH, shows how companies can use AI in quality management to efficiently manage deviations and create structured NCRs.
Cloud-based solution for precise data collection
A cloud-based DMS/QMS acts as a solid foundation for recording and processing non-conformances (NCs). Such systems enable structured data collection, which is essential for the use of AI applications. The use of cloud technology in certified data centers in the DACH region enables cross-company processes along the entire supply chain.
Systematic processing according to proven methods
Precise defect identification and systematic documentation by NCRs is crucial for efficient defect processing. Depending on the industry, different methodologies such as CAPA (Corrective and Preventive Action) or the 8D process can be used. A thorough root cause analysis is of central importance in order to prevent future defects through targeted preventive measures and to come closer to the goal of "predictive quality".
High-quality audits
The standard-compliant processing of non-conformities significantly increases the quality of audits. Different ISO standards define non-conformities (NCs) differently, which must be taken into account in the documentation. ISO 9001, for example, describes these as "deviations from the requirements of the quality management system (QMS)". In contrast, the FDA (Food and Drug Administration) guidelines for medical devices classify "any deviation in device performance" as a non-conformity. ISO 9101, the standard for aerospace and defense organizations, specifically defines NCRs as part of the requirements for documenting audit results.
A DMS/QMS enables specialist departments to independently design industry-specific BPMN processes and execute them as digital workflows. This system support means that employees automatically adhere to the standardized QM processes. A time travel function makes every processing step traceable and forms a valuable building block in audit management. Users thus have an overview of completed process steps at all times.
Proactive defect management for continuous improvement
Cross-plant, proactive defect management is the key to continuous improvement. The NCR data collected in the DMS/QMS serves as a valuable information base for various areas of the company and contributes to the approach to "predictive quality".
Supplier audits are a good example of this process: The cloud-based software connects customers and suppliers directly in the process. This reduces media disruptions and susceptibility to errors. A comprehensive NCR on customer complaints is highly relevant for after-sales processes. The collected NCR data can also serve as an information basis for negotiations in purchasing and contribute to supplier evaluation.
AI support for skills shortages
In quality management, it is crucial to consider extensive data and numerous documents in order to solve problems effectively. The know-how of experienced employees, which has been built up over the years, is invaluable. However, a shortage of skilled workers, staff turnover and retirements mean that this knowledge is lost, making it difficult to efficiently handle quality processes along the supply chain. AI technology in the DMS/QMS can help to counteract this loss of expertise. AI provides comprehensive insights into defects and helps with troubleshooting. For example, a material number in a technical drawing is used to retrieve order information or identify similar deviations. As part of the 8D process, AI analyzes related defects and quickly provides suggestions for corrective or immediate action.
Source: www.fabasoft.com/de
