Towards a New Generation of Assistance for RAMS, FMEA and Dependability Engineering Approaches
In the field of industrial engineering and systems engineering, risk analyses play a central role. They help manage the increasing complexity of products, systems, processes and organizations, while contributing to ensuring that performance, reliability, maintainability, dependability and safety requirements are achieved throughout the entire lifecycle.
Whether dealing with Design FMEA, Process FMEA, Equipment FMEA, functional analyses, Fault Tree Analyses, HAZOP, HARA, FMEDA analyses, or validation and monitoring plans, these methods have become essential tools for anticipating failures, assessing risks, prioritizing actions and securing technical decisions.
However, despite their widespread adoption and recognition in industrial standards and guidelines, these analyses are still often characterized by significant methodological and organizational diversity. In many companies, risk analysis processes remain fragmented across different disciplines, departments, sites and technical cultures. Mechanical, electronic, software, system, quality, dependability and industrialization teams may use their own models, terminology and analysis tools.
This disciplinary segmentation is accompanied by the coexistence of complementary methods that are still insufficiently integrated. Functional analyses, FMEAs, Fault Tree Analyses and preliminary risk analyses are often performed in parallel, with only partial or manual links between the different studies. This creates challenges in terms of consistency, traceability and consolidation, especially when projects become complex, international or involve multiple areas of expertise.
With 35 years of experience, KNOWLLENCE, part of the BASSETTI Group, has been helping industrial companies in the digital transformation of their risk analysis processes. Our dedicated software solutions enable organizations to structure, standardize and capitalize on their approaches for external and internal functional analysis, requirements identification, Design, Process, Equipment and MSR FMEA, Fault Tree Analysis, validation plans, monitoring plans, HARA analyses and FMEDA studies.
The deployment of specialized software platforms represents the first essential level of maturity. It helps consolidate compliance with recognized standards, secure the application of norms and methodological guidelines, and support FMEA facilitators, project managers and domain experts in carrying out their analyses.
In large organizations, centralizing analyses within a single database, combined with collaborative web interfaces, also facilitates access to information, harmonization of practices and standardization of deliverables.
However, once this first level of maturity has been achieved, new challenges emerge. Organizations are faced with geographically distributed teams, a diversity of languages and technical cultures, as well as differences in skills, experience and practices among the various contributors. These factors increase the heterogeneity of risk analyses and can lead to significant variations in the quality of the studies produced.
As a result, differences can be observed in the way analyses are structured, systems are decomposed, and functions, failure modes, effects or causes are defined. Variations can also be found in risk assessment, particularly regarding severity, frequency, occurrence and detectability criteria. These discrepancies limit the company’s ability to consolidate results, compare studies, reuse existing knowledge and build a true shared methodological knowledge base.
To overcome these limitations, it is necessary to go beyond the simple digitalization of forms or reference documents. The challenge is now to help teams make their risk analyses serve project objectives, while also enabling the organization to learn from its own data. This requires promoting knowledge reuse, improving analysis consistency, providing standards adapted to project contexts, and progressively transforming lessons learned into operational assets.
It is within this perspective that KNOWLLENCE has been developing advanced standardization and knowledge capitalization capabilities for several years, particularly through the use of generic blocks that can be synchronized with projects. These approaches make it possible to deploy robust models validated by the organization, while maintaining the flexibility required for each industrial context.
Today, a new step is being taken with the integration of artificial intelligence into risk analysis processes. AI is not intended to replace human expertise; it is designed to support, structure and enhance it. AI can help teams identify functions, suggest failure modes, retrieve similar cases, propose consistent wording, detect methodological deviations, and facilitate the exploitation of lessons learned.
One of the main research areas is to make AI-based assistance more reliable in the demanding context of reliability engineering. In particular, we are exploring the use of AI agents that enable AI suggestions to be supported by relevant data from controlled knowledge bases. The objective is to make responses more traceable, more verifiable and better aligned with company standards, reference frameworks and historical data.
During this webinar, we would like to share our vision, our research activities and our experience regarding the contribution of artificial intelligence to industrial risk analysis processes.
We will address the following key questions in particular:
- How can we improve the quality and consistency of risk analyses across an organization?
- How can we make better use of the knowledge generated from FMEAs, functional analyses, FMEDA, HARA and Fault Tree Analyses?
- How can AI be used to support teams while maintaining methodological control and preserving expert responsibility?
- How can we make AI recommendations more reliable by leveraging knowledge bases, project data and field experience feedback?
- How can we build a continuous learning loop between completed analyses, operational data and future studies?
This webinar is intended for dependability, quality, reliability and systems engineering managers, methods and innovation leaders, as well as FMEA facilitators, project managers and domain experts involved in industrial risk management.
Join us to discover how artificial intelligence can become a powerful enabler for standardization, knowledge capitalization and continuous improvement of risk analysis processes, supporting both projects and organizations.