Model-Based RAMS: Optimizing Model Development in a Distributed Working Environment
Paddy Conroy, Ho-Bin Kim, Khoi Tran, Daniel Chan · 2021
The challenges of conducting Reliability, Availability, Maintainability, Safety (RAMS) analysis in a distributed work environment have become exacerbated in the post-COVID world. The reality is that distributed operations may become the new "norm" for industry, and it is essential that RAMS (the cornerstone of system safety, reliability and cost of ownership attributes) can adapt to this paradigm, particularly in the context of increasingly complex systems that leverage Industry 4.0, the IoT and model-based engineering.To ensure that the analyses supporting RAMS are sufficiently comprehensive, consistent, and repeatable requires that a defined methodology to ensure optimized processes are in place. Model-based RAMS is the obvious answer.Model-based engineering platforms are primarily analytical in purpose and function, often overlooked is the use of them as a communication method between stakeholders (within and between teams, departments, organizations, and projects). A model functions as a technical baseline with the capability to act as a singular knowledge management solution for the decision making, assumptions, and soft institutional knowledge that goes into a product development process, improving traceability and accountability. Naturally, then, there is a need to explore the constraints of developing a RAMS model in a distributed work environment.This paper provides an approach and methodology for ensuring that organizations developing RAMS models in a distributed environment can address the issues of: segmented technical responsibilities (internal – between departments and groups; and external – with the supply chain); configuration management (of models, and their parameters at different stages in the product lifecycle), analysis consistency (the advantages of analysis process automation), data consistency (structure and nomenclature of functions and failures), and future-proofing by digitizing the process for extensibility.