AI based System of Systems Lifecycle Management of Digital Threads

Abstract

Over the past two decades, our understanding of product development has changed fundamentally. Disciplines that were once clearly distinct from one another, such as mechanical engineering, electrical engineering, and software development, have now become increasingly intertwined. Products are no longer isolated objects, but rather nodes in a network of systems, data, and processes. In this environment, it is no longer enough to manage individual data sets – they must flow, remain consistent, and be available to the right people at the right time. The AI based lifecycle management system of Digital Threads (Digital Thread for short) has therefore established itself as the backbone of modern system engineering. Early approaches to product data management (PDM) focused on central storage of CAD data and the management of versions and approvals. However, with the advent of complex mechatronic systems, networked vehicles, and modular plants, it became clear that this approach was not sufficient. Companies began to realize that it was not enough to simply store geometries; they needed a system that mapped the entire lifecycle of a product, from the initial requirement through development and production to operation and decommissioning. Today, Digital Threads’ AI based lifecycle management system acts as a digital nervous system that connects disciplines such as mechanics, electronics, software, and simulation. While system engineering provides methodical approaches for structuring requirements and architectures, Digital Thread ensures that the associated data are findable, traceable, and up to date. This object oriented approach creates transparency and enables end to end traceability, which is a key requirement of modern systems engineering. Another feature of modern digital thread approaches is the seamless integration of models and product data. Changes in one discipline, for example, in design, can automatically detect and forward all affected areas such as requirements, tests, and functions. This promotes the continuous flow of information and prevents unnoticed consequences of changes elsewhere in the system. The close link between AI based lifecycle management systems for digital threads and system engineering goes beyond technical necessities and promotes cultural change in companies. Organizations must not only introduce technology, but also clearly define processes and responsibilities in order to fully exploit the effectiveness of this system. Clear, transparent handling of changes, systematic management of variants and integration of real time data from operations are key success factors for the implementation of digital threads. The concept of ”‘digital twin’, which enables a continuous connection between the models of real and virtual products, can only be realized through the consistency and transparency offered by the digital threads. This, in turn, promotes more sustainable and data driven decision making processes that can be taken into account throughout the entire product lifecycle, from development to maintenance. Overall, it is clear that AI based digital thread lifecycle management systems not only provide the technical basis for system engineering, but are also an integral element for the successful development and operation of complex, networked systems. They promote innovation, improve quality, and enable companies to make their product development more flexible and efficient.