Keywords
lifecycle management
MBSE
PLM
CAD/CAE
Digital Thread
Digital Twin
variant and configuration management
How to Cite
Abstract
In modern development landscapes, products are no longer isolated artefacts, but densely networked systems comprising mechanics, electronics, software and data driven services. Changes are inevitable but their effects behave like waves in water: a local impulse can influence sensor technology, control logic, mechanical tolerances and service processes across interfaces.
Engineering change management (ECM) has the task of identifying such chain reactions early on, making them manageable and keeping them traceable. The text shows why classic, paper based processes are too slow, too siloed and too bureaucratic in this reality, and outlines how modern approaches combine speed, transparency and collaboration. At its core is a systemic view of change: change propagation analysis and models such as dependency structure matrices make dependencies visible before they become costly. Digital twins, graph based analytics and AI supported pattern recognition support the evaluation by deriving probabilities and risks from historical change data and real time usage. Equally important is the organisational side: clear roles (from requester to CCB), short decision cycles, interdisciplinary reviews and a culture in which uncertainty can be openly addressed. Model Based Systems Engineering (MBSE), SysML and System Driven Product Development bridge the gap between disciplines; platform and modularity strategies limit spillover effects. Blockchain based traceability can increase auditability without slowing down collaboration. Because products are increasingly operated as services and updated throughout their lifecycle, ECM is expanding its focus from the end of development to operation, maintenance and return: over the air updates, variant statuses and field feedback are becoming part of the change dialogue. In practical terms, this means that decisions are based less on gut feeling and Excel and more on connected models, data flows and agreed governance rules. The article summarises empirical findings from industry and research: mature ECM organisations avoid duplication of work, reduce miscommunication and shorten throughput times provided that processes are lived and not just described. The end result is a model: a ‘nervous system’ for product development that quickly recognises stimuli, systematically evaluates effects, responds in a targeted manner and documents the decision making process in its entirety. ECM thus evolves from a reactive correction mechanism to a proactive design tool for complex system networks with measurable benefits in terms of quality, time and costs. The key is to give equal attention to technology and collaboration: only when models, data and people work together can changes be truly absorbed and used as a lever for innovation and lasting competitiveness.
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