The purpose of our presentation is to explain how configuration management discipline can be seen as a connection between existing global approaches that focus on different aspects of Complex System Engineering: the design theories and methods, based on the design science that Herbert Simon widely contributed to develop, and the complex system engineering methodologies and frameworks. Indeed, Configuration Management is frequently seen as a subset of System Engineering Methodologies, while we consider it as the keystone that maintain both fluidity and reliability of the overall product generation process.
First, we will introduce how the two fields are mapped using one instance of each: the Concept - Knowledge Theory developed by A. Hatchuel and B. Weil, and the Model Based System Engineering framework. Example of this mapping exist in the literature. For instance, Jin, Yili & al. (Semantic Modeling Supports the Integration of Concept-Decision-Knowledge) present RFLP models that are output of MBSE methodology as ways to structure both Concept Space (the space of alternative solutions) and Knowledge Space. As such, this mapping is interesting. Indeed, CK theory is as theory for innovation, that overcomes simple decisions paradigms and optimization approaches. We consider it interesting, as a 1st outcome, to restate that System Engineering frameworks, although they are meant to ensure complicated artefacts design in a structured and industrialized way, are not antinomic with Innovation.
Second, we will try to expose how Configuration Management is way to ensure a safe conjunct exploration of both spaces, as described in C-K Theory. Indeed, as a discipline that ensure the reliability of data shared by teams and communities, Configuration Management can be seen as the infrastructure that allow collective actions to take place all along the Design Process. Based on a proposed extension of CK Theory to CDK Theory (Concept-Decision-Knowledge), proposed by Jin Yill and al. (supra), we will present how Configuration Management components (identification, baselining, change control processes) are enabling the process described in this formal framework, introducing a data behavior and processing approach that span through Concept, Knowledge and Decision Support, and complement the semantic modelling Jin Yill and al. present.
For the last part, we will present the implementation of this approach in examples from automotive and aerospace industries, and how it can be used to specify Computer Aided Design Systems and related Processes (such as Product Lifecycle Management capabilities). We will also discuss some limitations of this approach