Being able to efficiently and effectively provide custom products has been identified as a competitive advantage for manufacturing organizations. Product configuration has been shown to be an effective way of achieving this through a modularization, product platform and product family development approach. A core assumption behind product configuration is that the module variants and their constraints can be explicitly defined as product knowledge in terms of geometry and configuration rules. This is not always the case, however. Many companies require extensive engineering to develop each module variant and cannot afford to do so in order to meet potential customer requirements within a predictable future. Instead, they try to implicitly define the module variants in terms of the process for how they can be realized. In this way they can realize module variants on demand efficiently and effectively when the customer requirements are better defined, and the development can be justified by the increased probability of profiting from the outcome.

Design automation, in its broadest definition, deals with computerized engineering support by effectively and efficiently utilizing pre-planned reusable assets to progress the design process. There have been several successful implementations reported in the literature, but a widespread use is yet to be seen. It deals with the explicit definition of engineering process knowledge, which results in a collection of methods and models that can come in the form of computer scripts, parametric CADmodels, template spreadsheets, etc. These methods and models are developed using various computer tools and maintained within the different disciplines involved, such as geometric modeling, simulation, or manufacturing, and are dependent on each other through the product model. To be able to implement, utilize, and manage design automation systems in or across multiple disciplines, it is important to first understand how the disciplinary methods and models are dependent on each other through the product model and then how these relations should be constructed to support the users without negatively affecting other aspects, such as modeling flexibility, minimum documentation, and software tool independence.

To support the successful implementation and management of design automation systems the work presented here has focused on understanding how some digital product model constituents are, can, and, to some extent, should be extended to concretize relations between methods and models from different tools and disciplines. It has been carried out by interviewing Swedish industrial companies, performing technical reviews, performing literature reviews, and developing prototypes, which has resulted in an increased understanding and the consequent development of a conceptual framework that highlights aspects relating to the choice of extension techniques.

Manufacturing organizations often pursue the ability to efficiently and effectively provide custom products for its competitive advantage. Research has shown product configuration to be an effective way of achieving this goal through a modularization, product platform, and product family development approach. A core assumption behind this approach is that the module variants and their constraints are explicitly pre-defined as product knowledge. This is not always the case, however. Many companies require extensive engineering to develop each module variant but cannot afford to do so proactively to meet potential customer requirements within a predictable future. Instead, they attempt to implicitly define the module variants in terms of the process in which they can be realized. In this way, manufacturing companies develop module variants on demand efficiently and effectively when customer requirements are better defined, as justified by the increased probability of profiting from the outcome.

Design automation (DA), in its broadest definition, refers to computerized engineering support that efficiently and effectively utilizes pre-planned reusable assets to progress a design process. The literature has reported several successful implementations of DA, but especially widespread higher levels of automation are yet to be seen. One DA approach involves the explicit representation of engineering process and product knowledge in the engineers preferred formats, such as computer scripts, parametric geometry models, and template spreadsheets. These design assets are developed using various computer tools, maintained within the different disciplines involved, such as design, simulation, or manufacturing, and are dependent on each other through the product model. To implement, utilize, and manage DA systems in or across multiple disciplines, it is important to understand how the disciplinary design assets depend on each other throughout the product model and how these relations should be constructed to support users without negatively affecting other aspects, such as modeling flexibility, system transparency, and software tool independence.

To support the successful implementation and management of DA systems, this work focuses on understanding how digital product model constituents are, can, and, to some extent, should be extended to concretize relations toward and between design assets from different tools and disciplines. This research consists of interviews with Swedish industrial companies, technical reviews, literature reviews, and prototype developments, resulting in an increased understanding and the consequent development of a framework that highlights aspects regarding the choice and development of extension techniques.