Efficient monitoring of multi-disciplinary engineering constraints with semantic data integration in the Multi-Model Dashboard process

Abstract

In a multi-disciplinary engineering project, such as the parallel engineering of industrial production plants, domain experts want to efficiently monitor project-level constraints that depend on technical parameter values in local engineering models. However, the heterogeneous representations of constraint parameters in these engineering models make the automation of constraint monitoring difficult. In this paper, we introduce the Multi-Model Dashboard (MMD) process providing semantically integrated values of parameters and of constraints to domain experts, as parameter values in various local models change during the project. The tool-supported MMD process guides the definition and monitoring of MMD parameters and constraints. We evaluate the effectiveness and efficiency of the MMD process in a feasibility study with requirements and data from real-world use cases at industry partners. Major results are that the MMD process was effective and efficient in eliciting relevant project constraints and model dependencies and in providing data for change impact analysis.