A top-down method for the derivation of metrics for the assessment of design automation potential. (1st February 2020)

• Record Type: Journal Article
• Authors: Rigger, Eugen; Vosgien, Thomas; Shea, Kristina; Stankovic, Tino
• Is Part Of: Journal of engineering design. Volume 31:Number 2(2020)
• Page Start: 69
• Page End: 99
• Publication Date: 2020-02-01
• Subjects: Design automation -- failure mode and effect analysis -- process modelling -- performance metrics -- benefits evaluation; Engineering design -- Periodicals; Design, Industrial -- Periodicals; Industrial engineering -- Periodicals; 620.0042
• DOI: 10.1080/09544828.2019.1670786
• Languages: English
• ISSNs: 0954-4828

Abstract

ABSTRACT: Design automation supports design in several ways by automating design tasks based on computational tools. However, design automation use in industrial practice does not reflect the opportunities provided by state-of-the-art design automation. A lack of quantitative methods for assessing the potential impact of design automation in design practice contributes to this gap. Therefore, this research proposes a top-down method for the derivation of metrics for the potential estimation of design automation impact in practice. The method builds on collaborative workshops that focus on design process modelling, value stream mapping and failure mode effects analysis. The assumption is that if design automation is used, some of the failure mode causes are removed and harmful effects are mitigated. Validation of the method was conducted for two industrial case studies showing the applicability of the method in practice. The results highlight that both metrics and metrics specific to the use cases are attained. Thus, successive application of the method to different cases yields a generic metrics system that enables standardised estimation of design automation potential and the development of benchmarks for research on design automation. Therefore, this work lays the foundation for systematic and purposeful implementation of design automation in practice and research.