Auto-DSM: Using a Large Language Model to generate a Design Structure Matrix

The Design Structure Matrix (DSM) is an established method used in dependency modelling, especially in the design of complex engineering systems. The generation of DSM is traditionally carried out through manual means and can involve interviewing experts to elicit critical system elements and the relationships between them. Such manual approaches can be time-consuming and costly. This paper presents a workflow that uses a Large Language Model (LLM) to support the generation of DSM and improve productivity. A prototype of the workflow was developed in this work and applied on a diesel engine DSM published previously. It was found that the prototype could reproduce 357 out of 462 DSM entries published (i.e. 77.3%), suggesting that the work can aid DSM generation. A no-code version of the prototype is made available online to support future research.

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Publication Details

Journal
Natural Language Processing Journal
Published
2024-09-06
DOI
https://doi.org/10.1016/j.nlp.2024.100103
Citations
4
Primary Topic
Product Development and Customization
Type
article
Field-Weighted Citation Impact
2.92
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article

Auto-DSM: Using a Large Language Model to generate a Design Structure Matrix

Edwin C. Y. Koh
4 citations
Natural Language Processing Journal
Product Development and Customization
2.92
article

Auto-DSM: Using a Large Language Model to generate a Design Structure Matrix

Edwin C. Y. Koh
article en
4 citations

Abstract

The Design Structure Matrix (DSM) is an established method used in dependency modelling, especially in the design of complex engineering systems. The generation of DSM is traditionally carried out through manual means and can involve interviewing experts to elicit critical system elements and the relationships between them. Such manual approaches can be time-consuming and costly. This paper presents a workflow that uses a Large Language Model (LLM) to support the generation of DSM and improve productivity. A prototype of the workflow was developed in this work and applied on a diesel engine DSM published previously. It was found that the prototype could reproduce 357 out of 462 DSM entries published (i.e. 77.3%), suggesting that the work can aid DSM generation. A no-code version of the prototype is made available online to support future research.

Natural Language Processing JournalVol. 9
Singapore University of Technology and Design (SG)
Openalex Percentile: Top 9%
Product Development and Customization
2.92
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