The role of generative design and additive manufacturing capabilities in developing human–AI symbiosis: Evidence from multiple case studies

Elliot Bendoly*, Aravind Chandrasekaran, Robert Handfield, Siavash H. Khajavi, Samuel Roscoe, Mateus do Rogo Ferreira Lima

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

12 Citations (Scopus)
11 Downloads (Pure)

Abstract

The benefits of additive manufacturing (AM) extend beyond the attributes of physical products and production processes they enable. Experience with AM can augment the way design is approached and can increase opportunities to pivot toward less familiar design tasks. We begin this qualitative study with a natural experiment made possible by an exogenous shock: the COVID-19 pandemic. Through a three-stage case study approach using a grounded theory-building method, we contrast AM usage among a set of firms, half of which pivoted their resources away from their traditional production and toward a response to this shock. We engage in an abductive reasoning approach to consider common threads in AM capabilities that facilitated this pivoting. Our analyses suggest that the advanced use of generative design (GD), a category of computational technologies enabling novel and optimized design, is a critical attribute of these firms that ended up pivoting to make COVID-related products. Specifically, firms with experience applying this capability demonstrated a unique ability to pivot during this shock and emphasized their valuation of AM-enabled agility. We revisited these firms 2 years after initial contact and found that GD was associated with higher levels of innovation and was largely viewed by designers as a mechanism driving double-loop learning. Overall, our study provides insights into the symbiosis between human and artificially intelligent GD, and the role of such symbiosis in advancing AM capabilities.

Original languageEnglish
Pages (from-to)325-345
JournalDecision Sciences
Volume55
Issue number4
Early online date2023
DOIs
Publication statusPublished - Aug 2024
MoE publication typeA1 Journal article-refereed

Keywords

  • additive manufacturing
  • case study
  • generative design
  • theory building

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