Paper Number
1548
Paper Type
Short Paper
Abstract
Machine learning (ML) applications in product design workflows have grown in recent years. For highly complex analysis requiring in-depth ML skills, there is frequent interaction between domain experts and data scientists. ML-assisted domain-specific tasks reveal three major challenges: (1) absence of a digital assistance system for decision-makers, (2) too much dependency on data scientists, and (3) lack of ML skills among domain experts. This suggests rethinking data scientists' role in such knowledge-intensive work. We present ongoing design science research to design an ML system to support product design. We investigate existing design workflows within an industrial product development environment to understand the nexus between data scientists and design engineers. We discover several issues that hinder the success of ML-based product design systems. To address these issues, we derive requirements, propose design principles and an initial prototype. Thereby, we contribute with design knowledge for the intelligent augmentation of product design systems.
Recommended Citation
Johny, Lijo; Dechant, Hannes; and Schneider, Johannes, "Taking Data Scientists Out-of-the-Loop in Knowledge Intense Analytics — A Case Study for Product Designs" (2024). ECIS 2024 Proceedings. 17.
https://aisel.aisnet.org/ecis2024/track03_ai/track03_ai/17
Taking Data Scientists Out-of-the-Loop in Knowledge Intense Analytics — A Case Study for Product Designs
Machine learning (ML) applications in product design workflows have grown in recent years. For highly complex analysis requiring in-depth ML skills, there is frequent interaction between domain experts and data scientists. ML-assisted domain-specific tasks reveal three major challenges: (1) absence of a digital assistance system for decision-makers, (2) too much dependency on data scientists, and (3) lack of ML skills among domain experts. This suggests rethinking data scientists' role in such knowledge-intensive work. We present ongoing design science research to design an ML system to support product design. We investigate existing design workflows within an industrial product development environment to understand the nexus between data scientists and design engineers. We discover several issues that hinder the success of ML-based product design systems. To address these issues, we derive requirements, propose design principles and an initial prototype. Thereby, we contribute with design knowledge for the intelligent augmentation of product design systems.
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