Overcoming the semantic gap in the customer-to-manufacturer (C2M) platform: A soft prompts-based approach with pretrained language models

Jianhui Huang, Yue Wang, Stephen C.H. Ng and Fugee Tsung

International Journal of Production Economics, 2024, vol. 272, issue C

Abstract: Customer-to-Manufacturer (C2M) is a strategy in smart manufacturing where customers collaborate with manufacturers for customized product development on an online platform. The platform enables the shift from the traditional manufacturing process, which is driven by research and marketing, toward a customer-centric product development process. However, a challenge arises as customers lack technical knowledge to communicate their product specifications effectively, creating a semantic gap. This paper proposes a soft prompt-based network structure that utilizes pretrained language models to bridge the semantic gap on the C2M platform. To address limited customer needs data and imbalanced classes, a large corpus of product review texts is used to establish a mapping between reviews and product specifications. A smaller set of customer needs text is then employed to adapt this mapping to the target customer needs-product specifications relationship, thereby closing the semantic gap. The experimental results demonstrate the effectiveness of the proposed model adaptation operation and the prompting structure. Additionally, the experiments highlight the robustness of the proposed method against variations in training data size, thereby mitigating the challenges posed by imbalanced classes. The proposed method could potentially bring innovation to product customization and C2M platform development. By bridging the semantic gap, companies can better integrate customers in the co-design process and effectively translate customer needs into actionable product specifications.

Keywords: Advanced manufacturing; Configuration; Customer-to-manufacturer (C2M); Deep learning; Prompt; Pretrained language model (PLM) (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:272:y:2024:i:c:s0925527324001051

DOI: 10.1016/j.ijpe.2024.109248

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