Mass customization aims to provide goods and services that meet each individual customer's needs with a level of efficiency close to that of mass production. It is also a viable smart manufacturing strategy for companies that want to gain a competitive advantage in the current business environment. Product configurators are one of the major toolkits enabling mass customization. Existing product configurators require customers to choose from a set of predefined attributes or a list of component alternatives. However, customers may feel confused when configuring products if they do not have the necessary domain knowledge about the product. This article proposes a needs-based configurator mechanism that takes customer needs expressed in natural language as input to generate satisfactory product variants as output. This method leverages online product review data to distill the knowledge of customer preferences and needs, which then maps onto the product attribute specifications. A hierarchical attention network is applied to fully extract the information in the review text, which emphasizes the important keywords and phrases. We have obtained the promising experimental results, and our proposed needs-based configurators could help customers to find satisfactory product configurations with high recall rates.
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