Configuration-Based Smart Customization Service: A Multitask Learning Approach

Abstract

Smart customization service is an important element for smart manufacturing. The success of smart customization requires that designers, manufacturers, and customers with differences in context, semantics, and other cognitive aspects be engaged in a collaborative process. With product configurators reported to have positive impacts on product quality to meet customers' needs, this article attempts to explore an approach for smart customization service based on configurators. To better address the semantic gap between customers and designers/manufacturers, a new configuration mechanism is proposed that takes into consideration customer needs using natural language as the input and maps them to product specifications in the design stage. We collected a massive amount of review text from e-commerce websites and used ELMo, a contextualized word representation based on a deep bidirectional language model, to encode the text. A multitask learning-based neural network was adopted to build the mapping from layman customer needs to product specifications. Our experiments show that this approach can achieve a promising performance for the configuration task and, thereby, facilitate smart customization services. Note to Practitioners-Smart customization has been adopted by various industries to tailor companies' business streams and customize solutions for customers. It is a complicated service process involving cross-functional teams for identifying customer needs and establishing product design and manufacturing specifications. However, communication in the collaborative process may be challenging. Customers may express their needs using layman's terms. The expressed needs can even be ambiguous and imprecise. Miscommunication hinders the efficiency of smart customization services. This article uses natural language processing and machine learning techniques to map product review text, which is crawled from e-commerce websites to the corresponding product specifications. Given a new customer's needs in free-form text, the mapping can automatically identify the satisfactory product configurations. This has the potential to improve the efficiency of product customization and shield customers and companies from the back-and-forth communication procedure in the customization service.