The increasing complexity of smart products in the era of Artificial Intelligence (AI) presents new challenges for designing smart products, services, and product service systems. This paper aims to summarize the latest progress in the design of smart products and services, focusing on the concepts, design methods, and data types used in the design process of smart products and services. It also aims to explore how a data-driven approach can enhance product performance, improve user experience, and drive service innovation. A systematic literature search was conducted for studies published between 2004 and 2024 in the Web of Science (WoS) database. Keywords such as “smart product-service system (SPSS)”, “smart product design (SPD)”, “smart service design (SSD)”, “intelligent product service system design”, “intelligent product design”, and “intelligent service design” are used to retrieve relevant literature. A total of 803 research articles were searched and screened for relevance and eligibility based on predefined inclusion criteria, focusing on journals, papers, and conference proceedings. Ultimately, 694 valid articles were identified. Text analysis includes Term Frequency-Inverse Document Frequency (TF-IDF), Keywords cluster and knowledge graph, combined with research categories, data type, case study, and publication years to find core concepts and trends. The review identifies key applications of data in SPD, SSD, and SPSS, including requirements analysis, product optimization, fault diagnosis, and enhancing user experience. The findings highlight the importance of interdisciplinary integration and continuous innovation for developing SPD, SSD, and SPSS.

It has been widely reported that the reuse of previously created components, or features, in new engineering designs will improve the efficiency of a company’s product development process. Although the reuse of engineering components has established metrics and methodologies, the reuse of specific design features (e.g. stiffening ribs, hole patterns or lubrication grooves, etc.) has received less attention in the literature. Typically, researchers have reported approaches to partial design reuse that identify patterns predominately in terms of geometrically similar shapes (i.e. a set of features) whose elements are adjacent, cohesive, and decoupled from the overall form of a component. In contrast, this paper defines a common design structure (CDS) as collections of frequently occurring features (e.g. holes) with common parametric values (e.g. diameters) in a CAD database (irrespective of their locations or spatial connectivity between other features on a component). By exploiting the established data-mining technology of association rules and item-sets the authors show how CDSs can be efficiently computed for hundreds of 3D CAD models. A case study, with hole data extracted from a publicly available dataset of hydraulic valves, is presented to illustrate how item-sets associated with CDS can be computed and used to support predictive design by identifying potentially ‘substitutable features’ during an interactive design process. This is done using a combination of association rules and geometric compatibility checks to ensure the system’s suggestion are implementable. The use of the Kullback–Leibler divergence to assess the degree of similarity between components is identified as a crucial step in the process of identifying the “best” suggestions. The results illustrate how the prototype implementation successfully mines the CDSs and identifies substitutable hole features in a dataset of industrial valve designs.