A product configuration approach based on online data

Product design is greatly influenced by product configuration processes and can be suspended or result in failure if the configuration process consumes too much time, cost, or resources; such results can also occur if the end products manufactured based on configurations failed to satisfy customers. Therefore, a configuration approach that saves time, cost, and resources, as well as highly satisfies customers, is necessary and significant. Against the background, this study proposes a configuration approach that uses online data to map customer requirements into product configurations, including the product transaction data and customer review data. The approach generates feasible configurations initially by using transaction data. Next, the approach produces training samples based on positive customer review data. Lastly, the intelligent classifier is trained by the training samples and is utilized to select final configurations from feasible configurations to satisfy customer requirements. A real-world design case of smartphones is used to illustrate the proposed approach, and the results indicate that this approach saves time, cost, and resources and is competitive compared with other product configuration methods. This novel configuration approach provides designers and companies with a superior and efficient method to complete configuration tasks with competiveness and low risk and adds value to the usability and analysis of online data.