An integrated approach on new product development process with data mining: a case study on smartphone design

The New Product Development (NPD) process integrates data from various sources, with a primary focus on customer inputs. This study introduces a novel integrated approach that enhances the NPD process by systematically incorporating customer needs. Specifically, the approach extracts customer needs from social media data, integrates data mining techniques into the House of Quality (HoQ), and provides actionable recommendations for smartphone design improvements. The proposed methodology encompasses four stages: attribute extraction, review classification, transformation, and attribute deployment. Initially, X data is collected and analyzed using Latent Dirichlet Allocation for attribute extraction. Subsequently, customer reviews are classified using machine learning algorithms. The sentiment analysis scores are then transformed into Kano model parameters to inform Quality Function Deployment. Finally, the identified attributes are applied to smartphone design through the HoQ framework. The findings reveal that customer requirements, their significance, and competitive analyses can be effectively incorporated into the NPD process through the integration of data mining techniques. Notably, the study identifies security as the most critical attribute for smartphone design, with the sense of quality emerging as the foremost customer requirement. This integrated approach offers valuable insights for design managers, serving as a decision support system to guide new product design and development. The novelty of this study lies in its systematic combination of data mining methods with traditional NPD methodologies, offering a comprehensive framework for enhancing product design based on real-time customer feedback.