"product Innovation"

This book describes how to use computational intelligence and artificial intelligence tools to improve the decision-making process in new product development. These approaches, including artificial neural networks and constraint satisfaction solutions, enable a more precise prediction of product development performance compared to widely used multiple regression models. They support decision-makers by providing more reliable information regarding, for example, project portfolio selection and project scheduling.

Create a ChatGPT innovation machine to generate, implement, and advertise hundreds of product ideas

The objective of this book is to illustrate statistical methodologies that incorporate physical and numerical experiments and that allow one to schedule and plan technological innovation, similar to any other productive activity. This methodology should be implemented through a structured procedure aimed at reducing the high rate of commercial failure characterizing actual innovation processes. In fact, it is well known that:

Product Innovation Toolbox: A Field Guide to Consumer Understanding and Research brings together key thought-leaders and seasoned consumer researchers from corporate R&D, academia and …