Decision Support for Product Development [electronic resource] : Using Computational Intelligence for Information Acquisition in Enterprise Databases /

By:

Relich, Marcin [author.]

Contributor(s):

SpringerLink (Online service)

Material type: Text

TextSeries: Computational Intelligence Methods and ApplicationsPublisher: Cham : Springer International Publishing : Imprint: Springer, 2021Edition: 1st ed. 2021Description: XII, 114 p. 55 illus., 16 illus. in color. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783030438975

Subject(s):

Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification:

006.3 23

LOC classification:

Q334-342
TA347.A78

Online resources:

Click here to access online

Contents:

Product Development: State of the Art and Challenges -- Model for Formulating Decision Problems Within Portfolio Management -- Method for Supporting Product Development -- A Decision Support System for Portfolio Management of NPD Projects -- Implications for Management.

In: Springer Nature eBookSummary: 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. The book is appropriate for computer scientists, management scientists, students and practitioners engaged with product innovation and computational intelligence applications.

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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. The book is appropriate for computer scientists, management scientists, students and practitioners engaged with product innovation and computational intelligence applications.

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