Details
Decision Support for Product Development
Using Computational Intelligence for Information Acquisition in Enterprise DatabasesComputational Intelligence Methods and Applications
96,29 € |
|
Verlag: | Springer |
Format: | |
Veröffentl.: | 22.11.2020 |
ISBN/EAN: | 9783030438975 |
Sprache: | englisch |
Dieses eBook enthält ein Wasserzeichen.
Beschreibungen
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.<p>The book is appropriate for computer scientists, management scientists, students and practitioners engaged with product innovation and computational intelligence applications.</p>
<p>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.</p><br>
<p>Dr. <b>Marcin Relich</b> is a member of the Faculty of Economics and Management of the University of Zielona Gora. He is a lecturer in the Dept. of Controlling and Computer Applications in Economics, where he teaches IT in Economics and Management, Business Data Analysis, Management Information Systems, Decision-Support Systems, Decision Analysis, Business Simulations, and Project Management. His research interests include Management Information Systems (incl. Decision-Support Systems and Enterprise Resource Planning Systems), Project Management (incl. new product development), and early warning systems in business.</p>
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. <div><br></div><div>The book is appropriate for computer scientists, management scientists, students and practitioners engaged with product innovation and computational intelligence applications.</div>
Describes how to use computational intelligence and artificial intelligence tools to improve the decision-making process in new product development Approaches enable precise prediction of product development performance Valuable for computer scientists, management scientists, students and practitioners engaged with product innovation and computational intelligence applications