Decision Support for Product Development


Book Description

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.




Decision Support for Product Development


Book Description

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.




Multiple Criteria Decision Support in Engineering Design


Book Description

Multiple criteria decision making tools have been developing at an extremely rapid pace over the last few years. This work explores the nature of the pursuit, using the authors extensive experience in the field. With its clear, concise approach combining industrial examples and case studies, this book will be of interest to graduate students, practicing engineers, and project managers.




Multi-criteria Decision Analysis for Supporting the Selection of Engineering Materials in Product Design


Book Description

Multi-criteria Decision Analysis for Supporting the Selection of Engineering Materials in Product Design, Second Edition, provides readers with tactics they can use to optimally select materials to satisfy complex design problems when they are faced with the vast range of materials available. Current approaches to materials selection range from the use of intuition and experience, to more formalized computer-based methods, such as electronic databases with search engines to facilitate the materials selection process. Recently, multi-criteria decision-making (MCDM) methods have been applied to materials selection, demonstrating significant capability for tackling complex design problems. This book describes the rapidly growing field of MCDM and its application to materials selection. It aids readers in producing successful designs by improving the decision-making process. This new edition updates and expands previous key topics, including new chapters on materials selection in the context of design problem-solving and multiple objective decision-making, also presenting a significant amount of additional case studies that will aid in the learning process. Describes the advantages of Quality Function Deployment (QFD) in the materials selection process through different case studies Presents a methodology for multi-objective material design optimization that employs Design of Experiments coupled with Finite Element Analysis Supplements existing quantitative methods of materials selection by allowing simultaneous consideration of design attributes, component configurations, and types of material Provides a case study for simultaneous materials selection and geometrical optimization processes




Decision-making for New Product Development in Small Businesses


Book Description

What goes on in a small firm that lives or dies by its capacity to innovate? How are decisions made on new product development, and how does that feed into the ecological, social and financial sustainability of the firm? This book answers the questions through an in-depth look at a small business that manufactures high-end carpet yarn. Using advanced analytical techniques to interrogate rich qualitative data, the book draws together established theories of decision-making and new product development, coupled with thinking about business sustainability to improve our understanding of this important area of business practice. The book further reinforces the importance and role of organizational learning in organizational decision-making, based on novel analysis of empirically developed qualitative data.







Intelligent Support Systems for Marketing Decisions


Book Description

Intelligent Support Systems for Marketing Decisions examines new product development, market penetration strategies, and other marketing decisions utilizing a confluence of methods, including Decision Support Systems (DSS), Artificial Intelligence in Marketing and Multicriteria Analysis. The authors systematically examine the use and implementation of these methodologies in making strategic marketing decisions. Part I discusses the basic concepts of multicriteria analysis vis-à-vis marketing decisions and in new product development situations. Part II presents basic concepts from the fields of Information Systems, Decision Support Systems, and Intelligent Decision Support Methods. In addition, specialized categories of DSS (multicriteria DSS, web-based DSS, group DSS, spatial DSS) are discussed in terms of their key features and current use in marketing applications. Part III presents IDSS and a multicriteria methodology for new product development. Further chapters present a developmental strategy for analyzing, designing, and implementing an Intelligent Marketing Decision Support System. The implementation discussion is illustrated with a real-world example of the methods and system in use.







Decision Support Systems


Book Description

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference.




Cooperative Decision-Making in Modular Product Family Design


Book Description

The development of modular product families holds enormous economic potential for companies, as there are always great opportunities but also risks associated with all life phases of a product. However, these fundamental and far-reaching effects inevitably lead to conflicting objectives when defining modular product structures, which makes decision-making in product development particularly complex.Considering relevant theories from decision theory and product family design, this book presents an innovative method to support decision makers in the development of modular product families. The central element of the method is a novel Modularity Decision Dashboard (MDD), which interactively visualizes all decision-relevant data. The findings presented here confirm that applying the method to real-world decision-making problems leads to a more balanced ratio between internal and external variety, and thus significantly contributes to the efficient economic benefit of modularization.