Henk Jan Wassenaar

An Approach to Decision-Based Design With Discrete Choice Analysis for Demand Modeling

In this paper, we present the importance of using a single criterion approach to DecisionBased Design (DBD) by examining the limitations of multicriteria approaches. We propose in this paper an approach to DBD as an enhancement to Hazelrigg’s DBD framework that utilizes the economic benefit to the producer as the single criterion in alternative selection. The technique of Discrete Choice Analysis (DCA) is introduced for constructing a product demand model, which is crucial for the evaluation of both profit and production cost. An academic universal motor design problem illustrates the proposed DBD approach. It appears that DBD, when applied correctly, is capable of unambiguously selecting the preferred alternative in a rigorous manner. Open research issues related to implementing the DBD approach are raised. The focus of our study is on demonstrating the approach rather than the design results per se. @DOI: 10.1115/1.1587156#

Enhancing Discrete Choice Demand Modeling for Decision-Based Design

Our research is motivated by the need for developing an approach to demand modeling that is critical for assessing the profit a product can bring under the decision-based design framework. Even though demand modeling techniques exist in market research, little work exists on demand modeling that addresses the specific needs of engineering design, in particular, that facilitates engineering decision making. In this work, we enhance the use of discrete choice analysis to demand modeling in the context of decision-based design. The consideration of a hierarchy of product attributes is introduced to map customer desires to engineering design attributes related to engineering analyses. To improve the predictive capability of demand models, the Kano method is employed to provide econometric justification when selecting the shape of the customer utility function. A (passenger) vehicle engine case study, developed in collaboration with the market research firm, J. D. Power & Associates, and the Ford Motor Company, is used to demonstrate the proposed approaches.

Decision-Based Design: Integrating Consumer Preferences into Engineering Design

Building upon the fundamental principles of decision theory, Decision-Based Design: Integrating Consumer Preferences into Engineering Design presents an analytical approach to enterprise-driven Decision-Based Design (DBD) as a rigorous framework for decision making in engineering design. Once the related fundamentals of decision theory, economic analysis, and econometrics modelling are established, the remaining chapters describe the entire process, the associated analytical techniques, and the design case studies for integrating consumer preference modeling into the enterprise-driven DBD framework. Methods for identifying key attributes, optimal design of human appraisal experiments, data collection, data analysis, and demand model estimation are presented and illustrated using engineering design case studies. The scope of the chapters also provides: A rigorous framework of integrating the interests from both producer and consumers in engineering design, Analytical techniques of consumer choice modelling to forecast the impact of engineering decisions, Methods for synthesizing business and engineering models in multidisciplinary design environments, and Examples of effective application of Decision-Based Design supported by case studies. No matter whether you are an engineer facing decisions in consumer related product design, an instructor or student of engineering design, or a researcher exploring the role of decision making and consumer choice modelling in design, Decision-Based Design: Integrating Consumer Preferences into Engineering Design provides a reliable reference over a range of key topics.

A Choice Modeling Approach for Usage Context-Based Design

Usage context-based design (UCBD) is an area of growing interest within the design community. Usage context is defined as all aspects describing the context of product use that vary under different use conditions and affect product performance and/or consumer preferences for the product attributes. In this chapter, we propose a choice modeling framework for UCBD to quantify the impact of usage context on customer choices and exploit the rich contextual information existing in product usage. We start with defining the taxonomy for UCBD. By explicitly modeling usage context’s influence on both product performances and customer preferences, a step-by-step choice modeling procedure is proposed to support UCBD. Two case studies, a jigsaw example with stated preference data and a hybrid electric vehicle example with revealed preference data, demonstrate the needs and benefits of incorporating usage context in choice modeling.

Demand Analysis for Decision-Based Design of Vehicle Engine

Our research is motivated by the need for a rigorous engineering design framework and the need for developing a demand analysis approach that is critical for assessing the profit a product can bring. A Decision-Based Design framework is presented as a rigorous design approach and the method of Discrete Choice Analysis is applied in order to create a demand model that facilitates engineering decision-making in vehicle design with an emphasis on engine design. Through interdisciplinary collaborations, we illustrate how the gap between market research and engineering analysis can be bridged in product design.

Decision-Based Design Framework

With methods for modeling designer preference and customer preference presented in Chaps. 2 and 3, respectively, the decision-based design (DBD) framework and taxonomy is fully developed in this chapter. DBD is an integrated design approach that incorporates the interests from both producer and customers, where the discrete choice analysis (DCA) model for choice modeling, as well as cost models and enterprise objectives, are integrated. To demonstrate the DBD framework, a simple case study for an electric motor design is provided first. Next, an industrial example of vehicle engine design is presented, where techniques for implementing DBD for complex systems are introduced. In particular, determining attribute hierarchies in the DCA model and selecting the form of a DCA utility function using Kano’s method are discussed. The use of the DBD approach to engineering design decision making is illustrated through these examples.

Decision-Based Design: An Approach for Enterprise-Driven Engineering Design

The motivation for developing a decision-based design (DBD) approach for engineering design is introduced in this chapter. The DBD approach recognizes that engineering design is fundamentally an enterprise-driven decision-making process, and therefore uses principles of decision theory and economics principles in its formulation. Further, in this chapter an overview of DBD is provided as well as the organization of the book.

Fundamentals of Analytical Techniques for Modeling Consumer Preferences and Choices

The fundamental principles of decision theory for modeling designer (decision maker) preference are introduced in Chap. 2. In this chapter, the analytical techniques for modeling customer preferences in product design are introduced in the form of demand modeling, or alternatively, choice modeling. As concluded in Sect. 2.5, a primary feature of the enterprise-driven design approach is estimation of a demand function, which is critical for assessing both a customer’s willingness to purchase a product, as well as the benefit it brings to the producer. In this chapter, the demand modeling literature is first reviewed and the need for modeling the heterogeneity of customer preferences is shown. Next, a widely used choice modeling approach, discrete choice analysis (DCA) is introduced, which has the variants of Multinomial, Nested, and Mixed Logit Models. Ordered logit (OL) to model preferences for system attributes that are in the form of a rating is introduced next followed by a discussion of computational methods for estimating the DCA and OL models. Guidelines for addressing a wide range of issues, such as attribute and choice set selection, data collection, dynamic demand modeling, model validation, etc., in implementing demand modeling for product design are provided next. A walk-through example of model estimation and demand forecasting is presented in the final section.

A Decision-Based Design Approach to Product Family Design

In this chapter, we present an extension of the DBD approach to product family design. Products that share a common platform but have specific features and functionality required by different sets of customers form a product family. In product family design, achieving the commonality of the platform for minimizing producer’s cost is a competing objective with meeting the variability of consumer preference. The Decision-Based Design approach is employed here for complex decision makings in product family design. First, we introduce the market segmentation grid to help understand the different product variants needed, and then introduce the nested logit model to estimate demand considering product segmentation. We next create a product family formulation of DBD for making complex decisions in optimal product line positioning optimal “commonality” decisions, and optimal levels of engineering design attributes. We demonstrate the method using a case study for the design of a family of universal electric motors.

Hierarchical Choice Modeling to Support Complex Systems Design

In this chapter, we integrate the concepts of the previous chapters to develop a comprehensive hierarchical demand modeling approach for design of complex systems that typically consist of many subsystems and components. The hierarchical choice modeling framework utilizes multiple model levels corresponding to the complex system hierarchy to create a link between qualitative attributes considered by customers when selecting a product and quantitative attributes used for engineering design. The integrated Bayesian hierarchical choice modeling (IBHCM) approach utilizes choice data as well as other preference data, such as that collected using the human appraisals presented in Chaps. 6 and 7, to create a comprehensive choice model. To capture heterogeneous and stochastic customer preferences, the mixed logit choice model is used to predict customer system-level choices, and the random effects ordered logit model is used to model customer evaluations of system and subsystem-level design features. In the proposed IBHCM framework, both systematic and random customer heterogeneity are explicitly considered; the ability to combine multiple sources of data for model estimation and updating is provided using the Bayesian estimation methodology, and an integrated estimation procedure is introduced to mitigate error propagated throughout the model hierarchy. The new modeling framework is validated using several metrics and validation techniques for behavior models. The benefits of the IBHCM method are demonstrated in the design of an automobile occupant package, the same case study used in Chaps. 6 and 7.