Christopher Hoyle

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.

Optimal Experimental Design of Human Appraisals for Modeling Consumer Preferences in Engineering Design

Human appraisals are becoming increasingly important in the design of engineering systems to link engineering design attributes to customer preferences. Human appraisals are used to assess consumers’ opinions of a given product design, and are unique in that the experiment response is a function of both the product attributes and the respondents’ human attributes. The design of a human appraisal is characterized as a split-plot design, in which the respondents’ human attributes form the whole-plot factors while the product attributes form the split-plot factors. The experiments are also characterized by random block effects, in which the design configurations evaluated by a single respondent form a block. An experimental design algorithm is needed for human appraisal experiments because standard experimental designs often do not meet the needs of these experiments. In this work, an algorithmic approach to identify the optimal design for a human appraisal experiment is developed, which considers the effects of respondent fatigue and the blocked and split-plot structures of such a design. The developed algorithm seeks to identify the experimental design, which maximizes the determinant of the Fisher information matrix. The algorithm is derived assuming an ordered logit model will be used to model the rating responses. The advantages of this approach over competing approaches for minimizing the number of appraisal experiments and model-building efficiency are demonstrated using an automotive interior package human appraisal as an example. DOI: 10.1115/1.3149845

Choice Modeling for Usage Context-Based Design

Usage Context-Based Design (UCBD) is an area of growing interest within the design community. Usage context is the set of scenarios in which a product (or service) is to be used, including the environments in which the product is used, the types of tasks the product performs, and the conditions under which the product is purchased and operates. It is proposed in this work that in choice modeling for usage context-based design, usage context should be a part of the primary descriptors in the definition of a customer profile, in addition to the socio-demographic attributes for modeling customers’ heterogeneity. As customers become more technology-savvy and market-educated, current choice modeling methods in engineering design could greatly benefit from exploiting the rich contextual information existing in product usage. In this work, we propose a choice modeling framework for Usage Context-based Design (UCBD) to quantify the impact of usage context on customer choices. We start with defining a 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.

A hierarchical choice modelling approach for incorporating customer preferences in vehicle package design

The use of customer preference models to evaluate the economic impact of design changes and new product introductions has become prevalent in the literature. However, existing approaches do not sufficiently address the needs of complex design artefacts, which typically consist of many subsystems and components designed and manufactured with significant autonomy. Characteristics of complex systems, such as heterogeneity of consumer preferences throughout the system hierarchy, multiple sources of information and qualitative consumer-desired attributes, have not been adequately addressed. In this work, we propose a hierarchical choice modelling approach for complex systems to model customer preferences for attributes throughout the system hierarchy, and to subsequently predict consumer choice behaviours. A system of hierarchical models is used to link the design attributes used for engineering design to the attributes used by consumers to choose among competing products. The model framework utilises Discrete Choice Analysis at the top level to model customer choices and Ordered Logit regression at the lower levels to model ordinal survey responses as a function of product attributes. An approach for combining choice data from multiple sources based on the Nested Logit methodology is developed. The framework is demonstrated on the vehicle occupant package case study.

Integrated Bayesian Hierarchical Choice Modeling to Capture Heterogeneous Consumer Preferences in Engineering Design

Choice models play a critical role in enterprise-driven design by providing a link between engineering design attributes and customer preferences. However, existing approaches do not sufficiently capture heterogeneous consumer preferences nor address the needs of complex design artifacts, which typically consist of many subsystems and components. An integrated Bayesian hierarchical choice modeling (IBHCM) approach is developed in this work, which provides an integrated solution procedure and a highly flexible choice modeling approach for complex system design. The hierarchical choice modeling framework utilizes multiple model levels corresponding to the complex system hierarchy to create a link between qualitative attributes considered by consumers when selecting a product and quantitative attributes used for engineering design. To capture heterogeneous and stochastic consumer preferences, the mixed logit choice model is used to predict consumer system-level choices, and the random-effects ordered logit model is used to model consumer evaluations of system and subsystem level design features. In the proposed approach, both systematic and random consumer 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 approach 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.

Set-based design by simulation of usage scenario coverage

While the marketing literature has advocated for decades that new products should be designed for intended and anticipated consumer usages, the engineering literature mostly proposes optimisation of product performances independent of specific users’ skills, anticipated usage scenarios, and competing products in the market. In contrast to tedious market studies which assume an existing market experience for products and optimisation approaches based upon static product performances, we propose an adaptable approach to designing a product or product family: the set-based design by usage coverage simulation. It starts with generating a usage scenario space for a set of representative users. Next, considering a candidate set of products, one proceeds to the constraint satisfaction problem computations of feasible usage scenarios, assuming that physics-based models of performances are available. The comparison between the expected and feasible usage scenarios at the scale of a single user leads to Usage Coverage Indicators and finally to a preferred product which best covers the usage scenario space. At the level of a targeted consumer group, the approach provides a market share simulation for competing products or members of a scale-based product family. The design of a family of jigsaws thoroughly illustrates our approach.

INCORPORATING CUSTOMER PREFERENCES AND MARKET TRENDS IN VEHICLE PACKAGE DESIGN

Demand models play a critical role in enterprise-driven design by expressing revenues and costs as functions of product attributes. However, existing demand modeling approaches in the design literature do not sufficiently address the unique issues that arise when complex systems are being considered. Current approaches typically consider customer preferences for only quantitative product characteristics and do not offer a methodology to incorporate customer preference-data from multiple component/subsystem-specific surveys to make product-level design trade-offs. In this paper, we propose a hierarchical choice modeling approach that addresses the special needs of complex engineering systems. The approach incorporates the use of qualitative attributes and provides a framework for pooling data from multiple sources. Heterogeneity in the market and in customer-preferences is explicitly considered in the choice model to accurately reflect choice behavior. Ordered logistic regression is introduced to model survey-ratings and is shown to be free of the deficiencies associated with competing techniques, and a Nested Logit-based approach is proposed to estimate a system-level demand model by pooling data from multiple component/subsystem-specific surveys. The design of the automotive vehicle occupant package is used to demonstrate the proposed approach and the impact of both packaging design decisions and customer demographics upon vehicle choice are investigated. The focus of this paper is on demonstrating the demand (choice) modeling aspects of the approach rather than on the vehicle package design.Copyright

On Quantifying Cost-Benefit of ISHM in Aerospace Systems

Integrated systems health management (ISHM) is a desired system engineering capability to detect, assess, and isolate faults in complex aerospace systems to improve safety and reliability. At the conceptual design level, system-level engineers must make decisions regarding the extent of vehicle fault coverage using on-board sensors and the data collection, processing, interpretation, display, and action capabilities for the various subsystems, all considered essential parts of ISHM. In this paper, we propose a Cost-Benefit Analysis approach to initiate the ISHM design process. The key to this analysis is the formulation of an objective function that explicitly quantifies the cost-benefit factors involved with using ISHM technology in various subsystems. In the end, to determine the best ISHM system configuration, an objective is formulated, referred to as Profit, which is expressed as the product of system availability (A) and revenue per unit availability (R), minus the sum of cost of detection (CD) and cost of risk (CR). Cost of detection includes the cost of periodic inspection/maintenance and the cost of including ISHM; Cost of Risk quantifies risk in financial terms as a function of the consequential cost of a fault and the probabilities of occurrence and detection. Increasing the ISHM footprint will generally lower cost of risk while raising cost of detection, while Availability will increase or decrease based upon the balance of the reliability and detectability of the sensors added, versus their ability to reduce total maintenance time. The analysis is conducted at the system functional level, with ISHM allocated to functional blocks in the optimization analysis. The proposed method is demonstrated using a simplified aerospace system design problem resulting in a configuration of sensors which optimizes the cost-benefit of the ISHM system for the given input parameters. In this problem, profit was increased by 11%, inspection interval increased by a factor of 1.5, and cost of risk reduced by a factor of 2.4.

Understanding and modelling heterogeneity of human preferences for engineering design

In todays competitive market, it is essential for companies to provide products which not only achieve high performance, but also appeal to the tastes of consumers. Therefore, a key element of design is an understanding of consumer preferences for product features. In this work, the random-effects ordered logit model is proposed as the modelling framework to capture the impact of both product and human attributes on consumers’ ratings of qualitative system and sub-system attributes. To support the modelling, a series of methodologies are developed to both understand and model the influence of consumer heterogeneity upon product preferences. To illustrate the methodologies, a human appraisal experiment for understanding preferences for automobile occupant package design is analysed. An issue with analysing human appraisal experiments is that the effect of respondent heterogeneity must be understood to separate the influence of design factors from that of human factors. Hierarchical Bayes estimation and cluster analysis are used to gain an understanding of respondent rating styles, which are subsequently modelled explicitly in the ordered logit model. Smoothing spline regression is used to determine the functional form of the ordered logit model. The proposed ordered logit model is validated using a vehicle design case study.

Health Management Allocation During Conceptual System Design

Integrated Systems Health Management (ISHM) is an evolving technology used to detect, assess, and isolate faults in complex systems to improve safety. At the conceptual design level, system-level engineers must make decisions regarding the inclusion of ISHM and the extent and type of the sensing technologies used in various subsystems. In this paper, we propose an ISHM design tool to be used in conjunction with standard system modeling methods to help with the integration of ISHM into the system design process. The key to this analysis is the formulation of an objective function that explicitly quantifies the value derived by integrating the ISHM technology in various subsystems. Ultimately, to determine the best ISHM system configuration, an objective function is formulated, referred to as profit, which is expressed as the product of system availability (A(S)) and revenue per unit availability (R), minus the sum of cost of detection (C(D)) and cost of risk (C(R)). The analysis is conducted at the system functional level appropriate for conceptual design using standard system functional modeling methods, and ISHM is allocated to the functional blocks using the ISHM design tool. The proposed method is demonstrated using a simplified aerospace system design problem resulting in a configuration of sensors, which optimizes the value of the ISHM system for the given input parameters. In this problem, profit was increased by 11%, inspection interval increased by a factor of 1.5 and cost of risk reduced by a factor of 2.4 over a system with no ISHM. [DOI: 10.1115/1.3130775]