Reliability-Based Design Optimization (RBDO) for Electric Vehicle Market Systems

When designing a product, both engineering uncertainty and market heterogeneity should be considered to reduce the risk of failure in the market. Reliability-based design optimization (RBDO) approach allows decision makers to achieve target confidence in product performance under engineering uncertainty. Design for market systems (DMS) approach helps decision makers to find profit-maximized product design under market heterogeneity. This paper integrates RBDO and DMS approaches for an Electric vehicle (EV) design. Consumers’ preferences on warranted battery lifetime are heterogeneous while battery life itself is affected by various uncertainties such as battery characteristics and driving patterns. We optimized and compared four scenarios depending on whether engineering systems are deterministic or probabilistic, and whether a market is homogeneous or heterogeneous. The results provide some insight on how the optimal EV design should be altered depending on engineering uncertainty and market heterogeneity.

Agent-Based Modeling of Automobile Producer and Consumer Behavior to Support Design for Market Systems Analysis

Improving design for market systems analysis relies on understanding the motivations and interactions among producers and consumers. Producers should theoretically develop their strategies for designing new products based on consumer demand and the expected profits from their sales. In this study, an agent-based modeling approach is proposed to simulate consumer and producer behavior for use in market systems analysis, and it is demonstrated through a simplified automobile market. In the model, consumers make heterogeneous purchasing decisions based on product attributes, which provides the producers with insights into their preferences and how to improve upon these design attributes over time. Emergent behavior of the model shows that analyzing the behavior of consumers provides the opportunity for producers to compete which one another with different strategies to improve their designs by investing in technology improvements. This lays the foundation for future work that can model how different business and regulatory strategies, social structures, and policies influence consumer and producer behavior, which in turn influences economic, environmental, and social impacts.

Enhanced Adaptive Choice-Based Conjoint Analysis Incorporating Engineering Knowledge

Conjoint analysis from marketing has been successfully integrated with engineering analysis in design for market systems. The long questionnaires needed for conjoint analysis in relatively complex design decisions can become cumbersome to the human respondents. This paper presents an adaptive questionnaire generation strategy that uses active learning and allows incorporation of engineering knowledge in order to identify efficiently designs with high probability to be optimal. The strategy is based on viewing optimal design as a group identification problem. A running example demonstrates that a good estimation of consumer preference is not always necessary for finding the optimal design and that conjoint analysis could be configured more effectively for the specific purpose of design optimization. Extending the proposed method beyond a homogeneous preference model and noiseless user responses is also discussed.

A Bayesian Approach to Extrinsic Versus Intrinsic Uncertainty in Design for Market Systems

This article illustrates how variance in the predictive distribution of the profit objective function in a design for market systems model can be decomposed into two components using a simulation based Bayesian approach introduced in the econometrics literature. The first component, intrinsic uncertainty, would be retained in the model even if the model calibration parameter values, such as parameters representing customer preferences, were known with certainty. The second component, extrinsic uncertainty, stems from lack of precision regarding model calibration parameters such as customer preferences. The simulation based approach overcomes a key problem in decomposing uncertainty for the typical design for market systems problem by overcoming the difficulties associated with analytical treatment of non-normal distributions. The variance decomposition approach is demonstrated for the design of a handheld grinder power tool. Following the same Bayesian decision analysis framework the variance simulation method can be applied to other design for market system problems with other objective functions and with additional sources of uncertainty.

Customer-Driven Optimal Design for Convergence Products

Convergence products are multifunctional designs which are changing the way consumers use existing functionalities. Manufacturers’ ventures in developing convergence products abound in the marketplace. Smartphones, tablet computers, and internet TV are just a few examples. The complexity of designing a convergence product can differ significantly from that of single function products which most research in “design for market systems” aims at. In this paper, a new customer-driven approach for designing convergence products is proposed to address the following issues: (i) a design representation scheme that considers information from design solutions used in existing products. The representation facilitates the coupling of and combining multiple functionalities; (ii) a hierarchical Bayes model that evaluates consumers’ heterogeneous choices while revealing how usage of multiple functionalities impacts consumers’ preferences; and (iii) design metrics which help to evaluate profitability of design alternatives and account for future market penetration given evolving consumer preferences. An example problem for designing a tablet computer is used to demonstrate the proposed approach. The data for the example are collected by conducting a choice-based conjoint survey which yielded 92 responses. The proposed approach is demonstrated with three scenarios differentiated by the consideration of consumer heterogeneity and future market penetration, while comparing how the resulting optimal design solutions for the convergence product differ.

Strategic Design Decisions for Uncertain Market Systems Using an Agent Based Approach

Market players, such as competing manufacturing firms and retail channels, can significantly influence the demand and profit of a new product. Existing methods in design for market systems use game theoretic models that can maximize a firm’s profit with respect to the product design and price variables given the Nash equilibrium of the market system. However, in the design for uncertain market systems, there is seldom equilibrium with players having fixed strategies in a given time period. In this paper, we propose an agent based approach for design for market systems that accounts for learning behaviors of the market players under uncertainty. By learning behaviors we mean that market players gradually, over time, learn to play with better strategies based on action–reaction behaviors of other players. We model a market system with agents representing competing manufacturers and retailers who possess learning capabilities and based on some prespecified rules are able to react and make decisions on the product design and pricing. The proposed agent based approach provides strategic design and pricing decisions for a manufacturing firm in response to possible reactions from market players in the short and long term horizons. Our example results show that the proposed approach can produce competitive strategies for the firm by simulating market players’ learning behaviors when they react only by setting prices, as compared to a game theoretic approach. Furthermore, it can yield profitable product design decisions and competitive strategies when competing firms react by changing design variables in the short term—case for which no previous method in design for market systems has been reported.

Customer Driven Optimal Design for Convergence Products

Convergence products are multifunctional designs which are changing the way consumers use existing functionalities. Manufacturers’ ventures in developing convergence products abound in the marketplace. Smartphones, tablet computers, internet TV, are just a few examples. The complexity of designing a convergence product can differ significantly from that of single function products which most research in “Design for Market Systems” aims at. In this paper, a new customer-driven approach for designing convergence products is proposed to address the following issues: (i) a design representation scheme that considers information from design solutions used in existing products. The representation facilitates the coupling of and combining multiple functionalities; (ii) a hierarchical Bayes model that evaluates consumers’ heterogeneous choices while revealing how usage of multiple functionalities impacts consumers’ preferences; and (iii) design metrics which help evaluate profitability of design alternatives and account for future market penetration given evolving consumer preferences. An example problem for designing a tablet computer is used to demonstrate the proposed approach. The data for the example is collected by conducting a choice-based conjoint survey which yielded 92 responses. The proposed approach is demonstrated with three scenarios differentiated by the consideration of consumer heterogeneity and future market penetration, while comparing how the resulting optimal design solutions for the convergence product differ.

On the Suitability of Econometric Demand Models in Design for Market Systems

A goal of design for market systems research is to predict demand for differentiated products so that counterfactual experiments can be performed based on design changes. We review conventional methods and propose an additional method to evaluate the suitability of econometric demand models estimated from revealed preference data for use in product design studies. We evaluate one demand model form from literature and two newly constructed forms for new vehicle demand along existing metrics of fit and predictive validity as well as a newly developed metric of proportional substitution sensitivity. We show that a model that includes horizontally differentiated preferences for size performs better under metrics of fit and predictive validity but that no model relaxes the IIA property satisfactorily to avoid exploitation by design optimization. We conduct design studies separately, applying each demand model form assuming the automotive market is in Bertrand–Nash price equilibrium. Results illustrate that the influence of the demand model form on the optimum in terms of design variables and expected firm profit is significant.

Strategic Product Design Decisions for Uncertain Market Systems Using an Agent Based Approach

Market players, such as competing manufacturing firms and retail channels, can significantly influence the demand and profit of a new product. Existing methods in design for market systems use game theoretic models that can maximize a focal manufacturing firm’s profit with respect to product design and price variables given the Nash equilibrium of the market system. However, in the design for uncertain market systems, there is seldom equilibrium with players having fixed strategies in a given time period. In this paper, we propose an agent based approach for design for market systems that accounts for learning behaviors of the market players under uncertainty. By learning behaviors we mean that market players gradually, over a time period, learn to play with better strategies based on action-reaction behaviors of other players. We model a market system with agents representing competing manufacturers and retailers who possess learning capabilities and are able to automatically react and make decisions on the product design and pricing. The proposed approach provides strategic design and pricing decisions for a focal manufacturer in response to anticipated reactions from market players in the short and long term horizons. Our example results show that the proposed agent based approach can produce competitive strategies for a focal firm over a time period when market players react only by setting prices compared to a game theoretic approach. Furthermore, it can yield profitable product design decisions and competitive strategies when competing firms react by changing design attributes in the short term — a case for which no previous method in design for market systems has been reported.