Demand models play a critical role in enterprise-driven design by expressing demand and revenues as a function of price, and product attributes. Revenues and cost, expressed as a function of product attributes, provide the basis for predicting profits; the primary objective of corporate decision-making. However, existing demand modelling 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 modelling 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 behaviour. 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/subsystemspecific 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) modelling aspects of the approach rather than on the vehicle package design.
Enterprise-level business decisions are linked with engineering product decisions by integrating enterprise utility optimization and engineering design optimization under a hierarchical, multilevel, decision-based design framework. The enterprise problem sets attribute targets, that is, specifications, for engineering product development, which then optimizes product performance within the feasible design space to match the targets with minimum deviations. When the feasible domain imposed by engineering product development is disconnected in the space of attribute targets, an engineering design with the minimum deviation from the targets may not correspond to the design with the maximum utility value, even though the design is a converged solution from the multilevel optimization. To address this issue, a new algorithm is developed, which systematically explores the target space to lead the engineering product development to a feasible and optimal design in the enterprise context. Analytical examples and an automotive suspension design case study are presented to demonstrate the effectiveness of the proposed methodology.
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.
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