Management and forecast of dynamic customer needs : an immunity-based approach

With rapidly growing competition intensity, globalization, and technological advancement, high volatility of consumer needs has been observed. Product developers in volatile markets face the challenge of capturing fast moving targets - customer requirements. Customer needs can vary considerably across the period between the time of requirement elicitation and market introduction of a product. The variation contributes to the uncertainties of the product’s market acceptance. Therefore, it is crucial for developers to grasp the dynamism of customers’ needs in the processes of making decisions during product development cycles, especially in markets where requirements fluctuate considerably across time. Modern technology allows companies to gather vast amount of data concerning individuals and their habits on a periodical basis. An approach to deal with the issue of fast shifting customer needs is to dynamically acquire requirement data through effective media such as the Internet. Information derived from analyzing the time-series data may therefore serve to reduce the uncertainties of success that are inherent in product development projects. This study aims to advance the body of knowledge that is necessary to address the issue of dynamic customer needs. Research in the area of dynamic customer requirements and the potential solutions to the problem are reviewed. The boundaries of the research areas are identified and a set of research problems is accordingly defined.DOCTOR OF PHILOSOPHY (MAE

Heuristic-based approach to customer-driven conceptual design

Research has shown that about 80% of a product’s lifecycle cost is determined during the conceptual design phase. Despite its importance, the use of computer-aided design during this phase is uncommon. In the inherently large space of design, it is known that explicating all possible concept variants is astronomically costly, if at all possible. A strategy that can effectively assist designers in exploring and ascertaining solutions within this vast space is therefore necessary. This work adapts a General Best First heuristic algorithm for application on conceptual design problems. The algorithm is tailored to operate on a postulated knowledge organization structure, called Design Space Framework (DSF). Three case studies are presented in this thesis to illustrate the applicability and the logics of the proposed algorithm. The propositions made in this work are shown to advocate customer-oriented design philosophy and supports innovative design among other results. Finally, several promising tracks of future research are identified, based on the foundation of this project.MASTER OF ENGINEERING (MPE

A quality–time–cost-oriented strategy for product conceptualization

In general, product development companies aim to deliver products of optimal quality while incurring minimal cost in the shortest time possible. In this work, a quality–time–cost-oriented strategy (QTCOS) is proposed to facilitate product concept generation and selection. Firstly, general sorting is employed to elicit an initial product platform. The platform, constructed with a design space framework (DSF), serves as a base for generating a preliminary range of design options. Using the repertory grids elicitation method, designers contribute importance ratings with respect to a set of time and cost criteria for the range of design options. To account for trade-offs between cost and time related concerns, these ratings are employed to reduce the number of the derived design options, and thereby used as input features to a restricted coulomb energy (RCE) neural network. The RCE network function is applied to classify the set of design options into different patterns, i.e. cost–time-pairs. The classification results can subsequently serve as bases for the selection of preferred design options. A case study on wood golf club design is conducted to illustrate the proposed QTCOS