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In this paper a decision support framework is presented for the design of flexible engineering systems. The framework draws on concepts from multi-objective optimization, consumer choice theory, and utility theory. The framework supports the necessary decisions in the design of flexible engineering systems or systems that can change their functionality and embodiment to satisfy multiple performance requirements. The goal of the framework is to determine a design configuration that maximizes corporate utility while setting attribute and budget constraints for the conceptual design phase. The details of the framework are covered and then applied to a simple case study.

In this paper, a framework for the concept of flexibility in complex system design is presented. This is one of the first of many steps toward developing new design methods for designers that will aid them in the development of customizable systems that meet the requirements of multiple customers and multiple tasks. The hope is that this paper will provide both a starting point from which academia and industry can move forward in developing new design methods for flexible systems and a basis for establishing a standard lexicon for use when referring to flexible system design.

In this article, an argument for validation of design-decision methods is presented. In the process of justifying the need for validation, several criteria for a valid design-decision method are introduced. These criteria represent a starting point from which the research community can continue to debate and ponder the validation issue. Under these criteria, a critical empirical investigation of two popular decision support methods, the House of Quality and Suh’s Axiomatic Design, is presented via a simple design problem and both are shown to violate some portion of the proposed definition of validity. The goal of this article is to raise awareness of potential flaws in popular design-decision aids and to promote debate on design validation within the concurrent engineering research community.

The evolution of design thinking has seen numerous challenges and advances in transforming information into knowledge for engineers to design systems, products, and processes. These transformations occur in three stages throughout a design process. In simple form, the early, middle, and late stages of a design process serve to develop an understanding of the customer’s needs, arrive at the final concept of the design, and analyze and support the performance and usage profile of the deployed product, respectively. The quality and accuracy of the input information and the effectiveness of each transformation determine the success or failure of the product. Capturing good information and converting it to knowledge are two important tasks that have motivated a long history of research in design processes and tools. In this paper, we propose Design Analytics (DA) as a new paradigm for significantly enhancing the core information-to-knowledge transformations. The overall aim is to capture, store, and leverage digital information about artifacts, their performance, and their usage. The information is transformed into knowledge in each of the three stages using various analytics and cyber-enabled tools such as design repositories and concept generators. The ultimate result is better performing and functioning products. As web analytics has transformed how companies interact with consumers on the internet, we expect DA to transform how companies design products with and for consumers. An illustrative case study is performed to demonstrate some of the foundations of DA in the redesign of a refrigerator.