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In large engineering firms, most design projects are undertaken by teams of individuals. From the perspective of senior management, the overall project team must maintain scheduling, investment and return on the investment discipline while solving technical problems. Various tools exist in systems engineering (SE) that can reflect the value provided by the resources invested; however, the involvement of human decision makers complicates most types of analyses. A critical ingredient in this challenge is the interplay of the cognitive attributes of team members and the relationships that exist between them. This aspect has not been fully addressed in the literature, rendering many studies relatively oblivious to team dynamics and organization structures. To this end, we propose a framework to incorporate organization structure using a graph representation. This is then used to inform an agent-based model where team dynamics are simulated to understand the effects of cognitive attributes and team member relationships. In this work, we aim to understand team dynamics in the context of product development. The organization is modeled using the Barabasi–Albert scale-free network. The information regarding member relationships can be acquired through graph metrics such as the various centrality measures associated with the members and the distance between them. This is then used to model the dynamics of the members when they work on a technical problem, in conjunction with their other cognitive attributes. We present some results and discuss avenues for future work. [ABSTRACT FROM AUTHOR]

Product take-back and reuse is sometimes at odds with the rapidly evolving desires of some customers. For other customers, the environmental benefits of reuse more than compensate for minor drawbacks. 'Selling a service' (rather than a product) through leasing enables the manufacturer to control the timing and quality of product take-back but current methods assume a fixed leasing period. What is needed is a method for fine tuning the time span of customers' life cycles in order to provide each market segment the combination of features it most desires. This paper presents a new method for performing long range product planning so that the manufacturer can determine optimal take-back times, end-of-life design decisions, and number of lifecycles. The method first determines a Pareto optimal frontier over price, environmental impact and reliability using a genetic algorithm. Then, a multiattribute utility function is employed to maximize utility across different segments of the market and also across different lifecycles within each segment. Post-optimal studies help determine feasibility of component redesign in addition to parts consolidation. The proposed method is illustrated through an example involving personal computers. [DOI: 10.1115/1.4002142]

Product take-back and remanufacturing systems are difficult to implement cost effectively. One contributing factor is the complex nature of the inter-relationships among components of a product. Modeling of these relationships helps determine the product's overall performance as a function of the performances of individual components. Reliability, a commonly used measure of performance, is a good measure of the physical failure rate, but it does not always reflect value degradation as experienced by customers or experts. As a result, it is difficult to define the effective performance of remanufactured products when some components are reused while others are not. Legislated take-back mandates across the world increasingly make it necessary to understand this perceived performance. In this paper we propose a method for combining customers'/experts' assessments of value degradation using the maximum entropy principle. This value degradation information is then coupled with the components' failure rate information. A method for modeling performance of a product that is comprised of components of different ages is presented. Overall performance is measured in units of time (effective age) by aligning with that of a product that has never been disassembled. We demonstrate the approach using a personal computer as example. [DOI: 10.1115/1.3042146]