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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]

We present a new experiment demonstrating destructive interference in customers' estimates of conditional probabilities of product failure. We take the perspective of a manufacturer of consumer products and consider two situations of cause and effect. Whereas, individually, the effect of the causes is similar, it is observed that when combined, the two causes produce the opposite effect. Such negative interference of two or more product features may be exploited for better modeling of the cognitive processes taking place in customers' minds. Doing so can enhance the likelihood that a manufacturer will be able to design a better product, or a feature within it. Quantum probability has been used to explain some commonly observed "non-classical" effects, such as the disjunction effect, question order effect, violation of the sure-thing principle, and the Machina and Ellsberg paradoxes. In this work, we present results from a survey on the impact of multiple observed symptoms on the drivability of a vehicle. The symptoms are assumed to be conditionally independent. We demonstrate that the response statistics cannot be directly explained using classical probability, but quantum formulation easily models it, as it allows for both positive and negative "interference" between events. Since quantum formalism also accounts for classical probability's predictions, it serves as a richer paradigm for modeling decision making behavior in engineering design and behavioral economics. [ABSTRACT FROM AUTHOR]

Different operations, such as take-back, cleaning, and repair, lead to high system variability rendering remanufacturing systems difficult to manage. Even when a product is successfully remanufactured, there remains the problem of customer perception of remanufactured products being not able to perform as well as new ones. The possibility of several different options (reusing, remanufacturing, and recycling) further compound the complexity of the information set that should be considered for effective remanufacturing. This paper develops a method that can be employed for making component level decisions that accounts for aforesaid issues. A metric is proposed that measures the randomness or variability imposed by a reuse alternative. A measure of effective age is also proposed, extending the lines of previous research. A washing machine example illustrates the method and how the two measures can be incorporated into a design decision model. [ABSTRACT FROM AUTHOR]

An approach for testing methods for decision-making under uncertainty is proposed by the authors and their coworkers. The testing approach uses data for which the generating mechanism and probability distribution is unknown. The approach simulates a very large number of decisions and outcomes of uncertain events to test methods. The study presented in this paper follows the above approach to test methods for decision under uncertainty experimentally on a decision problem involving two decision makers, an inventor and a financier who want to develop and market a new device. Real-life data for simulating the outcomes of the project were collected using 133 slider-crank mechanisms that undergraduate students constructed. The mechanisms were constructed and measured to simulate the entire risky venture of developing and marketing a new device or product. The data obtained were used to simulate thousands of decisions of the inventor and the financier on a computer, using different methods for decision under uncertainty; standard probability, imprecise probability and Bayesian probability. These methods were then judged on the basis of the expected utilities that they produced when used by the two decision makers and also on their sensitivities to changes in the amount of available information or the risk attitudes of the decision makers. In general, the imprecise probability method was found insensitive to the changes in the problem parameters. Bayesian probability method, although sensitive to prior models of uncertainty, produced the highest utilities in general and it was also found insensitive to the information level of a decision maker, when a good prior was chosen. [ABSTRACT FROM AUTHOR]

This paper shows how reliability block diagrams can be used as a decision making tool. The premise behind the idea is that classical decision analysis (DA), while very powerful, does not provide much tractability in assessing utility functions and their use in making decisions. Our recent work has shown that a reliability block diagram which is a visual representation of systems, can be used to describe a decision situation. In decision making, we called these block diagrams decision topologies (DTs). We show that DTs can be used to make engineering decisions just as DA. The paper also proves that in the limit, using DTs is entirely consistent with DA for both single attribute and multi-attribute cases. The main advantages of the proposed method are that (1) it provides a visual representation of a decision situation, and (2) accommodates continuous and binary attributes together, as well as the tradeoff between them. The paper details the theoretical basis of the proposed method and highlights its benefits. An example is used to demonstrate how DTs can be used in practice. [ABSTRACT FROM AUTHOR]