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Improving Design Resource Management Using Bayesian Network Embedded in Task Network Method

[+] Author Affiliations
Hilario Lorenzo Xin Chen, Marie-Lise Moullec, Nigel Ball, P. John Clarkson

University of Cambridge, Cambridge, UK

Paper No. DETC2016-59709, pp. V007T06A034; 15 pages
doi:10.1115/DETC2016-59709
From:
  • ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 7: 28th International Conference on Design Theory and Methodology
  • Charlotte, North Carolina, USA, August 21–24, 2016
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5019-0
  • Copyright © 2016 by ASME

abstract

In Product Development (PD), there is an inherent complexity in deciding what resources should perform which tasks taking into account their effectiveness towards completing the task, while adjusting to their availabilities. The right resources must be applied to the right tasks in the correct order. In this context, process modeling and simulation could aid in resource management decision making. However, most approaches define resources as elements needed to perform the activities without defining their characteristics, or use a single classification such as “designers”. Despite their crucial importance to the delivery of the product, resources such as computational hardware, software, testing resources, amongst others have been overlooked during process planning stages.

This paper presents a new method to model different resource types (designers, computational, testing) and studies the impact of using different options of those resources by simulating the model and analyzing the results. Thus, the new approach, which extends a task network model with Bayesian Networks (BN), allows testing the influence of using different resources on process performance. The method uses BN within each task to model different instances of resources that carries out the design activities (computational, designers and testing) along with its configurable attributes (time, risk, learning curve etc.), and tasks requirements. Thus, activity behavior is shaped depending on the chosen resource option to perform it. The approach enhances the capability to explore resource combination design space. It was applied to an aerospace case study to identify insights such as the best performing resource combinations, critical resources, resource sensitive activities, and the probability of a resource reaching performance targets.

Copyright © 2016 by ASME
Topics: Design

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