Electronic Thesis/Dissertation

 

Developing a Recall Mitigation Framework for Complex Systems Open Access

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A product is recalled when it is deemed to pose a public safety hazard. As systems become more complex, identifying what factors influence recalls becomes increasingly important for engineering managers and systems engineers. With this in mind, this research endeavor highlights a novel safety framework that assists engineering teams in identifying recall risk factors early in the systems engineering process. The framework is applicable to complex systems, which usually have several stakeholders with requirements, and their needs are to be satisficed. Existing system engineering tools such as Failure Mode and Effects Analysis and Fault-tree Analysis are incorporated to identify risks. Five sequential activity phases are integral to the framework: Stakeholder Identification, Data Acquisition, Statistical Analysis, Safety Analysis, and Risk Review. The framework is especially useful in identifying and evaluating factors that could be associated with recalls prior to the next system design revision. For example, the framework can be applied by the engineering team during the redesign phase of a vehicle model (e.g. 2018 Toyota Camry), using historical data from the previous generation (e.g. 2011 Toyota Camry). Validation of the recall mitigation framework is highlighted through a case study involving the engineering of a new vehicle model in the automotive industry. Original Equipment Manufacturers (OEMs) of complex systems like cars issue recalls whenever the vehicle is perceived to have defects impacting public safety, whether due to airbag issues or excessive emissions. For over four years (2010 - 2013), the influence of recall factors for each automaker was analyzed. An additional, more qualitative, case study was performed of Lithium-Ion battery recalls, based on learnings from Sony’s woes in 2006. These case studies further validate the framework. Utilizing the framework within a new project environment can greatly assist engineering managers and their teams in identifying recall risk factors early in the systems engineering lifecycle.

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