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FUZZY-bAsED MONITORINg OF WARRANTY DATA This section describes the warranty data monitoring with the Early Warning Tool built upon an automated interpretation of differences between consecutive histograms. The trend detection and tracking process includes the following stages: data selection and preparation, computing fuzzy shifts between distributions, exploring the root causes of significant shifts, and fuzzy trend detection. Each stage is covered in a separate sub-section. Data selection and Preparation The tool Main Screen displays the filtering criteria that can be used for selecting the analyzed data: ? Vehicle selection: Make, Proceedings of the 20th conference on uncertainty in artificial intelligence, pp.611-618, 2004. ,
, Midwest) ? Labor code: the histograms can be based on a single labor code, a list of labor codes, a single Bill of Materials category, a single Vehicle Subsystem category or just all claims satisfying the other selection criteria. We assume here that each labor code is a part of a pre-defined taxonomy, where it is associated with a specific BOM code and its respective Vehicle Subsystem code. For example, the "Replace Battery" Labor Code belongs to the "Battery" BOM Category, which is part of the Electrical Subsystem. ? Histogram Selection. The user can choose one of the following variables for creating the histograms: ? TTF (Time to Failure): the software will build histograms for the Time to Failure elapsed since the previous claim having any labor code, ? ? Period selection (given as a range of dates): Model Build Dates: only cars manufactured between these dates will be included in the ? analysis. Delivery Dates: only cars delivered between these dates will be included in the analysis. ? Claim Dates: the histograms will be based only on claims submitted between these dates. ?
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Vehicle Subsystem: the software will build histograms for Mileage to Failure elapsed since the previous claim having the same Vehicle Subsystem or for the total car mileage ,
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