Early Warning from Car Warranty Data using a Fuzzy Logic Technique

Early Warning from Car Warranty Data using a Fuzzy Logic Technique

Mark Last (Ben-Gurion University of the Negev, Israel), Yael Mendelson (Formerly of Ben-Gurion University of the Negev, Israel), Sugato Chakrabarty (India Science Lab, GM Technical Center, India) and Karishma Batra (Formerly of India Science Lab, GM Technical Center, India)
DOI: 10.4018/978-1-60566-858-1.ch014
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Car manufacturers are interested to detect evolving problems in a car fleet as early as possible so they can take preventive actions and deal with the problems before they become widespread. The vast amount of warranty claims recorded by the car dealers makes the manual process of analyzing this data hardly feasible. This chapter describes a fuzzy-based methodology for automated detection of evolving maintenance problems in massive streams of car warranty data. The empirical distributions of time-to-failure and mileage-to-failure are monitored over time using the advanced, fuzzy approach to comparison of frequency distributions. The authors’ fuzzy-based early warning tool builds upon an automated interpretation of the differences between consecutive histogram plots using a cognitive model of human perception rather than “crisp” statistical models. They demonstrate the effectiveness and the efficiency of the proposed tool on warranty data that is very similar to the actual data gathered from a database within General Motors.
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Tracking of warranty trend of a particular product based on the claim distribution over time is an important problem of any company and industry. Most companies maintain warranty databases for purposes of financial reporting and warranty expense forecasting. Such warranty field data is largely extensive and messy, and hence special tools and algorithms are needed to extract useful information. In some cases, there are attempts to extract engineering information from such databases. Another important application is to use warranty data to detect potentially serious field reliability problems known as emerging issues, as early as possible. With detection of sudden emerging issues it is also important to track the other trends such as “bygone problem” (the failure rate has decreased back to normal), “emerging issue under control” and “emerging issue came gradually over a passage of time”. This is because after some action was taken by the manufacturing process or a precautionary measure taken by dealers through service enhancement it is important to study the behavior of the trends i.e. after process rectification whether the previous emerging issues trends for a group of failure components now changed to the “under control” or “bygone” trends.

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