Warranty Analytics

A warranty is a contractual obligation attached to a product sold by a manufacturer to a consumer. Typically, it states that if the product fails within a specified period and/or usage level, the manufacturer will remediate the situation, by either repairing, replacing, or recompensing the consumer.
Correct handling of warranty is important to manufacturers for several reasons.
Warranty is one of the biggest costs to a manufacturing company. Efficient processing of warranty claims is therefore a key activity, as is accurately forecasting future claim levels, and detecting emerging trends in claims patterns (particularly important where consumers safety may be at risk due to faults).
Warranty claims databases may be the sole data source a manufacturer has on the frequency and causes of failure of its products in the real world. Data mining and text mining techniques are used to better understand this (e.g. sequential pattern mining).
Warranty is increasingly being used as a tool that can provide a competitive edge in the marketplace. Selecting a suitable warranty policy is an optimisation process in which both costs and profits are considered from the manufacturer's perspective.
Warranty data analysis draws from the fields of statistics, computer science, and reliability engineering to tackle these issues. A recent seminal paper (Reliability Meets Big Data: Opportunities and Challenges, Hong & Meeker, 2014) observed that the nature of reliability data analysis is changing as more data sources become available for analysis (particularly that captured by a product's sensors), and called for research to make more use of these sources.
There are many practical challenges in working with reliability data - warranty data is incomplete and right censored, much of the information is contained in unstructured text fields, and there is a human element to consider (failed products not claimed for, non-failed products that are claimed for). Sensor data is of high volume, and merging data from siloed sources can be difficult.
Following a detailed literature review (around 40 papers) and discussions with several companies (Caterpillar, Jaguar Land Rover, Rolls Royce), I have identified three areas to explore in my research.
Further automate the analysis of warranty claims - In industry, assessment of claims is generally done by teams of warranty analysts who manually read each claim and decide if payment will be made or not. Due to the volume of claims received, business rules are used as a first stage of processing (e.g. all claims under a certain value are automatically paid). Some research has looked into developing text-mining tools to cluster claims by the technician's text comment. Other research flags outliers as potential cases of fraud. I propose using a supervised machine learning approach, (with for example a random forest or neural network), trained on historic claims that contain a warranty analyst's decision as a label. A stage of text mining could be performed and used as an input feature (e.g. identify key complaint word). Reinforcement learning capabilities could be added to improve system performance following implementation.
Provide individualised warranty terms based on product usage patterns -Manufacturers would like to be able to offer more favourable warranty terms where a warranty claim is less likely to arise. Parallels may be drawn with the insurance industry where pay-how-you-drive schemes are an accepted pricing model. Research has yet to explore the same idea in a warranty context. I propose analysing historic usage patterns in sensor data to understand the relationship with failure rates, and developing risk-based pricing models.
Improve accuracy of warranty claim forecasting using real-time sensor data - A large amount of research has focused on developing and fine-tuning the statistical models (falling within survival analysis) of warranty forecasting