Warranty Data Analysis: A Review

Warranty claims and supplementary data contain useful information about product quality and reliability. Analysing such data can therefore be of benefit to manufacturers in identifying early warnings of abnormalities in their products, providing useful information about failure modes to aid design modification, estimating product reliability for deciding on warranty policy and forecasting future warranty claims needed for preparing fiscal plans. In the last two decades, considerable research has been conducted in warranty data analysis (WDA) from several different perspectives. This article attempts to summarise and review the research and developments in WDA with emphasis on models, methods and applications. It concludes with a brief discussion on current practices and possible future trends in WDA

Hazard rate models for early warranty issue detection using upstream supply chain information

This research presents a statistical methodology to construct an early automotive warranty issue detection model based on upstream supply chain information. This is contrary to extant methods that are mostly reactive and only rely on data available from the OEMs (original equipment manufacturers). For any upstream supply chain information with direct history from warranty claims, the research proposes hazard rate models to link upstream supply chain information as explanatory covariates for early detection of warranty issues. For any upstream supply chain information without direct warranty claims history, we introduce Bayesian hazard rate models to account for uncertainties of the explanatory covariates. In doing so, it improves both the accuracy of warranty issue detection as well as the lead time for detection. The proposed methodology is illustrated and validated using real-world data from a leading global Tier-one automotive supplier

Forecasting terminal call rate with machine learning methods

This paper deals with the development of a model to predict the products’ terminal call rate (TCR) during the warranty period. TCR represents a key information for a quality management department to reserve the necessary funds for product repair during the warranty period. TCR prediction is often carried out by parametric models such as Poisson processes, ARIMA models and maximum likelihood estimation. Little research has been done with machine learning methods (MLM). Therefore, this paper addresses the utilization of machine learning methods (MLM), such as regression trees, ensembles of regression trees and neural networks in order to estimate the parameters of different models for TCR prediction. MLM were tested on exponential and logistic non-linear models, which best describe the shape of the cumulative density function of the failed products. The estimated cumulative density function was used to predict the TCR. The results have shown the ensembles of regression trees yield the smallest TCR prediction error among the tested MLM methods

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Are Risk-Averse Agents more Optimistic? A Bayesian Estimation Approach.

Our aim is to analyze the link between optimism and risk aversion in a subjective expected utility setting and to estimate the average level of optimism when weighted by risk tolerance. Its estimation leads to a non-trivial statistical problem. We start from a large lottery survey (1536 individuals). We assume that individuals have true unobservable characteristics. We adopt a Bayesian approach and use a hybrid MCMC approximation method to numerically estimate the distributions of the unobservable characteristics. We find that individuals are on average pessimistic and that pessimism and risk tolerance are positively correlated.Bayesian Estimation; MCMC Scheme; Importance Sampling; Pessimism; Risk Tolerance; Risk Aversion; Consensus Belief;

Are Risk Averse Agents More Optimistic? A Bayesian Estimation Approach

Our aim is to analyze the link between optimism and risk aversion in a subjective expected utility setting and to estimate the average level of optimism when weighted by risk tolerance. This quantity is of particular importance since it characterizes the consensus belief in risk-taking situations with heterogeneous beliefs. Its estimation leads to a nontrivial statistical problem. We start from a large lottery survey (1,536 individuals). We assume that individuals have true unobservable characteristics and that their answers in the survey are noisy realizations of these characteristics. We adopt a Bayesian approach for the statistical analysis of this problem and use an hybrid MCMC approximation method to numerically estimate the distributions of the unobservable characteristics. We obtain that individuals are on average pessimistic and that pessimism and risk tolerance are positively correlated. As a consequence, we conclude that the consensus belief is biased towards pessimism.Bayesian estimation, MCMC scheme, importance sampling, pessimism, risk tolerance, risk aversion, consensus belief

Are risk averse agents more optimistic? A Bayesian estimation approach

Our aim is to analyze the link between optimism and risk aversion in a subjective expected utility setting and to estimate the average level of optimism when weighted by risk tolerance.This quantity is of particular importance since it characterizes the consensus belief in risk-taking situations with heterogeneous beliefs. Its estimation leads to a nontrivial statistical problem. We start from a large lottery survey (1536 individuals). We assume that individuals have true unobservable characteristics and that their answers in the survey are noisy realizations of these characteristics. We adopt a Bayesian approach for the statistical analysis of this problem and use an hybrid MCMC approximation method to numerically estimate the distributions of the unobservable characteristics. We obtain that individuals are on average pessimistic and thatpessimism and risk tolerance are positively correlated. As a consequence, we conclude that theconsensus belief is biased towards pessimism.Bayesian estimation, MCMC scheme, importance sampling, pessimism, risk tolerance, risk aversion, consensus belief.

Are Risk Averse Agents More Optimistic? A Bayesian Estimation Approach

Our aim is to analyze the link between optimism and risk aversion in a subjective expected utility setting and to estimate the average level of optimism when weighted by risk tolerance. This quantity is of particular importance since it characterizes the consensus belief in risk-taking situations with heterogeneous beliefs. Its estimation leads to a nontrivial statistical problem. We start from a large lottery survey (1,536 individuals). We assume that individuals have true unobservable characteristics and that their answers in the survey are noisy realizations of these characteristics. We adopt a Bayesian approach for the statistical analysis of this problem and use an hybrid MCMC approximation method to numerically estimate the distributions of the unobservable characteristics. We obtain that individuals are on average pessimistic and that pessimism and risk tolerance are positively correlated. As a consequence, we conclude that the consensus belief is biased towards pessimism.Bayesian estimation, MCMC scheme, importance sampling, pessimism, risk tolerance, risk aversion, consensus belief

Retail Demand Management: Forecasting, Assortment Planning and Pricing

In the first part of the dissertation, we focus on the retailer\u27s problem of forecasting demand for products in a category (including those that they have never carried before), optimizing the selected assortment, and customizing the assortment by store to maximize chain-wide revenues or profits. We develop algorithms for demand forecasting and assortment optimization, and demonstrate their use in practical applications. In the second part, we study the sensitivity of the optimal assortment to the underlying assumptions made about demand, substitution and inventory. In particular, we explore the impact of choice model mis-specification and ignoring stock-outs on the optimal profits. We develop bounds on the optimality gap in terms of demand variability, in-stock rate and consumer heterogeneity. Understanding this sensitivity is key to developing more robust approaches to assortment optimization. In the third and final part of the dissertation, we study how the seat value perceived by consumers attending an event in a stadium, depends on the location of their seat relative to the field. We develop a measure of seat value, called the Seat Value Index (SVI), and relate it to seat location and consumer characteristics. We apply our methodology to a proprietary dataset collected by a professional baseball franchise in Japan. Based on the observed heterogeneity in SVI, we provide segment-specific pricing recommendations to achieve a service level objective

A Demand Estimation Procedure for Retail Assortment Optimization with Results from Implementations

We consider the problem of choosing, from a set of N potential stock-keeping units (SKUs) in a retail category, K SKUs to be carried at each store to maximize revenue or profit. Assortments can vary by store, subject to a maximum number of different assortments. We view a SKU as a set of attribute levels and also model possible substitutions when a customer\u27s first choice is not in the assortment. We apply maximum likelihood estimation to sales history of the SKUs currently carried by the retailer to estimate the demand for attribute levels and substitution probabilities, and from this, the demand for any potential SKU, including those not currently carried by the retailer. We specify several alternative heuristics for choosing SKUs to be carried in an assortment. We apply this approach to optimize assortments for three real examples: snack cakes, tires, and automotive appearance chemicals. A portion of our recommendations for tires and appearance chemicals were implemented and produced sales increases of 5.8% and 3.6%, respectively, which are significant improvements relative to typical retailer annual comparable store revenue increases. We also forecast sales shares of 1, 11, and 25 new SKUs for the snack cake, tire, and automotive appearance chemical applications, respectively, with mean absolute percentage errors (MAPEs) of 16.2%, 19.1%, and 28.7%, which compares favorably to the 30.7% MAPE for chain sales of two new SKUs reported by Fader and Hardie (1996)