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

Construction of asymmetric copulas and its application in two-dimensional reliability modelling

Copulas offer a useful tool in modelling the dependence among random variables. In the literature, most of the existing copulas are symmetric while data collected from the real world may exhibit asymmetric nature. This necessitates developing asymmetric copulas that can model such data. In the meantime, existing methods of modelling two-dimensional reliability data are not able to capture the tail dependence that exists between the pair of age and usage, which are the two dimensions designated to describe product life. This paper proposes two new methods of constructing asymmetric copulas, discusses the properties of the new copulas, and applies the method to fit two-dimensional reliability data that are collected from the real world

Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms: support vector regression forecast combinations

The motivation of this paper is to introduce a hybrid Rolling Genetic Algorithm-Support Vector Regression (RG-SVR) model for optimal parameter selection and feature subset combination. The algorithm is applied to the task of forecasting and trading the EUR/USD, EUR/GBP and EUR/JPY exchange rates. The proposed methodology genetically searches over a feature space (pool of individual forecasts) and then combines the optimal feature subsets (SVR forecast combinations) for each exchange rate. This is achieved by applying a fitness function specialized for financial purposes and adopting a sliding window approach. The individual forecasts are derived from several linear and non-linear models. RG-SVR is benchmarked against genetically and non-genetically optimized SVRs and SVMs models that are dominating the relevant literature, along with the robust ARBF-PSO neural network. The statistical and trading performance of all models is investigated during the period of 1999–2012. As it turns out, RG-SVR presents the best performance in terms of statistical accuracy and trading efficiency for all the exchange rates under study. This superiority confirms the success of the implemented fitness function and training procedure, while it validates the benefits of the proposed algorithm

Predictive Modelling and AI Integration for Enhanced Analysis of Warranty and Notification Data ; A Case Study in Manufacturing Data Analysis

This thesis explores the application of predictive modelling and AI integration in the analysis of warranty and notification data within a manufacturing company context. The research aims to develop a Power BI tool to analyze and visualize the relationship between warranty and notification data while utilizing predictive analytics. Design science methodology, specifically the Design Science Research Methodology (DSRM), is employed in the research process. The research objectives are threefold: (1) to investigate how predictive models can be utilized to analyze warranty and notification data, (2) to identify factors in the warranty and notification data connected to warranty claims, and (3) to explore potential future applications of AI in analyzing warranty and notification data in the next 10 years. Methodologically, logistic regression and time series analysis are employed to develop predictive models. Logistic regression is utilized to predict product claims, while time series analysis is used to visualize trends and offer forecasting options within Power BI. Additionally, AI-powered tools such as the key influencers in Power BI are utilized to analyze factors affecting product claims. Key findings indicate a significant positive relationship between notification count and the probability of a product claim, validating the original hypothesis. Recommendations for the case company include investing in augmented analytics, democratizing AI within the organization, and prioritizing clear communication to ensure effective and ethical use of AI technologies. While the research demonstrates the effectiveness of predictive modelling and AI integration in warranty and notification data analysis, several limitations exist. These include the inability to fully integrate the logistic regression model into Power BI and the focus primarily on the probability of product claims, leaving other potential areas unexplored. In conclusion, this thesis marks a step towards transforming warranty data into a tool for risk management and product improvement. Future research directions include refining predictive models, exploring advanced AI techniques, and increasing the generalizability of findings across different industries.Tässä tutkielmassa syvennytään ennustemallintamisen ja tekoälyn integroinnin vaikutuksiin takuu- ja notifikaatiodatan analysoinnissa valmistavassa yrityksessä. Tutkielman päätavoitteena on kehittää ennustemallintamiseen perustuva Power BI -työkalu, jonka avulla voidaan analysoida ja visualisoida takuu- ja notifikaatiodatan välistä suhdetta. Tutkimuksessa käytetään suunnittelutieteellistä metodologiaa, joka mahdollistaa järjestelmällisen lähestymistavan ongelmanratkaisuun ja uusien ratkaisujen kehittämiseen. Tutkielman tavoitteita on kolme: (1) tutkia ennustemallien soveltamista takuu- ja notifikaatiodatan analysoinnissa, (2) tunnistaa takuu- ja notifikaatiodatasta tekijöitä, jotka vaikuttavat reklamaatioihin, sekä (3) tutkia mahdollisia tapoja hyödyntää tekoälyä takuu- ja notifikaatiodatan analyysiin seuraavan vuosikymmenen aikana. Tutkielmassa ennustemallien luonnissa hyödynnetään logistista regressiota ja aikasarja-analyysiä. Logistista regressiota käytetään ennustamaan reklamaatioita, ja aikasarja-analyysiä hyödynnetään tulevan kehityksen ennustamiseen ja visualisointiin. Lisäksi työssä käytetään tekoälypohjaisia työkaluja, kuten Power BI:n tärkeimmät vaikuttajat -työkalua. Tutkielmassa löydettiin merkittävä positiivinen suhde notifikaatiomäärän ja reklamaatiotodennäköisyyden välillä. Näiden tulosten perusteella case-yritystä suositellaan panostamaan entistä enemmän tekoälypohjaiseen analytiikkaan sekä lisäämään ja laajentamaan tekoälyosaamista organisaatiossa. Lisäksi selkeä ja avoin kommunikaatio on keskeisen tärkeää, jotta tekoälyn tehokas ja eettinen käyttö voidaan varmistaa. Vaikka tutkimuksen tulokset osoittavat ennustemallintamisen ja tekoälypohjaisten työkalujen tehokkuuden takuu- ja notifikaatiodatan analysoinnissa, tutkimuksen rajoituksiin kuuluu logistisen regressiomallin epätäydellinen integrointi kehitettyyn työkaluun sekä keskittyminen pääasiassa tuotereklamaatioihin. Tulevaisuudessa tutkimusta voidaan laajentaa tarkastelemalla muita ennustemalleja ja tekoälytyökaluja sekä tutkimalla aihetta eri toimialoilla ja konteksteissa

Deep Learning Predictive Models for Terminal Call Rate Prediction during the Warranty Period

Background: This paper addresses the problem of products’ terminal call rate (TCR) prediction during the warranty period. TCR refers to the information on the amount of funds to be reserved for product repairs during the warranty period. So far, various methods have been used to address this problem, from discrete event simulation and time series, to machine learning predictive models. Objectives: In this paper, we address the above named problem by applying deep learning models to predict terminal call rate. Methods/Approach: We have developed a series of deep learning models on a data set obtained from a manufacturer of home appliances, and we have analysed their quality and performance. Results: Results showed that a deep neural network with 6 layers and a convolutional neural network gave the best results. Conclusions: This paper suggests that deep learning is an approach worth exploring further, however, with the disadvantage being that it requires large volumes of quality data

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

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 Government Bond Spreads with Heuristic Models:Evidence from the Eurozone Periphery

This study investigates the predictability of European long-term government bond spreads through the application of heuristic and metaheuristic support vector regression (SVR) hybrid structures. Genetic, krill herd and sine–cosine algorithms are applied to the parameterization process of the SVR and locally weighted SVR (LSVR) methods. The inputs of the SVR models are selected from a large pool of linear and non-linear individual predictors. The statistical performance of the main models is evaluated against a random walk, an Autoregressive Moving Average, the best individual prediction model and the traditional SVR and LSVR structures. All models are applied to forecast daily and weekly government bond spreads of Greece, Ireland, Italy, Portugal and Spain over the sample period 2000–2017. The results show that the sine–cosine LSVR is outperforming its counterparts in terms of statistical accuracy, while metaheuristic approaches seem to benefit the parameterization process more than the heuristic ones

Review of Health Prognostics and Condition Monitoring of Electronic Components

To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' vis-a-vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted