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

Abstract

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.

A Deep Learning Model for Insurance Claims Predictions

“…Other studies [2] applied deep learning to a different concept that predicts the terminal call rate. Although this study had a slight deviation for the insurance sector, it utilized a model similar to that adopted for this study, which is a deep neural network that has six layers combined with a convolutional neural network.…”

“…Reference [1] opined that the increasing demand for deep learning in various fields can be attributed to its ability to perform tasks swiftly at a summative frequency. This was acknowledged to have attracted ubiquitous relevance to their models and concepts; a significant among them is the convolutional neural network [2]. Most of these models have been utilized in everyday activities, such as speech recognition and picture classification, and in other more suffocated areas, such as predictions.…”

Data quality assessment in product failure prediction models

“…This article builds on the past research (Ferencek et al, 2019) where different approaches of predicting warranty call rates were compared such as Markov Modulated Fluid Model (Kofjač et al, 2014), modelling cumulative density functions using exponential and logistic models, estimating their parameters with Machine Learning Methods (MLM) (Kofjač et al, 2016) and using Neural Networks (NN) (Sašek, 2017).…”