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

Aljaž Ferencek,Andrej Škraba,Mirjana Kljajić Borštnar,Blaž Sašek,Davorin Kofjač

doi:10.2478/bsrj-2020-0014

Abstract

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.

Highlights

Todays' business environment is highly competitive businesses need to optimize their costs and improve their profit and/or market share
We addressed the problem of improving the product failure forecasting process within the warranty period in the selected company, which has been, up to now, done „manually“, and mainly using spreadsheets in Microsoft Excel
The proposed methodological approach is rooted in Design Science Research (DSR) (Hevner et al, 2004)

Summary

Introduction

Todays' business environment is highly competitive businesses need to optimize their costs and improve their profit and/or market share. If warranty conditions are good that usually indicates higher product quality, affects marketing of new products (Murthy & Djamaludin, 2002). This paper addresses the problem of products’ terminal call rate (TCR) prediction during the warranty period. 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. Conclusions: This paper suggests that deep learning is an approach worth exploring further, with the disadvantage being that it requires large volumes of quality data.

Objectives

Methods

Findings

Conclusion