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Forecasting customer servise demand by machine learning with real life implementation

Güçlükol, Simge (2020) Forecasting customer servise demand by machine learning with real life implementation. [Thesis]

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Official URL: https://risc01.sabanciuniv.edu/record=b2486363 _(Table of contents)

Abstract

The decision-making process has an important role in industry and customer service delivery is one of the essential parts of this process while it has a direct impact on customer satisfaction. In the first part of the study, the objective is forecasting monthly failure ratios for a line of the products for a Turkish multinational household appliances manufacturer in order to make better planning for spare parts and service personnel and, meet customer service demand on time. The real-life obtained data set from the company include the number of installed and failed products in a given month, and installation and failure dates of products individually. The data includes more than one time-series that is in multidimensional form and each one of time series has an impact on other time series. Machine learning-based approaches were applied in order to reveal this impact and achieve better forecasting results. As the second objective of the study, comparisons between statistical-based methods and machine learning-based approaches are made. The moving average for statistical-based methods and, artificial neural network and support vector regression methods for machine learning-based approaches are compared by the model performances.

Item Type:Thesis
Uncontrolled Keywords: failure ratio. -- machine learning. -- time series forecasting. -- bozulma oranı. -- makine ögrenmesi. -- zaman serisi tahminlemesi.
Subjects:T Technology > T Technology (General) > T055.4-60.8 Industrial engineering. Management engineering
ID Code:41185
Deposited By:IC-Cataloging
Deposited On:24 Oct 2020 15:38
Last Modified:24 Oct 2020 15:38

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