Forecasting customer servise demand by machine learning with real life implementation

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

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.