Location-Allocation Through Machine Learning for E-Commerce Logistic Services

Abstract

Companies desire to expand their businesses in such a way that there will not be any loss in their revenues. An e-commerce logistics company functions as the distribution and delivery of goods to buyers. To expand the business, opening new branches is a critical decision since determining the location of a branch correctly will not only help an e commerce logistics company to increase its revenue but also improve customer satisfaction. The logistic network, which is based on locations, is the most vital input for their business. For such decisions, data science is becoming an essential tool in recent years. Research shows that demographic information has a considerable impact on consumer behavior in e-commerce. In this thesis, the demand potential is studied by using demographic data and current demand for an e-commerce logistics company. The outcome of this work can be used to determine the location of new branches. Machine learning techniques are being used to decide the location of a new branch with the help of delivery demand potential prediction.