Leveraging Data Science for Impactful Logistics Automatons in the E-Commerce Sector

Automation technologies play a pivotal role in optimizing logistics operations within warehouse facilities. Retail companies invest in these technologies to maintain pace with the customers demands by increasing their production capacity while reducing their financial expenses. In this paper, we conduct a study on warehouse automation in the European e-commerce sector by analyzing historical data from three fulfillment centers. Accordingly, we explore diverse data science approaches applied to trained machine learning models to determine the automatons that have the greatest impact on financial costs. The purpose is to support supply chain managers in identifying the most profitable logistics automatons that merit consideration in future automation projects. The study offers a comprehensive analysis that encourages e-commerce companies to invest in tailored automation for future warehouse installations.