Cash Replenishment and Vehicle Routing Improvement for Automated Teller Machines

Abstract

Logistics has emerged as a crucial component in various business domains, playing a significant role in ensuring efficient operations. In addition to traditional applications, logistics principles are also being applied in the financial sector, specifically in the management of Automated Teller Machines (ATMs). ATMs offer a self-service and time-independent mechanism, providing financial institutions with an efficient means of serving their customers. However, the network design of cash distribution poses several challenges that necessitate an optimized solution. This solution aims to fulfill customer demands for ATMs while simultaneously minimizing losses for banks. This paper proposes a combined approach to address these challenges, integrating the demand forecasting with the vehicle routing problem. The replenishment policy begins with forecasting cash withdrawals, utilizing various methods such as statistical methodologies (e.g., ARIMA and SARIMA) and machine learning techniques (e.g., Prophet and DNN). To determine optimal routes for armored trucks and minimize costs based on the forecasted data, the VRP Spreadsheet Solver tool is implemented. By developing a decision support system, several methods are applied to facilitate ATM visitation using inventory control methodologies and vehicle routing techniques. This integrated approach seeks to achieve a balance between meeting ATM customer demands and optimizing the utilization of resources in cash replenishment and distribution. Overall, this research presents a comprehensive solution for addressing the challenges in cash network design for ATMs. By combining forecasting methods with vehicle routing optimization, it offers a decision support system that enhances the efficiency of ATM operations while minimizing costs and ensuring customer satisfaction.