Analyzing Replenishment Policies for Automated Teller Machines

Logistics has become one of the most significant parts of the process in many business areas. For an efficient logistics system, each stage of the operation needs to be designed carefully. Logistics approaches are also applied in the financial sector such as Automatic Teller Machines (ATM) cash management. ATMs provide efficient service for a financial institution to its customers through a self-service, time-independent, and simple-to-use mechanism. For daily financial transactions, ATM is the fastest, safest, and most practical banking tool. Many challenges have risen in the network design of the cash and these problems can be solved by using the optimized solution. This solution aims to satisfy the customer at the ATM and at the same time, minimize loss for banks. This paper states the solution for the replenishment of ATMs. Firstly, data is analyzed by using different policies from several approaches after then an efficient metric system is applied to compare the results of it. In the end, the method selected has the appropriate results according to metric. Furthermore, to avoid bottlenecks and become quicker in the procedures, inventory control connects the supply of cash and customer demand in the ATMs. The replenishment policy starts with forecasting cash withdrawals by applying various methods such as statical methodologies (ARIMA and SARIMA) and machine learning methods (Prophet and DNN). By creating a decision support system, several methods are applied in order to visit ATMs by using different inventory control methodologies.