Joint Optimization of Cash Management and Routing for New-Generation Automated Teller Machine Networks

Bati, Seyma; Gözüpek, Didem

doi:10.1109/tsmc.2017.2710359

Cited by 11 publications

(4 citation statements)

References 26 publications

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“…Formulating an integer linear program that jointly optimizes cash management and routing for new generation ATM networks. The objective of formulated problem is to minimize the total cost of cash management in ATMs, which consists of logistic cost and idle cash cost (Batı & Züpek, 2017). Combining VRP (Vehicle Routing Problem) and Inventory Allocation Problem is Inventory Routing Problem (IRP).…”

Section: Literature Reviewmentioning

confidence: 99%

Improving Cash Availability of Atm Using Lean Replenishment Pull for Sharia Bank in Indonesia

Herdi

Dores

2020

JMB

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To maintain the company's sustainability of quality and the increasingly rapid competition between banking institutions, a bank must continue to protect its customers from the ease and availability of services when needed anytime and anywhere. Automated Teller Machines (ATM) are the most common banking products and services used by the public. Previously, Activities carried out by the teller can already be done through an ATM. Availability of the ATM has been a special attention among the banking industry and CIT (Cash in transit) company. Factors that cause ATM unavailable are: hardware, receipt, network, and cash, one of the most critical factors is cash availability. Previous study shows some concern of cost and risk of inventory cash on ATM, that leads to study of cash prediction method to replenishment cash of the ATM. Current conditions, Bank ATMs have an average percentage of cash availability in the last 6 months of 92.86%, which means there is 7.1% of cash not available. The aim of this study is to adapt the lean replenishment pull system to manage cash replenishment of the bank ATMs and to achieve level 4 of sigma (99.38%) on ATM cash availability. By collecting, measuring and analyzing availability data and transactions data on both on-site and off-site ATMs samples for the certain period. The proposed model is to determine warning to do the cash replenishment and the Kmin of cash status. Thus, the cash supply at the ATM machine is sufficient, and no idle money occurs.

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Section: Literature Reviewmentioning

confidence: 99%

Improving Cash Availability of Atm Using Lean Replenishment Pull for Sharia Bank in Indonesia

Herdi

Dores

2020

JMB

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“…The framework is composed of inner loop optimization and outer loop optimization. Batı and Gözüpek (2017) formulate an optimization problem as an integer linear program, which jointly decides on when to visit an ATM, how much money to deliver to which ATM and which road should be followed for the distribution of cash to the ATMs. However, these studies do not to integrate it with the cash demand forecasting model, they assume that the demand is given; which is not realistic since the future cash demand is not realized at the beginning of the planning period.…”

Section: Literature Review: Motivating Our Approachmentioning

confidence: 99%

Optimal ATM replenishment policies under demand uncertainty

2019

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The use of Automated Teller Machines (ATMs) has become increasingly popular throughout the world due to the widespread adoption of electronic financial transactions and better access to financial services in many countries. As the network of ATMs is becoming denser while the users are accessing them at a greater rate, the current financial institutions are faced with addressing inventory and replenishment optimal policies when managing a large number of ATMs. An excessive ATM replenishment will result in a large holding cost whereas an inadequate cash inventory will increase the frequency of the replenishments and the probability of stockouts along with customer dissatisfaction. To facilitate informed decisions in ATM cash management, in this paper, we introduce an approach for optimal replenishment amounts to minimize the total costs of money holding and customer dissatisfaction by taking the replenishment costs into account including stock-outs. An important aspect of the replenishment strategy is that the future cash demands are not available at the time of planning. To account for uncertainties in unobserved future cash demands, we use prediction intervals instead of point predictions and solve the cash replenishment-planning problem using robust optimization with linear programming. We illustrate the application of the optimal ATM replenishment policy under future demand uncertainties using data consisting of daily cash withdrawals of 98 ATMs of a bank in Istanbul. We find that the optimization approach introduced in this paper results in significant reductions in costs as compared to common practice strategies.

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“…Aiming to maximize income from transactions and satisfy customer demand for cash, some banks might store as much as 40% more banknotes in ATMs than they actually need [38]. However, loading excess cash in ATMs, rather than only loading in what the demand roughly is, will increase operational and opportunity costs [9,20,21]. Conversely, if there is not enough cash loaded into ATMs, there will be "out of cash" transactions, resulting in the bank's reputation being damaged, as well as lowered income and customer satisfaction.…”

Section: Introductionmentioning

confidence: 99%

Forecasting ATM Cash Demand Before and During the COVID-19 Pandemic Using an Extensive Evaluation of Statistical and Machine Learning Models

et al. 2022

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The overarching goal of this paper is to accurately forecast ATM cash demand for periods both before and during the COVID-19 pandemic. To achieve this, first, ATMs are categorized based on accessibility and surrounding environmental factors that significantly affect the cash withdrawal pattern. Then, several statistical and machine learning models under different algorithms and strategies are employed. In aiming to provide the feature matrix for machine learning models, some new influential variables are added to the literature. Finally, a modified fitness measure is proposed for the first time to correctly choose the most promising model by considering both the prediction errors and accuracy of direction's change simultaneously. The results obtained by a comprehensive analysis-a statistical analysis together with grid search and k-fold cross-validation techniques-reveal that (i) category-wise prediction enhances forecasting quality; (ii) before COVID-19 and in times when there are only minor disturbances in withdrawal patterns, forecasting quality is higher, and in general, the machine learning models can more appropriately forecast ATM's cash demand; (iii) despite studies in the literature, sophisticated models will not always outperform simpler models. It is found that during COVID-19 and in times when there is a sudden shock in demand and massive volatility in withdrawal patterns, the statistical models of the autoregressive integrated moving average (ARIMA) and seasonal ARIMA (SARIMA) can mainly provide better forecasting likely due to high performance of such models for short-term prediction, while minimizing overfitting.

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Joint Optimization of Cash Management and Routing for New-Generation Automated Teller Machine Networks

Cited by 11 publications

References 26 publications

Improving Cash Availability of Atm Using Lean Replenishment Pull for Sharia Bank in Indonesia

Improving Cash Availability of Atm Using Lean Replenishment Pull for Sharia Bank in Indonesia

Optimal ATM replenishment policies under demand uncertainty

Forecasting ATM Cash Demand Before and During the COVID-19 Pandemic Using an Extensive Evaluation of Statistical and Machine Learning Models

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