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Optimal ATM replenishment policies under demand uncertainty

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Abstract

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 stock-outs 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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Notes

  1. We note that this is not a correct scoring rule in evaluating prediction errors. The MAPE can be used when only median predictions are derived. However, for most methods as they are based on L2-norm estimation or least squares estimation, mean square error needs to be used. This fundamental aspect in evaluating predictions is a topic of interest in the statistical literature (Gneiting and Raftery 2007; Gneiting 2011).

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Acknowledgements

This research was supported by the Scientific and Technological Research Council of Turkey scholarship, awarded for a postdoctoral research position for Dr. Yeliz Ekinci. Dr. Serban’s research was supported by the Coca-Cola Professorship in H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. The authors are also thankful to the anonymous bank that supplied the data and expert opinion. The interpretation and conclusions revealed in this study do not represent the official perspectives of the institutes stated above.

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Authors and Affiliations

  1. Business Administration, Istanbul Bilgi University, Istanbul, Turkey

    Yeliz Ekinci

  2. Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, USA

    Nicoleta Serban

  3. Industrial Engineering, Ozyegin University, Istanbul, Turkey

    Ekrem Duman

Authors
  1. Yeliz Ekinci
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  2. Nicoleta Serban
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  3. Ekrem Duman
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Correspondence to Yeliz Ekinci.

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Appendices

Appendix 1: Independent variables considered in the regression model

See the Table 4.

Table 4 Independent variables considered in the regression model
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Appendix 2: Prediction plots

See the Figs. 5, 6, 7 and 8.

Fig. 5
figure 5

Prediction intervals of the first week

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Fig. 6
figure 6

Prediction intervals of the second week

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Fig. 7
figure 7

Prediction intervals of the third week

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Fig. 8
figure 8

Prediction intervals of the fourth week

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Ekinci, Y., Serban, N. & Duman, E. Optimal ATM replenishment policies under demand uncertainty. Oper Res Int J 21, 999–1029 (2021). https://doi.org/10.1007/s12351-019-00466-4

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  • DOI: https://doi.org/10.1007/s12351-019-00466-4

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