Publication Type

Conference Proceeding Article

Publication Date

8-2015

Abstract

As part of its overall effort to maintain good customer service while managing operational efficiency and reducing cost, a bank in Singapore has embarked on using data and decision analytics methodologies to perform better ad-hoc ATM failure forecasting and plan the field service engineers to repair the machines. We propose using a combined Data and Decision Analytics Framework which helps the analyst to first understand the business problem by collecting, preparing and exploring data to gain business insights, before proposing what objectives and solutions can and should be done to solve the problem. This paper reports the work in analyzing passt daily ad-hoc ATM failures, forecasting ad-hoc ATM failures and then using the forecasted results to optimize the number of field service engineers to deploy in each geographical zone, to minimize the number of daily unattended ad-hoc ATM failures. The optimization model ensures that the least number of engineers are deployed in each zone on each day. However, to maintain a consistent number of engineers for a 2-week schedule, we recommend to deploy the maximum number of engineers in each within the 2 weeks. The resulting surplus engineer idle hours is reduced, and it represents a cost savings of 28.6% when compared with the bank's current practice.

Keywords

Data analysis, decision analytics, ATM failures, forecasting, optimization

Discipline

Artificial Intelligence and Robotics | Computer Sciences | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Data Management and Analytics; Intelligent Systems and Decision Analytics

Publication

IEEE International Conference on Automation Science and Engineering CASE 2015: August 24-28, Gothenburg, Sweden: Proceedings

First Page

1427

Last Page

1433

Identifier

10.1109/CoASE.2015.7294298

Publisher

IEEE

City or Country

Piscataway, NJ

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1109/CoASE.2015.7294298