Ad-hoc automated teller machine failure forecast and field service optimization

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 past 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 zone 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.