United Overseas Bank: Branch Crowd Analytics

This case focuses on the use of modern data analytics to alleviate crowding at the branches of United Overseas Bank, a full-service bank headquartered in Singapore. The case is set in 2020 against the backdrop of the COVID-19 global pandemic when vaccines were not yet available and social distancing was a key tool in the fight against the spread of the disease. How should the bank develop and deploy predictive analytics to accurately anticipate future branch crowds? How should the bank trade off key design considerations?

This case can be used in a graduate-level course in operations management, service operations, or business analytics. It can also be used in an undergraduate-level course with appropriate modifications to the teaching plan to avoid referencing students’ work experience. In a course on business analytics, this case can be discussed as a practical application of time-series forecasting methods, such as an autoregressive integrated moving average (ARIMA). <br><br>After working through the case and assignment questions, students should be able to<ul><li>understand the key challenges involved in managing retail banking operations, particularly during a period of sustained service disruption (e.g., a pandemic);</li><li>gain a deeper understanding of how data analytics can be operationalized to support even the offline aspects of a banking business, such as managing the flow of patrons who visit branches;</li><li>appreciate the challenges and complexities involved in translating data analytics from an idea to something tangible that an organization can implement; and</li><li>understand the trade-offs between different implementation choices typical of operationalizing an analytics solution.</li></ul>

• Data Analytics
• Forecasting
• COVID-19