Kavas, E.Alsan, H.F.Arsan, T.2025-01-152025-01-1520240979-835037943-3https://doi.org/10.1109/ASYU62119.2024.10756958https://hdl.handle.net/20.500.12469/7139IEEE SMC; IEEE Turkiye SectionElectric power systems are crucial for modern society, yet their reliability can be challenged by unforeseen disruptions, causing electricity supply disruptions. Call centers are essential for managing customer inquiries during such outages, acting as communication hubs for electricity utility companies. Effective capacity planning is vital for these call centers to maintain efficient operations and meet customer demands promptly. Proper workforce management ensures that enough skilled agents can handle calls effectively and maintain high service quality. Capacity planning begins with analyzing historical data to understand call volumes, patterns, and peak times. This data analysis identifies trends and factors influencing call patterns, enabling accurate forecasting of future demand and optimizing staffing levels. This paper provides a comprehensive overview of quantitative forecasting methods, focusing on Time Series Analysis applied to a dataset from a Turkish electric utility company that exhibits typical seasonal fluctuations. Specifically, the study examines the performance of AutoRegressive Integrated Moving Average and Seasonal AutoRegressive Integrated Moving Average models. Results indicate that both models perform well, with the Seasonal AutoRegressive Integrated Moving Average model demonstrating slightly superior performance compared to the AutoRegressive Integrated Moving Average model. This suggests that the Seasonal AutoRegressive Integrated Moving Average model may be more suitable for forecasting inbound calls at electricity utility call centers. This paper's detailed analysis and methodology offer valuable insights for optimizing operational efficiency, reducing costs, and enhancing customer satisfaction in dynamic and challenging operational scenarios. © 2024 IEEE.eninfo:eu-repo/semantics/closedAccessArimaCall CenterElectricity UtilitySarimaTime Series ForecastingConference Object10.1109/ASYU62119.2024.107569582-s2.0-85213361708N/AN/A