This study presents a data-driven approach to optimize operational efficiency in the freight forwarding industry by employing time series models to predict job volumes for sea exporting accurately. By gathering and pre-processing historical data, including forward freight information, job numbers, and spatial data from the Logi-Sys system, a software used by the logistics industry to manage data, valuable insights are derived. Through exploratory data analysis, patterns and relationships within the data are uncovered. Various time series models are then utilized to forecast industry growth, specifically focusing on predicting job volumes. Additionally, spatial analysis is incorporated to assess the business volume and reach across different locations. The performance of the models is thoroughly evaluated, and insightful recommendations are provided to facilitate data-driven decision-making within the freight forwarding industry. This research underscores the potential of leveraging data analysis techniques, particularly time series modeling, to enhance operational efficiency in the dynamic logistics sector, offering substantial benefits to industry practitioners seeking to optimize their performance.