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The increasing demand for legal contract services in today’s business environment requires efficient resource allocation. By leveraging data-driven approaches, organizations can better allocate resources, enhance efficiency, and improve overall performance. This project aims to develop a robust predictive model in forecasting work demand and provide actionable recommendations for resource allocation. Contract volume serves as the demand signal for workload forecasting. Traditional machine learning and time-series forecasting algorithms were assessed, including Random Forest, XGBoost, ARIMA, and Holt-Winters models. Statistical analysis were conducted to estimate the workload volume based on the predicted contract volume. Due to high spectrum of productivity rate, ABC classification method was employed to gain a realistic productivity rate. Integration of data science in the legal function has great potential in unlocking additional insights and opportunities for improvements.