<![CDATA[Inventory management of raw materials is a crucial aspect in ensuring the smooth production process of the palm oil industry. This study examines the effectiveness of integrating forecasting models and Material Requirement Planning (MRP) to optimize the inventory of Crude Palm Oil (CPO) raw materials at PT Salapian Indo Sawit. The palm oil industry in Indonesia represents about 3.5% of the national Gross Domestic Product (GDP) and provides employment for more than 17 million people. Inaccuracies in raw material inventory management lead to disruptions in the production process and inefficiencies in operational costs. The literature review shows that MRP methods with lot-for-lot techniques have been effectively implemented in various industries, but their application in the palm oil sector is still limited. The objective of this research is to analyze optimal forecasting methods for CPO raw material requirements and evaluate the efficiency of MRP implementation compared to conventional methods. The novelty of this research lies in the integration of forecasting methods (Moving Average and Single Exponential Smoothing) with MRP specifically for the palm oil industry, considering the unique characteristics of the palm oil supply chain. The study uses a descriptive quantitative approach with demand and historical production data from June 2020 to May 2021. The results indicate that the Single Exponential Smoothing method with ?=0.5 provides the best forecasting accuracy (MAPE=0.120; MSE=436.17). Implementing MRP with lot-for-lot techniques results in significant efficiencies, including a reduction in order frequency (26.67%), a reduction in order quantity (7.63%), and a reduction in initial inventory (8.10%). This study concludes that integrating an accurate forecasting model with the MRP system is effective for optimizing CPO raw material inventory and enhancing the operational efficiency of the company.]]>
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