In the middle of growing chemical market in Indonesia with CAGR (Compound Annual Growth Rate) of 6.8% from 2024 to 2032, currently PT MCP is facing business issue of sudden revenue and profit decrease in 2023. From the top three highest cost compared to another company in the same industry, inventories is the highest and operating cost is the second highest which show indication of inefficiencies. In order to survive in this competitive industry, the research is focused with objectives to identify to which extend the company implemented inventory management system, to identity the root cause of high inventories, to find the most suitable solution to the root cause that could be implemented and to find the best forecasting method and inventory model for the company. The methodology of the research is using mixed-methods approach, which are qualitative method by semi structured group-interview, and quantitative method for data processing and analysis. Primary data from group interview and secondary data from literature study and data from the company support the analysis of the research. The current gap that is having high concern is the high inventories with the issues of lost of order to competitor, higher selling price, inefficiency of work in supply chain department, high forecast gap, and current forecasting method manually calculated using Microsoft Excel formula that is frequently causing human error. From the issues, three root causes are found using CRT (Current Reality Tree) method which are absence of forecasting model, aggregate planning and inventory model to determine the inventory calculation. Three products from the top three revenue stream which represent 40.93% of the total company sales revenue are chosen, which are liquid glucose brix 85, maltodextrin DE10-12, and jelly mix JG 704. In order to improve the forecast accuracy, Minitab as software tools is being used for data analysis to choose the most suitable forecasting method with the least MAPE (Mean Absolute Percentage Error), MAD (Mean Absolute Deviation), MSD (Mean Squared Deviation) and TS (Tracking Signal) within 3MAD. Time series analysis is performed due to the historical data availability, the identified pattern of data, and the forecast horizon which is short. Data is further analyzed by comparing moving average, weighted moving average, simple exponential smoothing method, and exponential smoothing with trend. The best forecasting method with the least forecast error then further chosen to determine the most suitable inventory model to solve high inventories of the company business issue. Fixed order quantity model and fixed time period model are compared and chosen based on each product demand to find the optimum cost. Aggregate planning is also chosen for the company to manage the strategy of inventory and cost based on the most optimum cost. The final result indicates that the improvement in forecasting accuracy of the best forecasting method for each product could lower the forecast gap from 15,51% into into 2,1%, and save total annual cost by 27,81%.