Improving Crude Oil Supply Planning using Forecasing and Digital Twin-Decision Support: A Case Study at Refinary Company

PT. X is a Refinery Company with core activities focused on processing crude oil into fuel, non-fuel, and petrochemical products. Effective planning of crude oil demand and supply is therefore essential to ensure operational continuity and national fuel availability. This study focuses on two categories of crude oil feedstock used, namely heavy crude and super heavy crude. The objective of this research is to evaluate discrepancies between planned and realized crude oil orders and to generate forecasts for the subsequent 12 periods to support crude oil supply planning. Under existing conditions, crude oil supply scheduling relies on Linear Programming (LP) tools as input for the monthly Crude Master Program meeting. The initial Mean Absolute Percentage Error (MAPE) values were recorded at approximately 34% for heavy crude and 8% for super heavy crude, indicating deviations between planning and realization. To improve forecasting accuracy, this study applies a seasonal multiplicative decomposition method to time-series forecasting. The results demonstrate that the proposed method reduces the MAPE values to approximately 18% for heavy crude and 7% for super heavy crude, indicating a significant reduction in planning deviation. Beyond improved numerical accuracy, the forecasting results provide predictive insights that support early detection of supply deviations and proactive decision-making in crude oil supply planning. Furthermore, this study positions forecasting as a predictive intelligence component within a Digital Twin–ready decision support framework, contributing to more intelligent, data-driven, and coordinated crude oil supply management.