A Predictive Intelligence Framework for Digital Twin Ready Crude Oil Supply Planning Using Seasonal Time Series Decomposition
DOI:
https://doi.org/10.22441/oe.2026.v18.i1.161%20Kata Kunci:
Crude Oil Supply Planning, Predictive Intelligence, Digital Twin Ready Systems, Time-Series Forecasting, Decision Support SystemsAbstrak
Crude oil supply planning is critical to ensuring operational continuity and national fuel availability in refinery operations. This study evaluates discrepancies between planned and realized crude arrivals at a refinery and generates forecasts for the subsequent twelve periods to support supply planning decisions. Existing practices, crude scheduling relies on Linear Programming outputs used in the monthly Crude Master Program, resulting in notable deviations, with initial Mean Absolute Percentage Error (MAPE) 34% for heavy crude and 8% for super heavy crude. To improve predictive accuracy, a seasonal multiplicative time-series decomposition method is applied as a computational intelligence component for crude oil forecasting. The proposed approach reduces MAPE to approximately 18% for heavy crude and 7% for super heavy crude, demonstrating substantial improvement in planning accuracy. Forecasting outputs provide predictive insights that enable early detection of potential supply deviations and support proactive crude intake planning. Furthermore, forecasting is conceptualized as a predictive intelligence layer embedded within a Digital Twin–ready decision-support framework, linking physical supply operations with virtual scenario evaluation and managerial planning processes. 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.
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Hak Cipta (c) 2026 Operations Excellence: Journal of Applied Industrial Engineering
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