A Supply Chain-Oriented Model to Predict Crude Oil Import Prices in South Korea Based on the Hybrid Approach

Abstract

Although numerous studies have explored key variables for forecasting crude oil prices, the role of supply chain factors has often been overlooked. In the face of global risks such as COVID-19, the Russia–Ukraine war, and the U.S.–China trade dispute, supply chain management (SCM) has evolved beyond an individual company’s concern. This research investigates the impact of a supply chain-oriented variable on the forecasting of crude oil import prices in South Korea. Our findings reveal that models incorporating the Global Supply Chain Pressure Index (GSCPI) outperform those without it, emphasizing the importance of monitoring supply chain-related variables for stabilizing domestic prices for policy makers. Additionally, we propose a novel hybrid factor-based approach that integrates time series and machine learning models to enhance the prediction performance of oil prices. This endeavor is poised to serve as a foundational step toward developing methodologically sound forecasting models for oil prices, offering valuable insights for policymakers.

Supplier Evaluation in Supply Chain Environment Based on Radial Basis Function Neural Network

“…The supplier's delivery efficiency, product quality, inventory level, design advantage, and other aspects directly affect the manufacturer's income (Delgoshaei et al, 2023). At the same time, the supplier's product price and quality also determine the final customer satisfaction, product market competitiveness, possession, and viability and also have a certain impact on the core competitiveness of each component of the supply chain (Jo et al, 2023). With the development of economic globalization, enterprises devote themselves to their core business, and a large number of spare parts are purchased externally, which makes them more dependent on suppliers (Q. .…”