This study develops an integrated Business Intelligence (BI) and econometric framework for forecasting industrial chemical imports, validated through the case of Vietnam's Sodium Hydroxide (NaOH) market (HS Code 28151200). Utilizing a high-frequency dataset of 1,993 customs transactions spanning December 2024 to April 2025 - capturing 113,466 metric tons of NaOH valued at USD 41.4 million across 146 importers and 108 exporters - the research synthesizes transaction-level trade records with macroeconomic drivers including Vietnam's Industrial Production Index (IPI), GDP growth, VND/USD exchange rate and global caustic soda price benchmarks. A hybrid forecasting architecture combining log-linear structural demand estimation, Seasonal ARIMA and Vector Autoregression (VAR) is developed and validated. The proposed framework achieves a Mean Absolute Percentage Error (MAPE) of 6.2%, substantially outperforming traditional ARIMA (12.4%) and OLS (10.8%) benchmarks. VAR impulse response analysis confirms a statistically significant negative response of import volumes to exchange rate shocks, with a trough at Month 3 and an IPI spillover effect persisting for six months. The study makes three original contributions: (1) the first application of transaction-level customs data to econometric NaOH import demand modeling in Vietnam; (2) a validated hybrid ARIMA-VAR forecasting architecture; and (3) a replicable BI operationalization framework translating econometric outputs into procurement decision support. Results demonstrate that IPI and downstream industrial output are the dominant demand drivers, with a short-run price elasticity of -0.41 and exchange rate elasticity of -0.32. The framework is scalable to other HS-coded industrial inputs across emerging markets.
Trung Lai Quoc (Tue,) studied this question.