Zelin Wang
Hangzhou Shennong Jinjian Agricultural Technology Co., Ltd., Hangzhou 310000, Zhejiang, China.
*Corresponding author: Zelin Wang
Abstract
Bulk commodity supply chains operate under tightly coupled conditions shaped by price volatility, inventory accumulation, transportation delay and risk transmission, where experience-based judgement alone is often too slow to process multiple variables moving at the same time. In response to the need for coordinated procurement, inventory, logistics and risk decisions, a data-driven decision-making framework is developed based on literature synthesis, framework modelling and a 12-week anonymized scenario simulation; the framework integrates data access, indicator transformation, predictive optimization, decision output and feedback calibration, while bringing spot prices, futures curves, order demand, inventory coverage, in-transit volume and port congestion indicators into a unified analytical chain. The simulation comparison shows that the framework reduces demand forecasting error from 12.8% to 8.6%, lowers average inventory coverage from 34.5 days to 27.2 days, increases logistics delay warning lead time from 1.6 days to 4.8 days, and shortens the decision cycle from 48 hours to 12 hours, indicating that the proposed framework can improve procurement timing, inventory structure, delivery stability and risk response speed in bulk commodity supply chains.
References
[1] Giannelos S, Konstantelos I, Strbac G. Optimal supply chain design using machine learning, risk assessment and optimisation applied to coal distribution. EURO J Decis Process. 2025;13:100062.
[2] Guo L, Yu F, Sui C, et al. Country-level vulnerability in maritime bulk commodity supply chains: an integrated framework for identification, monitoring, and extrapolation. Systems. 2026;14(2):120.
[3] Tarei PK, Kumar G, Ramkumar M. A mean-variance robust model to minimize operational risk and supply chain cost under alea-tory uncertainty: a real-life case application in petroleum supply chain. Comput Ind Eng. 2022;166:107949.
[4] Seyedan M, Mafakheri F. Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities. J Big Data. 2020;7:53.
[5] Jha AK, Agi MAN, Ngai EWT. A note on big data analytics capability development in supply chain. Decis Support Syst. 2020; 138:113382.
[6] Yang M, Fu M, Zhang Z. The adoption of digital technologies in supply chains: drivers, process and impact. Technol Forecast Soc Change. 2021;169:120795.
[7] Bhandal R, Meriton R, Kavanagh RE, et al. The application of digital twin technology in operations and supply chain management: a bibliometric review. Supply Chain Manag. 2022;27(2):182-206.
[8] Zamani ED, Smyth C, Gupta S, et al. Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review. Ann Oper Res. 2023;327(2):605-632.
[9] Smyth C, Dennehy D, Wamba SF, et al. Artificial intelligence and prescriptive analytics for supply chain resilience: a systematic literature review and research agenda. Int J Prod Res. 2024;62(23):8537-8561.
How to cite this paper
Construction of a Data-driven Decision-making Framework for Bulk Commodity Supply Chains
How to cite this paper: Zelin Wang. (2026). Construction of a Data-driven Decision-making Framework for Bulk Commodity Supply Chains. Engineering Advances, 6(3), 187-191.
DOI: http://dx.doi.org/10.26855/ea.2026.09.011