University of Qom Journal of Data Analytics and Intelligent Decision-making 3115-8161 2 3 2026 07 01 Design of an Intelligent Predictive Framework for Financial Value Chain Dynamics and Stock Performance in Energy-Intensive Industries under Energy Price Fluctuations 240 255 4880 10.22091/jdaid.2026.16733.1095 EN Kourosh Kiyanfard Department of Industrial Management, AK.C., Islamic Azad University, Aliabad Katoul, Iran MohammadTaghi Khosravi Larijani Department of Management, AK.C., Islamic Azad University, Aliabad Katoul, Iran Ahmad Mehrabian Department of Industrial Engineering, AK.C., Islamic Azad University, Aliabad Katoul, Iran Razieh Naderkhani Department of Mathematics and Statistics, AK.C., Islamic Azad University, Aliabad Katoul, Iran Journal Article 2026 01 06 Purpose: The industrial sector, particularly the cement industry, is highly energy-intensive, with Iran's consumption reaching 1.31 times the global average. Energy price liberalization and subsidy reforms have challenged resource management in this sector. This study aims to design an intelligent decision support system (DSS) to evaluate and predict the sustainable financial value chain of cement companies under energy price fluctuations.<br />Design/methodology/approach: This research integrates fundamental management principles with advanced technical components. Using time-series data mining and a sequential artificial neural network (ANN) architecture equipped with an advanced optimizer, the study models the hidden patterns of electricity and gas price fluctuations on corporate financial statements. KMO and Bartlett’s tests were employed to confirm sampling adequacy, while RSI and MACD indicators were adapted to capture price momentum and trends.<br />Findings: The findings reveal that changes in energy carrier prices directly affect industry returns and financial sustainability by altering price momentum and trend indicators. The proposed intelligent model demonstrated high accuracy in predicting the production chain's financial behavior, achieving a significantly reduced Mean Squared Error (MSE) and an exceptionally high coefficient of determination.<br />Originality/value: This study contributes to the literature by bridging technical data mining techniques with financial sustainability management. It offers a novel, highly accurate predictive framework specifically tailored for evaluating the financial resilience of heavy industries facing energy policy shifts. https://jdaid.qom.ac.ir/article_4880_d64fe8e8bb7a21febcc705302c49eabf.pdf