Univrsity Of Tehran Press Journal of Information Technology Management 2980-7972 18 2 2026 04 01 Stochastic Decision-Support Modeling for Digital Supply Chain Management under Demand and Lead-Time Uncertainty 59 76 107168 10.22059/jitm.2026.107168 EN Maryna Iurchenko Associate Prof., Department of Informatics and Statistics, Klaipeda University, Klaipeda, 92294, H. Manto, 84, Lithuania. Vitalina Babenko Prof., Kharkiv National University of Radio Electronics, Kharkiv 61166, Ukraine; Kharkiv National Automobile and Highway University, Kharkiv, 61002, Ukraine. Liudmyla Lomovskykh Prof., Department of Global Economics, State Biotechnological University, Kharkiv, Ukraine. Tetiana Klymenko Associate Prof., Department of Business, Administration and Law, Higher Educational Institution «University of Future Transformation», Chernihiv, Ukraine. Journal Article 2026 05 28 In digital supply chain management, the effectiveness of managerial decision-making depends on the ability of information systems to support timely and cost-efficient delivery planning under uncertainty. This study develops a stochastic decision-support model for product delivery planning under random consumer demand and variable delivery lead time. The proposed approach addresses the limitations of deterministic inventory models, which often fail to reflect the uncertainty of real logistics in information-management environments. The model minimizes expected total costs by balancing inventory holding costs and losses caused by stockouts. Deviations between scheduled and actual delivery time, as well as between expected and actual inventory depletion time, are represented as continuous normally distributed random variables. This enables the analytical derivation of the expected cost function and reduces the optimization problem to determining the optimal scheduled delivery time. The optimality condition is obtained through an integral equation and is shown to have a unique solution due to the monotonic behavior of the corresponding probability function. A numerical example and graphical analysis demonstrate how holding costs, shortage losses, and uncertainty levels affect delivery timing decisions. The results show that higher holding costs shift the optimal delivery time toward later deliveries, whereas higher shortage losses require earlier scheduling. Increased uncertainty strengthens the sensitivity of the decision-support model to planning errors. The proposed model can be used as an analytical component of digital inventory-control and supply chain management systems. https://jitm.ut.ac.ir/article_107168_b3c1ccb9165d152a7685c8b52b3f2fed.pdf