Adaptive Lead-Time Prediction for Resilient and Sustainable Supply Chains

Reliable prediction of supplier lead times is important for understanding resilience in complex adaptive supply chains, which function as socio-technical systems characterized by high variability, dynamic interactions, and operational unpredictability. This study proposes a simulation-based adaptive lead-time prediction framework that unifies uncertainty-aware statistical modeling, digital twin-enabled simulation, IoT-linked operational adjustment, and AI-driven temporal learning within a single system-oriented architecture. Semi-synthetic datasets are used to emulate lead-time variability and disruption patterns across multiple operating scenarios under intermediate and elevated levels of uncertainty. The novelty of the study lies not in the use of individual techniques in isolation, but in their integration within a closed-loop predictive framework that links probabilistic modeling, adaptive correction, and digital twin-based system updating. The results indicate that the baseline statistical model performs satisfactorily under stable conditions; however, its performance declines significantly when exposed to parameter variations and extreme disruptions. Under high-variability conditions, for example, RMSE at μ = 3.0 and σ = 1.2 decreases from 65.00 weeks in the baseline model to 13.45 weeks in the IoT-adaptive model and to 3.00 weeks in the AI-enhanced model. These findings show that the proposed framework improves predictive accuracy, robustness, and adaptability relative to both the baseline statistical and IoT-adaptive alternatives. Overall, the proposed framework contributes to supply chain analytics by providing an integrated and simulation-based proof-of-concept for resilient lead-time prediction in complex supply environments. Its sustainability relevance should be understood as prospective: although the study does not directly measure emissions, energy use, or waste reduction, improved predictive stability and adaptive decision support may inform future sustainability-oriented planning and empirical evaluation.