Accurate demand forecasting is central to inventory optimization and resource allocation in modern retail. While traditional Enterprise Resource Planning (ERP) systems often rely on rigid heuristic-based rules or simple moving averages, this paper presents an ERP-native framework that bridges the gap between transactio...
Accurate prediction of transpacific container freight rates is critical for shipping companies to optimize resource allocation, manage risks, and maintain stable operations in a volatile market environment. To address the limitation that traditional linear models struggle to capture the nonlinear and dynamic dependenci...
This survey presents a comprehensive review of recent research on data-driven demand forecasting and decision support systems in agrochemical supply chain management and compares the strengths, limitations, application domains, and performance characteristics of existing approaches while identifying current research ga...
With economic development and the improvement of residents' consumption capacity, public awareness of pursuing healthy lifestyles has been continuously strengthened, leading to rising expectations for the variety and quality of fresh food products. Cold chain logistics, as a key link in ensuring the circulation quality...
Lead time reliability is an important performance metric in today's supply chain and logistics system. Differences between requested and actual delivery lead times can cause operational inefficiencies, inventory shortages, and increased logistics costs. This study uses shipment-level logistics data from a private secto...
It is demonstrated that a residual learning-based Hybrid ARIMA–LSTM framework can effectively improve daily digital retail demand forecasting by integrating statistical and deep learning models under identical experimental settings.
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