Impact of python-based demand forecasting on inventory performance in multi-SKU supply chains

The demand forecasting model based on Python has become a core aspect of digital supply chain transformation, especially in multi-SKU systems with heterogeneity, intermittency, and sophisticated lead-time architecture. The combination of scalable data streams, machine learning applications, and probabilistic modelling into Python environments can help companies go past the model of traditional point-forecast assessment and into decision-based inventory optimization. Nonetheless, better forecasting does not necessarily lead to better inventory performance in situations where there are multi-echelon constraints, demand ambiguity, and policy misfit. According to recent studies, probabilistic forecasting, hierarchical reconciliation, and cross-SKU learning can be regarded as tools to enhance service-level attainment and minimize cost volatility. Also, explainability, automation, and solid evaluation protocols will be essential to fill the divide between predictive analytics and operational inventory control. This review summarizes theoretical and empirical data on the effect of Python-based forecasting structures on inventory performance measurements of fill rate, safety stock, holding cost, shortage penalties, and bullwhip amplification. The conclusions are that combined forecasting-inventory analysis systems, uncertainty-aware replenishment policies, and scaled model governance need to be applied.