Inventory Segmentation and Demand Forecasting as Tools Supporting Sustainable Resource Management in a Manufacturing Company

This study investigates the integration of ABC/XYZ (value-based classification/demand variability classification) inventory classification with demand forecasting models (ETS—Error, Trend, Seasonality, ARIMA—AutoRegressive Integrated Moving Average, Prophet—type of additive model) in a manufacturing enterprise to support sustainable resource management. The research aims to evaluate the inventory structure, demand variability, and forecasting accuracy across different material categories. The results confirm a strong concentration of inventory value in A-class items and significant differences in forecast accuracy across ABC/XYZ segments. While AX items generally exhibit lower forecast errors, notable exceptions highlight the need for additional diagnostic analysis. The findings demonstrate that integrating classification and forecasting improves inventory decision-making, reduces excess stock, and supports sustainable resource utilization. The proposed approach provides practical guidance for optimizing inventory management in industrial environments.