The Role of Digital Twin Technology in Enhancing Production Forecasting and Inventory Optimization

Abstract

This study explores the impact of digital twin technology (DTT) on production forecasting accuracy and inventory optimization across various manufacturing sectors, including automotive, electronics, and consumer goods. Using a mixed-methods approach, data were collected from 25 companies that employed DTT in their production processes. Results indicate that DTT significantly enhances forecast accuracy by an average of 20%, improves inventory turnover by 15%, and reduces stockout occurrences by 18%. These findings support the potential of DTT to enable real-time data analysis, predictive modeling, and adaptive decision-making, thereby aligning with the goals of digital transformation. However, challenges such as high implementation costs, robust data infrastructure requirements, and a need for technical expertise were noted, particularly for smaller firms. Practical implications suggest phased implementation and staff training to optimize DTT adoption. This study contributes to the understanding of DTT's role in production management. It offers recommendations for industry-wide adoption, with future research suggested in broader sectors and integration with artificial intelligence.