Application of digital twins for forecasting and managing the product lifecycle in industrial enterprises of various profiles

digital twins are a powerful tool for optimizing the product life cycle in various industries. This article is dedicated to a comprehensive analysis of the possibilities of using digital twins for forecasting and managing the product life cycle in industrial enterprises. A conceptual analysis of literature over the past 5 years was conducted, key trends and unresolved issues in this field were identified. Clear terminology is proposed, and the relevance of developing new approaches to creating and using digital twins is substantiated. Based on empirical data obtained from a sample of 30 industrial enterprises with various profiles, a comprehensive set of methods was tested, including simulation modeling, data mining, and predictive analytics. The results of the study showed that the use of digital twins makes it possible to increase demand forecasting accuracy by 18%, reduce the time to market for new products by 23%, and decrease warranty service costs by 12% through proactive detection of potential defects. The findings have significant theoretical and practical importance and open up prospects for further development of the concept of digital twins in the context of Industry 4.0.

digital twin, product life cycle, industrial enterprises, Industry 4.0, modeling, intelligent data analysis