Search

The concept of Digital Twin (DT) has become increasingly prominent in both academia and industry, particularly in manufacturing. Despite its potential, the effective implementation of DT remains a challenge due to conceptual confusions, the need for integration of various technologies and the complexity of the DT ecosystem. This study aims to clarify the conceptual confusion and examine the complex structure of the DT ecosystem. A framework is developed to describe the impact of enablers, barriers, challenges on the performance of DT, incorporating insights from both literature and expert surveys. Using an improved Fuzzy Cognitive Mapping approach, the research conducts both static and dynamic analyses to prioritize critical factors and predict the performance of early-phase DT projects. Unlike previous studies that focus on physical asset-related metrics, this study introduces DT-specific performance measures. Simulations are performed for seven real cases across three industries and various possible scenarios. The simulations provide insights into the adoption and implementation of DTs, revealing that a balanced approach addressing both technological and non-technological factors is essential. The findings emphasize the need for comprehensive strategies encompassing infrastructure, data management, and collaboration to achieve successful DT projects.