Track: Supply Chain and Logisitcs Competition

Abstract

In the domain of supply chains, digital twins (DT) are reshaping traditional business approaches by offering diverse solutions that foster collaborative environments and data-driven decision-making. We examine digital twin technology, which involves creating virtual replicas of objects or processes to simulate the behavior of their real counterparts. We research the effectiveness of these digital twins in specific cases to enhance their performance. When applied to products, machines, and entire business ecosystems, the digital twin model can unveil information from the past, optimize the present, and even predict future performance in different areas. Drawing parallels with real-world examples, such as the supply chain management practices of major retailers like Walmart, we demonstrate the impact of digital twins on operational efficiency and overall supply chain performance. However, we critically address the challenges inherent in this system, such as costs, data quality, and model complexity. Through our research, a comparative analysis between digital twins and traditional simulations is provided, highlighting the distinct advantages and disadvantages of each approach in decision-making processes. This comparison aims to offer insights into the unique capabilities and constraints of digital twins in capturing the dynamics of real-world systems compared to simulation-based models.