Data-driven decision-making process can be defined to be the sequential activities of real-time data collection, data analytics, optimization and decision making. Developments in Industry 4.0 technologies have made it possible to realize that new quality decision-making process. When that decision-making process is performed under the simulation model of a system developed on real-time databased and end-to-end connection manner, to prevent the disruption risks and to improve resilience in a system, then it constitutes a digital twin (DT). A DT is a virtual representation of an object or system that can help organizations monitor operations, perform predictive analytics, and improve processes. For instance, a DT could provide a digital replica of the operations of a factory, communications network, or the flow of goods through a supply chain system. In this work, we focus on DT implementations in supply chain networks. We present state of the art implementation of DTs in supply chains and their prospective utilizations towards creating intelligent supply chains.