# A Data-Intelligence-Driven Digital Twin Framework for Improving Sustainability in Logistics
## 1 Introduction
### Related Work  
The backbones of DTs are the enabling technologies based on Industry 4.0 concepts [8], a solid two-way automatic dataflow between the physical object and the digital object via a connector [9] and the use of this physical-digital fusion to extract knowledge [10]. Some works in the literature have explored the concept of a supply chain digital twin. For instance, the work in [11] described a supply chain digital twin as a simulation model of a supply chain capable of predicting dynamic behaviors to make immediate decisions (short to medium term). The authors of [11] discussed how the need for DTs in supply chains is important to overcome the limitations of ERPs in including real-time feedback and responses to dynamic supply chain environments when dealing with orders, processes, and assets. One of the solutions highlighted in the literature is the digital twin features provided by AnyLogistix supply chain software [12], which provides tools for leveraging ERP data for integration with IT infrastructure and business intelligence (BI), along with supply chain resilience analysis. In the systematic review in [2], the insights from analyzing the performance benefits of applying key emerging technologies to different supply chain companies included an improvement in productivity, demand forecasting, traceability, customer satisfaction, responsiveness, decision making and so much more. While [2] did not describe a case study for a supply chain DT with company data, the capability of a DT to utilize the Supply Chain 4.0 technologies to handle both commercial and sustainability benefits was evident. Our work showcases the potentials of DTs with some more context. As a supply chain DT must handle big data to be responsive in real time, there exist challenges. The work in [13] identified some of the challenges when handling big data for transportation systems--from the modeling, processing, and analyzing to visualizing aspects. The authors highlighted some challenges of intersecting the Supply Chain 4.0 technologies of the IoT, cloud computing and big data to include real-time data analytics (lambda architectural issues), data sources and security and privacy. Challenges associated with traffic management are often solved by machine learning approaches, as discussed in [14]. Some of the traffic management challenges adopted in our work include the use of real-time geo-positioning as a data source for some of the algorithm workflows in the proposed DT. In relation to routing optimizations, reinforcement learning has been explored in [15] for multi-modal transport in supply chains, focusing on optimizing the costs, including emissions, but this requires further refinement for optimal decision making. The proposed DT in this work considers the human feedback for such a solution. While the development of DTs is not fully standardized, hence the lack of an all-purpose technological platform that could perform all the activities peculiar to a business case study, adopting standards such as ISO-23247 can significantly help improve the benefits of applying DTs [7]. Researchers continue to explore strategies for achieving NET Zero by 2050, such as alternative energy sources like adopting synthetic fuels for decarbonizing transport [16], swarm intelligence for traffic optimization [17], and the application of AI approaches for optimizing power electronics [18]. Furthermore, Industry 5.0 presents opportunities for sustainability through adopting 5G for intelligent transport [19], the Internet of Things (IoT) as an enabler of transport sustainability [20], and fault diagnosis [21]. The study in [22] identified the challenges of verifying the confidence interval of optimization algorithms based on machine learning approaches. The work in [23] showcased the framework and implementation of a digital twin for collaborative logistics solutions to the emissions and resources wastage associated with truck logistics.