# A Data-Intelligence-Driven Digital Twin Framework for Improving Sustainability in Logistics
## 2 Materials and Methods
### Data Processing and Integration  
To create a digital twin representing the physical assets and processes of the logistics operations, the data sources described above must be processed and integrated into the DT model. Processing the data involves extracting raw data and processing it to infer context to  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Name** & **Description** & **Type/Latency** \\ \hline \multirow{2}{*}{1} & Transport Management & ERP for planning and assigning pick-up & \multirow{2}{*}{ERP/Batch stream} \\  & System & and drop-off goods in-trailer to drivers & \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{Truck Telematics} & On-vehicle sensors transmitting vehicle & \multirow{2}{*}{API/Realtime stream} \\  & & telematics such as location, trips, carbon & \\  & & emission, driving events, etc. & \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{Trailer Telematics} & On-trailer sensors transmitting trailer & \multirow{2}{*}{API/Realtime stream} \\  & & telematics such as location, temperature, & \\  & & speed, weight, etc. & \\ \hline \end{tabular}
\end{table}
Table 1: Data description.  
Figure 1: Case study.  
be fed into the DT model and intelligence-driven model. An example of this is identifying whether a trailer is empty or full. To achieve this, the weight in tonnes of the trailers over a certain period must be processed to identify the threshold that depicts an empty or full trailer. Figure 2 below provides a description of achieving this for a certain trailer type in which the data over a month were processed to identify this threshold.  
In terms of integration, the DT model needs to integrate all the different data sources to provide a full picture of the assets relating to events. An example of this is achieving integration in the DT by turning data into information that translates business processes into features for the model. This integration helps answer the following questions.  
1. Which driver and truck are assigned to a certain job and what is its status at any given point in time?
2. Where is the truck (assigned or unassigned to a trailer) at any given point in time?
3. Where is the trailer (loaded or unloaded) at any given point in time?
4. What are the nearest vehicles to a given location or trailer?
5. What truck/driver based on all the information from 1-4 is best suited to be assigned an incoming job to optimally utilize resources and reduce costs and carbon emissions?  
From the questions above, question 5 is ultimately the goal of our intelligent DT model as identifying this achieves our goal. Nonetheless, Figure 3 below provides an example of this integration within our DT interface, where trailers (blue markers) within a geozone (green highlight) are identified, with the closest vehicles identified as well. This closest vehicles feature enables us to build a proximity score of all the available trucks/drivers, taking into account their status, distance and availability to take on jobs.  
With data coming from the different systems, and the integration as described above, the DT gathers information to feed into our model and ultimately allows our model to train and infer what actions best optimize the next step of a business process, with the most recent data.  
Figure 2: Example of data processing to identify the load status of trailers.