# A Roadmap to Integrate Digital Twins for Small and Medium-Sized Enterprises
### Rationale of This Article  
The overarching theme of the relationship between DT and PLM is the data and the Digital Thread (DTH). At each PLM phase, data are harvested, communicated between all stakeholders, stored and accessed to enable informed managerial decisions. The DTH links information generated at each stage of the product's lifecycle in order to enhance present \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Communication Protocol** & **Advantages** & **Disadvantages** & **References** \\ \hline \hline MODBUS & Simple, Efficient & \begin{tabular}{c} Lacks \\ sophisticated security \\ \end{tabular} & [23; 24] \\ \hline \hline \multirow{4}{*}{OPC UA} & Publisher-Subscriber model & \
\begin{tabular}{c} Complicated \\ to implement, \\ Device specific limitations \\ \end{tabular} & [25; 26; 27] \\  &
\begin{tabular}{c} Distributed \\ Security layers \\ \end{tabular} & & \\ \hline \hline \multirow{4}{*}{MQTT} & Lightweight, & \multirow{4}{*}{Vulnerable security} & \multirow{4}{*}{[28]} \\  & & & \\ \cline{1-1} \cline{4-4}  & & & \\ \cline{1-1} \cline{4-4}  & & & \\ \cline{1-1} \cline{4-4}  & & & \\ \cline{1-1} \cline{4-4}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristic of machine-to-machine communication protocols in the automation process. and future decision making [36]. It aims to be the 'authoritative data' that contains every iota of data known about the system at that point in time that is both current and complete [37]. [PERSON] et al. [38] proposed the use of a supply chain DT to address a specific supply chain issue in the aircraft industry relating to the traceability and transparency of operations information. The authors presented a case study of a DT of the supply chain, enabled through blockchain technology in conjunction with additive manufacturing. The supply chain DT would serve as the historical data source providing reference and transparency. [PERSON] et al. [39] proposed a cloud-based DT that communicates with distributed edge DTs, i.e., DTs that are at different stages of the product lifecycle, for metal additive manufacturing data management. They developed a conceptual framework that consists of a number of DTs, known as edge DTs, that each represents a segment of the product lifecycle, ranging from product design to manufacturing and quality measurement. At each stage of the product lifecycle, the edge DTs perform their designated tasks, which include device control, analysis, report generation and data storage. The edge DTs are further managed by a cloud-based DT which harvests edge data and applies advanced data analytics. Although the individual components, e.g., sensors, computational hardware, modelling and analytical software and databases, of the DT are readily available, albeit sometimes requiring modifications, the link between them still requires some development. In other words, the DTH that collects and shares the harvested data over the product lifecycle is not yet built to the point that it is a 'one-size-fits-all' model. This applies for DT modelling as well, where each DT model must be built from the ground upwards. There is no standard method yet developed. The majority of sophisticated DTs can be found in larger companies such as GE and Siemens due to the fact that integrating such components poses a large challenge [40]. In the case of small and medium-sized enterprises (SMEs) that are looking to transition towards the digital transformation, they may face obstacles in specific steps, expectations, costs and learning curve. According to a 2017 report by Deloitte [41], Australian SMEs are steadily becoming more digitally engaged but are still unable to maximise the potential of I4.0 tools. They face issues, barriers and limitations such as: a lack of expertise to manage complex I4.0 structures; concerns about data and cyber security; lack of appropriate digital infrastructure; lack of knowledge of digitalization and extract valuable data to help business and visualise the data [41; 42].