# Defining SMEs' 4.0 Readiness Indicators
## 4 Defining SMEs 4.0 Readiness Aspects
### Industry 4.0 Aspects Identifications by Bibliometric Analysis  
As a result of the data searching in Table 1, the research obtained a database from the Web of Science of 641 journals, and Scopus of 907 journals. After that, the abstracts' data were extracted amounting to 1541 data and 336 keywords from the co-occurrence in bibliometric analysis. The irrelevant and duplicate keywords are deleted such as academic, fog, and systems. Hence, the researcher selected the 34 aspects from the most occurrences and relevance scores in the interested aspects and literature review. The number of occurrences and VOS's relevance score are shown by [PERSON] and [PERSON] [46]. In terms of the relevance score, this presents the trends of specific keywords that covered all text in the database. In visualization mapping, aspects are represented by circle labels, while the cluster and their links determine each aspect by color [47]. Accordingly, the research finds 34 aspects, we update the visualization map, and, finally, the research concludes five dimensions (pillars) for SMEs' 4.0 readiness aspects. Figure 2 presents the relationship and group of the dimensions from the visualization of similarities (VOS) techniques which generate maps using VOS mapping and VOS clustering techniques. These are new alternative techniques to multidimensional sizing approaches [54]. The techniques performed the normalizing co-occurrence frequencies that observed the number of co-occurrences of node (aspects) \begin{table}
\begin{tabular}{c c} \hline \hline
**Database** & **Scopus, ISI Web of Science** \\ \hline Time Limitation & 2008–2020 \\ \hline \multirow{3}{*}{Category} & Engineering; Computer Science; Business Management and Accounting; Decision Sciences; Mathematics; Social Sciences; Materials Science; Energy; Environmental Science and related. \\ \hline Source & Article; Book and Book Chapter \\ \hline Language & English \\ \hline \multirow{2}{*}{Keywords Search} & Industry 4.0, Smart Manufacturing, Smart Factory, Maturity, Readiness, Assessment, Roadmap, Implementation, Strategy, Factor Successful, Critical Factor, Indicator \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Keywords parameters. i and node (aspects) j [43; 44] which is called the association strength. There are often significant differences between node in the number of edges per other nodes [56]. Table 4 presents the keyword from the bibliographic analysis. Industry 4.0 aspects with the most relevance score are Supply chain management, Infrastructure, Circular economy, Business model, and Data management. These results indicate that supply chain management, such as partnership, stakeholders, and the customer, has the most relevant industry 4.0 development. The infrastructure, real-time data, digital technology, and protection are essential for the data exchange in the operational SMEs toward Industry 4.0. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Aspects** & **Occurrences** & **Relevance Score** \\ \hline
1 & additive manufacturing & 28 & 0.6743 \\
2 & artificial intelligence technology & 56 & 0.2877 \\
3 & blockchain technology & 18 & 0.4721 \\
4 & business model & 73 & 1.4799 \\
5 & business strategy & 13 & 0.6441 \\
6 & circular economy & 17 & 1.6295 \\
7 & cloud manufacturing & 153 & 0.3343 \\
8 & collaborative robot & 77 & 0.6114 \\
9 & customized product & 11 & 1.3406 \\
10 & big data analytic & 25 & 0.3971 \\
11 & data acquisition & 16 & 0.888 \\
12 & data connected & 19 & 0.7684 \\
13 & data management & 9 & 1.4141 \\
14 & cybersecurity & 7 & 0.8387 \\
15 & digital transformation & 56 & 1.3022 \\
16 & financial resource and investment & 47 & 0.7214 \\
17 & governance & 55 & 0.6979 \\
18 & human resource & 15 & 0.901 \\
19 & industrial automation & 14 & 0.7769 \\
20 & infrastructure & 7 & 2.0728 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Industry 4.0 Aspects selection keywords from the visualization of similarities techniques. Figure 2: Industry 4.0 Aspects relationship from visualization of similarities mapping.