## 3 Results
### Brief Overview of Current LSS Publications
#### 3.1.3 Keywords  
For comparative purposes, it is interesting to have an overview of the keywords employed to classify the reviewed works. Table 4 shows the ranking of the top ten relevant key terms used by the authors. \begin{table}
\begin{tabular}{c c} \hline \hline
**Search Term** & **Articles** \\ \hline Lean Six Sigma & 1572 \\ Lean Six Sigma Manufacturing & 547 \\ Lean Six Sigma Manufacturing Chemicals & 22 \\ Lean Six Sigma Manufacturing Chemical Industry & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Scientific publications between 2002 and 2022; Scopus database. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Subject Area** & **Items Including** & **\% From Total Articles** \\ \hline Business, Management and Accounting & 810 & 30.1 \\ Engineering & 661 & 24.5 \\ Decision Sciences & 367 & 13.7 \\ Medicine & 301 & 11.1 \\ Computer Science & 173 & 6.4 \\ Social Sciences & 110 & 4.0 \\ Nursing & 103 & 3.8 \\ Environmental Science & 71 & 2.6 \\ Materials Science & 55 & 2.0 \\ Energy & 43 & 1.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Top ten research areas of LSS articles. \begin{table}
\begin{tabular}{c c} \hline \hline
**Country** & **Articles** & **\%** \\ \hline United States & 481 & 38.9 \\ India & 256 & 20.7 \\ United Kingdom & 187 & 15.1 \\ Netherlands & 60 & 4.8 \\ Brazil & 54 & 4.3 \\ Ireland & 51 & 4.1 \\ Italy & 43 & 3.5 \\ Malaysia & 42 & 3.4 \\ United Arab Emirates & 33 & 2.7 \\ Canada & 32 & 2.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Main contributions made by country. From the data collected in Table 4, a graph-based map was elaborated using VOS Viewer tool (Figure 3). It indicates the frequency of occurrence of each term, number of connections to other terms, and strength of these connections based on the frequency of their occurrence. The proximity between nodes, which indicates the simultaneous appearance in multiple published articles, and the thickness of the links are directly related to their strength [47]. To provide a more representative picture, each term must be mentioned at least 30 times to be involved in mapping. Furthermore, irrelevant terms such as \"article\" or \"human\", which do not add value to the search, were excluded. Finally, a total of 64 from 6521 keywords remained for mapping. The frequency analysis shows the most relevant keywords as well as the co-occurrence patterns found in the publications on this topic. As seen, all are grouped into three cluster: red, green and blue. The green cluster includes words such as total quality management, quality improvement, organization and management, whereas the red one includes lean production, six sigma, work simplification and process monitoring, among others. The blue cluster is the smallest and gathers words such as lean six sigma, lean or process improvement. From the analysis of these clusters, it can be deduced that there are three main pillars that define LSS research: (i) the red pillar, which is headed by the term Six Sigma, is mainly focused on management; (ii) the green pillar, led by total quality management, focuses on quality; and (iii) the blue pillar, which is driven by Lean Six Sigma, is related to the analysis. The multitude of connections between clusters indicates the mutual dependence of these pillars. It should be noted that the largest cluster is related \begin{table}
\begin{tabular}{c c c} \hline \hline
**Keywords** & **Quantity** & **Total Link Strength** \\ \hline Six Sigma & 618 & 2182 \\ Lean Six Sigma & 602 & 1664 \\ Total Quality Management & 284 & 1089 \\ Lean & 249 & 808 \\ Work Simplification & 244 & 1211 \\ Lean Production & 228 & 1142 \\ Process Engineering & 195 & 1004 \\ Quality Improvement & 187 & 768 \\ Process monitoring & 182 & 1096 \\ Organization and Management & 141 & 635 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Top ten keywords and key terms used in LSS ranking of published articles. Figure 3: Keyword concurrency LSS analysis. Groups of repeated keywords are shown in the same color (clusters).