# Trends and Recommendations for Enhancing Maturity Models in Supply Chain Management and Logistics
## 2 Materials and Methods
### Literature Search  
The scientific papers analyzed were selected by using the following approach. The first step of the methodology involves identifying MMs in SCLM. Then, Scopus was chosen as the search engine since it is considered the best research solution for scientific journal coverage [49]. To generate the initial sample of records (identification phase), the identified keywords were combined. These keywords were classified into two semantic areas: Semantic Area 1, related to maturity tools, and Semantic Area 2, related to SCLM. To form the search string, keywords within each semantic area were combined using the 'OR' operator, indicating that any of the keywords could be present. The two groups of keywords were then combined using the 'AND' operator, indicating that both groups of keywords needed to be present in the search results. Additionally, the operator \"' was used to maintain the consecutive order of multiple words and the operator \"' was used to include words with the same root but different suffixes. Based on the previous considerations, the following search query was obtained:  
_( \"maturity model\" OR \"maturity framework\" OR \"maturity roadmap\" OR \"maturity grid\" OR \"maturity assessment\" OR \"maturity level\" OR \"maturity index\") AND (\"supply chain\" OR \"supply chain\" management\" OR logistic\")_  
After inserting the search query on Scopus, the obtained results were further refined through a screening phase based on specific inclusion criteria. The inclusion criteria consisted of selecting articles within the subject areas of 'Engineering', 'Business, Management, Accounting', and 'Decision Science'. Only articles falling under the categories of 'Articles', 'Conference Papers', and 'Reviews' were considered. Moreover, only articles written in English were included in the analysis. The next step involved evaluating the relevance of the articles in relation to MMs for SCML. Initially, a screening process was conducted by reviewing the title, abstract, and keywords of each article. Subsequently, the full text of the selected articles was examined to determine their suitability for the study. Through this process, articles of interest were identified and included in the final sample of scientific papers for further analysis. Cross-referencing was employed to ensure comprehensive coverage and to enhance the robustness of the selected articles. More in detail articles of interest among the selected document references were analyzed, and the most relevant ones were included. The initial search string yielded 547 documents. After filtering the results based on reference area, document type, and English language, 293 records remained. Following a review of the title, abstract, and keywords, 149 documents were selected, while 144 were discarded. Subsequently, upon reading the full text, 17 additional results were excluded for various reasons. One document did not directly address SCML, some papers had incomplete (6 of the 17 articles excluded) or nonexistent models (4 of the 17 articles excluded), and others were not focused on processes (6 of the 17 articles excluded), as product or people-oriented. Here are examples provided to clarify the exclusion criteria. [PERSON] et al. [50] presents a model aimed at enhancing inventory management, which comprises two dimensions and seven sub-dimensions that do not align with any maturity level. [PERSON] et al. [6] does not introduce a maturity model; rather, they compare an operational standard with a generic CMMI to assess its applicability as a model-based proposal. [PERSON] et al. [51] proposes a people-based model for evaluating the excellence of logistics operators. Then by cross-referencing, 5 articles were included, resulting in a final sample of 137 documents. This final sample, consisting of 126 articles and conference papers, along with 11 reviews, was used to analyze the state of the art of MMs for SCML.