# Trends and Recommendations for Enhancing Maturity Models in Supply Chain Management and Logistics
## 1 Introduction
These examples underscore the significance of MMs, as they not only assess individual systems but also facilitate comparison among multiple systems, revealing common factors and guidelines for management and performance enhancement. While the origin of MMs lies in software development, specifically with the Capability. Maturity Model (CMM) and the Capability Maturity Model Integrated (CMMI) [23], they are frequently referenced and adapted across various domains. MMs can be categorized into different assessment tools, including CMM-based models, Likert-like questionnaires, and Maturity Grids. CMM-based models define maturity from generic to specific objectives for each process area or dimension [24; 25; 26]. Likert-like questionnaires employ questions on a scale to assess maturity [27; 28; 29]. Maturity grids, represented in matrix forms, describe best practices for each maturity level [30; 31; 32]. Regardless of the assessment tool used, MMs define maturity levels through the use of levels, dimensions, and sub-dimensions. Moreover, entity assessment through MMs can have different objectives [33]. Descriptive models evaluate the current state of the system without making comparisons. Comparative models allow benchmarking between elements but do not provide guidance for improving maturity. Prescriptive models, although less common, emphasize providing improvement guidelines and roadmaps. It is important to note that these three objectives accompany the entire life cycle of a model [34]. Initially, a model is created to describe the current state (descriptive nature), followed by providing guidance for improvement (prescriptive nature), and finally, enabling comparisons between entities (comparative nature). MMs are practical tools that play a crucial role in evaluating and improving business processes. Despite their usefulness, there is a lack of comprehensive literature reviews that provide a complete overview of MMs in SCML, addressing the needs of both academics and practitioners. Numerous maturity models have been crafted with a specific focus on supply chain management, exemplified by the renowned SCMMM by [PERSON] [35] and S(CM)2 by [PERSON] and [PERSON] [36]. However, scant attention has been directed toward exploring their interrelation, impact, and roles in shaping models that encompass logistics processes in sustainable supply chains [37]. As a matter of fact, existing reviews prove to be limiting, with a specific focus and a small number of analyzed articles (Table 1). Among the most recent reviews, the study by [PERSON] et al. [38] analyzes 19 models for smart manufacturing. Additionally, the review conducted by [PERSON] et al. [39] focuses on the analysis of 11 MMs specifically addressing Sustainable Supply Chain Management. Similarly, [PERSON] [40] examines 11 MMs related to logistics in the service sector. Furthermore, [PERSON] et al. [41] provides a comprehensive summary of the contribution of 28 MMs to Digital Supply Chains. Other reviews instead only compare selected models without conducting in-depth content analysis. [PERSON] et al. [42] compares 15 models that contribute to the introduction and application of Industry 4.0 in the manufacturing sector, while [PERSON] et al. [1], [PERSON] et al. [43] and [PERSON] and [PERSON] [44] compare 49, 13, and 28 supply chain management models. Finally, reviews integrating bibliometric and content analyses have also recently emerged, such as those by [PERSON] et al. [45] and [PERSON] et al. [46], which are not fully representative due to the small sample size (19 and 17 papers, respectively). In contrast, the recent work by [PERSON] et al. [47] addresses this limitation by utilizing a more appropriate sample size for the analysis, covering a considerable time span. However, it is worth mentioning that this bibliometric analysis focuses primarily on organizational maturity models, with SCML being a subgroup within that context.