## 2 Background Literature
### Related Studies
\begin{table}
\begin{tabular}{c c} \hline \hline
**Study** & **Main Features** \\ \hline
[9] & Handles resilience and efficiency by managing the number of suppliers, degree of differentiation, and interrelationships in the SC \\
[10] & Evaluates complexity by theoretically developing an entropy-based measure \\
[11] & Evaluates complexity by identifying drivers and applying AHP \\
[12] & Provides an extensive and insightful review of complexity drivers in SC \\
[13] & Studies drivers that boost complexity in SC and propose mechanisms to overcome their effects and reduce complexity \\
[14] & Provides a useful methodology to link SC complexity with plant performance, considering plant design and operation strategy, which will be useful in future research \\
[15] & Manages SC complexity by system dynamics tool, highlighting more effective drivers for complexity level \\
[16] & Provides a theoretical description of an entropy-based measure as a proxy variable for complexity in SCs and introduces conditional (relative) complexity, which was employed in our study. \\ Our study & Manages complexity to achieve efficiency, resilience, responsiveness, and agility in SC. The evaluation uses twenty key information elements encompassing five aspects: planning, logistics, workforce, finance, and technology. The study also relates complexity to competitive priorities. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related studies and their contributions, along with the positioning of this study. operational strategy, directly impacts manufacturing outcomes, thereby reinforcing the relevance of complexity management as a vital component of performance improvement. Study [15] analyzes SC complexity through the lens of performance measurement and system dynamics. This research posits that examining performance indicators can enhance the understanding of complexity while acknowledging that complexity itself significantly influences performance. It identifies impactful complexity drivers that warrant further investigation. Finally, study [16] expands upon the entropy-based approaches introduced in study [10] by developing an algorithm for assessing SC complexity that incorporates the notion of conditional (relative) complexity. This innovative methodology enables a more focused assessment of specific SC components, enhancing the applicability of complexity metrics for managerial diagnosis and decision making. In summary, the selected studies can be categorized into four thematic areas:  
* Transition from a qualitative understanding of complexity [12] toward quantitative, metric-driven approaches [10; 16];
* Identification and analysis of complexity drivers [11; 12; 15];
* Exploration of performance as a resultant outcome from complexity [13; 14; 15];
* Conceptualization of complexity as a management variable rather than a static attribute [9], required to achieve other objectives, such as efficiency and agility. Our study aligns with these thematic groups by offering a quantitative approach, identifying and quantifying drivers of complexity, investigating the performance-complexity relationship as a point for future exploration, and framing complexity as a variable to be optimized rather than merely reduced. Therefore, the research hypothesis is that it is possible to measure complexity in SCs by a quantitative model that identifies drivers and serves as a feedback loop to control complexity according to strategic concerns.