## 1 Introduction
The success of an SC operation hinges not only on each company achieving its partial or local objectives but on the attainment of global objectives that entail satisfying customer requirements [19]. In short, only the achievement of global objectives ensures the overall success of the SC. This study focuses on applying entropy, as conceived in information theory, to the realm of administrative sciences, more specifically, supply chain management (SCM). It employs Shannon entropy to assess the required information exchanged between agents during SCM. The underlying assumption is that information entropy serves as a proxy measure for the complexity within the SC. Complexity is not the same as complicatedness [20]. In scientific realms, complexity refers to the inherent nature of a system composed of many interconnected and mutually dependent parts, usually exhibiting emergent, non-linear properties that cannot be explained by individual, isolated components [21]. Replacing even a single component yields sensible variation in outcomes, as the replacement influences internal mutually dependent relationships. Complicatedness refers to a system that is difficult to understand or deal with due to having a high number of parts, but these parts are mutually independent [22]. Replacing one part with another that is similar does not change outcomes. In essence, complexity is inherent to nature, while complicatedness arises from the number of parts. For example, an aircraft engine that entails thousands of parts may be complicated but not complex. A soccer team, even entailing only eleven agents, may be complex but is not complicated. In short, complex systems may show emergent behavior difficult to predict and control, while complicated systems, even when difficult to handle, can be predicted and linearly escalated [23]. Given the uncertainties in SCM, it becomes important to not only assess but also manage the inherent complexity that arises from SC dynamics [24]. Controlling complexity does not solely imply reduction; in some instances, it may imply an increase. For example, in stable environments, reducing complexity can translate to cost reduction, making the SC more efficient. In unstable scenarios, in which innovation and variety are essential, an increase in complexity is often required, making the SC more flexible [25]. This study employs a measure of entropy that connects the concepts of uncertainty and complexity. According to this measure, as a system becomes more uncertain, it also becomes more complex [11]. To describe and monitor a complex system, it is necessary to access a range of information to manage unpredictability, underscoring a fundamental relationship. The greater the complexity, the more information is required to understand the system's behavior and anticipate possible outcomes presented by complex systems due to their unpredictable nature. A search on Scopus from 2020 to 2025 using the keywords \"Complexity\" AND \"Supply Chains\" yielded 4468 results. In contrast, the search for \"Complexity measurement\" AND \"Supply Chain\" returned only three results, and there was just one article found for \"Complexity\" AND \"Supply Chains\" AND \"Shannon entropy\". This indicates that while the topic of complexity in SCs is significant, there are gaps in the recent, high-impact literature concerning the measurement of complexity and, specifically, the application of Shannon entropy. Therefore, the purpose of the study is to propose a quantitative modeling method, employing [PERSON]'s entropy model as a proxy to assess the complexity in SCs. The research method is quantitative modeling. The core principle is to use the theoretical framework of [PERSON]'s entropy to quantify informational flow entropy as a proxy variable of complexity. The model measures the uncertainty in the flow of information permeating the SC. The underlying assumption is that the complexity of the SC is directly proportional to the amount of information required to manage it effectively [26].