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Harnessing Computer Science to Drive Sustainable Supply Chains Facing Resilience Organizational Complexity

2024; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-031-52517-9_5

1865-0937

Pablo Guerrero Sánchez, Belem Gabriela Hernández Jaimes, José Guerrero Grajeda, Víctor Pacheco-Valencia, Rosa Álvarez-González, Felipe de Jesús Bonilla Sánchez,

Complex Systems and Decision Making

In an era of increasing complexity, where sustainability has become a pressing global concern, the role of computer science in supply chain in managing has never been crucial. With limited resources, growing populations, and environmental degradation, businesses and industries are compelled to adopt sustainable practices. Fortunately, advancements in computer science offer innovative solutions to address the complexities of supply chains, enabling companies to achieve sustainability goals while minimizing environmental impact and maximizing efficiency. This article explores the intersection of sustainability, computer science, supply chains, and complexity, highlighting the transformative potential of this multidisciplinary approach. Modern supply chains face numerous sustainability challenges, including resource depletion, greenhouse gas emissions, waste generation [1], and social issues such as labor rights [28]. The complexity of global supply networks exacerbates these challenges, as companies must track and manage a multitude of interconnected processes, stakeholders [23], and geographic locations. Moreover, the need for timely and accurate data, efficient decision-making, and effective risk management further amplifies the difficulty of achieving sustainable supply chains. The objective of this article is to lay the foundations for the construction of a heuristic optimization model that will be developed from the elements raised. The methodology involves the construction of a heuristic optimization model, exploring complex dynamic elements with a simple example of Monte Carlo simulation, with the objective of analyzing processes.