International Journal of Economics Finance & Management Science

This study explores the potential of Large Language Models (LLMs) in optimizing supply chain decision-making by comparing their performance with traditional machine learning models, including Random Forest (RF), Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and Deep Neural Networks (DNN). The evaluation focuses on four key supply chain tasks: demand forecasting, supplier selection, inventory management, and logistics optimization. Results indicate that LLMs significantly outperform traditional models, particularly in tasks involving both structured and unstructured data. The LLM achieved superior accuracy in demand forecasting, supplier selection, and logistics optimization, demonstrating its capability to analyze complex, multi-dimensional data from sources such as transactional records, supplier feedback, and market trends. Although the LLM required more computational resources, its overall performance highlighted its potential to revolutionize supply chain management. The findings suggest that LLMs offer a promising approach to optimizing supply chain decisions, improving efficiency, reducing costs, and enhancing overall decision-making accuracy. Future research should focus on addressing the computational challenges and exploring broader applications of LLMs in supply chain contexts.

Modern software development uses CI/CD pipelines to speed up software systems' delivery timelines. Most technical teams face pipeline system expansion as a critical engineering hurdle. The paper presents a detailed framework for the automation of CI/CD pipelines, which combines Terraform and GitLab specifically to achieve maximum scalability and efficiency. Organizations can create affordable and secure cloud infrastructure deployment management through a GitLab CI/CD platform integrated with Infrastructure as Code (IaC) frameworks. This allows them to manage infrastructure deployment simultaneously with application deployments while ensuring repeatability. Application and process efficiency and automated infrastructure deployment stem from the connection between IaC technology and GitLab CI/CD tools. The document shows deployment processes by demonstrating actual code, which helps organizations gain competence in tool usage. During the actual implementation of the framework, deployment speed increased by 55%, as the framework reduced infrastructure costs by 25% and improved deployment reliability to 70%. Terraform and GitLab work together to transform DevOps operational frameworks based on the provided results. Implementing such a framework enables organizations to optimize their DevOps workflows, lowering manual tasks while expanding their CI/CD pipeline capabilities. The paper presents essential best practices and integration methods that provide essential knowledge about present-day software development requirements for automated deployments.