Using Machine Learning And Best - Worst Method To Evaluate 3pls Partnerships: A Case Study Of A 3pl Company In Vietnam

Abstract

Third-party logistics (3PL) providers offer a range of services to streamline the supply chain for businesses. These services typically encompass warehousing, inventory management, order fulfillment, and transportation. However, managing communication and data exchange with a 3PL can be a complex task. This abstract explores how Python code can be utilized to bridge this gap and achieve seamless integration with 3PL providers then by applying a MCDM method called Best Worst Method (BWM) could select the best option for a 3PL service to customers through its Linear model to examined whether the highest score belongs to any carriers. The abstract will delve into the core functionalities of 3PL services, highlighting areas like inventory tracking, order processing, and shipment management. It will then discuss the challenges associated with manual data exchange and the potential for errors from from an interval analysis. Subsequently, the abstract will introduce the concept of Tokenized texts scripting as a solution. It will explain how Python's versatility and wealth of libraries can facilitate tasks like integration, data manipulation, and automation of routine communication with the 3PL. By leveraging Python code, businesses can achieve real-time data visibility, improve order fulfillment accuracy, and optimize inventory management. The abstract will conclude by emphasizing the potential of applying machine learning, especially tokenized texts for enhancing efficiency and cost savings within the 3PL integration process.