| dc.description.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. | en_US |
