Recent changes in the global economy and the increase in digital technology, plus the customer's demand for

better service and communication from the logistics provider, have driven rapid changes in the logistics industry.

In this context, there is a growing reliance on Less than Container Load (LCL) consolidation services as they

coordination and communication practices in the management of the consolidation-based logistics service

process. The study involved administering a structured questionnaire to 50 respondents and using various

statistical analysis methods such as percentage calculations, One-Way ANOVA, Independent samples t-tests and

Chi-Square tests to evaluate the responses for the research purpose.

Manual processes and the time it takes to coordinate work are the main causes for inefficiency in operations.

Employees surveyed indicated they are generally aware of and accept the use of AI-based systems, such as

automated booking and documentation management. The results show AI-driven process automation can

increase operational efficiencies, reduce the time it takes to book shipments, increase the accuracy of

communications, and increase customer satisfaction for logistics operations.

Keywords: Artificial Intelligence, LCL Consolidation, Logistics Automation, Shipment Booking, Customer

logistics services available, Less than Container Load (LCL) consolidation has become an important shipping

model where multiple customers share the space in a container based on similarities in their destination and

shipping schedule.

Using an LCL consolidation service creates cost-effective transportation solutions for customers that do not have

enough product volume to fill an entire container. Although the LCL consolidation model provides shipping

options to customers, it also adds additional complexity to the operational processes as multiple shipments,

customers, and departments must all be coordinated at the same time. If one customer's shipment is delayed, it

can cause other customers in the same consolidated container to experience delays as well. In the current business

environment, customers expect faster booking confirmation, real-time updates, transparency, and efficient

communication. However, many logistics operations continue to depend heavily on manual coordination,

predictive analytics and AI-powered customer service.

This research project will explore the operational difficulties surrounding LCL consolidation services and the

use of AI-based automation to improve shipment booking processes and enhance customer satisfaction.

Research conducted in the past has emphasized many aspects that lead to efficient logistics operations including

freight consolidation, process efficiency, and the use of technology.

According to Campbell, J. F., the concept of freight consolidation allows for lower transportation costs by

improving route efficacy and allowing economies of scale to be achieved through a planned freight consolidation

process. Ha, K. H. explored how freight consolidation positively affects logistics service performance through

improving coordination efficiency and delivery urgency related to how well logistics firms coordinate with their

environments.

Key findings in the literature indicate that while consolidation greatly improves transportation efficiencies, many

logistics firms continue to have operational problems related to communication lags, manual workflows, and

fragmented operations for coordinating supplier and client processes. There has been limited research

specifically related to AI-based consolidated freight automation specifically for booking efficiency and customer

communications.

Primary Objective

 To study the role of AI-based automation in improving LCL shipment booking efficiency and customer

experience.

coordination, communication practices and artificial intelligence (AI) automated fulfillment functions for

logistics fulfilment activities. The process of consolidating LCL (Less than Container Load) shipments requires

multiple operational fulfilment processes, including booking confirmation, documentation, communicating with

customers as well as out the shipment transaction. Any delays or inadequacies experienced throughout the

fulfilment processes can have an effect on overall service quality and ultimately affect customer satisfaction.

The framework provides the assumption that any of these operational challenges (e.g., manual processes, gaps

in communication, and delays in coordination) will negatively impact the overall booking efficiency and

customer experience. AI based automation will serve as the independent variable that will improve workflow

satisfaction with logistics services. There is very little research exploring operational inefficiencies found within

the processes related to the consolidation of LCL freight. Furthermore, there is little research looking at how

automating processes using AI will improve shipment booking, internal coordination, and customer

communication in logistics operations that utilize consolidation as an operational means.

Existing literature also primarily focuses on the optimization of transportation costs and supply chain efficiency

as areas of concern while limiting their focus on operational workflow challenges for organizations such as

manually following up with customers, delays in communication and poor process standardization. Because of

these previously identified gaps, this study intends to provide an analysis of operational coordination issues and

automation readiness for LCL logistics services.

The researchers used several different data sources for their secondary data collection including research

journals, logistic reports, books and online academic resources related to the supply chain management and

automation. The research sample consisted of 50 respondents. Statistical analysis of the collected data was

conducted utilizing One-Way ANOVA, Independent Samples t-test and Chi-Square tests.

Statistical Analysis

One-Way ANOVA

The results indicate that there is no significant difference between male and female employees regarding comfort

with AI tools.

Chi-Square Test

The results indicate that there is no significant association between years of experience and perceptions regarding

members encounter three main barriers to efficiently obtain bookings and deliver superior client experiences,

namely manual coordination, communication delays, and fragmented processes.