Matching shippers and carriers via AI-enabled platform

Development team and time 

  • Product Owner in Singapore & Thailand  
  • 2 Scrum Teams (15 people) – 8 sprints to MVP GO-LIVE  
  • Integration team in Germany 
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Background

Vehicle routing and scheduling are essential yet complex tasks that consistently challenge carriers and shippers, who share the objective of finding the most efficient and economical pathways for delivery. This quest for optimization is not just about saving time and resources; it’s about enhancing overall operational efficiency and customer satisfaction in the highly competitive logistics sector. 

Such a concern has urged corporations to figure out innovative and effective solutions that leverage the latest technologies. The client is among the leading global transportation and logistics providers with more than 80,000 employees in over 100 countries. The company is known for its long history of freight forwarding activities and overland business. 

In our collaborative project, GEM and the client worked together to unlock a self-service platform that can process instant quotations and competitive rates for overland shipments. The differentiation of this platform is the application of Artificial Intelligence (AI) to match shippers’ requirements with carrier availability. This initiative is expected to be realized in Thailand, followed by other countries in the Asia Pacific region.

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Challenges

The challenges faced in the project fell into the following categories: 

Data management

Each carrier had its own way of collecting and storing data, leading to a disorganized and inconsistent database. This issue became even more complex when the platform also needed to include booking and tracking systems that allowed customers to monitor the package’s delivery and contract payment. This made the system highly difficult to manage. 

Implementation of AI

The lack of uniformity and organization in the database made it challenging to implement Artificial Intelligence (AI) solutions. This prevented AI implementation from reaching its fullest potential because AI relies on structured and coherent data to function optimally.  

Performance and responsiveness

The platform is expected to process a massive amount of data while maintaining a responsive and stable performance to ensure a smooth user experience. Balancing these requirements with the other technological and operational challenges was a critical task. 

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Solution

After defining the requirements and challenges, GEM’s scrum teams worked with the integration team in Germany to carry out the project. The team employed multi-tenant architecture to facilitate multiple users on the same code base. Thus, the software application can share a dedicated instance of configurations, data management, and other properties. White labeling procedures also took place so that the platform can use services from various providers across geo-locations to create offers under its name. This could save approved carriers time, energy, and money regarding infrastructure and marketing costs.  

In terms of data processing, the execution team collected the database of all logistics companies joining the platform to analyze and produce a better and uniform one. The standardized and patterned inputs enabled seamless queries of shipment details. Then, the AI-powered engine stepped in to streamline and automate the quotation comparison process to present the most competitive options that fit individual needs. The whole process was run on the operating system to ensure high availability, real-time visibility, and responsiveness. 

Tech stack

  • Multi-tenant & white-labeling service design to allow customization & localization across geo-location  
  • Service Oriented Architect and Event Sourcing architecture to enable high availability and loose coupling services  
  • ReactJS for responsive web, and Java Spring Boot for backend application 
  • Graph Database, Elasticsearch, and Redis Cache for Rate Engine service to calculate the best rate over millions of matches among shippers & carriers.

Output

The platform was successfully developed and functioned as efficiently as expected. The proprietary rate engine leveraged AI to match shipper’s requirements with carrier availability to eliminate manual efforts dedicated to these tasks. It also stood out with its ability to provide instant quotations and competitive rates for overland shipments through a self-service platform.  

The platform was launched in Thailand and then in other countries in the Asia Pacific region. 

Impacts

The platform’s significance was proven by the following factors: 

  • It provided an innovative and technology-enabled approach to operating logistic activities via one unified platform. 
  • It bridged the supply and demand of overland logistics, provided approved carriers with the opportunity to profit from idle fleets, and offered local and regional shippers the most competitive freight services. 
  • It made the client one of the first logistics providers in the APAC region to apply AI technology to a digital freight platform.  

Closing remarks

This case study showcases an AI-driven breakthrough in the logistics sector that transformed traditional freight forwarding and overland business operations. GEM’s team helped the client develop a robust platform that not only streamlined the matching process between shippers and carriers but also set a new standard for efficiency and user experience in the logistics industry across the APAC region.