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Digitization of Container Management and Tracking for a European Shipping and Logistics Leader

About the Customer:

Our customer is a giant shipping and logistics company based in Europe. They have operations globally.

They wanted to associate with an expert consulting team to help them out with technical solutions and implementation for their operations.

Business Challenges:

The challenge that our customer was facing was that they lacked visibility of the location and status of their containers. Due to their large and complex organizational structure, they found it difficult to streamline the container tracking process.

They had their containers docked across places such as ports, third-party warehouses, or their own storage spaces. But they did not have a centralized system to track and manage these containers or any information regarding these containers.

This led to inefficiencies and high costs when they had to reassign the containers to another ship panel. Many containers were left on the port or other leased spaces without proper planning or optimization.

The project aimed to solve this problem by creating a digital platform where every source could upload their data regularly, regardless of the format. This enabled them to have a clear picture of where each container was and how to optimize the reassignment process. The priority was to clear the containers from the port, which was the most expensive place to store them, and move them to the company’s own space or third-party spaces, which were cheaper alternatives.

Embitel Solution:

We developed a digital solution that automates the tracking and management of the customer’s containers efficiently. As a pilot project, they decided to deploy the solution for their operations in the US.

For our customer, this resulted in less waiting time for drivers, more savings by retrieving containers that incurred extra fees, and more. Our team has designed tools to monitor these operations on a real-time dashboard.

The dashboard helps to address the following processes:

  1. Predicting and planning ahead for incoming containers using graphs and tables as a tool to identify peak periods on the port. We did this by:
    • Integrating data from 2 data sources and transforming the manual calculations into an algorithm to show this data in real time.
  2. Managing driver capacity and scheduling of containers for better visibility and action using KPI’s, graphs and tables. We accomplished this by:
    • Filtering the data to get the relevant information. Creating rules for the end user to filter through the table based on the desired information. For example, the end user can filter by appointment date to display results only with a specific appointment date.
    • Using color coding of graphs, the correct metrics are calculated in the back end to give the user an intuitive understanding of the state of appointments in the next days.
  3. Tracking and monitoring of containers and trucks to know exactly where and when containers are and for how long, using a map with additional information in tables.
    • By combining the data from a few different sources, we were able to collect and consolidate the driver and yard information to draw the live data of containers and drivers on the map.
    • By creating tables that store the information, we were able to create rules so that the end user would be able to see drivers and containers that have been idle so that these could be investigated.

The administrators can easily find the information they need by using the features on their website. They have search options, filters and user interface layers that help them access the relevant data from the databases.

Embitel Impact:

This project has resulted in total savings of $1.18M to our customer in just one week and for one particular location.

The project’s success reinforced our customer’s belief in our capabilities, and we are now working on Phase 2 of the project.

Tools and Technologies:

The Data Engineering

Tech stack includes:

  • Azure platform
  • Azure Data factory
  • Python
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