We implemented the ML-based solution and helped counter frequent logistics & supply chain issues.

About the Industry

The logistics industry is the lifeline that carries business at a steady flow. Right from material handling and production to inventory and warehouse management, all complex operations are handled with a robust supply-chain solution.

Analytics plays a crucial role in the logistics industry. The real-time analysis of tasks, paired with an unparalleled vehicle management system, helps maintain the safety, security, and integrity of the organization. Trained ML-Models are simplifying the overall functioning of the tasks by phasing out manual operations. With the intervention of high-end business intelligence solutions, reporting and visualization have accelerated the identification and mitigation of challenges in real-time. 

About the Client

Our client is an American logistics and supply chain giant. They have presence in more than two hundred and twenty countries and accounts for 3.6 million shipments daily. Our client holds an upper hand over its competitors. The service deliverable of the organization is recognized at an enterprise level. As a part of the business expansion, they have opened subsidiaries in major geographic areas.

The Business Challenges

The following challenges were identified at the enterprise level:

  • The need for real-time updates of air freights and cargoes, their movement, and order status details
  • Identifying the reason behind the delays, and coming up with the best-fit solution for such inevitable incidences
  • Lack of real-time weather forecasting, which sometimes led to chaos
  • The revenue generation was also getting impacted at the granular level

Our Solution

We analyzed the challenges and modeled the solution to overcome roadblocks.

  • We introduced Azure ML Studio and created data sets that included airport ID number, wind-speed, visibility, etc.
  • We deployed Join Module to join flight and weather data, and created a model using the Two-class Boosted Decision Tree Model
  • For comparison analysis of any two parameters, the Two-Class Logistic Regression Model was implemented
  • Similarly, the Score and Evaluate Model was deployed to analyze new data sets for future analysis

Key Results

  • Achievement of real-time insights on weather forecasting
  • Real-time acknowledgement of cargoes and flights
  • Achievement of actionable insights for business improvements


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