Machine Learning Solution to Tackle Business Challenges

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

Explore how our implementation of machine learning transforms businesses by upgrading existing tracking systems to effectively counter various challenges. Our case study highlights the power of advanced technology in optimizing business operations and driving success.

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. Even 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. With its presence in more than two hundred and twenty countries, the company has given employment to more than four lakh individuals. The revenue of the organization stands at US$ 70 billion and connects markets that comprise more than 90% of the world’s Gross Domestic Product. This company 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. They have also deployed smart IT and analytical solutions for technical excellence.

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 were urgently required.
  • Identifying the reason behind the delays, and coming up with the best-fit solution for such inevitable incidences was the fundamental challenge.
  • Lack of real-time weather forecasting led to a lack of preparedness.
  • The revenue generation was also impacted at the granular level.

The Business 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, windspeed, 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

  • We achieved real-time insights on weather forecasting.
  • Real-time acknowledgement of cargoes and flights improved the business process.
  • Achievement of actionable insights for business improved the revenue generation of the enterprise.

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