Case Study

Predictive Analytics in Logistics: ML-based Government Scores Prediction for On-Time Performance

A global logistics giant transforms its performance evaluation process through predictive analytics and machine learning.

Predictive Analytics in Logistics for On-time Performance

A leading logistics provider improves customer satisfaction and operational excellence  by leveraging predictive analytics and machine learning to forecast the empirical impact of Government Scores and other key factors on On-Time Performance (OTP).

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Industry: Logistics, Global

Technology Stack:

RDBMS: MS SQL Server; ML Models: XGB & Random Forest; Integrated Development Environment (IDE): Jupyter Notebook; Cloud Deployment: Azure Pipeline, Azure Functions, Azure Data Factory, Azure Blob; Code Library: Python libraries

Client Overview

Our client is a leading logistics services provider, offering a comprehensive range of relocation and transportation services. Operating on a global scale, the company embraces advanced technologies to revolutionize processes and prioritize seamless customer experiences.

Business Challenge

The client uses a critical metric known as On-Time Performance (OTP) as part of its performance evaluation process. The challenge was identifying and understanding the key factors, particularly Government Scores, that impact OTP and influence operational efficiency. To enhance On-Time Performance, the client sought to leverage Machine Learning (ML), integrating it into its existing rules-driven forecasting process, for more accurate prediction of variables like Government Scores that affect OTP effectively.

Solution Offered

To improve the client’s On-Time Performance and forecasting capabilities, KANINI created a machine learning-driven methodology proficient in precise Government Score predictions and effectively communicating the model’s results alongside their corresponding business advantages. The solution involved:

  • Data Analysis and Model Building: Historical data was gathered from silos using data modeling and meticulously analyzed to identify and preprocess relevant data elements required to build effective ML models. These models were specifically designed to predict government scores.
  • Machine Learning Implementation: Following data preparation, appropriate ML techniques were chosen based on the specific dataset and objectives. These techniques were then implemented to create robust and accurate predictive models.
  • ML Integration into Existing Forecasting Process: The ML methodologies were seamlessly integrated into the existing rules-driven forecasting process to enhance the overall accuracy and efficiency of government score predictions.
  • Business Translation: The solution not only predicted Government Scores effectively, but it also articulated model outcomes along with their business benefits to stakeholders for informed decision-making.
Value Delivered
  • Improved On-Time Performance: Minimized delays and enhanced overall operational efficiency.
  • Enhanced Forecasting Accuracy: Enabled more precise and reliable predictions of Government Scores by utilizing historical data and appropriate ML techniques.
  • Informed Decision-making: Empowered stakeholders to make strategic decisions that positively impact business performance and customer satisfaction

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