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Executive Summary

A retail company with multiple regional distribution centers and stores faced inefficiencies in its logistics operations, including high transportation costs, delayed deliveries, and underutilized fleets. By leveraging a route optimization solution powered by Google OR-Tools and using a data integration tool for automated data ingestion, we streamlined logistics operations. The solution reduced logistics costs, improved delivery timelines, and required minimal manual intervention.

About Client

A retail company with a widespread network of regional distribution centers and stores, the client faced rising transportation costs, delivery delays, and inefficiencies in fleet utilization. Manual data handling further complicated logistics operations. Seeking to optimize their delivery routes, reduce costs, and improve service levels, the client partnered with us to implement an AI-powered route optimization solution.

Problem Statement

The retail company faced the following logistics challenges:

Rising Transportation Costs

  • Inefficient routing increased fuel expenses and vehicle wear-and-tear.

Inconsistent Deliveries

  • Poorly optimized routes led to frequent delivery delays, impacting operations and customer satisfaction.

Manual Data Management

  • Delivery and fleet data was handled manually, leading to errors and inefficiencies.

Solution Overview

We implemented an automated route optimization system that minimized manual effort and improved logistics efficiency. The solution focused on:

Data Ingestion & Integration

Automating the collection of delivery, vehicle, and distance data using a data integration tool.

Route Optimization

Using Google OR-Tools to generate optimized routes for the fleet.

Visualization & Monitoring

Providing actionable insights via dashboards to track route efficiency and identify potential delays.

Key Components of the Solution

#1. Automated Data Ingestion

  • Tools Used: Azure Data Factory (ADF).
  • Process:
  • Delivery data (e.g., addresses, demands, vehicle capacities) was ingested from the company’s ERP system and logistics platform into a centralized database.
  • ADF pipelines automated the daily extraction, transformation, and loading (ETL) of data, ensuring accuracy and up-to-date information.

#2. Route Optimization

  • Tools Used: Google OR-Tools.
  • Process:
  • The distance matrix was generated using latitude and longitude values from the delivery data.
  • OR-Tools solved the Vehicle Routing Problem (VRP) by minimizing travel distance and balancing vehicle loads while adhering to constraints like time windows and vehicle capacities.
  • Real-time traffic data (via a third-party API) was incorporated into the solution to refine routes dynamically.

#3. Visualization & Reporting

  • Tools Used: Power BI.
  • Process:
  • Dashboards were built to display optimized routes, fleet utilization metrics, and key performance indicators (KPIs) such as cost savings and delivery timeliness.

Results

15% Reduction in Logistics Costs

Achieved a 15% reduction in transportation costs, saving $750,000 annually through optimized routing and consolidation strategies.

20% Improvement in Delivery Timelines

Reduced delivery delays by 20%, ensuring 95% on-time deliveries and significantly improving service levels for stores and customers.

12% Enhanced Fleet Utilization

Increased fleet efficiency by 12%, reducing the number of trips required by 1,200 trips annually, resulting in lower fuel consumption and maintenance costs.

Tech Stack & Tools

Data Ingestion

  • Azure Data Factory (ADF) for automated ETL processes.

Optimization

  • Google OR-Tools for solving the Vehicle Routing Problem (VRP).

Visualization

  • Power BI for route visualization and monitoring.

Team Composition

  • Data Engineer : 1
  • Data Scientist : 1
  • Business Analyst : 1
  • Project Manager : 1

Clientele

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+1 770 796 0077

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