Predictive Logistics + Optimization (DHL Supply Chain)

Role

Data Scientist (Logistics)

Timeline

8 Months

Team

Logistics Managers, Operations Analysts, Data Engineers

My Focus

Operations Research, Demand Forecasting, Route Optimization

PythonSQLXGBoostProphetLinear ProgrammingOperations Research

Business Impact

+30% efficiency; +5% backhaul

Scale

USA & Mexico operations

Predictive Logistics + Optimization (DHL Supply Chain)

The Challenge

The Challenge: The "Empty Mile" Problem

In logistics, an empty truck is lost revenue. DHL needed to optimize fleet utilization to reduce costs and improve delivery speed.

The Bottleneck: Manual planning could not account for dynamic demand or identify "backhaul" opportunities (return trips), resulting in trucks running empty 20% of the time.
The Goal: Build an automated system to predict daily demand and mathematically optimize route assignments to minimize empty miles.

The Architecture

I developed a Predictive Logistics Pipeline that combined Machine Learning with Operations Research:

Data Transformation: Used dbt to clean and transform historical shipment data from SQL databases into analysis-ready features.
Demand Forecasting: Trained an XGBoost model to predict shipment volumes 24 hours in advance, allowing for proactive planning.
Route Optimization: Applied Linear Programming (PuLP/OR-Tools) to solve the vehicle routing problem, mathematically guaranteeing the most efficient route allocation.

System Architecture Diagram

graph LR
    A[Shipment Data<br/>SQL Database] --> B[Feature Engineering<br/>dbt Transformations]
    B --> C[XGBoost<br/>Demand Forecast]
    C --> D[Linear Programming<br/>PuLP/OR-Tools]
    D --> E[Route Assignment<br/>Engine]
    E --> F[PowerBI<br/>Operations Dashboard]

    G[Airflow<br/>Orchestration] -.->|Daily Batch| B
    H[Backhaul<br/>Optimizer] -.->|5% Increase| D

    style A fill:#0066ff,stroke:#0052cc,stroke-width:2px,color:#fff
    style B fill:#4C9AFF,stroke:#0066ff,stroke-width:2px,color:#fff
    style C fill:#0066ff,stroke:#0052cc,stroke-width:2px,color:#fff
    style D fill:#4C9AFF,stroke:#0066ff,stroke-width:2px,color:#fff
    style E fill:#0066ff,stroke:#0052cc,stroke-width:2px,color:#fff
    style F fill:#4C9AFF,stroke:#0066ff,stroke-width:2px,color:#fff
    style G fill:#666,stroke:#444,stroke-width:1px,color:#fff
    style H fill:#666,stroke:#444,stroke-width:1px,color:#fff

The Impact

We successfully automated the daily route planning process, reducing manual work and fuel waste.

Metric	Manual Planning	AI-Optimized Routing
Planning Time	4+ Hours / Day	< 10 Minutes (Automated)
Route Logic	Human Intuition	Mathematical Optimization
Asset Utilization	20% Empty Returns	+5% Backhaul Capacity
Efficiency	Baseline	+30% Operational Efficiency

Collaboration & Operations

Success in logistics requires buy-in from the operations floor:

Operational Adoption: I worked directly with dispatchers to design a PowerBI Dashboard that visualized the optimized routes, ensuring the output was actionable and trusted by the team.
Scalability: The system was deployed via Airflow to run reliable daily batches, scaling to handle 500+ shipments per day across multiple distribution centers.

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