About
Starting Semester: Fall 2026Assigned: No
Location: remote/ DHL eCommerce Atlanta Dist Center - Mableton
DHL eCommerce
Client Profile
DHL eCommerce is one of four DHL business units, providing international and domestic e-commerce delivery services in select markets in the Americas, Asia Pacific and Europe. The division is focused 100 percent on servicing B2C online merchants, shipping lightweight packages and parcels domestically and internationally.Project Description
DHLeC currently relies on a third party vendor to manage all commercial airline cargo movements across the U.S. network. This is a multi million dollar annual operation supporting time critical parcel flows to and from 18 national distribution centers.This project will focus on designing a next generation, in house air cargo management platform and operating model that could replace the existing outsourced solution. Students will architect a fully integrated logistics technology stack—combining data engineering, transportation network optimization, automation, and airline operations analytics—to determine the financial, operational, and strategic feasibility of internalizing this function.
The scope includes:
• Automating commercial air cargo booking across multiple carriers
• Developing predictive ETA and risk monitoring capabilities for freight in transit
• Modeling airport to DC pickup and delivery flows using network simulation techniques
• Designing the airline relationship management framework, SLAs, and compliance metrics
• Quantifying expected cost savings vs. current third party model
Ultimately, students will design the future-state DHLeC Air Cargo Control Tower powered by modern data infrastructure and predictive analytics.
Project Objectives (Technical + Strategic)
1. Advanced Cost & Network Optimization Analysis
• Build a comparative cost model using SQL/Python to evaluate the current vendor solution vs. an in house operational model.
• Analyze cost drivers such as linehaul, tendering, cargo handling, airport access, and SLA penalties.
• Use network design tools and simulation to identify optimal hub airport configurations and routing logic.
2. Workforce Modeling & Operational Architecture
• Determine required FTE roles using workload modeling, transaction volume analytics, and process cycle time analysis.
• Recommend staffing profiles (Ops Analysts, Control Tower Specialists, Data Technicians) with expected productivity metrics.
3. Implementation Blueprint
• Develop a phased roadmap including system integration, process migration, KPI establishment, and service continuity risk mitigation.
• Propose data flows, automation opportunities, and a future state architecture diagram for an internal Control Tower model.
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Technical Environment & Communication Structure
• Teams will meet weekly with the DHL project lead, accessing real operational data.
• Collaboration will take place in a dedicated Microsoft Teams channel with integrated file sharing, whiteboarding, and workflow tracking.
• A large scale operational dataset is provided via a state of the art Databricks platform, enabling advanced analytics, SQL queries, ML modeling, and scenario simulations.
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Deliverables (Elevated for Technical Rigor)
1. Current State System Audit
o End to end mapping of the third party operating model, system integrations, API touchpoints, and process gaps.
2. Cost Model + Workforce Requirements
o SQL/Python-driven cost to serve model
o Forecast of required FTEs with defined skill sets, job architectures, and labor cost impact
o Sensitivity analysis across demand volumes, airport combinations, and SLA expectations
3. Future-State Air Logistics Model & Operating Plan
o Network design recommending optimal airline partners, routes, and airport flows
o Control Tower process design with predictive monitoring and automated exception reporting
o System architecture proposal including data pipelines, dashboards, and workflow automation
4. End to End Implementation Roadmap
o Timeline, deliverables, risk assessment, required technologies, and transition plan
o RACI matrix and recommended KPIs for go live and stabilization phases
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Skills
• Value Stream Mapping & Lean Process Engineering• Transportation Network Design & Air Cargo Flows
• Advanced Data Analysis (Databricks / SQL / PySpark / Python)
• Simulation Modeling (AnyLogic, SimPy, or Python based tools)
• Predictive Analytics & ETA Modeling
• Cost-to-Serve Modeling & Financial Impact Analysis
• Systems Design & Control Tower Architecture
• Creative Problem Solving for Multi Constraint Logistics Systems
This project is ideal for students interested in data engineering, transportation systems, supply chain technology, network science, and operational automation