Nissan Parts Packaging Optimization
Tableau Dashboards · Data Engineering · Supply Chain Analytics
Team Lead | Data Science in Teamwork Practice × Nissan North America | Jan 2025 - Apr 2025
Led a team-based consulting project in collaboration with Nissan North America as part of a graduate course in Data Science in Teamwork Practice. The project focused on identifying cost-saving opportunities in their supply chain logistics and packaging workflows, involving direct interaction with Nissan's data and leadership teams.
The final presentation was delivered to a panel of executives including Nissan's CFO, with insights projected to enable approximately 20% cost savings through standardization.
The Problem
Nissan wanted to evaluate inefficiencies in how parts were packaged and transported across their North American supply chain. Working closely with supply chain managers and engineers, we identified three key areas for potential optimization:
Repackaging Rates
Quantifying how often parts require repackaging and why
Cost-per-Shipment
Analyzing shipping costs across lanes and facilities
Geographic Distribution
Understanding parts flow and warehouse utilization
My Approach
Data Collection & Analysis
- Worked with multi-source logistics data from Nissan's supply chain systems
- Analyzed packaging specifications across product families
- Identified patterns in highly incomplete datasets requiring significant data engineering
Stakeholder Engagement
- Held regular check-ins with Nissan stakeholders to validate assumptions
- Collaborated with supply chain managers and engineers to understand end-to-end processes
- Iterated on deliverables based on executive feedback
Key Deliverables
1. Interactive Tableau Dashboards
Designed and implemented a suite of dashboards that visualized:
- Monthly logistics data across the U.S. warehouse network
- High-cost shipping lanes requiring optimization
- Underutilized facilities and capacity opportunities
- Packaging inconsistencies across product families
- Repackaging trends over time
2. Data Engineering Pipeline
Data Cleaning
Python (Pandas) for handling messy datasets
Part Grouping
Custom algorithms for product classification
Imputation
Statistical methods for missing values
3. Strategic Recommendations
Delivered actionable insights projected to enable ~20% cost savings through improved packaging standardization, optimized shipping lane selection, and better warehouse utilization.
Impact
~20%
Projected cost savings through standardization
CFO Presentation
Delivered to Nissan NA executive panel
What I Contributed
- Led data wrangling and preprocessing of multi-source logistics data
- Built dynamic Tableau dashboards to track cost drivers and repackaging trends
- Developed part grouping logic and imputation strategies for incomplete data
- Represented the team in presenting results to Nissan's executive leadership
Technologies Used
Recognition
Received Vanderbilt Certificate of Recognition for the Nissan Packaging Optimization project (Apr 2025).