Identifying Profit Leakage in High-Volume Logistics

Strategic Assessment: Supply Chain Risk & Margin Analysis

Dashboard Link: [View Interactive Tableau Dashboard] https://tinyurl.com/bdfz6kch

Business Problem

The Global Supply Chain division generates $35.2M in revenue but faces unexplained margin erosion in specific sectors. Stakeholders lacked visibility into how operational inefficiencies (specifically shipping delays) were impacting net profitability across different global regions.

Objective: Isolate specific products and regions where shipping delays correlate with negative profit margins to optimize logistics and reduce "profit leakage."

The Solution: Executive Command Center

I engineered a full-stack analytics solution to visualize the correlation between Shipping Speed and Profit Margin.

1. Data Architecture (SQL & Python)

• Data Cleaning: Used Python to preprocess raw order data, handling null values and formatting dates.
• Logic Layer (SQL): Utilized Common Table Expressions (CTEs) to:
  • Join Orders, People, and Returns tables.
  • Filter for valid transactions (excluding returns).
  • Calculate Actual Shipping Days vs. Scheduled Days.
  • Create a dynamic Profitability Status flag (Profitable vs. Unprofitable).

2. Interactive Dashboard (Tableau)

Designed an Executive Command Center using the "F-Pattern" layout for rapid decision-making:

• Risk Matrix (Scatter Plot): A 4-quadrant view isolating "High Risk / Low Profit" markets (e.g., Central Asia).
• Root Cause Analysis (Bar Chart): A sorted view identifying "Strength Training" as the primary loss leader (-107.8% Margin).
• Financial Health Trend: A longitudinal analysis proving the company maintains net-positive profitability despite category-specific losses.

Key Findings

1. The Trap: The "Strength Training" category is a critical deadweight, operating at a -107.8% margin despite high sales volume.
2. The Risk Zone: Regions with an average shipping time >4 days (e.g., Southern Africa, Central Asia) show a strong correlation with negative profitability.
3. The Stability: Despite these leaks, the overall business model remains healthy with a 10.8% Net Margin ($3.8M Profit).

Technical Stack

• SQL (PostgreSQL): CTEs, Window Functions, Joins.
• Tableau Public: Advanced Visualizations, LOD Expressions, Dashboard Actions.
• Python: Pandas for initial data profiling.

Data Source

The analysis is based on the Global Superstore dataset

• Format: CSV
• Size: 51,000+ rows
• Source: [Download Dataset via Kaggle] (https://www.kaggle.com/datasets/apoorvaappz/global-superstore-dataset)

Author: Tabassum K. Senior Business Data Analyst Portfolio