Operations Data Analysis

Python, SQL, and Power BI analysis of fulfillment performance, late deliveries, revenue, defects, and returns. The reproducible data pipeline uses only the Python standard library: csv for ingestion and SQLite for analysis.

Results at a Glance

Question	Finding from the sample	Operational follow-up
Where is delivery performance weakest?	Midwest had the highest late-order rate at 62.5% (5 of 8 orders).	Review FC-ORD capacity, pick-pack time, and carrier handoffs.
Which center performs best?	FC-PHX delivered 100.0% of 6 orders on time.	Compare its workflow with lower-performing centers.
Which category needs attention?	Electronics produced the most revenue ($4,879.88) and the highest return rate (30.0%).	Segment returns by defect, packaging, and expectation mismatch.

These are descriptive findings from a small synthetic dataset, not causal conclusions.

Power BI Dashboard

Open the tracked Power BI project in Power BI Desktop to explore the one-page dashboard. It includes region, category, and month slicers; four KPI cards; a monthly revenue and late-rate trend; fulfillment-center rankings; and a region-by-category late-rate matrix.

The PBIP uses source-controlled PBIR and TMDL files. Its semantic model embeds the same 40-row synthetic snapshot as data/orders.csv, so it opens without a machine-specific data path or credential prompt. See the dashboard notes.

Business Questions

1. How do monthly revenue, volume, delivery time, late orders, and returns change?
2. Which regions and fulfillment centers have the strongest delivery performance?
3. Which region-category combinations are associated with late orders?
4. Which product categories have elevated defect or return rates?

Run the Analysis

Requirements: Python 3.9 or newer. No third-party packages are required.

python3 src/analyze_operations.py

On Windows, use python src/analyze_operations.py if the launcher is named python.

The script validates data/orders.csv, loads it into an in-memory SQLite database, runs five SQL analyses, and regenerates:

• outputs/monthly_kpis.csv
• outputs/region_performance.csv
• outputs/fulfillment_center_performance.csv
• outputs/late_order_drivers.csv
• outputs/category_performance.csv
• visuals/region_late_rate.svg

Project Structure

data/orders.csv                    Synthetic order-level source data
src/analyze_operations.py          Ingestion, validation, analysis, and exports
sql/analysis_queries.sql           Standalone SQLite analysis queries
outputs/*.csv                      Reproducible analysis results
visuals/region_late_rate.svg       Generated portfolio chart
visuals/operations_dashboard.png   Verified Power BI dashboard preview
powerbi/                            Source-controlled PBIP, PBIR, and TMDL files
insights.md                        Findings, limitations, and next steps
resume_project_entry.md            Resume and interview-ready project summary

Dataset and Method

The dataset contains 40 synthetic orders from January through April 2026 across four US regions, six fulfillment centers, and four product categories. Each row includes order value, promised and actual delivery time, and binary defect and return indicators.

The pipeline:

1. Validates required columns, dates, numeric ranges, and binary flags.
2. Loads the CSV into an in-memory SQLite table.
3. Calculates monthly, regional, center, late-order, and category KPIs with SQL.
4. Exports dashboard-ready CSVs and a regional late-rate chart.
5. Presents the same sample through reusable DAX measures and interactive Power BI visuals.

See the SQL queries and the full findings.

Skills Demonstrated

• Python data ingestion and validation
• SQL aggregation, conditional metrics, grouping, and ordering
• SQLite workflow automation
• Operations KPI design and business interpretation
• Reproducible CSV reporting and SVG visualization
• Power BI dashboard design, DAX measures, slicers, and interactive reporting
• Source-controlled PBIR and TMDL project authoring

Portfolio Summary

Built a dependency-free Python and SQL pipeline that validates order data, calculates fulfillment and quality KPIs, identifies late-order patterns, and exports five analysis-ready reports. Presented the results in a source-controlled, interactive Power BI dashboard.