FedEx Project Ops Decision Support (v1)

A Python-based operations decision-support and reporting tool modeled after an internal enterprise analytics system. The tool transforms synthesized shipment data into KPI tables, trend visualizations, and an executive-ready PDF report to support operational decision-making.

DISCLAIMER: “FedEx” is used strictly as a placeholder name. All data in this repository is 100% synthetic.

What this tool does

• Ingests shipment-level operational data (CSV)
• Computes core operational KPIs (service level, delays, exceptions, throughput)
• Generates clean charts for trend and performance analysis
• Produces a multi-page, stakeholder-ready PDF report
• Packages insights, assumptions, and roadmap into a single deliverable

Problem This Tool Solves

Large-scale logistics operations generate extensive daily performance data, but that data is often reviewed in fragmented reports that make prioritization difficult. Decision-makers may see metrics but lack a structured way to interpret operational risk, compare sites, and identify where intervention is most urgent. This tool consolidates operational KPIs into a ranked, decision-oriented report that supports faster, more consistent operational oversight.

Output

• PDF report: FedEx_Project_Ops_Report.pdf
• Screenshots:

Executive Summary

KPI Table

Trend Chart

Tech stack

• Python
• pandas
• matplotlib
• ReportLab
• Dependencies: requirements.txt

Project Structure

fedex-project-ops-decision-support/
├── README.md
├── LICENSE
├── requirements.txt
├── reports/
│   └── FedEx_Project_Ops_Report.pdf
├── src/
│   ├── export_pdf.py   # report orchestration
│   ├── metrics.py      # KPI computation
│   ├── rules.py        # deterministic decision logic
│   └── insights.py     # optional narrative inputs
├── data/
│   └── sample/
│       └── shipments_sample.csv
└── docs/
    └── screenshots/

How to run

By default, the tool runs on a synthetic sample dataset included at
data/sample/shipments_sample.csv.

You may substitute your own CSV if it follows the same schema.

Input CSV Requirements

To generate a report using your own data, provide a CSV file with the following columns:

Column Name	Description
date	Date of operations (YYYY-MM-DD)
scenario	Operating scenario (e.g. NORMAL, PEAK, DISRUPTION, LABOR_SHORTAGE)
package_volume	Total packages processed that day
network_capacity	Maximum network capacity for the day
on_time_rate	On-time delivery rate (0–1)
exceptions	Number of delivery exceptions
labor_hours	Total labor hours worked
cost_per_package	Average cost per package

Additional columns may be present but are ignored. You may use the provided sample dataset as a template.

Environment setup

python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -r requirements.txt
2. Generate the report
bash
Copy code
python src/export_pdf.py
The script outputs a PDF report to the reports/ directory.

Data

Sample data mirrors realistic operational patterns and distributions

Assumptions & limitations
Metrics are illustrative and simplified for demonstration purposes

Root-cause attribution is not automated in v1

Designed as a reporting artifact, not a live dashboard

Roadmap
Parameterized report configuration (date ranges, thresholds)

Automated anomaly flagging

Lane- and region-level drilldowns

Unit tests and CI workflow