Airport Parking Operations Performance Dashboard

Overview

An operations analytics dashboard designed to simulate airport parking management workflows and support data-driven operational decision-making.

This project demonstrates Excel reporting, data analysis, KPI tracking, dashboard development, and operational performance monitoring through both an Excel workbook and an interactive Streamlit dashboard.

---

Technologies Used

• Microsoft Excel
• Python
• Pandas
• Streamlit
• Plotly
• XlsxWriter
• OpenPyXL

---

Project Structure

Operations_Performance_Dashboard.xlsx

The primary Excel workbook containing:

• Project Summary
• Dashboard
• Raw Data
• Lookup Tables
• Pivot Tables
• KPI Calculations
• Business Insights

app.py

Interactive Streamlit dashboard for operational analytics and KPI visualization.

generate_dashboard.py

Python automation script used to generate datasets, create Excel sheets, apply formulas, and configure charts.

---

Key Features

Operations Dashboard

• Interactive Airport Hub Filter
• Parking Type Filter
• KPI Monitoring
• Revenue Analysis
• Customer Satisfaction Tracking
• Booking Status Analysis

Key Performance Indicators

• Total Bookings
• Total Revenue
• Average Revenue per Booking
• Completion Rate
• Average Customer Rating
• Airports Served

Analytics & Reporting

• Revenue by Airport
• Monthly Revenue Trend
• Revenue Share by Parking Type
• Booking Status Distribution
• Revenue by Vehicle Type

---

Excel Skills Demonstrated

Lookup Functions

VLOOKUP

Used to retrieve city information from airport lookup tables.

=VLOOKUP(B2,'Lookup Tables'!$A$3:$C$8,3,FALSE)

XLOOKUP

Used to dynamically retrieve vehicle category and airport information.

=XLOOKUP(G2,'Lookup Tables'!$I$3:$I$6,'Lookup Tables'!$J$3:$J$6,"N/A")

---

Logical Functions

IF

Used to calculate revenue based on booking status.

=IF(I2="Cancelled",0,J2*VLOOKUP(D2,'Lookup Tables'!$F$3:$G$5,2,FALSE)*(MOD(ROW(),5)+1))

---

Aggregation Functions

• SUMIF
• SUMIFS
• COUNTIF
• COUNTIFS
• AVERAGEIF
• AVERAGEIFS

Used for KPI calculations, revenue analysis, booking tracking, and customer satisfaction reporting.

---

Dataset Details

The project contains a simulated dataset of 500 airport parking transactions across multiple airport hubs.

Airports

• Bangalore Airport
• Chennai Airport
• Hyderabad Airport
• Kochi Airport
• Mumbai Airport
• Delhi Airport

Parking Types

• Economy
• Premium
• Valet

Vehicle Types

• Sedan
• SUV
• Hatchback
• Luxury

Booking Status

• Completed
• Pending
• Cancelled

---

Operational Insights

Revenue Performance

Mumbai Airport generated the highest overall revenue due to higher adoption of premium and valet parking services.

Parking Type Analysis

Valet parking contributed a significantly larger share of revenue compared to its booking volume, indicating higher revenue yield per booking.

Cancellation Trends

Cancellation rates were highest during peak travel periods, highlighting opportunities for cancellation control policies.

Customer Satisfaction

Economy parking recorded lower customer ratings compared to premium services, suggesting opportunities for service improvement.

Resource Planning

Peak-hour service demand patterns indicate the need for optimized staffing and operational scheduling.

---

Key Skills Demonstrated

• Operations Reporting
• Data Analysis
• Excel Dashboard Development
• KPI Monitoring
• Process Optimization
• Business Intelligence
• Data Visualization
• Stakeholder Reporting
• Operational Analytics
• Excel Functions (VLOOKUP, XLOOKUP, IF, SUMIF, COUNTIF)

---

Running the Streamlit Dashboard

Install dependencies:

pip install pandas streamlit plotly openpyxl xlsxwriter

Run the application:

python -m streamlit run app.py

Open:

http://localhost:8501

---

Dashboard Preview

Add screenshots from the dashboard here:

• Main Dashboard
• KPI Overview
• Revenue Analytics
• Operational Insights

---

Author

Mahadev Ambadi SS

B.Tech Computer Science and Engineering Christ University