PowerBI Project: Apocalypse Food Prep Visualisation

Project Overview

This PowerBI project focuses on visualising data for Apocalypse Food Prep, a company specialising in emergency food supplies. The dataset consists of various CSV files containing information on customers, locations, products, sales, sales teams, and state regions.

Data Sources

The project uses the following CSV files:

1. Customers
2. Locations
3. Products
4. Sales
5. Sales_team
6. State_regions

Each of these CSV files contains specific information critical to understanding and analyzing the business operations and performance.

CSV File Details

Customers

• Contains details about the customers.

Locations

• Information about different store locations.

Products

• Contains the list of products sold by Apocalypse Food Prep.

Sales

• Records of sales transactions.

Sales_team

• Details about the sales team members.

State_regions

• Information on state regions where the stores are located.

PowerBI Dashboard

The PowerBI dashboard provides several interactive visualizations to explore the dataset:

1. Sales Analysis: Visualizes total sales, sales trends over time, and sales breakdown by product, location, and sales team.
2. Customer Insights: Analyzes customer demographics and purchase behaviour.
3. Product Performance: Shows the performance of different products in terms of sales volume and revenue.
4. Geographical Analysis: Maps sales data and store performance by state regions.
5. Team Performance: Evaluate the performance of sales team members.

Summary Reports

Key Metrics:

1. Total Sales Revenue: Achieved a total sales revenue of £43.08K.
2. Production Costs:
   • Stainless Steel Axe: £32.45
   • Weatherproof Jacket: £30.59
   • Backpack: £26.92
   • Water Purifier: £17.93
3. Total Units Sold: 3001 units across various products.
4. Top Selling Products:
   • Stainless Steel Axe: Highest production cost (£32.45) and a significant contributor to revenue.
   • Weatherproof Jacket: Second highest production cost (£30.59).
   • Backpack: £26.92 production cost, showing strong sales.
5. Sales Distribution by State:
   • Texas: 951 units sold (31.69%)
   • Minnesota: 828 units sold (27.59%)
   • New York: 613 units sold (20.43%)
   • Missouri: 609 units sold (20.29%)
6. Average Units Sold by Year and Day: Peaked in sales around mid-January 2022, with an average of 60 units sold on the highest days.

Achievements:

1. Successful Multi-State Sales Strategy: Led to significant sales distribution, with Texas and Minnesota being the top-performing states.
2. Optimized Product Portfolio: Focused on high-revenue products like the Stainless Steel Axe and Weatherproof Jacket, leading to efficient production cost management.
3. Data-Driven Insights: Utilized Power BI to identify peak sales periods and optimised inventory accordingly, resulting in maximized sales during critical times.
4. Comprehensive Cost Analysis: Conducted detailed cost analysis across products, leading to informed pricing strategies that balanced profitability with market competitiveness.

Getting Started

To view the visualisations, follow these steps:

1. Download the Repository: Clone the GitHub repository to your local machine.

   git clone https://github.com/gappeah//Apocalypse-Food-Prep-Report.git

2. Open the PowerBI File: Use Microsoft PowerBI Desktop to open the Apocalypse Food Prep - Visualisations.pbix file.
3. Load Data: Ensure that all the CSV files mentioned above are in the same directory as the PowerBI file. PowerBI should automatically link to these files. If not, you can manually load them through the PowerBI interface.

File Structure

root
│
├── README.md
├── Apocalypse Food Prep - Visualisations.pbix
├── Customers.csv
├── Locations.csv
├── Products.csv
├── Sales.csv
├── Sales_team.csv
└── State_regions.csv

How to Use

• Open the PowerBI file to access the interactive dashboard.
• Use filters and slicers within the dashboard to explore specific aspects of the data.
• Hover over visualisations to see detailed information and insights.

Contributing

If you would like to contribute to this project, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Commit your changes (git commit -m 'Add new feature').
5. Push to the branch (git push origin feature-branch).
6. Open a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.