Requirement Document for AI Cash & Carry Store Power BI Report

1. Introduction

This document outlines the requirements for the AI Cash & Carry Store Power BI report. The report is designed to provide insights into sales, shipping, profit, and customer behavior for improved business decision-making.

2. Purpose

The purpose of this Power BI report is to track and analyze key performance metrics related to sales, profit, shipping costs, customer behavior, and product performance across different segments and categories.

3. Scope

The Power BI report includes:

• Sales analysis by category, segment, and customer
• Profit analysis by category and segment
• Shipping cost analysis
• Customer insights (Top and Bottom customers)
• Product performance (Top and Bottom products)
• Sales trends and goal tracking
• Regional and country-wise sales analysis

4. Data Sources

The report utilizes data from:

• Sales transactions
• Shipping details
• Customer and product databases

5. Key Metrics

The following key metrics are tracked:

• Total Sales: Aggregate revenue generated.
• Shipping Cost: Total cost incurred for product shipments.
• Quantity Sold: Total units sold.
• Profit by Segment: Profit categorized by Consumer, Corporate, and Home Office segments.
• Sales by Category: Revenue generated from Office Supplies, Furniture, and Technology.
• Profit by Category: Profit earned from different product categories.
• Shipping Cost by Category: Distribution of shipping costs across categories.
• Top 10 Customers: Highest revenue-generating customers.
• Bottom 10 Customers: Customers contributing the least to revenue.
• Top 10 Products by Sales: Best-selling products.
• Bottom 10 Products by Sales: Least-selling products.
• Sales Trends: Monthly and yearly sales performance.
• Sales and Target by Month: Comparison of actual sales vs. target sales.
• Sales by Country and Market: Geographical sales distribution.
• Customer’s Sales & Profit by City and Segment: Sales and profit performance by city.

6. Visualizations

The Power BI report contains the following visualizations:

• Sales & Profit by Segment (Bar Chart)
• Shipping Cost by Segment (Bar Chart)
• Sales by Category & Segment (Stacked Bar Chart)
• Profit by Category & Segment (Stacked Bar Chart)
• Shipping Cost by Category & Segment (Clustered Bar Chart)
• Top 10 and Bottom 10 Customers (Bar Chart)
• Top 10 and Bottom 10 Products (Bar Chart)
• Sales Trends Over Time (Line Chart)
• Sales & Profit by Sub-Category (Tree Map)
• Sales by Country & Market (Map Visualization)

7. Functional Requirements

• Ability to filter data by date range, category, segment, and region.
• Drill-down capabilities for detailed analysis.
• Comparison between different years for trend analysis.
• Highlight sales and profit outliers.
• Real-time data updates.

8. Performance Considerations

• Report should load efficiently with large datasets.
• Optimized queries and DAX measures for quick response time.
• Data model should be structured for scalability.

9. Security & Access Control

• Role-based access to ensure data confidentiality.
• Secure sharing options for stakeholders.

10. Project Completion Method

The project completion follows these steps:

10.1 SQL Queries

• Extract sales, customer, and shipping data from the database using optimized SQL queries.
• Example Query:

SELECT CustomerID, CustomerName, SUM(Sales) AS TotalSales, SUM(Profit) AS TotalProfit
FROM SalesData
GROUP BY CustomerID, CustomerName;

• Ensure indexes and proper joins for performance optimization.

10.2 Power Query (ETL)

• Data cleaning and transformation using Power Query.
• Merge tables to create a comprehensive dataset.
• Remove duplicate records and handle missing values.
• Create calculated columns for further analysis.

10.3 DAX Measures & Calculations

• Create DAX measures for advanced calculations.
• Example:

TotalSales = SUM(SalesData[Sales])ProfitMargin = DIVIDE(SUM(SalesData[Profit]), SUM(SalesData[Sales]), 0)

• Implement time intelligence functions for trend analysis.
• Create calculated tables and relationships for better data modeling.

11. Conclusion

This Power BI report provides critical insights for decision-making in AI Cash & Carry Store operations, helping to track performance, identify trends, and optimize business strategies.

sample data files in CSV format for Power BI report:

1. Sales Data (sales_data.csv)
2. Customer Data (customer_data.csv)
3. Shipping Data (shipping_data.csv)

Here are three sample data files based on the_AI Cash & Carry Store Power BI Report