(SQL Server + MySQL)

https://github.com/Arun-Kumar-Venugopal/Demand-Availability-Analysis-PowerBI

This project analyzes product demand, availability, supply shortages, profit, and loss using an end-to-end Power BI workflow across SQL Server and MySQL databases.

This project helps operations and supply chain teams analyze demand versus availability across products and dates. It identifies supply shortages, excess inventory, profit, and loss trends to improve inventory planning and business optimization.

The business faces challenges in balancing product demand and availability, leading to supply shortages or excess inventory that impacts profitability.

• Average Demand per Day
• Average Availability per Day
• Total Supply Shortage
• Total Profit
• Total Loss
• Average Daily Loss

• Demand and availability are treated as daily aggregates
• Profit and loss calculations assume constant unit price
• Data refresh depends on source system availability

SQL Server / MySQL
→ Power BI Desktop (Power Query, DAX, Visuals)
→ Power BI Service (Multiple Workspaces)

• Single semantic model reused across report pages
• Same report transitioned between SQL Server and MySQL backends
• Published to separate workspaces for each data source

Organizations need visibility into:

• Daily demand vs availability
• Supply shortages
• Profit and loss trends
• Product-level performance
• Data consistency across test and production environments

This dashboard helps stakeholders monitor operational efficiency and make informed inventory decisions.

• Built an end-to-end Power BI solution using SQL Server and MySQL
• Implemented test → production environment migration
• Designed reusable DAX KPI framework
• Migrated report from SQL Server to MySQL without rebuilding visuals
• Validated metrics across multiple data sources

• SQL Server
  • Test Environment
  • Production Environment
• MySQL Database
  • Production Environment

• Imported inventory and product datasets
• Performed data quality checks
• Created reporting table using SQL LEFT JOIN

• Loaded prepared table into Power BI Desktop
• Standardized data types
• Created calculated columns
• Designed report layout with custom backgrounds

• Created a dedicated Measures Table
• Implemented KPIs using DAX:
  • Average Demand per Day
  • Average Availability per Day
  • Total Supply Shortage
  • Total Profit
  • Total Loss
  • Average Daily Loss

• Switched data source from Test DB to Prod DB
• Ensured schema consistency
• Validated metric accuracy

• Installed MySQL connector
• Recreated equivalent tables in MySQL
• Updated Power Query source using Advanced Editor
• Validated results across databases

• Published reports to Power BI Service
• Created separate workspaces for SQL Server and MySQL sources

• Average Demand per Day
• Average Availability per Day
• Total Supply Shortage
• Product & Date filters

• Total Profit
• Total Loss
• Average Daily Loss
• Product & Date filters

• CALCULATE & FILTER
• SUMX for row-level calculations
• Time-based aggregations
• Conditional logic for profit/loss analysis

• Microsoft SQL Server & SSMS
• MySQL Server & MySQL Workbench
• Power BI Desktop
• Power BI Service
• DAX
• SQL

🔗 View-only reports published to Power BI Service
(Microsoft login required)

• SQL Server Data Source Report:
  https://app.powerbi.com/links/v6oAGXMnuK?ctid=4b05e781-4500-43c7-a12b-f7a45bcd4a54&pbi_source=linkShare

• MySQL Data Source Report:
  https://app.powerbi.com/links/jzcVhIHipP?ctid=4b05e781-4500-43c7-a12b-f7a45bcd4a54&pbi_source=linkShare

Note: Both reports use the same Power BI visuals and DAX logic.
The purpose is to demonstrate data source migration from SQL Server to MySQL without rebuilding the report.

• Designing an end-to-end Power BI solution using multiple databases (SQL Server & MySQL)
• Handling test-to-production database migration without rebuilding reports
• Switching Power BI data sources using Power Query Advanced Editor
• Building reusable KPI measures using DAX
• Validating business metrics across different database platforms
• Managing separate Power BI workspaces for different data sources
• Publishing and maintaining reports in Power BI Service

• Automate refresh using gateway scheduling
• Add alerting for supply shortages
• Implement product-level row-level security
• Extend analysis to forecasting demand trends

Created by: Arun Kumar Venugopal