🚀 AI-Driven Supply Chain & Operations Intelligence Dashboard

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📊 Dashboard Overview

🟦 Supply Chain Intelligence Dashboard(Landing Page)

📊 Executive Overview

🚚 Operations & Logistics Dashboard

👥 Customer & Product Insights

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📌 Overview

This project presents a comprehensive end-to-end Power BI solution designed to analyze supply chain performance, operational efficiency, and customer-product insights.

The dashboard enables stakeholders to make data-driven decisions through interactive and visually rich reports.

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🎯 Objectives

• Analyze sales and profitability trends
• Monitor delivery performance and logistics efficiency
• Identify high-value customers and top-performing products
• Evaluate discount impact on profitability
• Deliver an interactive and intuitive dashboard experience

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🧱 Data Model

📊 Tables Used

• orders → Sales, Profit, Discount
• customers → City, Segment
• products → Category
• shipments → Delivery & Shipping data
• suppliers → Supplier performance

🔗 Relationships

• orders → customers (CustomerID)
• orders → products (ProductID)
• orders → suppliers (SupplierID)
• orders → shipments (OrderID)

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📄 Dashboard Pages

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🟦 Page 1: Supply Chain Intelligence Dashboard(Landing Page)

• Clean UI design with background and branding
• Navigation button for smooth user flow
• Professional dashboard entry screen

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📊 Page 2: Executive Overview

KPIs:

• Total Revenue
• Total Profit
• Total Orders
• Avg Order Value
• Avg Discount

Visuals:

• Revenue Trend
• Revenue by Category
• Revenue by City
• Profit vs Discount

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🚚 Page 3: Operations & Logistics

KPIs:

• On-Time %
• Avg Days
• Late Orders
• Shipments
• Avg Cost

Visuals:

• Delivery Trend
• Supplier Performance
• On-Time vs Late Deliveries
• Shipping Cost by City

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👥 Page 4: Customer & Product Insights

KPIs:

• Total Customers
• Avg Order Value
• Profit Margin %
• Top Revenue
• CLV (Customer Lifetime Value)

Visuals:

• Top Customers
• Top Products
• Segment Analysis
• Profit vs Sales

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⚙️ DAX Measures

On-Time % = 
DIVIDE([On Time Orders], [Total Shipments])

Profit Margin % = 
DIVIDE(
    SUM('orders'[Profit]),
    SUM('orders'[SalesAmount])
)

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🎨 Design & UX

• Dark-themed modern UI
• Clean KPI cards with icons
• Rounded containers for visuals
• Minimal slicers for better usability
• Cross-filtering enabled

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🔄 Deployment

• Published to Power BI Service
• Configured daily scheduled refresh
• Ensures real-time data updates

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💡 Key Insights

• Revenue shows consistent growth
• High discounts reduce profitability
• Certain cities dominate sales performance
• Supplier delays affect delivery efficiency
• Top customers contribute majority revenue

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🛠️ Tools & Technologies

• Power BI Desktop
• DAX (Data Analysis Expressions)
• Power BI Service
• Data Modeling (Star Schema)

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💼 Business Value

• Enables executive decision-making
• Improves operational efficiency tracking
• Supports customer segmentation
• Helps optimize profitability

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📌 Project Highlights

✔ End-to-end dashboard development ✔ Multi-page analytical reporting ✔ Advanced DAX calculations ✔ Real-world deployment ✔ Automated data refresh

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🔗 Project Link

👉 (https://app.powerbi.com/links/mwwmULnb1n?ctid=691c09c3-ed03-46e8-bbc9-5278f3f7ca81&pbi_source=linkShare)

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📁 Project Structure & Usage

📂 Folder Details

• Datasets/ Contains all raw CSV files used in the dashboard

• Screenshots/ Stores dashboard images for all pages

• .pbix File Main Power BI report file

• README.md Documentation file

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⬇️ How to Use

🔹 Step 1: Download Project

git clone https://github.com/Mehtab161/Anudip-Project.git

OR download ZIP from GitHub

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🔹 Step 2: Open Dashboard

1. Open Power BI Desktop

2. Click:

   File → Open
   

3. Select .pbix file

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🔹 Step 3: Load Data (if needed)

• Go to Transform Data
• Update file paths from Datasets folder

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🔹 Step 4: Explore Dashboard

• Use slicers for filtering
• Click charts for cross-filtering
• Navigate between pages

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⚠️ Notes

• Use latest Power BI Desktop version
• Update dataset paths if required

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👤 Author

Mehtab Khan Aspiring Data Analyst | Power BI Developer

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⭐ Support

If you like this project, give it a ⭐ on GitHub!

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