Project Title: Sales & Operations Performance Dashboard

1. Data Cleaning (via Python Pandas)

-Deduplication: Cleansed the Order ID column of duplicates to ensure accurate, transaction-based sales figures.

-Handling Nulls: Removed Null values to prevent errors in mathematical and -statistical operations.

Formatting: Standardized data types across all columns for seamless modeling compatibility.

2. Data Modeling & DAX (in Power BI)

-Financial Metrics: Calculated Total Sales, COGS, and Profitability %. Accurately computed Average Order Value (AOV) to determine average invoice sizing.

-Shipping Metrics: Applied DATEDIFF and AVERAGEX to calculate Average Delivery Time (ADT) and track historical logistics efficiency.

-Analysis & Ranking: Used TOP N to identify top states by sales (California) and delivery speed (West Virginia). Applied COUNT to aggregate and map regional order distribution.

3. Dashboard Visualizations (UI)

-Sales db: Displays financial KPIs and tracks actual sales against a $300K annual target, featuring a Scatter Plot correlating discounts and sales volumes.

-Shipping & Region db: Monitors delivery efficiency and maps order distribution by shipping modes (Standard, Second Class, First Class, Same Day).

-UX: Integrated a side navigation panel with smart Slicers for dynamic filtering by Category, Sub-Category, Segment, Region, Ship Mode, and State.

Business Impact: Empowers management to accurately monitor profit margins, identify high-yield products and regions, and detect supply chain bottlenecks. These insights directly enhance customer satisfaction and optimize pricing strategies.