An interactive Power BI dashboard analyzing 3 years of warehouse operations across Bangalore, Chennai, and Hyderabad — covering ₹9.24bn in revenue, 50K orders, and multi-category performance from 2022–2024.
This project simulates a real-world Warehouse Operations Analyst Dashboard for Flipkart's South India fulfillment network. It tracks revenue performance, order fulfillment health, cancellation impact, and category-level profitability — enabling data-driven decisions across warehouse operations.
Built entirely in Power BI with a star schema data model, time intelligence DAX measures, and a clean KPI-first layout.
| KPI | Value |
|---|---|
| 💰 Total Revenue | ₹ 9.24 Billion |
| 📈 Total Profit | ₹ 2.0 Billion |
| 📦 Total Orders | 50,000 |
| ❌ Cancel Rate | 17.89% |
| 💸 Cancellation Loss | ₹ 2 Billion |
⚠️ Key Finding: The 17.89% cancel rate caused ₹2bn in revenue loss — nearly equal to total profit. Reducing cancellations is the single highest-ROI operational lever.
Line chart tracking monthly revenue fluctuations between ₹220M–₹280M. Reveals volatility patterns and a notable dip in mid-2023.
Bar chart comparing warehouse contribution across 3 cities:
- 🥇 Bangalore — ~₹3.8bn (highest)
- 🥈 Chennai — ~₹2.9bn
- 🥉 Hyderabad — ~₹2.2bn
5-category breakdown with near-equal distribution across Fashion, Home Appliances, Accessories, Footwear, and Electronics — all between ₹1.4bn–₹2.0bn.
Multi-line chart overlaying Q4→Q3 performance for 2022, 2023, and 2024. Shows that 2023 had the sharpest Q1 dip before recovering strongly in Q3.
| Status | Share |
|---|---|
| ✅ Delivered | 72% |
| ❌ Cancelled | 18% |
| 🔄 Returned | 10% |
Horizontal bar chart — Electronics leads with the highest margin (~20%+), followed by Footwear and Fashion. Home Appliances records the lowest margin.
The dashboard is fully interactive with cross-filtering across all visuals:
| Slicer | Options |
|---|---|
| Date Range | 01-01-2022 to 31-12-2024 (date picker + slider) |
| Year | 2022, 2023, 2024 |
| Quarter, Year | Q1–Q4 per year |
| Category | Accessories, Electronics, Fashion, Footwear, Home Appliances |
| Location | Bangalore, Chennai, Hyderabad |
┌──────────────────┐
│ Fact_Orders │
│ (Central Table) │
└────────┬─────────┘
┌─────────────────┼─────────────────┐
▼ ▼ ▼
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ Dim_Product │ │ Dim_Date │ │ Dim_Location │
│ (Category, │ │ (Year, Qtr, │ │ (Bangalore, │
│ SKU, etc.) │ │ Month, Day) │ │ Chennai, │
└──────────────┘ └──────────────┘ │ Hyderabad) │
└──────────────┘
-- Total Revenue
Total Revenue = SUM(Fact_Orders[Revenue])
-- Total Profit
Total Profit = SUM(Fact_Orders[Profit])
-- Cancel Rate %
Cancel Rate % =
DIVIDE(
CALCULATE(COUNTROWS(Fact_Orders), Fact_Orders[Status] = "Cancelled"),
COUNTROWS(Fact_Orders), 0
) * 100
-- Cancellation Revenue Loss
Cancellation Loss =
CALCULATE(SUM(Fact_Orders[Revenue]), Fact_Orders[Status] = "Cancelled")
-- Profit Margin % by Category
Profit Margin % =
DIVIDE([Total Profit], [Total Revenue], 0) * 100
-- YoY Revenue Growth
YoY Revenue Growth % =
VAR CY = CALCULATE([Total Revenue], DATESYTD(Dim_Date[Date]))
VAR PY = CALCULATE([Total Revenue], SAMEPERIODLASTYEAR(Dim_Date[Date]))
RETURN DIVIDE(CY - PY, PY, 0) * 100
Flipkart_Warehouse_Operations/
│
├── 📊 FlipkartWarehouse.pbix # Power BI report file
├── 📂 Data/
│ ├── Fact_Orders.csv
│ ├── Dim_Product.csv
│ ├── Dim_Date.csv
│ └── Dim_Location.csv
├── 📂 Screenshots/
│ └── Flipkart_Warehouses_Report.png # Dashboard preview (above)
└── README.md
| Tool | Purpose |
|---|---|
| Power BI Desktop | Dashboard design, data modeling, publishing |
| DAX | KPI measures, time intelligence, margin calculations |
| Power Query (M) | Data cleaning, type casting, table relationships |
| Excel / CSV | Source data preparation |
-
Clone the repo
git clone https://github.com/Suresh-Note/Flipkart_Warehouse_Operations-.git
-
Open in Power BI Desktop
- Download Power BI Desktop (free)
- Open
FlipkartWarehouse.pbix
-
Fix data source paths (if needed)
Home → Transform Data → Data Source Settings- Update paths to your local
Data/folder → click Refresh
-
Interact with the dashboard
- Use date, year, category, and location slicers to filter all visuals
- Hover over charts for exact values and tooltips
- Bangalore dominates revenue at ~41% of total — a potential single-point risk if operations there face disruption
- ₹2bn cancellation loss equals the entire profit pool — a 5% reduction in cancel rate would directly improve net margin
- Electronics has the highest profit margin despite not being the top revenue category, suggesting strong pricing power and lower return rates
- 2023 Q1 was the weakest quarter across all three years, likely driven by post-festive demand drop before a Q3 recovery
- 72% delivery success with 10% returns — healthy last-mile performance, but return logistics optimization could recover additional margin
Suresh Kanchamreddy
- 🔗 GitHub: @Suresh-Note
- 💼 LinkedIn: linkedin.com/in/suresh-kanchamreddy
- 🎓 B.Tech CSE | Data Analyst | Power BI · Python · SQL · ML
Portfolio project. Dataset is synthetic and does not represent actual Flipkart operational data.