SAP Sales PnP Dashboard

A full SAP Analytics Cloud (SAC) project featuring store sales analytics with performance KPIs and planning simulation for future periods.

This project demonstrates a Performance and Planning (PnP) dashboard using SAP Analytics Cloud (SAC) with real-world retail sales data. We walk through the entire workflow — from raw CSVs to a fully interactive dashboard — highlighting forecasting, planning, and KPI tracking.

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📁 Project Folder Structure

sap-sales-pnp-dashboard/
├── data/                   # Cleaned and raw data CSVs
│   ├── train.csv
│   ├── stores.csv
│   ├── transactions.csv
│   └── sales_clean.csv
│   ├── target_sales_city.csv
├── figures/                # Charts and SAC screenshots
│   ├── performance-total_sales.png
│   ├── performance-sales_by_store.png
│   ├── performance-top_products.png
│   ├── performance-monthy_sales.png
│   ├── planning-actual_target_variance.png
├── notebooks/              # Data prep and forecasting notebooks
│   ├── 01_clean_sales_data.ipynb
│   └── 02_generate_target_sales_by_city.ipynb
├── docs/                   # Exported SAC dashboard as PDF
│   └── sap_sales_pnp_dashboard_preview.pdf
├── README.md               # Project overview and setup instructions

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✅ Project Summary

Goal: Build an interactive dashboard that shows store sales performance and compares it with planning targets using SAP Analytics Cloud.

Key Features:

• Data preparation using Python (pandas, Jupyter)
• Forecasted targets by city
• SAC dashboard with KPI cards, blended charts, planning table

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🔄 Workflow Summary

0. Environment Setup

• Managed using pipenv
• Required packages: pandas, matplotlib, seaborn, jupyter

1. Data Cleaning

• Source: Kaggle Store Sales Forecasting dataset
• Files used:
  • train.csv – Daily sales per store/product
  • stores.csv – Store metadata
  • transactions.csv – Daily store footfall
• Merged and cleaned to create sales_clean.csv

2. Forecast Target Generation

• Aggregated average 2017 sales by city
• Simulated targets with avg_sales
• Output saved as target_sales_city.csv

3. SAP Analytics Cloud Setup

• Activated free SAC trial (14 days)
• Uploaded sales_clean.csv → sales_model
• Uploaded target_sales_city.csv → target_model
• Created blended dataset on city
• Designed the dashboard with:
  • Performance KPIs:
    • Total Sales, Sales Distribution by Stores, Top Products, Monthly Sales
  • Planning KPI:
    • Actual sales vs target (+/- variance)

4. Final Dashboard Export

• Dashboard exported as PDF (see docs/)
• Screenshots saved in figures/

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📊 Tools Used

• Python (Jupyter, pandas)
• SAP Analytics Cloud (modeling, story building)
• GitHub (portfolio packaging)

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📝 Author Notes

This project is part of my analytics portfolio to demonstrate:

• Forecasting and planning skills
• Enterprise BI tool experience (SAC)
• Dashboard development for stakeholders

Built with 💡 and coffee.

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🔗 Medium Blog Post

A full narrative blog post will accompany this repo. Stay tuned!