Demand Elasticity Modeling • Revenue Simulation • Inventory-Aware Pricing
Retail and e-commerce companies often optimize prices using theoretical demand models, without considering inventory constraints.
This leads to:
• Stockouts during promotions • Lost revenue opportunities • Inefficient discount strategies • Misalignment between pricing and operations
This project builds an end-to-end pricing analytics framework that evaluates whether pricing strategies are operationally feasible, not just theoretically profitable.
The analysis integrates price elasticity modeling, scenario simulation, and inventory constraints to identify pricing strategies that maximize realized revenue.
Can a pricing strategy that maximizes demand actually be fulfilled with available inventory?
The project evaluates how pricing decisions interact with:
• demand elasticity • revenue potential • inventory availability • stockout risk
Interactive dashboard highlights:
• Pricing scenario KPIs • Theoretical vs feasible revenue • Category-level pricing recommendations • Elasticity vs inventory risk analysis
Live App https://pricing-inventory-optimization-2appeg9cg3dxgr9u6utkhjx.streamlit.app/
• Cleaned raw pricing and demand data • Handled missing values and inconsistencies • Created analysis-ready datasets
Analyzed how demand responds to price changes.
Identified:
• Elastic product categories • Inelastic product categories • promotion-sensitive segments
A regression model was trained to estimate demand at different price points.
This allowed simulation of revenue curves across pricing ranges.
Simulated multiple pricing strategies:
• Baseline price • −10% discount • +5% price increase • +10% price increase
Each scenario evaluates:
• predicted demand • projected revenue • demand sensitivity
Inventory constraints were applied to determine feasible sales volume.
Key metrics evaluated:
• fulfilled demand • stockout risk • realized revenue
This step highlights the difference between theoretical revenue and achievable revenue.
Categories were segmented based on:
• price elasticity • stockout probability
This enables identification of:
• safe discount ranges • high-risk promotional strategies
• Deep discounts increase demand but significantly raise stockout risk • Inventory constraints reduce achievable revenue versus theoretical forecasts • Moderate pricing strategies deliver the most reliable revenue outcomes • Pricing decisions must align with supply planning
Avoid aggressive discounts without inventory readiness.
Moderate pricing adjustments produce more stable revenue outcomes.
Promotional campaigns should be aligned with:
• inventory availability • replenishment cycles • demand forecasts
The most profitable pricing strategy is not always the one that maximizes demand — it is the one that maximizes fulfilled revenue.
Programming
• Python • Pandas • NumPy
Modeling
• Linear Regression (Demand Modeling) • Price Elasticity Analysis
Visualization
• Matplotlib
Dashboard
• Streamlit
Version Control
• Git & GitHub
pricing-inventory-optimization
│
├── 01_app
│ ├── app_pricing_and_inventory_optimization.ipynb
│ └── app_pricing_and_inventory_optimization.py
│
├── 02_data
│ ├── category_decision_table.csv
│ ├── ecommerce_pricing_cleaned.csv
│ ├── ecommerce_pricing_featured.csv
│ ├── ecommerce_pricing_featured_02.csv
│ ├── ecommerce_pricing_raw.csv
│ ├── elasticity_risk_table.csv
│ └── scenario_summary.csv
│
├── 03_notebooks
│ ├── 01_Category_Strategy.ipynb.ipynb
│ ├── 02_Demand_Modeling.ipynb.ipynb
│ ├── 03_Elasticity_Analysis.ipynb.ipynb
│ ├── 04_Elasticity_Risk_Segmentation.ipynb.ipynb
│ ├── 05_Inventory_Feasibility.ipynb.ipynb
│ ├── 06_Pricing_Scenarios.ipynb.ipynb
│ └── 07_Final Summary Notebook.ipynb.ipynb
│
├── 04_insights.md
├── 05_Dashboard Price And Inventory.pbix
├── 06_Pricing and Optimization.pptx
├── README.md
└── requirements.txt
• Inventory levels are simulated • Demand model uses linear regression for interpretability • Results illustrate pricing strategy trade-offs
Production deployment would require:
• real inventory feeds • advanced elasticity models • continuous monitoring
• Non-linear demand modeling • Dynamic pricing optimization • Supply-chain lead time integration • Explainable AI for price decisions • Automated price recommendation engine
Amneet Kaur Data Analyst | Pricing Analytics | Inventory Optimization Canada