A comprehensive end-to-end Inventory Data Analysis project focused on identifying inefficiencies, reducing inventory costs, and improving operational decision-making using data-driven inventory management techniques.
This project simulates a real-world manufacturing inventory case study, covering demand forecasting, ABC analysis, EOQ modeling, inventory turnover, lead time analysis, carrying cost evaluation, and process improvement recommendations.
Inventory management plays a critical role in balancing product availability and cost efficiency.
This project analyzes historical purchase, sales, inventory, and procurement data to uncover:
- Overstocking and slow-moving inventory
- Stockout risks due to poor demand planning
- High inventory carrying costs
- Supplier lead-time inefficiencies
The analysis delivers actionable insights and a sustainable inventory strategy for business decision-makers.
- Forecast future product demand using historical sales data
- Classify products using ABC (Pareto) Analysis
- Evaluate inventory efficiency using Inventory Turnover
- Optimize ordering using EOQ & Reorder Point
- Analyze supplier lead time and procurement delays
- Calculate inventory carrying costs
- Identify process bottlenecks and improvement areas
- Propose a data-driven inventory management strategy
├── README.md
├── requirements.txt
├── Inventory Data Analysis Report.pdf
│
├── visuals/
│ ├── 01_overall_daily_demand.png
│ ├── 02_Monthly_Demand_Trend.png
│ ├── 03_Top_10_High_Demand_Products.png
│ ├── 04_Demand_Forecast_using_Rolling_Average.png
│ ├── 05_Demand_Forecast_using_30-Day_Rolling_Average.png
│ ├── 06_ABC_Analysis_Pareto_Chart.png
│ ├── 07_Top_20%_High-Value_Products.png
│ ├── 08_Sales_Value_Contribution_by_ABC_Category.png
│ ├── 09_Top_10_Fast-Moving_Products_by_Inventory_Turnover.png
│ ├── 10_Bottom_10_Slow-Moving_Products_by_Inventory_Turnover.png
│ ├── 11_Top_10_Products_by_EOQ.png
│ ├── 12_Top_10_Products_by_Reorder_Point.png
│ ├── 13_Top_10_Vendors_by_Average_Lead_Time.png
│ ├── 14_Top_10_Vendors_by_Payment_Delay.png
│ ├── 15_Top_10_Items_by_Annual_Carrying_Cost.png
│ └── 16_Top_Vendors_with_Long_Procurement_Lead_Times.png
│
├── notebooks/
│ ├── Data Cleaning of Inventory Data.ipynb
│ └── Inventory Data Analysis.ipynb
│
├── Raw_data/
│ ├── 2017PurchasePricesDec.csv
│ ├── BegInvFINAL12312016.csv
│ ├── EndInvFINAL12312016.csv
│ ├── InvoicePurchases12312016.csv
│ ├── PurchasesFINAL12312016.csv
│ └── SalesFINAL12312016.csv
│
└── Clean_data/
├── Beg_Inv.csv
├── End_Inv.csv
├── Final_Purchase.csv
├── Final_Sales.csv
├── Invoice.csv
└── Purchase_Price.csv
🔗 Download All Datasets (Google Drive):
👉 [https://drive.google.com/drive/folders/1PnDEPSJaZa8Pj9G5yGrA9SqJfRqSWp0w?usp=sharing]
Inventory_Data_Analysis_Datasets/
├── Raw_data/
└── Clean_data/
- Download the dataset folder from Google Drive
- Place it in the project root directory
- Ensure folder names match exactly:
Raw_data/Clean_data/
- Run notebooks in this order:
Data Cleaning of Inventory Data.ipynbInventory Data Analysis.ipynb
- Python
- Pandas, NumPy
- Matplotlib, Seaborn
- Jupyter Notebook
- CSV Data Processing
- Data Visualization
Analyzed daily and monthly sales trends to identify seasonality and demand patterns.
Overall Daily Demand
Monthly Demand Trend
Rolling Average Forecast
📌 Outcome: Enables forecast-based purchasing and stock planning.
Classified products into A, B, and C categories based on revenue contribution.
ABC Pareto Chart
Sales Contribution by Category
📌 Outcome: Helps prioritize high-value inventory and reduce excess stock.
Measured how efficiently inventory is sold and replenished.
Fast-Moving Products
Slow-Moving Products
📌 Outcome: Identifies obsolete stock and improves working capital efficiency.
Optimized order quantities and replenishment timing.
Economic Order Quantity
Reorder Point
📌 Outcome: Minimizes ordering and carrying costs while preventing stockouts.
Evaluated supplier performance and procurement delays.
Vendor Lead Time
Payment Delay Analysis
📌 Outcome: Supports supplier optimization and negotiation strategies.
Calculated annual holding costs to identify cost-intensive inventory.
Top Carrying Cost Items
📌 Outcome: Encourages lean inventory practices.
Identified procurement bottlenecks and inefficiencies.
Procurement Bottlenecks
📌 Outcome: Improves procurement cycle time and operational efficiency.
- ABC-based inventory control
- Demand-driven replenishment
- EOQ & Reorder Point automation
- Supplier lead-time optimization
- Regular inventory turnover review
- Reduce excess stock of low-value items
- Focus capital on high-value, fast-moving products
- Implement automated reorder alerts
- Optimize vendor selection based on lead time
- Conduct periodic inventory performance reviews
This project demonstrates how data-driven inventory analytics can significantly improve:
- Cost efficiency
- Inventory availability
- Working capital utilization
- Supplier performance
The proposed inventory strategy enables scalable, sustainable, and optimized inventory management.
📘 Detailed Analysis & Insights:
➡️ Inventory Data Analysis Report.pdf
👤 Harsh Belekar
📍 Data Analyst | Python Developer | SQL | Power BI | Excel | Data Visualization
📬 LinkedIn | 🔗GitHub
⭐ If you found this project helpful, feel free to star the repo and connect with me for collaboration!