This project aims to develop a BI platform that analyzes inventory aging data to identify slow-moving and dead stock. The system will help organizations reduce holding costs, improve inventory turnover, and make informed procurement decisions using data-driven insights.
Collect inventory datasets including purchase dates, stock levels, sales frequency, and expiry details.
Clean and preprocess data to remove inconsistencies and missing values.
Design a data warehouse model suitable for inventory lifecycle analysis.
Implement ETL processes to consolidate inventory and sales data.
Develop dashboards displaying stock aging categories, turnover ratios, and dead stock percentages.
Analyze product-wise and category-wise inventory performance.
Identify patterns contributing to excess inventory accumulation.
Implement trend analysis to forecast potential dead stock risks.
Create interactive filters for warehouse, product type, and time period.
Validate data accuracy and dashboard responsiveness.
Document insights and recommendations for inventory optimization strategies.
