Inventory Optimization
Leveraging predictive analytics to streamline inventory management in the fashion industry.
Back to ProjectsProject Overview
Methodology
1. Data Collection
Gathered historical sales data, inventory levels, and seasonal trends from multiple sources, including POS systems and e-commerce platforms.
2. Data Preprocessing
Cleaned and preprocessed the data, including handling missing values, normalizing data, and extracting relevant features.
3. Model Training
Trained machine learning models, including time series forecasting and regression models, to predict demand and optimize inventory levels.
Key Insights
Demand Forecasting
Predicted future demand for specific products using advanced time series analysis, achieving an accuracy of 90%.

Inventory Optimization
Optimized inventory levels to reduce overstocking by 30% and understocking by 25%, ensuring a balanced supply chain.

Results
Reduced Waste
Reduced inventory waste by 30% through accurate demand forecasting and optimized stock levels.

Increased Profitability
Increased profitability by 25% by minimizing stockouts and overstocking, leading to better cash flow.

Improved Efficiency
Streamlined inventory management processes, reducing manual effort and improving operational efficiency.

Visual Insights
Demand Forecasting

A line chart showing predicted demand vs. actual demand over time.
Inventory Optimization

A bar chart comparing optimized inventory levels before and after the project.