AI Driven Demand Forecasting and Inventory Optimization Workflow

AI-Driven Demand Forecasting and Inventory Optimization

1. Data Collection

1.1 Historical Sales Data

Gather historical sales data from various channels such as online platforms, retail stores, and wholesale partners.

1.2 Market Trends Analysis

Utilize tools like Google Trends and social media analytics to identify current fashion trends and consumer preferences.

1.3 External Factors

Incorporate external data such as economic indicators, seasonal trends, and fashion events that may impact demand.

2. Data Preprocessing

2.1 Data Cleaning

Use AI tools like Talend or Trifacta to clean and preprocess the data, ensuring accuracy and completeness.

2.2 Feature Engineering

Identify key features that influence demand, such as price changes, promotions, and marketing campaigns.

3. Demand Forecasting

3.1 AI Model Selection

Select appropriate AI models such as time series forecasting algorithms (ARIMA, Prophet) or machine learning models (Random Forest, XGBoost).

3.2 Model Training

Train the selected models using historical sales data and external factors to predict future demand.

3.3 Model Evaluation

Evaluate model performance using metrics like Mean Absolute Percentage Error (MAPE) and adjust parameters as necessary.

4. Inventory Optimization

4.1 Stock Level Analysis

Analyze current inventory levels using tools like NetSuite or SAP Integrated Business Planning to determine optimal stock levels.

4.2 Reorder Point Calculation

Implement AI algorithms to calculate reorder points based on predicted demand and lead times.

4.3 Automated Reordering

Utilize AI-driven inventory management systems such as Stitch Labs or TradeGecko to automate the reordering process based on forecasts.

5. Continuous Improvement

5.1 Performance Monitoring

Regularly monitor forecast accuracy and inventory turnover rates to assess the effectiveness of the AI-driven approach.

5.2 Feedback Loop

Establish a feedback loop where insights from sales performance are fed back into the forecasting model to enhance future predictions.

5.3 Technology Upgrades

Stay updated with advancements in AI technology and continuously integrate new tools and methodologies to improve forecasting and inventory management.