AI Driven Inventory Management and Demand Forecasting Workflow

Inventory Management and Demand Forecasting

1. Data Collection

1.1 Sales Data

Collect historical sales data from various channels including online and physical stores.

1.2 Inventory Levels

Monitor current inventory levels across all locations.

1.3 Market Trends

Analyze market trends and consumer behavior using AI tools.

2. Data Analysis

2.1 AI-Driven Analytics Tools

Utilize AI-driven analytics platforms such as Tableau or Google Analytics to process collected data.

2.2 Demand Forecasting Models

Implement machine learning models to predict future demand based on historical data and market trends.

3. Inventory Optimization

3.1 Automated Replenishment Systems

Use AI tools like Relex Solutions or Blue Yonder to automate inventory replenishment based on forecasted demand.

3.2 Stock Level Adjustments

Adjust stock levels dynamically based on AI recommendations to avoid overstocking or stockouts.

4. Performance Monitoring

4.1 Key Performance Indicators (KPIs)

Establish KPIs such as inventory turnover rates and forecast accuracy to measure performance.

4.2 Continuous Learning

Implement feedback loops using AI to refine forecasting models and inventory strategies over time.

5. Reporting and Decision Making

5.1 AI-Powered Reporting Tools

Employ AI-powered reporting tools like Microsoft Power BI to generate insights and visualizations for stakeholders.

5.2 Strategic Planning

Utilize insights from reports to inform strategic decisions regarding product launches, promotions, and supply chain management.