AI Driven Demand Forecasting and Inventory Management Workflow

Demand Forecasting and Inventory Management

1. Data Collection

1.1 Sources of Data

• Historical Sales Data
• Market Trends
• Customer Feedback
• Supply Chain Information

1.2 Tools for Data Collection

• Google Analytics
• Salesforce
• Tableau

2. Data Processing and Analysis

2.1 Data Cleaning

Utilize AI algorithms to identify and rectify anomalies in the data.

2.2 Demand Forecasting Models

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

• ARIMA (AutoRegressive Integrated Moving Average)
• Exponential Smoothing
• Facebook Prophet

2.3 Tools for Analysis

• IBM Watson Studio
• Microsoft Azure Machine Learning
• Amazon Forecast

3. Inventory Management Optimization

3.1 Inventory Tracking

Utilize AI-driven inventory management systems to monitor stock levels in real-time.

3.2 Reorder Point Calculation

Employ AI algorithms to calculate optimal reorder points based on forecasted demand.

3.3 Tools for Inventory Management

• Fishbowl Inventory
• NetSuite ERP
• Zoho Inventory

4. Integration with Supply Chain

4.1 Supplier Collaboration

Use AI to facilitate communication and collaboration with suppliers for timely restocking.

4.2 Tools for Supply Chain Integration

• SAP Integrated Business Planning
• Oracle SCM Cloud

5. Continuous Improvement

5.1 Performance Monitoring

Implement AI analytics to continuously monitor inventory performance and demand accuracy.

5.2 Feedback Loop

Establish a feedback mechanism to refine forecasting models based on real-time data.

5.3 Tools for Continuous Improvement

• Google Cloud AI
• Qlik Sense