AI Driven Predictive Demand Forecasting Workflow for Businesses

Predictive Demand Forecasting System

1. Data Collection

1.1 Identify Data Sources

• Sales Data
• Market Trends
• Customer Behavior Analytics
• Supply Chain Metrics

1.2 Gather Historical Data

• Utilize ERP Systems (e.g., SAP, Oracle)
• Leverage CRM Tools (e.g., Salesforce)

2. Data Preprocessing

2.1 Data Cleaning

• Remove Outliers
• Handle Missing Values

2.2 Data Transformation

• Normalization and Scaling
• Feature Engineering

3. AI Model Development

3.1 Select AI Techniques

• Time Series Analysis
• Machine Learning Algorithms (e.g., ARIMA, LSTM)

3.2 Tool Selection

• TensorFlow for deep learning models
• Scikit-learn for machine learning algorithms
• Power BI for data visualization

4. Model Training

4.1 Split Dataset

• Training Set
• Validation Set
• Test Set

4.2 Train Models

• Utilize cloud computing platforms (e.g., AWS, Google Cloud)

5. Model Evaluation

5.1 Performance Metrics

• Mean Absolute Error (MAE)
• Root Mean Square Error (RMSE)

5.2 Model Refinement

• Hyperparameter Tuning
• Cross-Validation

6. Deployment

6.1 Integration with Existing Systems

• Connect with Supply Chain Management Software
• APIs for real-time data access

6.2 Monitor Model Performance

• Set up dashboards for real-time monitoring
• Regularly update the model with new data

7. Continuous Improvement

7.1 Feedback Loop

• Collect user feedback on forecast accuracy
• Adjust models based on operational changes

7.2 Ongoing Training

• Regularly retrain models with new data
• Incorporate advanced AI techniques as they emerge