AI Driven Machine Learning Workflow for Demand Forecasting

Machine Learning-Based Demand Forecasting Process

1. Data Collection

1.1 Identify Data Sources

Utilize various data sources such as:

• Historical sales data
• Market trends
• Customer feedback
• Seasonal factors

1.2 Data Integration

Employ AI collaboration tools like:

• Tableau: For data visualization and integration.
• Apache Kafka: For real-time data streaming.

2. Data Preprocessing

2.1 Data Cleaning

Use AI algorithms to identify and rectify errors in the dataset.

2.2 Feature Engineering

Extract relevant features that can influence demand, such as:

• Promotional activities
• Economic indicators

3. Model Selection

3.1 Choose Appropriate Algorithms

Implement machine learning models such as:

• Time Series Analysis: ARIMA, Seasonal Decomposition.
• Regression Models: Linear Regression, Random Forest.

3.2 Tool Utilization

Utilize platforms like:

• Google Cloud AI: For scalable model training.
• Amazon SageMaker: For building, training, and deploying machine learning models.

4. Model Training and Validation

4.1 Train the Model

Use historical data to train the selected models.

4.2 Validate Model Performance

Assess model accuracy using metrics such as:

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

5. Demand Forecasting

5.1 Generate Forecasts

Utilize the trained model to predict future demand.

5.2 Visualization of Results

Leverage visualization tools such as:

• Power BI: For presenting forecast data.
• Qlik Sense: For interactive data visualization.

6. Implementation and Monitoring

6.1 Integrate with Supply Chain

Collaborate with supply chain management systems to align production with forecasts.

6.2 Continuous Monitoring

Utilize AI tools for ongoing performance monitoring and adjustment of the forecasting model:

• IBM Watson: For predictive analytics.
• Microsoft Azure Machine Learning: For model retraining and updates.

7. Feedback Loop

7.1 Collect Feedback

Gather feedback from stakeholders regarding forecast accuracy.

7.2 Model Refinement

Refine the model based on feedback and new data inputs.