AI Driven Predictive Analytics for Demand Forecasting Workflow

Predictive Analytics for Demand Forecasting

1. Data Collection

1.1 Identify Data Sources

Gather historical sales data, customer behavior data, market trends, and external factors such as seasonality and economic indicators.

1.2 Tools for Data Collection

• Google Analytics – for website traffic and user behavior insights.
• Salesforce – for customer relationship management data.
• Tableau – for visualizing and analyzing sales data.

2. Data Preparation

2.1 Data Cleaning

Ensure data accuracy by removing duplicates, correcting errors, and filling in missing values.

2.2 Data Transformation

Convert data into a suitable format for analysis, including normalization and categorization.

3. Model Development

3.1 Select Predictive Analytics Models

Choose appropriate algorithms for demand forecasting, such as time series analysis, regression models, or machine learning techniques.

3.2 AI Tools for Model Development

• TensorFlow – for building machine learning models.
• Azure Machine Learning – for developing, training, and deploying predictive models.
• IBM Watson Studio – for collaborative model development and deployment.

4. Model Training and Testing

4.1 Split Data into Training and Testing Sets

Divide the dataset to train the model on one portion and validate its accuracy on another.

4.2 Evaluate Model Performance

Utilize metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) to assess model effectiveness.

5. Forecasting Demand

5.1 Generate Demand Forecasts

Use the trained model to predict future demand based on current and historical data.

5.2 Visualization of Forecasts

Implement visualization tools to present demand forecasts clearly to stakeholders.

6. Implementation of Insights

6.1 Integrate Forecasts into Business Strategy

Align inventory management, marketing strategies, and supply chain operations based on forecasted demand.

6.2 Tools for Implementation

• SAP Integrated Business Planning – for aligning demand forecasts with supply chain planning.
• Oracle Demand Management Cloud – for managing forecasts and inventory levels.

7. Continuous Improvement

7.1 Monitor Model Performance

Regularly review model accuracy and make adjustments based on new data and changing market conditions.

7.2 Update Models Periodically

Retrain models with the latest data to ensure ongoing accuracy in demand forecasting.