AI Driven Demand Forecasting and Inventory Optimization Workflow

Demand Forecasting and Inventory Optimization

1. Data Collection

1.1 Gather Historical Sales Data

Collect sales data from various sources, including ERP systems, CRM platforms, and e-commerce websites.

1.2 Integrate External Data Sources

Incorporate external factors such as market trends, seasonal changes, and economic indicators using APIs from platforms like Weather API and Market Research Tools.

2. Data Preprocessing

2.1 Data Cleaning

Utilize AI-driven tools like DataRobot to clean and preprocess the data, removing outliers and handling missing values.

2.2 Data Transformation

Transform data into a suitable format for analysis using tools such as Apache Spark for large datasets.

3. Demand Forecasting

3.1 AI Model Selection

Select appropriate AI models for demand forecasting, such as ARIMA, Prophet, or Machine Learning Algorithms available in TensorFlow or PyTorch.

3.2 Model Training

Train the selected models using historical sales data to predict future demand. Tools like H2O.ai can facilitate automated machine learning processes.

3.3 Model Evaluation

Evaluate model performance using metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) with the help of Scikit-learn.

4. Inventory Optimization

4.1 Inventory Analysis

Analyze current inventory levels and turnover rates using AI tools like IBM Watson Supply Chain to identify slow-moving and fast-moving products.

4.2 Optimization Algorithms

Implement optimization algorithms (e.g., Genetic Algorithms or Linear Programming) to determine optimal stock levels and reorder points.

4.3 Automated Reordering

Utilize AI-driven inventory management systems such as NetSuite or TradeGecko to automate reordering processes based on forecasted demand.

5. Continuous Improvement

5.1 Monitor Performance

Continuously monitor inventory performance and demand forecasts using dashboards created with Tableau or Power BI.

5.2 Feedback Loop

Establish a feedback loop to refine forecasting models and inventory strategies based on actual sales data and market changes.

5.3 Update AI Models

Regularly update AI models with new data to improve accuracy and adapt to changing market conditions.