AI Driven Demand Forecasting and Inventory Optimization Workflow

AI-Driven Demand Forecasting and Inventory Optimization

1. Data Collection

1.1 Source Identification

Identify relevant data sources including sales history, market trends, seasonal variations, and external factors such as economic indicators.

1.2 Data Aggregation

Utilize tools like Google Cloud BigQuery or AWS Data Pipeline to aggregate data from multiple sources into a centralized database.

2. Data Preprocessing

2.1 Data Cleaning

Implement data cleaning techniques to remove duplicates, fill in missing values, and correct inconsistencies using tools like OpenRefine.

2.2 Data Transformation

Transform the data into a suitable format for analysis using Pandas or Apache Spark to facilitate efficient processing.

3. Demand Forecasting

3.1 Model Selection

Select appropriate AI models for demand forecasting such as ARIMA, Prophet, or Long Short-Term Memory (LSTM) networks.

3.2 Model Training

Train the selected models using historical data. Tools like TensorFlow or PyTorch can be employed for building and training neural networks.

3.3 Model Evaluation

Evaluate model performance using metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) to ensure accuracy.

4. Inventory Optimization

4.1 Inventory Analysis

Analyze current inventory levels and turnover rates using Tableau or Microsoft Power BI for visualization and insights.

4.2 Optimization Algorithms

Implement AI-driven optimization algorithms such as Genetic Algorithms or Linear Programming to determine optimal inventory levels.

4.3 Simulation and Scenario Planning

Use simulation tools like AnyLogic to model different inventory scenarios and assess the impact of various demand forecasts on inventory levels.

5. Implementation and Monitoring

5.1 Integration with Supply Chain Systems

Integrate the forecasting and optimization models with existing supply chain management systems, utilizing APIs and middleware solutions.

5.2 Continuous Monitoring

Establish a monitoring system to track forecast accuracy and inventory performance using dashboards created in Google Data Studio.

5.3 Feedback Loop

Create a feedback mechanism to continuously improve demand forecasting models based on actual sales data and inventory performance.

6. Reporting and Decision Making

6.1 Reporting Tools

Utilize reporting tools like Looker or QlikView to generate insights and reports for stakeholders.

6.2 Strategic Decision Making

Leverage insights gained from the AI-driven forecasts to make informed decisions regarding procurement, production, and distribution strategies.