Leveraging Advanced Techniques for Demand Planning in Supply Chain Management

 

In the ever-evolving landscape of supply chain management, accurate demand planning is crucial for optimizing inventory and meeting customer needs. AI, ML, and statistical forecasting techniques are invaluable tools to tackle seasonality, trends, and cyclic behavior. These methods help select the best forecasting model for each SKU at every location, ensuring high accuracy. The process begins by cleansing historical data to remove errors and outliers, creating a reliable dataset for prediction.

 

Demand Planning vs. Supply Planning

 

Multi-Level Forecasting for Better Demand Planning

Multi-level forecasting offers a top-down or bottom-up approach to develop a consensus-driven demand plan. This technique allows stakeholders to collaborate and aggregate or disaggregate demand at any level—whether across regions, product categories, or time periods. By aligning data from all levels, a comprehensive view of demand emerges, fostering better decision-making in supply chain management.

 

Configurable Hierarchies and Workflows

Configurable hierarchies and workflows offer flexibility for demand planning. By establishing adaptable product and organizational structures, companies can create a robust framework for forecasting, supporting both bottom-up and top-down methods.

 

Primary Sales Forecast Generation

AI/ML-based forecasting tools generate accurate primary sales forecasts using secondary sales data and customer inventory. By aligning secondary sales and inventory data, supply chain management teams can produce reliable forecasts that enhance demand planning.

 

Measuring Forecast Accuracy

Monitoring forecast accuracy through methods like Volume Error, MAE, and Bias ensures continuous improvement in demand planning and supply chain management.

 

Ease of Use and Collaborative Tools

User-friendly interfaces with drag-and-drop functionality, Excel-like environments, and offline working modes make these tools accessible for seamless demand planning in supply chain management.

 

By leveraging AI, ML, and statistical forecasting techniques, businesses can optimize demand planning and enhance overall efficiency in supply chain management.



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