Forecasting for low-volume products has always been a tricky endeavor. The unpredictable demand patterns and limited data make it difficult to generate reliable forecasts. However, by adopting a Hierarchical Forecasting approach, you can significantly improve forecast accuracy even for these challenging products. Here's how Hierarchical Forecasting can transform your low-volume product predictions and align them with higher-level forecasts for optimal results.

What Is Hierarchical Forecasting?

Hierarchical Forecasting is a structured method that organizes forecasts at multiple levels of aggregation. The hierarchy typically includes different layers, such as top-level (e.g., total product category), middle-level (e.g., regional or segment-level), and bottom-level (e.g., individual product level). Each of these levels can use different forecasting methods, allowing for a more nuanced approach that leverages the strengths of each technique.

This hierarchical structure offers a dual benefit: it provides stability to the forecast at higher levels while still accounting for the variability seen at lower levels. When done correctly, Hierarchical Forecasting ensures that all levels are aligned, resulting in a coherent and unified forecast that accounts for external factors and demand volatility.

Read more about hierarchical forecasting.

How Does It Work for Low-Volume Products?

Different Forecasting Methods for Different Levels

One of the primary advantages of Hierarchical Forecasting is the ability to apply different forecasting techniques at each level of the hierarchy. This approach allows you to make the most of available data and tailor your forecasting methods to the nature of each level:

• Top-Level Forecasts: Use robust models enriched with external factors such as economic indicators, market trends, or seasonality. These forecasts benefit from having more aggregated data, which often exhibits clearer and more stable patterns.
• Middle-Level Forecasts: Incorporate external variables and segment-specific insights to refine predictions further. These middle-level forecasts are crucial for bridging the gap between stable top-level data and the more volatile bottom-level details.
• Bottom-Level Forecasts: At this level, where demand is erratic and data is sparse, simpler univariate models like moving averages or exponential smoothing are more effective. By keeping models simple, you minimize the risk of overfitting and make the most of the limited information available.

By distributing forecasting complexity across different levels, Hierarchical Forecasting can optimize performance and increase accuracy for low-volume products, even when data is scarce.

This reconciliation benefits forecasts at every level of the hierarchy in terms of accuracy.

Improving Forecasts with External Factors

Top- and middle-level forecasts benefit greatly from the integration of external factors. External variables can include macroeconomic trends, competitor actions, and broader market signals, all of which influence aggregate demand. When these factors are effectively incorporated into your models, they provide a richer understanding of overall demand patterns, which, in turn, informs bottom-level forecasts.

The ripple effect is that the insights gained at higher levels help stabilize and improve the accuracy of forecasts for low-volume products. The top- and middle-level forecasts act as a stabilizing force, guiding the bottom-level models and mitigating their inherent volatility.

Smart Weighting Method for Enhanced Alignment

To ensure all forecasts in the hierarchy are aligned, Hierarchical Forecasting uses a smart weighting method. This approach assigns weights to each forecast based on its past accuracy, giving more importance to those models that have historically performed well. For example:

• If a top-level forecast has proven reliable over time, it receives a higher weight, influencing the bottom-level forecasts more significantly.
• Conversely, if a bottom-level forecast shows a pattern of inaccuracy, its influence on the overall forecast is minimized.

This weighted system ensures that the forecasts are balanced and coherent across all levels. It also dynamically adjusts to changing demand patterns, continuously refining the weighting scheme to optimize overall accuracy.

Seeing is believing

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