Forecasting the demand for products with low volume can be a challenging task, (but not impossible) particularly when it comes to detecting market trend shifts and knowing how to manage inventory. Yet, it remains essential for companies to invest time and effort to get this done. 

Why?

Maximizing resource allocation and efficiency

Do you want to avoid overstocking or understocking these products, and prevent unnecessary inventory costs? Forecasting demand for your low-volume products accurately ensures you can optimize your resource allocation and operational efficiency. 

How is this done?

Typically, traditional univariate models such as exponential smoothing, are commonly used to forecast products with low volumes. They focus on a single variable to predict future outcomes and have their merits in forecasting. However, it's important to recognize that they have their limitations.

What are these limitations? We list the top 3.
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1. Limited context and information

Univariate models consider only one variable, disregarding the potential influence and interactions of other relevant factors. By overlooking the broader context, these models may miss crucial information that could significantly impact the accuracy of the forecast. Neglecting external variables and their relationships can lead to incomplete or misleading predictions, potentially hindering effective decision-making.

2. Inadequate detection of market trend shifts:

Univariate models may struggle to detect and adapt to market trend shifts, particularly in low-volume product scenarios. These models often rely on historical data, assuming that future patterns will mimic the past. However, market dynamics can change rapidly, and univariate models may fail to capture sudden shifts, emerging trends, or disruptive events, resulting in inaccurate forecasts.

3. Overlooking Multidimensional Insights 

Univariate models focus solely on one aspect of the forecasted variable, potentially overlooking multidimensional insights that could be valuable for decision-making. By neglecting the influence of related variables or contextual information, these models may provide limited perspectives on the underlying dynamics driving the forecast. Incorporating additional dimensions through multivariate models allows for a more comprehensive understanding and more robust predictions.

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Taking a hybrid approach to forecasting

The key issue in forecasting is coming to terms with the fact that a single methodology cannot effectively address all forecasting challenges. 

If you were taking a hybrid approach instead, you’d be able to:

1. Use both univariate and multivariate models, which comes in a wide range, to optimize accuracy

By utilizing these models, businesses can gain valuable insights into the demand patterns of products with low volume. However, when it comes to identifying market shifts, the integration of additional data sources becomes crucial. This is where multivariate models shine, as they enable the analysis of leading indicators and provide a more comprehensive understanding of the market dynamics.

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2. Benefit from leveraging hierarchical forecasting 

It’s a powerful technique that allows businesses to combine low-volume product data with higher-volume category or region-level data. By incorporating this hierarchical structure, the forecasting process becomes more robust and accurate. The identification of market shifts at the category or region level can significantly impact the demand for low-volume products. Through hierarchical forecasting, businesses can capture these shifts and incorporate them into their predictions, leading to improved forecast accuracy. Here’s how hierarchical forecasting works in Indicio.

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How does hierarchical forecasting help with aligning the results of these forecasts?

By using optimal forecast reconciliation, forecasting every possible level in the hierarchy is possible.

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

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How is this done?

Let's begin by taking the individual forecasts and specifying the respective hierarchy and the residuals of the involved models. These forecasts are then calibrated using a method that weights them based on their historical accuracy; a step called reconciliation. The residuals are used as a measure of certainty, giving higher weight to certain forecasts and lesser weight to uncertain ones.

This means that an accurate worldwide forecast will cause less precise regional or product-level forecasts to be adjusted more than the other way around. This reconciliation benefits forecasts at every level of the hierarchy in terms of accuracy.

Even for products with low-volume.

-> Schedule a demo and we'll show you how it can work for your data.

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