Over coffee about the supply chain

Feature Importance in Demand Forecasting

In the data age, where predicting future trends and behavior is critical to business success, machine learning techniques are playing an increasingly important role. One of the most important elements in the process of building predictive models, including demand forecasting, is understanding which features (traits) have the greatest impact on the model’s results. This is where the concept of feature importance comes into play.

What is Feature Importance?

Feature importance is a measure of how significantly each feature used in the analysis affects the model’s predictions. In other words, it indicates which variables are most important to the accuracy of the prediction. Understanding which features are most important helps not only in interpreting the model, but also in optimizing it and making better business decisions.

How does Feature Importance work?

Different machine learning algorithms can measure feature validity differently. Let’s look at some popular methods:

1. Decision Trees and Random Forests (Random Forests): For decision trees and random forests, feature importance is often measured by assessing how much a feature reduces uncertainty (entropy) in the tree. Features that lead to greater reductions in uncertainty are considered more important.
2. Shapley values method (SHAP): SHAP is a method derived from game theory that assigns a value to each attribute based on its impact on the model’s outcome. It is a more advanced and accurate technique that can be applied to different types of models.
3. Gradient boosting: In algorithms such as XGBoost, feature importance can be measured by counting the number of times a feature is used to split data in decision trees, or by assessing how much each feature contributes to reducing model error.

Why is Feature Importance Important in Demand Forecasting?

1. Better model interpretation: Understanding which traits are most important allows analysts and managers to better interpret model results. For example, if price-related characteristics are key, the focus can be on price analysis and its impact on demand.
2. Model optimization: Knowing the importance of features can optimize the model by eliminating less important features, which can lead to simplifying the model and increasing its performance.
3. Business Decision Making: Feature importance provides valuable information that can be used to make better decisions. For example, if weather is an important factor affecting demand, marketing strategies can be adjusted depending on weather forecasts.

Application Example Feature Importance

Consider an ice cream company that wants to forecast demand for its products. Inputs to the model may include characteristics such as temperature, holidays, competitor prices, advertising spending, etc. After applying random forests, the company discovers that temperature and holidays have the greatest impact on demand. This allows the company to focus its marketing and logistics efforts on these key factors to better anticipate and meet customer needs.

Challenges and Limitations

While feature importance is a powerful tool, there are some challenges and limitations:

• Complexity of models: In very complex models, it can be difficult to determine exactly which features are most important.
• Colinearity: high colinearity between features can lead to misinterpretations of validity.
• Interpretation: feature importance results may vary depending on the method used, which may lead to inconclusive conclusions.

Summary

Feature importance is a key tool in the process of analyzing data and building demand forecasting models using machine learning. It allows for better understanding, interpretation and optimization of models, resulting in more accurate forecasts and better business decisions. Despite some challenges, the use of feature importance can significantly improve the efficiency and effectiveness of demand forecasting, which is invaluable in a dynamic and competitive market environment.

DATURE ENTERPRISE software uses artificial intelligence and machine learning in the process of demand forecasting and inventory optimization. The Dature system uses the Feature Importance measure to determine the impact of independent variables on the dependent variable, which is demand.

The system provides methods for forecasting seasonal demand and demand influenced by calendar days. Inventory management methods allow for both pre-season inventory building approaches, dynamic safety stock control and JIT.

TheDATURE application ENTERPRISE can also use expertise in the demand forecasting process. Authorized users can enter expert forecasts and adjust statistical forecasts with them. The process is fully auditable in terms of who changed the forecast when and how. This makes it possible to track the accuracy of both statistical and expert forecasts. As a result, the organization learns how to forecast more accurately and improve process efficiency.