Demand Forecasting

6 Pitfalls to avoid in AI Demand Forecasting

September 7, 2020

Demand forecasting is the field of predictive analytics that incorporates the process of estimating the customers’ demands by analyzing historical data. All the indicators and demand forecasting models that we used previously simply help us to make better predictions about what will happen. In this article, we would like to discuss the 6 most common pitfalls we encounter in Demand Forecasting.

1. Not including promotion data in Demand Forecasting

For successful promotion, the model should be trained with the data connected with marketing and ad history. By having information about the previous successful promotions, the Demand Forecasting model can make more accurate predictions.

2. Estimating the demand for a new product 

It is possible to estimate the demand for a new product without the help of historical data. Many innovations have been done in this area, and techniques such as product DNA (comparing demand with similar product attributes) can help a company uncover similar products in its past or current portfolio. A similar product’s data can efficiently predict the demand for new products.

3.Underestimating the impact of weather on prediction

Weather can sometimes be more important than the price of the product itself and it can affect demands in numerous contexts. It is tough to predict the weather, but the good news is, you can still use it in your model to explain historical variations in demand.

4.Not incorporating the changes in updating the dataset 

To achieve short and long term goals, companies constantly make changes in their offerings, inventory and processes. When these changes are not annotated in the dataset, the model encounters sudden changes and shifts in demand. In reality, it is not a big issue but has a significant impact on overall performance.

5. Inconsistent information 

Models can only predict accurately if the dataset is consistent. If any information about the product undergoes any changes, it must be noted separately to remove confusion from the model. 

6. Overfitting the model 

The use of a machine learning model can lead to a vicious problem of overfitting. A model works fine with the training data set but sometimes it becomes inflexible and gives wrong predictions when new data is delivered in a model. This problem can be solved by our expert data scientists.

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