What are the challenges of machine learning in demand forecasting?

What are the main challenges of using machine learning for demand forecasting?

Some main challenges include data quality, model complexity, and the need for continuous updates.

How does data quality affect the accuracy of forecasts?

Poor data quality can lead to inaccurate models, resulting in unreliable forecasts and poor decision-making.

What types of data are most important for effective demand forecasting?

Key data types include historical sales data, market trends, seasonality, and external factors like economic indicators.

Can you explain the role of model complexity in forecasting?

Model complexity can improve accuracy but may also lead to overfitting, where the model performs well on training data but poorly on new data.

What strategies can be used to overcome these challenges?

Strategies include improving data collection processes, using ensemble methods, and regularly updating models with new data.