Can Retailers Trust Their Machine Learning Models? Posted on : Oct 21 - 2022

As we inch closer to Black Friday and the start of the holiday buying extravaganza, retailers are putting the final touches on the demand forecasts they’re using to predict the mix of goods they’ll carry this winter. There are lot of variables to juggle, including COVID, the economy, and the weather. It seems like a perfect use case for the increasingly sophisticated machine learning models that are in vogue in the industry. But can they trust their predictions?

Over the past decade, retailers and other companies in the consumer goods supply chain have started upgrading their demand forecasting systems in hopes of gaining ground in this super competitive industry.

Forward-looking retailers, in particular, are replacing the largely deterministic approaches that were favored in the past–which used simple linear regression models based on historical data with relatively static assumptions about the state of the world–with probabilistic approaches that bring more data into the equation and rely on more sophisticated machine learning algorithms, like neural nets and XGBoost, to generate more detailed forecast ranges.

The new probabilistic approaches hold the potential to provide more accurate forecasts for demand planning than the older deterministic approaches, according to supply chain consultant Stefan de Kok. View More