Intermittent demand is often marked by many periods with zero demand and occasional periods with nonzero demand, making it challenging for demand planners. In this article I examine the performance of four machine learning models—LSTM/RNN, SARIMA, XGBoost, and Croston—for estimating intermittent demand. To evaluate these models, I conducted 40 rounds of time series simulations using two patterns: 20 series based on a lognormal distribution (featuring numerous zeros and low-de

You've probably heard of AI agents, right? If this term sounds strange to you, you may have already interacted with these systems in their simplest form: chatbots. However, the concept of agents goes beyond simple chatbots. They are powerful artificial intelligence tools that tend to impact the future of how we currently work in companies. Trying to simplify the concept of AI agents in a few words: They are artificial intelligence architectures “empowered” with tools. These p

Consider the following situation: Imagine a company planning to launch a new version of its product. It must choose between two flavors: spicy or smooth. To make an informed decision, the company randomly surveys 200 people for their preferences. The overall result is shown in the table below: The result shows that 80% of users liked the spicy flavor and 75% liked the smooth flavor, leading us to believe that launching the spicy product would be the best decision. However, wh