Predict Tomorrow’s Demand Using Yesterday’s Data

While AI models have taken the spotlight, traditional statistical models remain highly valuable tools for demand forecasting

Hello Medium readers!

Today, we’ll dive into forecasting techniques applied to demand planning, a field that I’m highly invested in due to my supply chain background and passion for data science. Recently, I’ve been reading up on this topic, revisiting books and articles on demand forecasting to provide you with some fresh insights.

To kick things off, let me share a thought-provoking quote by British statistician George E. P. Box:

“All models are wrong, but some are useful.”

As you reflect on this quote, you might wonder: why even bother forecasting the future if no model can ever be entirely accurate? Think of it like weather forecasting: it helps us plan ahead. Should I bring an umbrella tomorrow? Should I put on sunscreen? Should I take shelter from a hurricane? Forecasts, while imperfect, guide us in making better decisions.

In demand planning, it’s no different. Demand planners and other company stakeholders use forecasts to anticipate future needs and adjust…