Demand Forecast

To plan for future inventory, we would need to know what is our sell-through data (what's sold to end customers) and then roll that forecast up to determine the sel in-figures (what retailers or suppliers should order)

When it comes to managing inventory and forecasting demand, two terms often come up: sell in and sell through. While they may sound similar, they play very different roles in your supply chain

MAE (Mean Absolute Error) measures the average difference between your predictions and actual values, ignoring the direction of the error.

MAPE (Mean Absolute Percentage Error) is a simple metric that tells you how far off your forecasts are, on average, in percentage terms. A lower MAPE means your forecasts are more accurate, while a higher MAPE suggests there’s room for improvement.

Forecasting is the backbone of supply chain management. By measuring how accurate your predictions are, you can make better decisions, manage inventory more efficiently, and ultimately boost your bottom line.

Time series analysis might sound complex, but at its core, it’s about understanding patterns in your past data to better predict future demand

Demand forecasting involves predicting future sales to help businesses plan more effectively. One common method is regression analysis, a statistical tool that helps identify the relationship between various factors and demand.

Exponential smoothing is a forecasting method that blends recent actual sales with previous forecasts using a smoothing constant (alpha).

Demand forecasting is simply predicting how much of a product customers will want in the future. By doing this, businesses can make better decisions about how much inventory to hold, how to plan production, and how to manage costs. Let’s break down the basic ideas and common methods in