peer response to Forecasting can be divided into four types: qualitative, time series analysis, causal relationships, and simulation. (Jacobs & Chase, 2020). Time series is based on the idea that related data is past the demand that can be predicted for future demand. Some items that could be past data are the following: several components, such as trend, seasonal, or cyclical influences. Causal forecasting deals with linear regression, and the demand is assumed to be related to underlying environmental factors. This involves using independent variables rather than the time-predicted future demand. When it comes to simulation models, they help with forecasting and run through a range of different examples for forecasting conditions. Working in finance and accounting for a few years, I would say that time series method and pulling historical data to predict future costs. But sometimes, businesses face unexpected things that have also caused "challenges" to our budget, which is a blanket dollar amount typically spread across the enterprise that we must hit. An example I have seen is the predicted budget would have been $100M, and the challenge given to us was ($10M); our new budget target would be $90M, which can result in layoff and postponement