Deacom has released new forecasting methods to help manufacturers make stronger business predictions within a single ERP system. Deacom’s new forecasting methods allow users to predict future trends more accurately by taking into consideration market volatility by strategically weighting specific variables, measuring short-term demands, or applying multiple variables to a forecast.
With DEACOM ERP, all data and forecasting capabilities reside in its core system, enabling users to automatically generate forecasts with a single software login. Users can leverage a variety of different forecasting methods to create strong expectations for future demands. These methods include:
Straight Line Forecasting—Commonly used when a company’s growth rate is constant, straight line forecasting provides a straightforward view of continued growth at the same rate. This is the simplest method to implement as it uses basic math and historical data to provide predictions that guide financial and budget goals.
Moving Average Forecasting—When there is a need to follow trends and identify patterns, moving average forecasting is often used. It calculates an average performance around a specific metric within a shorter time frame, like days, months, and quarters rather than years. This method is suitable for industries where sales and revenue fluctuate so executives can identify the peaks, dips and valleys that occur from month to month.
Simple Linear Regression Forecasting—This method is used to chart a trend line based on a relationship between two variables. The analysis shows changes to x and y variables so a correlation can be made to create a graph line that indicates a trend moving up, down, or remaining constant. Often, simple linear regression forecasting is used to identify trend lines for sales and profits to see a company’s profit margin over a set time period.
Multiple Linear Regression Forecasting—A multiple linear regression forecast takes things a step further than a simple linear regression forecast by including two or more independent variables against a dependent one to create a prediction.