Are you ready to learn more about the Fishbowl software? Fishbowl now has a Plugin module that lets users manage all of the plugins they currently have available. And one of those plugins is the Forecast module. This is a very useful feature. It allows you to use previous sales data to project what could happen in the future. This helps businesses plan for their upcoming inventory needs. So let’s talk about how to install the Forecast module and how to set it up to get the information you need.
InstallationIt’s pretty easy to install the Forecast module. Just go into the Plugin module, select Forecast, and click Install. Then restart the Fishbowl Server and you should be able to find the Forecast module in the Accounting module group. Easy peasy.
Forecasting FiltersIt has a number of filters that let you focus on the information you want to measure: Display Data: Sales, Cost, or Quantity This is the most basic part. What do you want to measure – the total sales price or total cost of the inventory you’ve sold or the quantity of the inventory you’ve sold, scrapped, or consumed on work orders? Part By selecting a single part, you can track only that part’s sales, costs, and quantities used over time. If you want to see changes in all of your inventory just leave this field blank. Location Group To home in on a certain location group, simply select it in this field. Select <All> to look at all of your location groups, and click the Combine Location Groups box to group them all into a single graph instead of multiple ones. Formula There are three formulas you can use to graph the data:
- Constant Average – This puts a flat line through the graph at the data’s mean average. This clearly shows you the historical average, which could prove useful if you don’t expect much growth in the future.
- Linear Regression – This puts a slanted line through the middle of the data points to show what the future might hold if things continue as they have in the past.
- Exponential Moving Average – This puts a line that keeps moving up or down to reflect trends from one data point to the next.