Improving forecast accuracy is a key requirement for any forecasting system, and is one of the most frustrating issues operations and executive teams face when running a business. NEHANET Global Forecasting addresses three key areas that are required to create an accurate forecast.
These areas are:
- Data entry environment
- Availability and the ease-of-use of ancillary data sets such as shipments, backlog, uncommitted backlog, annual plans, etc.
Let us look at these areas in a little more detail.
Too often forecasting is an end-of-the month exercise that focuses more on completion than accuracy. NEHANET provides a forecasting environment that is available 24/7, allowing the forecaster to enter updated information on a regular basis, at their convenience, and when it is freshest in their minds. Inherently this leads to a more accurate forecasting process.
Excel is okay for a while and everyone knows how it works — but it quickly gets too complex to be an efficient tool to do corporate forecasting. If you’re doing it this way you probably know about the spreadsheet from hell that you have to deal with at the end of the month. NEHANET emulates its simple and effective data entry environment in the Global Forecast module, with significant improvements added such as auto-fill algorithms and grid edit that allows for updating a complete list of forecast line items on a single page.
The final piece of the puzzle is the availability and usage of ancillary data, the most critical being BBB data or bookings, billings and backlog. This data is typically available in one form or another, but the real issue is how easy it is to use. NEHANET’s forecasting environment integrates BBB data directly into the forecasting data entry environment, allowing the forecaster to easily reference this data while entering the forecast information for a specific customer/part combination.
Through personal experience, forecasting accuracy can be improved from +/- 25% to less than +/- 8%, a 60% reduction of inaccuracies. Based on this experience and discussions with customers, I believe that optimizing these three areas will lead to at least a 30% improvement in forecasting accuracy.