1. Forecasting is an art that requires a mix of professional judgment and objective statistical analysis. Successful demand forecasts require a baseline prediction leveraging statistical forecasting methods. Once established, the process can focus on how best to adjust statistical forecasts based on your own insights and business knowledge.

2. The forecasting process is usually iterative. You may need to make several refinements of your initial forecast before you are satisfied. It is important to be able to generate and compare alternative forecasts quickly and easily. Tracking accuracy of these forecasts over time, including alternatives that were not used, helps inform and improve the process.

3. The credibility of forecasts depends heavily on graphical comparisons with historical data.  A picture is worth a thousand words, so always display forecasts via instantly available graphical displays with supporting numerical reports.

4. One of the major technical tasks in forecasting is to match the choice of forecasting technique to the nature of the data. Effective demand forecasting processes employ capabilities that identify the right method to use.  Features of a data series like trend, seasonality or abrupt shifts in level suggest certain techniques instead of others. An automatic selection, which selects and uses the appropriate forecasting method automatically, saves time and ensures your baseline forecast is as accurate as possible.

5. Successful demand forecasting processes work in tandem with other business processes.   For example, forecasting can be an essential first step in financial analysis.  In addition, accurate sales and product demand forecasts are fundamental inputs to a manufacturing company’s production planning and inventory control processes.

6. A good planning process recognizes that forecasts are never exactly correct. Because some error creeps into even the best forecasting process, one of the most useful supplements to a forecast are honest estimates of its margin of error and forecast bias.