All statistical methods either even out the peaks and troughs in sales history to produce trend-based forecasts, or else they look for repeated patterns in the historical peaks and troughs to make future forecasts.
However, if the peaks and troughs in the sales of real-world products are caused by what are often 'random' events, such as promotions or competitor activity, how can statistical methods help you forecast? On the one hand, a smoothed forecast has little value if the primary purpose for forecasting is to predict the short term sales peaks and troughs. On the other hand, how valid is the second approach given the random nature of historical peaks and troughs?
If you cannot use statistics, what can you use? In the majority of situations, informed judgment (or 'finger to the wind' as cynics might describe it) is actually more likely to produce better results within fmcg markets.
The essence of judgmental forecasting is the application of the business manager's knowledge and interpretation of past events and activities, and their effects on sales, to planned future events and activities. The result is a 'judgmental' forecast for the future sales periods.
The key factors to consider are fairly well known:
- Trade promotions
- Launch / relaunch activity
- Promotions / special packs
- Historical out of stocks
- Distribution changes
- Seasonality (if relevant)
- Competitor activity
- Advertising effect
- Market trends
Although there is never the same thing twice, developing and using an understanding of how sales respond to different types and combinations of events is the most effective way of generating a forecast. It has spin-off benefits too, because it forces marketing and sales people to think long and hard, and hopefully objectively, about which factors really drive their sales.
The method most likely to succeed is forecasting from the 'bottom up', and reviewing from the 'top down'. This means generating the forecasts at the lowest (relevant) level of detail using the process described above : the 'bottom up' method. One then compares how the resulting forecasted year on year growth rates and Moving Annual Totals compare to expectation, historical or current growth rates and Moving Annual Totals. If the 'bottom up' results are out of line with the 'top down', then the 'bottom up' forecasts need to be revisited to identify the sources of the difference.
This process must continue until the 'top down' and 'bottom up' forecasts are consistent.
1 komentar:
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