Although most systems adjust historical data before using it to forecast future sales, many systems don t do enough. Depending on what caused that spike, it should or should not be removed from the data or adjusted in size. The same is true for a unique dip in sales. Many systems also fail to add lost sales to actual sales. Averaging prior unit sales is not an accurate method of forecasting.
Averages don t take sudden up or down trends into account fast enough for buyers to react in time.
They have negative effects on distributors whose products are subject to sudden, rapid changes in demand. Some systems vainly attempt to overcome this problem by using a weighted average that puts greater emphasis on more recent sales, but this doesn t do much to improve accuracy. Another deficiency found in some systems is the use of historical sales instead of demand when forecasting. This can result in not having enough stock on hand. If sales growth is slowing for a particular product, the better systems can adjust purchasing recommendations downward, even though recent sales have been much higher than they were only a few months earlier.