Forecast quality is inevitably dependent on the quality of the underlying data. One-off events, such as a dramatic increase in sales or an unusual drop in demand, can dramatically change the forecast for the worse. Therefore, one must pay attention to the historic sales to produce accurate forecasts.
Historic sales or the time series may be classified based on ABC sales/volume analysis, this classification will be useful when reviewing and deciding whether judgmental adjustments should be made to the data to minimize the forecasting error. The ABC classifications are as follows;
A-items are usually 20% of the overall items that account for 80% of the sales, thus these items are of great importance to the firm. It is recommended to examine the forecasting error for these products on a regular basis and make judgmental adjustments to the sale history when appropriate.
B-items are approximately 30% of the overall items and account for 15% of the total sales. These items are therefore not as crucial to the firm as A items, the forecasting methods selected by the Forecasting Module are usually fairly accurate and human intervention is usually only required when exceptions occur in the sales.
C-items are of lowest volume and may include as many as 50% of the company’s items. Many of the C-items will have zero sales in the sales time series, with the occasional small sales and, more rarely, a large sale. The percent error of forecasting is likely to be high, which is why you would probably want to have a small confidence limit to keep your safety stock costs down. These items are inexpensive and only produce about 5% of the company’s sales, so for these items the cost of forecasting error is probably more acceptable than the cost of employees adjusting time series data. Thus, automation is key.