Seasonal Indexes To Seasonalize And Deseasonalize Data

Seasonal Indexes
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Seasonal indexes are employed in the adjustment of demand forecast. In most cases, they are used in the measurement of seasonal changes, which basically mean the measurement of the demands depending on the season. The adjustment is made by the multiplication of the base forecast's product and the multiplier (Wayne, 2014). The effect of this either raises or lowers the demand forecast over a given period which is usually a month or a week. The results are usually helpful in the calculation of the inventory which supports the sales. The good thing about it is that it reduces the errors that usually come during time-series forecasting. This therefore improves the quality of the forecasts that are made out of judgment. It however, has its own disadvantages as well.
The difficulty with seasonality is that it is always changing year in year out. The fiscal year may fail to map back to the indexes of the season leading to inaccuracy (William, 2017). This is because the sales of the previous season may impose a significant impact on the base demand forecast of the present season. It is usually very difficult to ascertain the actual situation of the events on the ground if seasonal adjustment is done on data. Seasonalizing and deseasonalizing data usually calls for an analysis of a large number of additional parameters.
The impact seasonality has on lead time is significant. This is the time required for the ordered, purchased goods to be shipped, picked and sold. As a result of this, product shelf life is also impacted. It means that due to this seasonality, products can expire before they are all sold leading to losses (Ryan, 2013).This is a major problem of seasonality. It can change each year...