Seasonality is a fact of demand for many products. The demand changes with the time, however, displays the same general pattern of ramping up/down time after time. Such repeating demand patterns generally occur due to seasonality. It does not have to be annual, just cyclic in nature & repeated at regular intervals. To tell if demand is seasonal, simply look at the demand curve over a long enough period of time and if you see the same pattern repeated over time, it most likely shows seasonal pattern. How much history do you need to make a firm determination? Well – that is a question that depends on the length and frequency of the season. Usually, you should have data that shows more than three complete cycles of seasonal demand to be sure that seasonality exists. Based on the frequency and the length of the season, you may do fine with historic demand data for as less as 4-5 months or 3-4 years or longer to be sure that there is a seasonal pattern in the data. For example, if the pattern was monthly and showed a spiked demand in the first week of the month followed by a low-stable demand for the rest of the month, then the historical data for just a few months will be sufficient to establish the pattern. Now, consider a demand pattern that shows cyclic activity once a year, say in early summer that lasts for 45-60 days: then you will need 3-4 years worth of history to establish the pattern.
Forecasting techniques normally de-seasonalize the historical demand data before projecting the demand into the future. The projected demand is then re-seasonalized to produce the final projected demand. Why go through all this trouble? Because, the de-seasonalized historical demand generally represents a time-series that is much more stable and lends itself easily to statistical projection. Chances are, that this process will enhance your forecast accuracy substantially.
So, how do you de-seasonalize data? That is not particularly hard either. The forecasting solutions find a base-line demand that is stable and then create a seasonal index on top of the base-line demand that recreates the seasonal cycle over the length of the season. The index is created using the historical seasonal demand over the base demand and can be reviewed frequently to make ensure the indices reflect the demand patterns correctly. The seasonal indices can be created by week or by month depending on the total length of the demand cycle and the build-up and decline patterns. In some cases, the indices may be created even by the day to reflect a high ramp-up of demand.
Want to know more about supply chain processes? How they work and what they afford? Check out my book on Enterprise Supply Chain Management at Amazon. You will find every supply chain function described in simple language that makes sense, as well as see its relationship to other functions.
© Vivek Sehgal, 2009, All Rights Reserved.