The classic supply-chain approach has been to try to forecast future inventory demand as "accurately" as possible, by applying statistical trending and "best fit" techniques based on historic demand and predicted future events. The advantage of this approach is that it can be applied to data aggregated at a fairly high level (e.g. category of merchandise, weekly, by group of customers), requiring modest database sizes and small amounts of manipulation. Unpredictability in demand is then managed by setting safety stock levels, so that for example a distributor might hold two weeks of supply of an article with steady demand but twice that amount for an article where the demand is more erratic. Universally accepted statistical methods such as Standard Deviation and Mean Absolute Deviation are often used for calculating safety stock levels.