as . Issues with this approach No universal formula exists for safety stock, and application of the one above can cause serious damage. It makes several implicit assumptions: The assumption that demand is a succession of independent normal random variables: First, real demand cannot be negative. If the ratio of standard deviation to mean is quite high, this will skew the distribution (compared to the normal distribution), leading to consistent overestimation of safety stock by this formula. Second, and more importantly, demand is often influenced by external random factors which persist for more than one time period, so that the successive demands are not independent. With a very large number of sources (for example, consumers of a central retail warehouse), that may not be an issue, but otherwise it is (for example, for a manufacturer that supplies these retail warehouses) The use of average and standard demand assumes it is constant. For seasonal demand (for example high in summer, low in winter), the formula will consistently produce stock outs in summer and waste in winter. Similar errors apply for demand that grows or declines. That does not invalidate the formula, but it influences the parameters to be input into the formula in each time period. Lead time is extremely hard to quantify in complex manufacturing and/or purchase environment, which has become the norm in global supply chains that span many independent partners. In practice, lead time is estimated by a rule of thumb that hardly improves on estimating safety stock with a rule of thumb. Even when lead time is correctly quantified, the formula assumes supply (production and purchase) is statistically constant, which is not always the case. Type II service Another popular approach described by Nahmias uses the standardized unit loss integral L(z), given by: Where is cumulative distribution function for the standard normal. Let β be the proportion of demands met from stock (service level), Q the order quantity and σ the standard deviation of demand, then the following relationship holds: In this case, the safety stock is given by: and the expected number of units out of stock during an order cycle is given by σL(z). See also Buffer stock scheme Service level Reorder point References ''This is a dead link Chockalingam, Mark (2001) "Tracking and Measurement of Demand Forecast Accuracy and Implications for Safety stock Strategies", DemandPlanning.Net Business terms Inventory Inventory optimization