cause a much larger change in perceived demand, which in turn can mislead companies to make bigger changes in inventory than are really necessary. Non-optimized approach Without inventory optimization, companies commonly set inventory targets using rules of thumb or single stage calculations. Rules of thumb normally involve setting a number of days of supply as a coverage target. Single stage calculations look at a single item in a single location and calculate the amount of inventory required to meet demand. Deterministic vs. stochastic Inventory optimization models can be either deterministic—with every set of variable states uniquely determined by the parameters in the model – or stochastic—with variable states described by probability distributions. Stochastic optimization takes supply uncertainty into account that, for example, 6 percent of orders from an overseas supplier are 1–3 days late, 1 percent are 4–6 days late, 5 percent are 7–14 days late and 8 percent are more than 14 days late. Stochastic optimization also accounts for demand volatility which is a top priority among the challenges faced by supply chain professionals. For example, management predicts a 65 percent probability of selling 500 units, a 20 percent probability of selling 400 units and a 15 percent probability of selling 600 units. High service levels can be achieved with cost overruns, excessive inventory and firefighting, but higher profitability can be achieved by understanding the sources of volatility and planning appropriately. The result is a better understanding of the inventory requirements than with a deterministic approach. Single vs. multi-echelon Single-echelon location problems are single-type problems such that either the material flow coming out or the material flow entering the facilities to be located is negligible. In multiple-echelon problems, both inbound and outbound commodities are relevant. This is the case, for example, when distribution centers (DCs) have to be located taking into account both the transportation cost from plants to DCs and the transportation cost from DCs to customers. In multiple-echelon problems, constraints aiming at balancing inbound and outbound flows have to be considered. A sequential single-echelon approach forecasts demand and determines required inventory for each echelon separately. Multi-echelon inventory optimization determines the correct levels of inventory across the network based on demand variability at the various nodes and the performance (lead time, delays, service level) at the higher echelons. Multi-echelon inventory optimization looks at inventory levels holistically across the supply chain while taking into account the impact of inventories