there is a bird-in-the-hand decision that forces the seller to reject lower revenue generating customers in the hopes that the inventory can be sold in a higher valued segment. The tradeoff is sometimes mistakenly identified as occurring at the intersection of the marginal revenue curves for the competing segments. While this is accurate when it supports marketing decisions where access to both segments is equivalent, it is wrong for inventory control decisions. In these cases the intersection of the marginal revenue curve of the higher valued segment with the actual value of the lower segment is the point of interest. In the case illustrated here, a car rental company must set up protection levels for its higher valued segments. By estimating where the marginal revenue curve of the luxury segment crosses the actual rental value of the midsize car segment the company can decide how many luxury cars to make available to midsize car renters. Where the vertical line from this intersection point crosses the demand (horizontal) axis determines how many luxury cars should be protected for genuine luxury car renters. The need to calculate protection levels has led to a number of heuristic solutions, most notable EMSRa and EMSRb, which stands for Expected Marginal Seat Revenue version a and b respectively. The balancing point of interest is found using Littlewood's rule which states that demand for should be accepted as long as 2 11 where is the value of the lower valued segment is the value of the higher valued segment is the demand for the higher valued segment and is the capacity left This equation is re-arranged to compute protection levels as follows: 1−121 In words, the seller wants to protect 1 units of inventory for the higher valued segment where 1 is equal to the inverse probability of demand of the revenue ratio of the lower valued segment to the higher valued segment. This equation defines the EMSRa algorithm which handles the two segment case. EMSRb is smarter and handles multiple segments by comparing the revenue of the lower segment to a demand weighted average of the revenues of the higher segments. Neither of these heuristics produces the exact right answer and increasingly implementations make use of Monte Carlo simulation to find optimal protection levels. Since the mid-1990s, increasingly sophisticated mathematical models have been developed such as the dynamic programming formulation pioneered by Talluri and Van Ryzin