Lot sizing for varying degrees of demand uncertainty

Abstract

The average costs for some well-known lot-sizing techniques are investigated by simulation. We study an inventory system with periodic review, instantaneous (or fast) delivery of every replenishment order and backlogging of unfilled demand. At review point t, the amount to be delivered in period t+ j consists of firm demand from customers who have ordered j periods or more before their desired delivery in period t+ j and normally distributed random demand from customers who order less than j periods in advance. If j is big then all demand is random. We use the profile of advance ordering to specify various degrees of demand uncertainty. If the demand uncertainty is low then the best lot sizes are those computed with a rolling horizon by Deterministic Dynamic Programming. For increased demand uncertainty, an (s, S) policy becomes a better choice. We suggest a policy-iteration algorithm to compute the reorder point s SDP1 and the order-up-to level S SDP1 for the model which assumes that the actual demand is known one period in advance only. We recommend to implement the reorder point s SDP1 and to specify the actual order-up-to level as S SDP1 plus a term adjusting for the neglected actual information about advance orders.