No abstract

Research on lot sizing has mostly assumed single echelon systems. Even when multiple echelon systems have been used, capacity constraints are seldom considered. However, in manufacturing capacity constraints can lead to significant queuing effects. Commonly used lot sizing policies like Lot-For-Lot (LFL) and Period Order Quantity (POQ) do not take these effects into account. This research compares these policies with a Fixed Order Quantity (FOQ) policy, within which lot sizes are based on minimizing estimated lot flowtimes at capacity-constrained machines. Simulation is used to study a small production and distribution network using time-phased planning. Results show that the FOQ policy performs better than both LFL and POQ when inventory levels and delivery performance are of concern.

This paper summarizes one group of recent simulation studies comparing replenishment strategies. Time-phased planning, implemented using DRP and MRP logic, continuous-review reorder point (ROP) and single-card Kanban (KBN) systems are considered. These differ in terms of decision-making information, logic and integration requirements. Experimental results have been statistically analyzed and explained using simple stochastic models.Steps have also been taken to evaluate which strategies are most suitable under various demand patterns, levels of uncertainty and capacity constraints. Results show that DRP/MRP is superior under time-varying demand, regardless of whether or not capacity constraints are present. With no capacity constraints and level demand, ROP is superior to KBN, in part because it considers backorder information. With capacity constraints behavior is complicated by queuing effects. Under level demand, KBN may slightly outperform ROP, given assumptions of equal lot sizes, order placement delays and transportation times.