This algorithm is of great benefit in situations where multiple products are being produced at capacity-constrained resources with large Setup and Change Over times. Typical examples that come to mind are: A Press Shop feeding components to a Weld Shop assembling Car/Truck bodies or a component manufacturing facility feeing an Engine Assembly shop. In fact similar situations are also commonplace in White Goods, Compute Hardware and High-tech industries.
The task of supplying components from capacity constraints resources is becoming even more challenging as a result of:
It is a Catch-22 situation. If you increase batch sizes to improve capacity utilization, you cannot supply on time all components needed by the assembly shop. If you cut down batch sizes too much, you lose vital capacity and cannot meet demand. Lot Size optimizing algorithm decides the most optimum batch sizes and their production sequence so that you can meet demand with a greater certainty with your limited capacity.
At the heart of Lot Size optimizer is a LP/IP base engine. It considers a variety of constraints such as Minimum/Maximum buffer limits for each component, production capacity, minimum batch size for each component and so on and arrives at optimum batch sizes to achieve the twin objectives of meeting demand and maximizing capacity utilization.