A Requirement-Dependent Inventory Allocation Model for Dynamic Allocation Process in LED Chip Manufacturing Plants

Abstract

LED chip manufacturing (LED chip manufacturing, LED-CM) factory play an important role in the LED supply chain. Basically, product specifications (for LED-CM BIN plant) are expressed by BIN. Required specifications of an order are composed by several BINs. Because the processing of LED-CM production is unstable, LED chip factory could produce the products which are not fit required specifications and further, side-product could be generated. The side-product is not defective product, and it could also meet the inventory requirement for subsequent orders. For the reason, while LED-CM plant receives a new order, it must provide inventory to meet demand at first. Then, insufficient quantity would be produced by further orders. In order to response to different customer demands, there are two types of demand, dynamic allocation procedures and static allocation procedures. Dynamic allocation process means that factory receives the order and it should allocate inventory for immediate shipment to customers. Static allocation process can wait until the orders are accumulated to a certain volume, then the entire batch simultaneous is distributed and shipped to customers. Although the static allocation process can efficiently use factory inventory and get maximum output by optimizing distribution model, but it will lengthen the response time and shipping time. When a customer requires an immediate response, how to immediately allocate chip combination of each BIN in warehouse to have maximum shipments or to make maximum subsequence order shipped efficiently is a dynamic allocation decision-making problem for improving customer server level and profiting effectively. In practice, there are two most common used dynamic allocation process shipping method for LED-CM factory, the average method (Average, AVG) and inventory quantity ratio method (Inventory Proportion, IP). Although both two methods are simple and practical, but they ignore different level consumptions of orders to each BIN. In order to use inventory of factory efficiently and increase subsequent using probability for different BIN, this study proposes a dynamic allocation procedure which takes into account inventory and demand proportion. This study verifies the effectiveness of proposed method by simulation and experiment design. The results show that under different demand environments, performance of proposed method which could meet the demand of dealing more orders and the performance of proposed method is better than the performances of AVG and IP under the same inventory.