The article spare parts inventory management with demand lead times and rationing attempts to analyze the inventory system of two demand classes. For the first class, the order has to be met and satisfied immediately while the second class has a reorder level and therefore, provides a lead time. The research is carried out on a spare parts service system of a manufacturing company that faces both the first and second class demands. The company is on top of in its industry supply chain with its research, development, manufacturing facilities and distributors across the United States, Europe and in the Far East. The company has a spare part network that provides the customers with the spare parts to customers for routine repairs and maintenance. It does this because it acknowledges of how expensive the storage of this inventory could be to the customers, manufacturing facility. It, therefore, has agreements with the leading customers to manage their stock stores and godowns. It has an immediate service response on four hours that cater to down orders for equipment failures and lead time orders for scheduled maintenance in their regional and local depots.
The article delves into the criticality of the two classes, where it identifies the service level for the critical class as a mere approximation, and on the other hand, the non- critical category is exact. The researcher conducts a computation analysis to justify the estimate of the service level of the critical class, the benefits of rationing inventory and illustrates the value of lead time when the demand is of the critical class. The article is based on a case study where the researcher focuses on sixty-four spare parts items to different customers with critical down orders that demand to be satisfied immediately and the non-critical maintenance orders that can be met with a fixed demand lead time.
At times both orders on the local and the regional depot have proven challenging as the company uses a regular base stock inventory system that uses one service level.The system is therefore inefficient because it is ineffective for all types of demand as it fails to account for the lead time differences. This happens when a local depot is facing a need and lead time for varying internal customers while the region has demanding orders from the external customers as well as the replenishing orders to the local depots. Kocaga and Sen, therefore, suggest the use of an inventory model that best fits demand lead times and multiple demands classes and allows differentiation of service levels.
Multiple demands class best suits when it uses different stockpiles for each demand class. The spare parts stock of each class can be managed separately to meet the various service level of each level. It accommodates different demands of varying criticality or an item that is sold to many customers of different criticality. However, this system has its drawbacks that theres no substantial gain on pooling risks and there is an increase in inventory cost since the spare parts would be offered at the highest reorder level. The other model that could be utilized is rationing. In this system, critical orders are prioritized once theres a backorder. When the replenishing stock from the regional depots arrives, the critical order is first satisfied, and in the case, it is above the critical order, the rest is supplied to the non-critical backorder. The primary challenge facing rationing policy is that the concept meets theoretical difficulties and therefore, it is limited to static procedures.
The research by Kocaga and Sean aim was to derive the service levels for both classes in the form of the cost-effectiveness of no stock out. The lead order was an approximation that depended on how replenishing ordered was utilized. The model assumes that theres consideration of the spare parts is done for each separately and that the critical levels are time invariant. The model applies the optimizing and differentiation approach to calculate the factors in question, which is the service level of the inventory item as well as the lead time. In the optimization of the service level, the objective is to minimize the base stock as opposed to reducing the average inventory on hand. The model constructed assumed that each item was subject to both classes of demand. Class one had to be satisfied immediately while the second class allowed demand lead time. The two types are satisfied from the same pool of inventory which is managed by a base stock policy. Each order triggers the need to replenish the stock with a predetermined lead time.
The researchers apply simulation study to evaluate the accuracy of the approximation of the high service level. They also test the performance of the estimate by varying a single parameter of each class with that of the other. For example the arrival rate of the critical class and the arrival rate for a non-critical demand class. The analysis concluded that the performance of the approximation improves as the service level of the critical class increases. The research ion the article also shows that the recognition of demand lead times and rationing is cost saving to the company whether accurate or proximate figures are used as the service level of the critical demand class.
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