Abstract
The life spans of durable goods are longer than their warranty periods. To satisfy the service demand of spare parts and keep the market competition advantage, enterprises have to maintain the longer inventory planning of spare parts. However, how to obtain a valid number of spare parts is difficult for those enterprises. In this paper, we consider a spare-part inventory problem, where the inventory can be replenished by two ways including the final production order and the remanufacturing way. Especially for the remanufacturing way, we consider the acquisition management problem of used products concerning an acquisition pricing decision. In a multiperiod setting, we formulate the problem into a dynamic optimization problem, where the system decisions include the final production order and acquisition price of used products at each period. By stochastic dynamic programming, we obtain the optimal policy of the acquisition pricing at each period and give the optimal policy structure of the optimization problem at the first period. Then, a recursion algorithm is designed to calculate the optimal decisions and the critical points in the policy. Finally, the numerical analyses show the effects of demand information and customer’s sensitive degree on the related decisions and the optimal cost.
Highlights
With the improvement of market competition, customers have larger difficulty in distinguishing the products with similar functions
Users are more dependent on the services provided by the manufacturer, ignoring the after-sale service outside the regular warranty period due to more serious results, which is showed by Nagler [1]: “you buy a car and eight years later cannot get it serviced for a reasonable price because the manufacturer has discontinued a particular part, you will remember it when you buy your vehicle”
In order to gain a competitive advantage in an increasing competitive market, many enterprises have improved the concerning for the after-sale service in a regular warranty period
Summary
With the improvement of market competition, customers have larger difficulty in distinguishing the products with similar functions. The shortage of spare parts is the most difficult problem for providing after-sale service at outside the regular warranty period This especially holds for durable products, because the production line has been closed. The main methods for forecasting demand are still statistical methods, such as moving average and exponential smoothing ( see Axsater, 2006 [19]), under more advanced information technologies (such as Radio-Frequency Identification (RFID), cloud computing, and Internet of Things), firms might obtain more in advance and more accurate information, such as the real-time status of one product, historical sales data, customers’ position, the correlation between the historical service data, the using environment of the product, and customers’ types These possibilities enable firms to change the operations policy, such as making a better and longer spare-part inventory planning. We will consider the inventory decisions problem in a multisource spare-part system, where we adopt the final production order and remanufacturing ways to replenish spare parts.
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