Spare parts management of aging capital products
Başlık çevirisi mevcut değil.
- Tez No: 402324
- Danışmanlar: PROF. DR. ROMMERT DEKKER
- Tez Türü: Doktora
- Konular: Endüstri ve Endüstri Mühendisliği, Endüstri Ürünleri Tasarımı, Industrial and Industrial Engineering, Industrial Design
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2015
- Dil: İngilizce
- Üniversite: Erasmus Universiteit Rotterdam
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 218
Özet
Özet yok.
Özet (Çeviri)
The economic lifetime of capital products is much longer than its production phase. Keeping capital products in operation requires a stable spare parts supply. When capital products are in-production it is easy to obtain spare parts, since Original Equipment Manufacturers (OEMs) can increase their order sizes to ship spare parts to their customers. After the end-of-production announcement of the OEM, obtaining spare parts becomes more difficult due to since suppliers' profitability and capacity utilization becomes an important factor for the ow of spare parts. As a consequence of this dependence this spare part ow is subject to increasing supply risk towards the end of the economic lifetime. From the suppliers' perspective, producing spare parts of out-of-production systems may yield a steady revenue stream or it may be a source of efficiency loss due to an increasing number of production setups. To prevent excessive set-ups, suppliers might choose to maintain some inventory which then, however, would increases their holding cost and lower their financial performance. Naturally all of these decisions depend on the demand rate of spare parts. On the one hand, for capital products with large installed bases, keeping a spare part stock might be beneficial for the supplier. On the other hand, when the installed base of a capital product is small, holding the inventory increases the amount of non-moving parts, whereas producing them might cripple the average utilization of the supplier. Therefore, to consolidate current orders with future ones, suppliers choose to delay their production for incoming orders and eventually stop their spare part support. The theoretical explanation of this phenomenon is illustrated by a queuing system with two customer classes and a batch server in Chapter 3. From the OEMs' perspective, this situation implies an increasing supply risk for spare parts of aging capital products. End-of-support announcements from suppliers stand for their main source of supply risk.Another factor that aggravates the supply risk is that spare parts suppliers may be too busy to notify OEMs of their end-of-support decisions. Dealing with large supplier bases, it is impossible for OEMs to check their suppliers continuously for an unannounced supply loss. Therefore, they can only discover a supplier's end-of-support decision when they need to buy a part. If there is no alternative supplier (and no inventory), OEMs fail to satisfy their customers' spare parts demand. Failing to satisfy the demand not only means a revenue loss, it also makes the reliability of OEMs' after-sale services questionable and might motivate asset owners to replace their capital products. Therefore, early detection, and mitigation of supply risk of spare parts is an important item in OEMs agendas. To overcome the supply risk, OEMs take proactive and reactive actions such as maintaining spare parts stock, cultivating a back-up supplier and utilization of secondary markets, if any. Since each action incurs a cost, it is important to evaluate inventory and replenishment policies of spare parts through mathematical models that explicitly take into account supply risk. Among these means of supply risk mitigation, the utilization of secondary markets requires more attention then others. Next to being an alternative supply source, secondary markets might be a source of competition for OEMs' after-sales services. It is known that OEMs are subject to fierce competition with third party service providers for after-sale services of their capital products, especially after the end of their warranty period (Cohen et al., 2006). The existence of secondary markets make this competition even more erce since different traders (or even customers) can trade their surplus spare parts. This situation forces OEMs to consider secondary markets in their tactical and operational decisions. In this thesis, we consider four different spare parts management problems. Each is motivated by a business an OEM, Fokker Services, that provides maintenance services for out-of-production aircraft in the Netherlands. From a broad perspective, our study started with an empirical analysis on supply risk using purchase history and demand data from the company. After generating empirical evidence for supply risk and insight for its underlying reasons (Chapter 2), we develop mathematical models for optimal inventory control and replenishment policies in Chapters 3 and 4. In Chapter 5, we address pricing and replenishment policy for spare parts when secondary markets are used as a supply source by OEMs as well as customers. An empirical analysis on supply risk for spare parts of aging capital products is presented in Chapter 2. At the beginning of this study, managers in the company had an incomplete understanding of supply risk for their spare parts. The company was experiencing supplier loss due to various reasons and each supply problem case triggered a solution procedure, depicted in Chapter 2, which could take a year. At the end of that procedure, the company would find a new supply source and restart its supply chain. Since this reactive approach threatened the service rate and profitability, it was necessary to develop indicators for future supply problems. The main motivation was having a risk measure which can trigger advance actions to mitigate the effects of supply disruptions on the company. To this end, we hypothesize relationships between disruptions and various supply chain features, e.g. lead time, price, order frequency, order size etc., and test them using purchase histories of spare parts whose suppliers failed before the date of analysis. Results indicate that supply risks are closely related with changes in lead times as well as the time period since the last purchase. This result not only led to another empirical study by Li et al. (2015), who considered a more advanced model for the same problem, but also it indicated the necessity of considering random lead times and supply disruptions in a single inventory control model, which is the focus of Chapter 3. Random lead times coupled with supply disruptions are considered in Chapter 3. Our review of the queuing literature indicates that coupled lead times and supply disruptions are a natural consequence of suppliers' optimal manufacturing schedules. To address this problem, we develop a mathematical model considering Markov-modulated random lead times with supply disruptions. After proving structure of the optimal policy, we run extensive numerical experiments and evaluate different scenarios. The main output of this study is the importance of the coupled effect of random lead times and supply disruptions. Our analysis indicates that the coupled effect may give rise to extreme cost increases, especially for high service levels. Furthermore, we find that proactive actions using advance indicators are most important for mitigating the effeect of supply risk. Replenishment of spare parts from secondary markets and a regular supplier is considered in Chapter 4. Although secondary markets are useful for mitigating the supply risk, they have particular qualities which must be considered explicitly. First, secondary markets have limited and random availability of spare parts. In each decision epoch there are only a limited number of parts and the spare part availability varies over time. Second, secondary markets are cheaper and faster than regular suppliers in most cases. Third, spare parts on secondary markets may be in different conditions which brings substitution into the equation. We consider all of these factors in a dual sourcing setting (a regular supplier and secondary markets) and develop efficient heuristics using a myopic cost function. Later we extend our heuristic with nonstationary demand and evaluate both policies in extensive numerical experiments. Our results indicate that our policy is fast and efficient while producing near-optimal solutions when the lead time of the regular supplier is equal to one period. For larger lead times, our method deviates 17% from the optimal policy on average. Despite the significant deviation from the optimal cost, the best available alternative policy is only marginally better (about 1%), while our method is much more efficient in terms of computation time. In Chapter 5, secondary markets are considered to be competitors for OEMs' aftersale services. Secondary markets are accessible for OEMs as well as customers. Due to competitive prices, some customers choose to buy spare parts from secondary markets. For OEMs this means demand and revenue loss. This implies the necessity of considering price competition from secondary markets in the replenishment and pricing policies. We consider a single-period model for this problem setting. Our analysis indicates that for the replenishment policy, secondary markets should be considered as the main source, whereas regular suppliers should be used as a complementary one. For pricing, a modified list price policy is optimal in which the sum of existing inventory and the market availability is the main factor for the optimal price value.
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