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|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|5438||2013||32 صفحه PDF||سفارش دهید|
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|شرح||تعرفه ترجمه||زمان تحویل||جمع هزینه|
|ترجمه تخصصی - سرعت عادی||هر کلمه 90 تومان||10 روز بعد از پرداخت||595,260 تومان|
|ترجمه تخصصی - سرعت فوری||هر کلمه 180 تومان||5 روز بعد از پرداخت||1,190,520 تومان|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Reliability Engineering & System Safety, Available online 23 May 2013
Inventory management of spare parts is one of the most critical issues in the aeronautical industry, given the required level of system availability related to the strategic importance and high stocking costs of the components. Even if a large number of spare parts increases warehousing costs, every single shortage have a greater impact: the adoption of best-in-class inventory management techniques becomes crucial. On these considerations, the paper presents an innovative model of spare parts allocation for the Italian Air Force with the aim of minimizing backorders and, at the same time, ensuring an availability of 99% depending on the actual flight plan. The model, solved by a marginal analysis, considers an original configuration of features combining different skills of maintenance centres in a hierarchical multi-echelon, multi-item, multi-indenture structure. A real case is provided in order to analyse the solving method and the results.
Spare parts inventory management is usually based on data provided by different sources, as for suppliers, that need to be analyzed in accordance to the target of the organization. Commonly used models can follow two different approaches: • the item approach tries to define an economic order quantity and period for each item, without considering possible interactions among different items and availability constraints. These methodologies of inventory control aims at reducing inventory and ordering costs of spare parts while progressively checking the resulting availability. Many examples can be found in ; • the system approach tries to define the logistic support to ensure a specified availability. In this case, an availability-cost function is created in order to evaluate inventory costs associated to a required service level. These methodologies present a common starting point in the studies of Sherbrooke on METRIC, as detailed in the following paragraphs. The latter approach is certainly the most appropriate in all that context where time, cost and availability targets can be achieved through different means and have to respect the different features of complex systems: • Commonality: different systems may have parts in common. In this case there can be two possible solutions: parts in common can be stocked separately for each system or they can be destined to many systems, thus reducing inventory costs (risk pooling); •Service differentiation: systems may require differentiated availability levels. The service differentiation can be seen as a cost saving opportunity when it is possible to allocate more resources only on the most critical systems; •Multi-transportation modes: transportations of items from a central to a local warehouse can be done in different ways, depending on the emergency of the situation. For example, if a local warehouse is out of stock and no lateral transshipment is possible, a faster transportation could be required to reduce system down-time; •Multi-echelon structure: a service supply chain can consist of central and local warehouses, where central warehouses replenish stock of local ones. Spare parts allocation should consider stock availability both for central depot and local sites; • Lateral transshipment: this aspect concerns the possibility that a local warehouse provides a spare part to another local site that is out of stock. Generally, different local warehouses can operate individually and, when out of stock, ask for a replenishment from a central warehouse. Sometimes, it may be useful to have a quicker backup from other local warehouses as well. Consider a lateral transshipment in the inventory management model means that the inventory of local warehouses should be optimized jointly. In particular, the aeronautical industry is one of the sectors most characterized by complex systems which require high levels of backups to comply with availability requirements. Furthermore, a multi-echelon structure is a standard requirement: organizations generally control many sites with different targets and competences. Not all the sites can have a warehouse or a maintenance center as any action on critical items (e.g. motors, shafts, and sensors) require certified skills that can be commonly centralized in dedicated structures. Most components have both a strategic and an economic high value and the problem of minimizing inventory costs, assuring at the same time a high availability, is then crucial, also considering the failure rate of the items. Flight plans, maintenance and testing procedures generates different consumption of components thus requiring models that allow service differentiation. The spare parts allocation problem is widely discussed, as clearly presented by the literature review in : many detailed mathematical models have been developed in order to explore and provide models of single features but few papers analyze the effects of possible configurations of multiple features. This paper gives a contribution to this last stream of research, presenting an original combination of features. It is organized as follows. A literature review is provided in Section 1, reporting the evolution of the multi-item approach (in particular for aircraft maintenance), presenting different models and algorithms that face the problem with a special focus on the marginal analysis method to improve computational efficiency. In Section 2, the complex system is described, presenting the features according to the above classification, explaining what kind of supply processes are activated when a failure occurs. In Section 3, the mathematical representation of the model, its description and hypothesis are presented. In Section 4, the target function and the marginal analysis to solve the problem are explained. A numerical example is presented in Section 5, basing a real case study of a maintenance two-echelon, two-indenture, multi-item for military aircraft. Summary, results and future works are given in Section 6.
نتیجه گیری انگلیسی
In this paper a multi-echelon, multi-item, multi-indenture spare parts allocation method was analyzed, combining the results of previous works available in literature to describe the features of a complex system. The original combination deals with a network where repair centers have different skills and certifications so that they are not always able to execute the maintenance or repair processes. To fit constraints on budget and on a specific operational availability target (related to the flight plan of any site), a model was developed to minimize the system wide expected backorder. A solving algorithm, based on marginal analysis method, determines the stocking level for each type of spare part at each local warehouse and at the central depot. The results of a numerical example show the applicability of the model and show the evolution of the availability level of the system according to the increase of the cost due to spare parts allocation. As the repair processes are often conditioned by the capability of the repair centers and the total availability of a system should consider the operative plan of elements, the results in this paper are important references to test other resolution methods and improvements. Motivations of this paper are strictly related to the requirements of the military industry, both aeronautical and naval, where the spare parts values are very high and availability cannot be decreased. Producers or buyers of such critical products can use the presented model to calculate the economic effort and the best allocation of inventory while at the moment of the design of the logistic support of a fleet. This is always more important in the definition of contractual issues of global service that includes maintenance. This research can be extended in several directions. One possible extension is to model the use of lateral transshipment, possibly with partial or complete pooling strategies. Another extension is to analyze the requirements of specific scenarios (i.e. during missions, where it is possible to introduce a weighted availability value considering flight hours) where endurance length creates a risk related to the lead time of spare parts supply from the producer to the central depot and from the central depot to the local warehouses. As a further improvement, the risk of obsolescence of repairable systems can be introduced, since the lifespan of the model is very wide and spare parts in inventory can face obsolescence and cause greater losses.