On the demand distributions of spare parts

Spare parts have become ubiquitous in modern societies, and managing their requirements is an important and challenging task with tremendous cost implications for the organisations that are holding relevant inventories. Demand for spare parts arises whenever a component fails or requires replacement, and as such the relevant patterns are different from those associated with ‘typical’ stock keeping units. Such demand patterns are most often intermittent in nature, meaning that demand arrives infrequently and is interspersed by time periods with no demand at all. A number of distributions have been discussed in the literature for representing these patterns, but empirical evidence is lacking. In this paper, we address the issue of demand distributional assumptions for spare-parts management, conducting a detailed empirical investigation on the goodness-of-fit of various distributions and their stock-control implications in terms of inventories held and service levels achieved. This is an important contribution from a methodological perspective, since the validity of demand distributional assumptions (i.e. their goodness-of-fit) is distinguished from their utility (i.e. their real-world implications). Three empirical datasets are used for the purposes of our research that collectively consist of the individual demand histories of approximately 13,000 SKUs from the military sector (UK and USA) and the Electronics Industry (Europe). Our investigation provides evidence in support of certain demand distributions in a real-world context. The natural next steps of research are also discussed, and these should facilitate further developments in this area from an academic perspective.     

Cost allocation in inventory pools of spare parts with service-differentiated demand classes

Holding inventory of spare parts is critical to assure safety and production. In order to save costs, different producers may collaborate through an inventory pool. We consider an inventory pool of spare parts, subject to a service level constraint, where the members of the pool may have different target service levels, so that they represent different demand classes. The pool is implemented either by round-up or rationing policies. The members should agree on how to share the costs. Based on cooperative game theory concepts, we show the important effects that different targets can have in the core stability for this problem. We perform a computational study in a large number of instances, providing insights on the emptiness of the core and the performance of seven allocation methods. We also propose the novel Minimum Deviation from Service Level Referential Cost Method (MIND). This method looks for a stable allocation such that the maximum difference between a cost allocated to a player and its service level referential cost is minimised. The MIND allocation is the solution to a linear programming model and is core guaranteed, in the sense that if the core is not empty, the allocation belongs to the core.     

Optimal service parts contract with multiple response times and on-site spare parts

This study examines user outsourcing of spare parts management to vendors through a service contract. The user’s selection of a fixed-price service parts contract is formulated as a stochastic integer programming model that decides multiple response times and on-site spare parts, while considering component breakdown with uncertain failure rates. We analytically derive the optimality conditions for the continuous case and subsequently design an efficient algorithm. Numerical illustrations and analyses are conducted to evaluate decisions under various scenarios. Our analysis shows that when both failure rate and expedited contract cost are high, coupled with low part cost, users would prefer the purchase of spare parts for all components to expedited contracts. A fixed-price expedited contract has a lower marginal cost with respect to failure rate than a fixed-price next day contract and a usage-based contract. We also examine inventory behaviour for a single part, multiple types of parts, and multiple groups of parts. It is shown that there is a cost-saving pooling effect in spare parts for identical items, which significantly raises the likelihood of having on-site stored parts. The problem becomes more complex for multiple items, reflecting bundling effects between items for a given contract.     

基于随机提前期的备件携行量计算方法

舰艇出海执行任务期间，需要携行一定种类和数量的物资自给。文中研究了这种单级供应条件下，如何确定备件最优携行量的问题。建立了以服务水平为目标，以资源为约束的目标规划模型，模型考虑了随机提前期和备件重要度。提出一种基于边际效费比的增量法用于求解这个模型。最后，给出一个算例并得到了一些结论。     

Sensor-driven prognostic models for equipment replacement and spare parts inventory

Accurate predictions of equipment failure times are necessary to improve replacement and spare parts inventory decisions. Most of the existing decision models focus on using population-specific reliability characteristics, such as failure time distributions, to develop decision-making strategies. Since these distributions are unaffected by the underlying physical degradation processes, they do not distinguish between the different degradation characteristics of individual components of the population. This results in less accurate failure predictability and hence less accurate replacement and inventory decisions. In this paper, we develop a sensor-driven decision model for component replacement and spare parts inventory. We integrate a degradation modeling framework for computing remaining life distributions using condition-based in situ sensor data with existing replacement and inventory decision models. This enables the dynamic updating of replacement and inventory decisions based on the physical condition of the equipment.     

二维保证下汽车维修备件动态库存控制策略

以汽车产品为例,针对失效率以时间或里程为计量单位的单位价值高、需求率低的不可修零部件,以未来销售预测为起点,依据二维保证政策的特点确定某时点处于保证时效内的汽车数量,基于产品可靠性对保证备件的需求进行预测,针对保证备件需求的特点,提出动态分段(Q,r)库存控制策略,通过案例验证了该库存策略可有效降低库存成本支出。     

流程式生产企业备件两级存储模式研究

从流程式生产企业的视角给出了慢速流动备件与快速流动备件的参照定义及其特性比较;分析了流程式生产企业慢速流动备件存储的两难困境;总结了我国三大钢铁企业的备件管理实践;提出了慢速流动备件行业内企业合作存储、快速流动备件流程式生产企业分散存储的备件两级存储的新模式及其运行机制。     

基于(R,S)策略的多品种零备件库存结构优化

对多品种零备件库存采用(R,S)周期检查库存策略,当总安全库存金额确定时,以年期望缺货次数最少为优化目标建立规划模型,计算各零件的安全系数和库存水平,以合理分配安全库存金额,优化库存结构.以单位库存持有成本和单位缺货成本的比值M为拉格朗日系数,应用拉格朗日乘数法求解该模型,并应用边际理论说明了M的实际意义.对不同总安全库存金额求解该模型,得到的"总安全库存服务水平曲线"说明了安全库存与服务水平之间的交换关系.     

零件寿命函数的备件库存控制

针对价值高、存储时间长的A类备件最佳再订货时间和订货批量问题,通过对零件历史使用寿命进行统计分析,拟合得到了此零件的寿命函数,由此函数得到零件在某一时间段内出现故障的概率,结合此零件备件的缺货损失,计算出这一时间段内缺货损失的期望值,通过权衡缺货损失期望值和存储费用,构建了使总成本最低的库存模型,得到备件的最佳再订货时间和订货批量.经实例验证,使用此模型后存储时间和存储费用降低了76.29%.     

需求受相关性影响的设备备件库存优化

备件多级库存模型通常基于备件需求相互独立的假设，但随着库存系统层次的增加以及协同管理方式的应用，备件需求的相关性将显著影响库存优化决策。针对需求具有相关性的备件库存问题，以服务响应时间为约束条件，以库存成本及缺货成本最小化为目标建立备件两级库存决策模型。引入Nataf概率变换法，利用已知的备件需求边缘概率密度函数构造满足特定相关性条件的随机需求样本，并将蒙特卡洛仿真与遗传算法相结合求解最优库存分配方案。仿真算例证明，设备备件库存的最优决策随着需求相关性系数的增大而发生变化，根据需求相关性的变化适当地调整库存决策，有利于降低备件库存系统总成本，提高库存系统对顾客需求的响应能力。     

基于设备使用周期的备件库存控制模型研究

由于设备系统的复杂性、设备故障的随机性,备件的需求难以准确确定,相关费用也难以做出准确评估,从而使得备件这一类库存控制问题变得相当复杂.基于备件的需求量与设备使用周期密切相关这一关键点,从设备的整个使用周期入手,结合设备的使用情况,在备件需求率随时间变化的情况下,对备件库存控制问题进行了研究,建立了在设备整个使用周期内使备件总订货成本和存储费用最低的数学模型,给出了备件最佳订货次数、订货时间和订货批量的计算公式.最后给出了一个算例以证明模型的实用性.     

The Design and Implementation of a Computerized Spare Parts Management Information System

1IntroductionInamodernindustrialenterprise,equipmentinallagemcntisaveryimportantfactortoensurenormalopetation[fi;howevef,spa.re--partmanagementisofprominentimportanceinequipmentmanagement.Withoutsupplyingspafrs-partsit'rmaintcn:.mcew')rk,onecanhardlyfulfilltheproductiontask.Whilesupplyingthespare--part,toestablisheconomicstockquotatoreduceinventories,tospeedupturnoverofthespare--partfund,andtoworkoutmodernmethodsofthespare-partmanagement,areallctiticaltaskswhichspare--partmanagersmustface.When…     

The influence of additive manufacturing on the configuration of make-to-order spare parts supply chain under heterogeneous demand

Additive manufacturing (AM), alongside technological developments, has been used in the production of spare parts with positive results for spare parts supply chains. In this study, we investigate spare parts supply chains serving heterogeneous demands from multiple service locations under the mode of make-to-order. We aim to compare different configurations (i.e. centralised and distributed) of spare parts supply chains in terms of their performance (e.g. sojourn time and cost) and to further propose suggestions to better configure AM-based spare parts supply chains by effectively allocating AM machines at service locations (SLs) or regional distribution centres (RDCs). In order to realise these research objectives, the simulation approach is used as the main research method. Different from the existing perception, our results illustrate that the distributed deployment of AM machines does not always guarantee a quick response, and that centralised configuration is desirable when the demand rate is relatively high due to the pooling effect. The distributed configuration, however, can still be suitable, considering the development of AM technology. Our results also indicate the possibility of a mixed configuration of AM-based supply chains with the potential for outperforming the purely centralised/distributed configuration. The criteria to design such a mixed configuration are also offered.     

Inventory Management of a Plant Spare Parts Supply Chain

In spare parts industries, firms are dealing with a situation which is more and more uncertain due to the supply chain structure and variable demands. This paper presents a Bayesian approach to forecast demand and subsequently determine the appropriate parameter S of an (S - 1; S) inventory system for controlling plant spare parts. We apply the Bayesian approach in an innovative way to specify the initial prior distributions of the failure rates, using the initial estimates and the failure history of similar items. According to the proposed method, a lower base stock than the one currently used is sufficient to achieve the desired service level.     

A survey of semiconductor supply chain models Part II: demand planning,inventory management,and capacity planning

Part I of this three-part series described semiconductor supply chains from the decision-making and functional perspectives, using this as a framework to review the industrial engineering and operations research literature on the problems arising in these supply chains. Part I then reviewed the literature on Strategic Network Design, supply chain coordination, sustainability and simulation-based decision support. This paper, Part II, reviews the areas of Demand Planning, Inventory Management and Capacity Planning in semiconductor supply chains. Part III concludes the series by discussing models to support Master Planning, Production Planning and Demand Fulfilment in this industry.     

Optimal production and inventory decisions under demand and production disruptions

Unanticipated events may take place and disrupt demand and/or production in a supply chain. Conditional on the type, magnitude and duration of disruptions, changes may be called to revise the original production plan. We analyse different disruption scenarios and propose optimal production–inventory models for products facing demand and production disruptions. To lower the cost, we optimise the production run time, purchasing times and order quantity for the manufacturer. Numerical experiments are conducted to examine the influences of disruption time and magnitude on optimal production and purchasing decisions.     

Multi-item inventory model with variable backorder and price discount under trade credit policy in stochastic demand

In this paper, a two echelon supply chain with one manufacturer and one retailer is developed for multi products. The retailer faced with the uncertain demand for all products which follows a normal distribution. The production process is assumed to be imperfect, and the defectiveness is assumed to follow a beta distribution. The manufacturer produces and delivers the products in a number of equal-sized batches to the manufacturer's warehouse, and thereby it is delivers in a number of equal batches to the retailer's warehouse. Shortages are allowed to occur, at the retailer side, and it is backordered partially. The retailer offers a price discount for backordered items to his customers. Both the lead time crashing cost and the partial backorder ratio are considered as the inverse function of lead time. Under these assumptions, there are three inventory models proposed in this paper, one with non-integrated approach, the other with an integrated approach without trade credit and finally an integrated approach with trade credit. A new iterative algorithmic procedure has been developed to minimise the total cost. Finally, numerical examples are given to illustrate the models and the sensitivity analysis is conducted over various model parameters.     

Genetic-algorithms-based algorithm portfolio for inventory routing problem with stochastic demand

This paper presents an algorithm portfolio methodology based on evolutionary algorithms to solve complex dynamic optimisation problems. These problems are known to have computationally complex objective functions, which make their solutions computationally hard to find, when problem instances of large dimensions are considered. This is due to the inability of the algorithms to provide an optimal or near-optimal solution within an allocated time interval. Therefore, this paper employs a bundle of evolutionary algorithms (EAs) tied together with several processors, known as an algorithm portfolio, to solve a complex optimisation problem such as the inventory routing problem (IRP) with stochastic demands. EAs considered for algorithm portfolios are the genetic algorithm and its four variants such as the memetic algorithm, genetic algorithm with chromosome differentiation, age-genetic algorithm, and gender-specific genetic algorithm. In order to illustrate the applicability of the proposed methodology, a generic method for algorithm portfolios design, evaluation, and analysis is discussed in detail. Experiments were performed on varying dimensions of IRP instances to validate different properties of algorithm portfolio. A case study was conducted to illustrate that the set of EAs allocated to a certain number of processors performed better than their individual counterparts.