System optimization-oriented spare parts dynamic configuration model for multi-echelon multi-indenture system

In order to optimize the spares configuration project at different stages during the life cycle, the factor of time is considered to relax the assumption of the spares steady demand in multi-echelon technique for recoverable item control (METRIC) theory. According to the method of systems analysis, the dynamic palm theorem is introduced to establish the prediction model of the spares demand rate, and its main influence factors are analyzed, based on which, the spares support effectiveness evaluation index system is studied, and the system optimization-oriented spares dynamic configuration method for multi-echelon multi-indenture system is proposed. Through the analysis of the optimization algorithm, the layered marginal algorithm is designed to improve the model calculation efficiency. In a given example, the multi-stage spares configuration project during its life cycle is gotten, the research result conforms to the actual status, and it can provide a new way for the spares dynamic optimization.

Location and allocation problem for spare parts depots on integrated logistics support

In equipment integrated logistics support(ILS), the supply capability of spare parts is a significant factor. There are lots of depots in the traditional support system, which makes too many redundant spare parts and causes high cost of support. Meanwhile,the inconsistency among depots makes it difficult to manage spare parts. With the development of information technology and transportation, the supply network has become more efficient. In order to further improve the efficiency of supply-support work and the availability of the equipment system, building a system of one centralized depot with multiple depots becomes an appropriate way.In this case, location selection of the depots including centralized depots and multiple depots becomes a top priority in the support system. This paper will focus on the location selection problem of centralized depots considering ILS factors. Unlike the common location selection problem, depots in ILS require a higher service level. Therefore, it becomes desperately necessary to take the high requirement of the mission into account while determining location of depots. Based on this, we raise an optimal depot location model. First, the expected transportation cost is calculated.Next, factors in ILS such as response time, availability and fill rate are analyzed for evaluating positions of open depots. Then, an optimization model of depot location is developed with the minimum expected cost of transportation as objective and ILS factors as constraints. Finally, a numerical case is studied to prove the validity of the model by using the genetic algorithm. Results show that depot location obtained by this model can guarantee the effectiveness and capability of ILS well.

Spare Parts Demand Forecasting:a Review

Spare parts are very common in industry and military fields, and the investigations of spare parts demand forecasting methods have draws much attention in recent years. However,to the best of our knowledge,only few papers reviewed the forecasting papers systematically. This paper is an attempt to provide a novel and comprehensive view to summarize these methods. A new framework was proposed to classify the demand forecasting methods into four categories,including empirical methods,methods based on historical data,analytical methods and simulation methods. Some typical literatures related to each category were reviewed.Moreover, a general spare parts forecasting procedure was summarized and some evaluation criteria were presented. Finally,characteristics of different forecasting methods and some avenues for further research were illustrated. This work provides the managers with a systematical idea about the spare parts demand forecasting and it can be used in practical applications.

Joint optimization of maintenance inspection and spare provisioning for aircraft deteriorating parts

With the wide application of condition based maintenance(CBM) in aircraft maintenance practice, the joint optimization of maintenance and inventory management, which can take full advantage of CBM and reduce the aircraft operational cost, is receiving increasing attention. In order to optimize the inspection interval, maintenance decision and spare provisioning together for aircraft deteriorating parts, firstly, a joint inventory management strategy is presented, then, a joint optimization of maintenance inspection and spare provisioning for aircraft parts subject to the Wiener degradation process is proposed based on the strategy.Secondly, a combination of the genetic algorithm(GA) and the Monte Carol method is developed to minimize the total cost rate.Finally, a case study is conducted and the proposed joint optimization model is compared with the existing optimization model and the airline real case. The results demonstrate that the proposed model is more beneficial and effective. In addition, the sensitivity analysis of the proposed model shows that the lead time has higher influence on the optimal results than the urgent order cost and the corrective maintenance cost, which is consistent with the actual situation of aircraft maintenance practices and inventory management.

Forecasting Spare Parts Demand Using Statistical Analysis

Spare parts are very essential in most industrial companies. They are characterized by their large number and their high impact on the companies’ operations whenever needed. Therefore companies tend to analyze their spare parts demand and try to estimate their future consumption. Nevertheless, they face difficulties in figuring out an optimal forecasting method that deals with the lumpy and intermittent demand of spare parts. In this paper, we performed a comparison between five forecasting methods based on three statistical tools;Mean squared error (MSE), mean absolute deviation (MAD) and mean error (ME), where the results showed close performance for all the methods associated with their optimal parameters and the frequency of the spare part demand. Therefore, we proposed to compare all the methods based on the tracking signal with the objective of minimizing the average number of out of controls. This approach was tested in a comparative study at a local paper mill company. Our findings showed that the application of the tracking signal approach helps companies to better select the optimal forecasting method and reduce forecast errors.

Research on Three-Echelon Inventory Model and Algorithm for Valuable Spare Parts in Weapon Equipment

According to the current situation of repair and supply for valuable spare parts in the weapon equipment of the PLA,using the research results of the repairable spare parts inventory theory,an inventory model of the valuable spare parts in three-echelon repair and supply system is established by expanding the classical METRIC model and theory,and the genetic algorithm is utilized to solve the model.In the algorithm,the chromosome representation and initial population production,the crossover and mutation operators are designed.By using an example,a simulation analysis is carried out to verify the model's correctness.

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.

Prioritizing Maintenance Spare Parts Based on Supportability Analysis and Neural Network

In order to facilitate spare parts management,an integrated approach of BP neural network and supportability analysis(SA)was proposed to evaluate the criticality of spare parts as well as to prioritize spare parts.Influential factors of prioritizing spare parts were detailedly analyzed.Framework of the integrated method was established.The modelling process based on BP neural network was presented.As the input of the neural network,the values of influential factors were determined by supportability analysis data.Based on the presented method,spare parts could be automatically prioritized after supportability analysis for a new system.A case study results showed that the new method was applicable and effective.

Purchase-oriented Classification Model of the Spare Parts of Agricultural Machinery

Based on the classification of spare parts and the research results of the demand of spare parts,a three-dimensional classification model of spare parts of agricultural machinery is established,which includes the application axis sorted by technical characteristics,the cost axis classified by ABC method,and the demand axis classified by the demand of the spare parts of agricultural machinery.These dimension axes represent different factors,and the application of factors in purchase is analyzed.Guiding value of each dimension axis is summarized in the field of the spare parts purchase;and corresponding strategy instruction is put forward.Integrated application of these strategies by model makes the purchase have more realistic operational meaning.Application field of the three-dimensional model of spare parts is discussed;and the direction for further research is pointed out.

Maintenance Strategies and Spare Parts Inventory Management for Use of Reconditioned Items

The maintenance management of construction equipment occupies a prominent place in the overall strategy for managing the equipment fleet. It is also linked to the management of the stock of spare parts and particularly reconditioned spare parts. Because of the relatively low purchase cost of these, most companies are tempted by this option. The purpose of this article is precisely to study the impact of this category of parts in the stock and also in the optimization of maintenance costs, knowing that to do this;their use must obey certain constraints. It is therefore important to be able to characterize these reconditioned parts by estimating their optimal age, and studying their advantages and also their limits. The determination of the optimal age of reconditioned parts will be such that the total cost of replacement with used parts is at most equal to the total cost of replacement with new parts at the end of the operating period.

Ship spare parts demand forecast based on RBF neural network

Due to the fact that in ship maintena n ce process,the method of determining the number of spare parts is not scientific and the actual operation is complicated,this paper analyzes four major facto rs affecting the number of ship spare parts,including number of main planned op eration s,total times of disassembling in maintenance,accumulated working time and mea n t ime between failures.It also establishes a spare parts demand forecast model b ased on the affecting factors and radial-basis function(RBF) neural network.F inally,the paper provide s forecast examples and makes a comparison between the examples and back propaga tion(BP) neura l network forecast result.The comparison results s how that the forecast based on RBF neural network is simple and the forecast res ult fits the actual situa tion and fitting effect is better than that based on BP.

Analysis and Improvement Measures for the Current Situation of Spare Parts Management (SPM)

Based on an investigation on the current situation of SPM in some enterprises, this paper presents the common probletns and poor fields existing in SPM in enterprise; analyzes the reasons which resulted in the situation, and then puts forward some feasible measures to improve it. Lastly, some fields that should be paid more attention in SPM are provided.

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

This paper introduces the design and implementation of an equipment spare-part-management information system, which was developed by Microsoft Visual Foxpro5.0 and runs under the Windows95 environment, its basic principle, the information flow diagram and functional modules, and finally applications of the system.

Spare Parts Surgery—Salvage of a Below Knee Amputation Stump

“Spare parts surgery” is well-described in the plastic surgery literature. In the setting of trauma, otherwise discarded parts can be utilized for reconstruction resulting in a superior functional and aesthetic outcome for the patient. We describe the use of spare part surgery in order to preserve a functional knee articulation in a patient undergoing amputation for compartment.

基于改进Croston方法的多需求模式零备件预测

维修备件管理是提高产线可靠性、实现降本增效的关键。针对具备间歇性与随机性特征的维修备件需求预测问题,提出了基于改进Croston方法的备件需求预测模型。依据Syntetos准则基于间断性与波动性特征将备件需求划分为4类。针对含有波动性特征的需求,基于Croston方法主要思想将备件需求预测分解为需求发生状态预测和需求量预测两类问题,设计了集合经验模态分解(ensemble empirical mode decomposition,EEMD)-长短期记忆网络集成(long short-term memory,LSTM)预测模型。EEMD方法将剧烈波动序列分解为若干相对平稳的分量,进而采用LSTM方法对各分量进行预测。针对含有间断性特征的需求,引入信号处理技术中的信号调制技术,将需求发生状态0-1二值序列进行连续化处理。所提方法解决了备件需求波动性强、间断性大的难题,已应用于湖北中烟武汉卷烟厂,证明了方法的优越性与可行性。

基于主成分分析-BP神经网络的风电备件需求预测

风电机组具有结构复杂,运维困难,且长期处于恶劣的工作环境的特点。风电备件的需求预测有助于为风电场配备最合适的备件数,以确保风电场的平稳、高效运行。构建主成分分析-反向传播(principal component analysis-back propagation,PCA-BP)模型,针对受多因素影响的复杂备件,先利用PCA将影响风电备件的要素进行筛选,再利用BP神经网络算法,得到最为精确的预测结果。比较自回归积分滑动平均(autoregressive integrated moving average,ARIMA)模型、BP神经网络预测和PCA-BP神经网络预测的结果。结果表明:PCA能显著降低神经网络预测误差,预测的精度为93.94%,高于BP神经网络预测的88.39%和ARIMA模型的85.31%,所以PCA-BP神经网络模型的预测精度准确且有可靠结果,能够适用于风机备件的需求预测。

基于质量问题统计数据的某型车辆备件配置研究

基于交付用户使用后的某型特种车辆质量问题统计数据,对发生的质量问题进行了分类和分析,梳理出车辆易损件备件清单。最后对该产品备件配置存在的问题进行了探讨并提出了建议,以期对其他产品备件配置提供一定的借鉴。

基于飞机状态的备件动态规划技术

伴随民航业的竞争日趋激烈,航空公司迫切需要控制成本以提升效益。民机备件既是直接影响飞行安全的关键因素,备件成本又在航空公司可控成本中占比最高。因此,在保证飞行安全的前提下做好备件规划,对航空公司有至关重要的意义。根据民机备件的安全级别、故障修复时限等对备件分级设置保障率水平,采用符合备件故障特性的马尔可夫生灭过程,建立满足给定保障率水平的备件量计算模型,并在此基础上建立以保障成本最低为目标函数,满足保障率水平为约束函数的备件量动态规划模型。基于飞机状态分析历史故障数据、季节性、日利用率、机队规模对备件保障的影响,在所建模型中将故障率季节性差异、日利用率和飞机停场损失淡旺季差异进行评估以减小计算结果与实际需求的偏差。以H航空公司ERJ-190机型显示组件为例,对所建模型进行应用验证,计算结果与H航空公司运营实际相吻合,证明所建模型可为航空公司备件规划提供技术方法支持。

数据与知识双驱动的备件需求模糊预测模型

针对知识驱动型需求预测模型所需的专家知识稀缺、数据驱动型需求预测模型可解释性不足的问题,提出了数据与知识双驱动的备件需求模糊预测模型。该模型基于模糊聚类算法将数值型数据聚类为结构简单、可解释性强的规则库,运用模糊逻辑将领域专家知识表示为Mamdani型规则库。在此基础上,引入了一种新型智能计算理论——模糊网络理论对两类规则库进行合并运算,形成初始预测模型。采用遗传算法优化模型规则库的模糊集参数来提高模型预测准确性。通过与模糊聚类算法进行对比,提出的模型在可解释性以及准确性指标上均具有优势。

备件需求预测中的不确定性问题研究综述

备件需求不确定性表现为随机性、多样性、时变性、信息不充分性,预测过程中很难精确地描述备件消耗与影响因素之间错综复杂的关系。以智能计算理论为代表的处理不确定性的各种方法和工具迅速发展。梳理处理备件需求预测不确定性的相关文献,按照不确定性的随机性、模糊性、不完全性、复合不确定性四大类属性,对每个大类分别进行了综述,并总结了相关研究的局限性与发展方向。研究成果可为装备备件管理提供参考。