Dynamic Prediction Method for Valuable Spare Parts Demand in Weaponry Equipment Based on Data Perception

Missile is an important weapon system of the army.The spare parts of missile equipment are significant effect on military operations.In order to improve the mission completion rate of missile equipment in wartime,this paper introduces data sensing method to forecast the demand of valuable spare parts of missile equipment dynamically.Firstly,the information related to valuable spare parts of missile equipment was obtained by data sensing,and the sample size was determined by Bernoulli uniform sampling probability.Secondly,according to the data quality of multi-source and multi-modal,the data requirement for dynamic demand prediction of valuable spare parts of missile equipment was obtained.Finally,according to the characteristics of the spare parts,the life of the spare parts was predicted,realizing the dynamic prediction of the demand for valuable spare parts of missile equipment.The results show that the demand of valuable spare parts of missile equipment can be predicted dynamically by using this method,the accuracy is higher than 95%,and the real-time performance is more excellent.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Auto Spare Parts Export: At a Crossroad

Chinese car spare parts sup-pliers have arrived at a cross-roads,as the golden age for component export has gone forever,when the production method was simple,costs were low and profits were high.What will be the way out in front of the tremendous export pres-sures?

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.

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

Based on an invesrigarion on the current situation of SPM in some enterprises, thispaperpresents the common problems and poor fields existing in SPM in enterprise; analyzes thereasons which resulted in the situation, and then puts forward some feasible measures to improveit Lastly, some fiethe that should be paid more attention in SPM are

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

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

Optimal Allocation of Fleet's Spare Parts for System Reliability

Allocation of fleet's spare parts is rarely studied due to its complexity. However, this task is extremely important because the warship's service level highly relies on the maintenance logistics' level. In this study, the readiness ratio is proposed as a critical index in measuring the system's reliability. A well-established mathematical model adopting the optimization method of spare part allocation is also introduced. The objective is to minimize the number of each spare part while satisfying the fleet's system reliability. The fault tree analysis(FTA) is applied to analyze the system's failure logic and stratify the units on ship. As a result, the strategy of spare part sharing can be introduced in detail. The solution algorithm is developed, and the simulation experiments to obtain the key parameters are conducted. The proposed model and algorithm are applied to an actual fleet of two warships, and results show that the method above is feasible and can be directly applied into practice.

Warship Spare Parts Configuration Optimization for Stock Control:Investigating the Gap Between Qualitative and Quantitative Constraints

This paper investigates the gap between qualitative and quantitative constraints in spare parts stock control, with specific reference to warship spare parts support projects. A critical literature review of theoretical contributions about qualitative or quantitative factors for warship spare parts warehouse management is firstly provided, which allows to analyze the reasons for this qualitative-quantitative gap by addressing the limitations of spare parts models developed in the literature. Therefore a model including cloud model, marginal analysis and Lagrange multiplier method(CML) for study is proposed in this paper to bridge the gap. The model is used to solve the mix-constraints(both qualitative and quantitative constraints are considered) problem in a logic decision diagram particularly at the different decision nodes of the diagram. Finally, verifying test results show that the algorithm is feasible and its optimal support project meets the needs of engineering practices.

Classification methodology for spare parts management combining maintenance and logistics perspectives

Spare parts management is a function of maintenance management that aims to support maintenance activities,giving real-time information on the available quantities of each spare part and adopting the inventory policies that ensure their availability when required,minimizing costs.The classification of spare parts is crucial to control the vast number of parts that have different characteristics and specificities.Spare parts management involves mainly two areas,maintenance and logistics.Therefore,the integration of both input information is recommended to make decisions.This paper presents a multicriteria classification methodology combining maintenance and logistics perspectives that intends to differentiate and group spare parts to,subsequently,define the most appropriate stock management policy for each group.The methodology was developed based on a case study carried out in a multinational manufacturing company and is intended to be included in its computerized maintenance management system to support decision-making.

Modeling on Spare Parts Inventory Control Under Condition Based Maintenance Strategy

In order to optimize the spare parts inventory, we present a decision-making model under condition based maintenance policy for a single equipment system subjected to continuous and random deterioration. Firstly,a probability model of the spare parts support is established, according to the requirement of a predetermined probability of stockout. It can determine the optimal spare parts stock level. Secondly, the spare parts ordering decision is made according to the equipment deterioration level, and it can optimize the spare parts ordering. The objectives of this model are to minimize the spare parts inventory, and the expected total operating cost. Thirdly,a numerical example is given to illustrate this model. The results prove that the optimal preventive maintenance threshold obtained from the proposed model can satisfy the spare parts support requirements.

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.