Spare Parts Stock Research Articles

Modelling and numerical assessment of a maintenance strategy with stock through piecewise deterministic Markov processes and quasi Monte Carlo methods

To ensure a power generation level, the French national electricity supply (EDF) has to manage its producing assets by putting in place adapted preventive maintenance strategies. In this article, a fleet of identical components is considered, which are spread out all around France (one per power plant site). The components are assumed to have stochastically independent lifetimes, but they are made functionally dependent through the sharing of a common stock of spare parts. When available, these spare parts are used for both corrective and preventive replacements, with priority to corrective replacements. When the stock is empty, replacements are delayed until the arrival of new spare parts. These spare parts are expensive, and their manufacturing time is long, which makes it necessary to rigorously define their ordering process. The point of the article is to provide the decision maker with the tools to take the right decision (make or not the overhaul). To do that, two indicators are proposed, which are based on an economic variable called the net present value. The net present value stands for the difference between the cumulated discounted cash-flows of the purely corrective policy and the preventive one which including the overhaul. Piecewise deterministic Markov processes are first considered for the joint modelling of the stochastic evolution of the components, stock and ordering process with and without overhaul. The indicators are next expressed with respect to these piecewise deterministic Markov processes, which have to be numerically assessed. Instead of using the most classical Monte Carlo simulations, we here suggest alternate methods based on quasi Monte Carlo simulations, which replace the random uniform numbers of the Monte Carlo method by deterministic sequences called low-discrepancy sequences. The obtained results show a real gain of the quasi Monte Carlo methods in comparison with the Monte Carlo method. The developed tools can hence help the decision maker to take the right decision.

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.

Condition based spare parts supply

We consider a spare parts stock point that serves an installed base of machines. Each machine contains the same critical component, whose degradation behavior is described by a Markov process. We consider condition based spare parts supply, and show that an optimal, condition based inventory policy is 20% more efficient on average than a standard, state-independent base stock policy. We further propose an efficient and effective heuristic policy.

MANAGEMENT PROCEDURE OF SUPPLIES OF UNITS, MATERIALS AND CONSTITUENT PARTS FOR REPAIR AND MAINTENANCE SUPPORT OF TRANSPORT AND PRODUCTION MACHINES

This article considers a question of creation of schemes and sequence of passing of the main stages of process of execution of repair and maintenance works order taking into account formation of stocks of necessary spare parts, materials and constituent parts. Models of an input stream are offered for inventory control system, which are rather adequately described by autoregressive models of order two.

Approximation neighborhood evaluation for the design of the logistics support of complex engineering systems

This paper deals with the problem of designing the logistics support of complex multiindenture and multi-echelon engineering systems, with the aim of determining the spare parts stock and the maintenance resources capacity, as well as the level of repair. The problem is modeled as an integer program with a nonlinear probabilistic constraint on the expected availability, whose satisfaction can only be evaluated by means of very time-consuming simulation experiments. Thus, we use an optimization via simulation approach, in which the search space is eciently explored through an approximated neighborhood evaluation mechanism, which makes use of several parameters estimated by means of simulation. Experimental results on a number of instances show the eectiveness of the proposed approach.

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.

Determination of optimal order-up to level quantities for dependent spare parts using data mining

Consumable spare parts play an important role during regular and periodic maintenance activities depending on equipment criticality of a manufacturing firm. Usually, these spare parts are consumed together on a regular basis to different equipment with a high percentage of commonality. Complete or partial shutdown of maintenance activity may happen if there is shortages in any of these common parts and at the same time, maintaining a high inventory of these parts lead to more cost. If there is a shortage of a spare part, then there is a chance that other dependent spares may remain as idle inventory and incur the opportunity cost. In this paper, a noble approach is adopted to incorporate dependency of items in periodic review (T, S) policy to determine the optimal stock of dependent spare parts considering common cycle time and fill rate for each spares. Our results show that there is considerable reduction of stock level and total cost of inventory when associated spares are considered together for management of inventory instead of considering them individually. Finally, the developed model is applied to a case company.

Customer stock in repairable item systems

Many enterprises that use expensive capital goods outsource the process of spare parts supply and stocking to a maintenance-, repair-, and overhaul provider (MRO-provider). Because of the high value, these spare parts are stocked in repairable item systems, in which defective parts are repaired after removal from the machine and put back into stock. When setting inventory levels, the providers do not consider all of the spare parts that are stocked in the system because customers hold own stock to bridge the delivery time from the MRO-provider. As these spare parts are not property of the MRO-provider, the provider does not take them into account when planning inventory, incurring overstocking at the central depot. A model, which considers customer stock when determining depot inventory levels, is developed by extending the METRIC model with a heuristics. The heuristics calculates backorder levels according to the depot inventory levels as well as customer stock levels and allows for an inventory reduction. Using the proposed model, savings of depot inventory can be up to 100 %, while keeping the backorder level of part requests below a preset threshold.

Spare parts stocking analysis using genetic programming

Optimal solutions to the Level of Repair Analysis (LORA) and the Spare Parts Stocking (SPS) problems are essential in achieving a desired system/equipment operational availability. Although these two problems are interdependent, they are seldom solved simultaneously due to the complicating nature of the relationships between spare levels and system availability (or expected backorder) thus leading to sub-optimal solutions for both problems. This paper uses genetic programming-based symbolic regression methodology to evolve simpler mathematical expressions for the expected backorder equation. In addition to making the SPS problem more tractable, the simpler mathematical expressions make it possible for a combined SPS and LORA model to be formulated and solved using standard optimization techniques. Three sets of spare parts stocking problems are presented to study the feasibility of the proposed approach. Further, a case study for the joint problem is solved which shows that the proposed methodology can tackle the integrated problem.

Forward management of spare parts stock shortages via causal reasoning using reinforcement learning

Our role in this paper was to search the appropriate means serving as a decision support tool for the choice of a policy and procurement planning of spare parts, contributing to the maintenance in operational conditions of industrial equipment and enabling to avoid stock outs and all at a lower cost. For this, we present a generic Bayesian model of consumption of spare parts in a replenishment policy type (T, s, S) adapted (mT, s *, S). The originality of this research is the fact that we characterize the process of consumption of spare parts by a set of typical scenarios, called "consumption configurations" and identified by a system of performance indicators using variables state in a Bayesian model. After defining all these indicators, the research enchain to deploy a Bayesian network which allow, through Bayesian simulation, obtaining a replenishment planning indicating the optimal combination by period: the durations of these replenishment periods, quantities to purchased, types of SP (new or revalorized), costs and associated risk of rupture, purchasing costs and induced storage.

Optimization of Gross Margin in Outsourcing of Management of Inventory of Spare Parts of Production Equipment

The subject of the paper is the presentation of the case-study of the company outsourcing the spare parts stock. As the result of the changes in the agreement between the outsourcer and the large commissioning company, the task of the outsourcing company was to standardize the margin for the individual groups of products, so far highly diversified. The research problem was to analyze the development of margins using CVA (Cash Value Added) analysis and to establish such a level of the above which will satisfy the client’s requirements and provide further employment and generating profit for the outsourcing company.

Research on Joint Optimization of Level of Repair Analysis and Spare Parts Stocks

According to the real background of repair and supply of spare parts, aims to reasonably plan that how to and where to repair the failure parts, where to deploy the repair resources and develop the initial spares configuration program such that the availability target of equipment is achieved with the lowest investment cost, the joint optimization model for level of repair analysis (LORA) and spare parts stocks is established consisting of variable cost and fixed cost that produced in maintenance as well as the equipment availability, and a new joint optimization method for the model is proposed combined Invasive Weed optimization (IWO) and margin analysis, then the optimization is realized. In a given example, the optimization result is gotten, through which the validity of the model and method is proved.

Researches into the Stochastic Modelling of the Brake Shoes Stock Used in Railway Wagon Maintenance

The brake system maintenance of railway wagons consists, amongst other matters, brake shoe replacement whose wear in time exceeds the maximum allowable limit. Their absence in stock leads to the maintenance process stagnation. This causes additional costs of procurement and very high costs because of shortage. Thus, the main objective in control of the brake shoe stocks, in a company carrying out maintenance activities of railway wagons is that of determining the quantity ordered and the interval between orders. Their optimal values will be reached when storage costs, expressed by the cost function (the total mean value of the costs) are minimal, leading to a normal unfolding of the corrective maintenance activities. For the management of the spare parts stocks to be applied various methods, depending on the certainty or uncertainty knowledge of the level demand; of their delivery time and of their supply costs. For avoiding increasing penalty costs due to lack in stock the authors propose, after the estimation of the relative variability of demand for these spare parts, the stochastic modelling utilisation, the demand and delivery time being discrete random variables (depending on season, type and random rhythmicity of the falls), known being the request repartitions of the brake shoes on the basis of statistical records. The research results led to the determination of the value necessary to brake shoes stock so that, the total losses of the company in which carried out the maintenance activities to be minimised, and to establish the range of cost in which is allowed the acquisition of a such spare part when she is missing from the stock.

BMED: A WEB BASED APPLICATION TO ANALYZE THE PERFORMANCE OF MEDICAL DEVICES

In this study, a software application was developed to analyze the performance test results of medical devices. The software (BMED) provides a medical device database and analyses problems with medical devices. The BMED application analysis reports incompatible performance test results in accordance with international safety and inspection standards. The BMED also contains performance test measurement history of all medical devices in the BMED medical device database. The main purpose of this study is to make analysis of the performance control of medical devices faster, easier, mobile and more efficient. The increasing number of medical devices in a hospital is another reason to use applications such as this. The BMED software application was tested using sample data collected during performance test results of a total of 1553 medical devices. The devices were defibrillator, electrocardiography, pulse oximeter, anaesthesia unit, vaporizer, ventilator, electrosurgical unit, physiological monitor, sphygmomanometer, surgical aspirator, phototherapy unit and infant incubator. The performance tests of these medical devices were performed by the biomedical personnel in accordance with the inspection and preventive maintenance system (IPM) procedures, which were developed by the emergency care research institute (ECRI institute) and their results were also interpreted in accordance with the same procedures. The results of application testing showed that the BMED is a very successful application used to analyze performance test results of medical devices. Its usage is very easy, fast and comfortable. The main advantage of BMED is that it can be accessed with mobile devices from anywhere. This feature increases the performance and efficiency of biomedical staff. Additionally, this study showed that the BMED application provides consistent data for medical device problems. Spare part stock management and preventive maintenance activities decreased the repair costs and minimized the number of fault device conditions within the BMED application. All the analysis results also affected the selection and purchasing decisions of medical devices and their technologies. In short, the BMED served a manageable solution to combine all distributed data from a large number of medical devices located in different departments of a hospital — the location of the device, the biomedical number, the device name, the manufacturer, the serial number, the interpretation of the performance test results and the analysis results — into one platform. Thus, the management of medical device inventory was made easier. The future study will be the evaluation of the analysis for other medical devices. It is expected that usage of the tool will increase and it will be a very useful application for biomedical personnel.

Challenges of Maintenance Management Systems

Corporate focus ever more on profitability placing more emphasis on the revenue producing equipments. Correctly maintained and serviced equipment, well managed spare parts stocks as well as plant management decisions based on integrated information, are today’s demands. The integration, not just the interconnection of information and knowledge from the process level, up to the enterprise management level, is tomorrow’s challenge. The positioning of the computerized maintenance management system CMMS today and in the future and the method of selecting and assessing the optimal solution is described.

Application of the Monte Carlo method to marine system maintenance studies considering failure modes and spare part stock control

This paper presents a methodology using Monte Carlo methods to analyse factors affecting the operational efficiency of marine systems. The aim is to show how Monte Carlo methods can take into account a wide range of factors to facilitate the decision making process for maritime operations. Monte Carlo methods have been used to model the varying effects of different failure modes and the items which contribute to failure for a given system. The methods used have been applied to a case study to examine system failure probability, downtime and maintainable item contributions for a marine cooling system.

Integrating inventory control and capacity management at a maintenance service provider

In this paper, we study the capacity flexibility problem of a maintenance service provider, who is running a repair shop and is responsible for the availability of numerous specialized systems which contain a critical component that is prone to failure. Upon a critical component failure, the component is sent to the repair shop and the service provider is responsible for the repair as well as the down-time costs resulting from the system unavailability. In order to decrease the down-time costs, the repair shop keeps an inventory for the critical components, such that a failed critical component can be replaced with a spare one immediately, if it is available. The component inventory stock level and the repair shop capacity level decisions have to be taken jointly by the service provider. The shop floor manager resorts to two different capacity modes in order to make use of capacity flexibility. First one is the single-level capacity mode, in which the capacity level is fixed and is the only capacity related decision. The best results in this mode serve as a reference to the two-level capacity mode, in which there are low (permanent) and high (permanent plus contingent) capacity levels. In this mode, the permanent capacity is always available in the shop, whereas the deployment of the contingent capacity is decided at the start of each period based on the number of components waiting to be repaired in the shop. The relevant capacity decisions of this mode are the permanent and contingent capacity levels, the period length and the states (in terms of number of failed components waiting) where the contingent capacity is deployed. We develop quantitative models based on queuing theory that integrate the inventory level decision with the capacity related decisions for the repair shop, in each of the two capacity modes, in order to minimize the total cost rate of the service provider. Our numerical results suggest that two-level capacity mode can bring substantial savings compared to the best fixed capacity mode and these savings are mostly resulting from lower repair shop capacity usage. Moreover, we find that the system, in most cases, chooses the shortest period length possible, indicating the overarching importance of a fast response to the system state. The system switches to the high capacity mode if the spare part stock is just one or two, then it uses a quite high contingent capacity level, in order to avoid out of stock. If contingent capacity costs are high, system chooses a high level of permanent capacity to prevent frequent capacity switches.

In This Issue

In This Issue

Demand Planning based on Performance Measurement Systems in Closed Loop Supply Chains

The operational readiness of an aircraft fleet is based on a reliable maintenance, repair and overhaul (MRO). In order to reduce the downtimes of aircrafts, damaged components are replaced on site. The need for immediate replacements associated with long lead times require spare parts stocking. To operate on a competitive basis, the optimization of the spare parts inventory level has to take place under consideration of financial and non financial aspects. In order to be able to evaluate and improve the spare parts supply process in the civil aviation industry, the formulation of a performance measurement system is subject matter of this paper. The performance measurement system presented in this paper is based on a balanced scorecard. Herewith, the complex coherences deriving from multiple participants in the closed loop supply chain can be considered by means of cause and effect relationships between the parameters used. The holistic approach leads to a performance measurement system that enables planning divisions to meet the requirements of complex closed loop supply chains and improve operating efficiency.

Joint optimization of level of repair analysis and spare parts stocks

In the field of after sales service logistics for capital goods, generally, METRIC type methods are used to decide where to stock spare parts in a multi-echelon repair network such that a target availability of the capital goods is achieved. These methods generate a trade-off curve of spares investment costs versus backorders. Backorders of spare parts lead to unavailability of the capital goods. Inputs in the spare parts stocking problem are decisions on (1) which components to repair upon failure and which to discard, and (2) at which locations in the repair network to perform the repairs and discards. The level of repair analysis (LORA) can be used to make such decisions in conjunction with the decisions (3) at which locations to deploy resources, such as test equipment that are required to repair, discard, or move components. Since these decisions significantly impact the spare parts investment costs, we propose to solve the LORA and spare parts stocking problems jointly. We design an algorithm that finds efficient solutions. In order for the algorithm to be exact and because of its computational complexity, we restrict ourselves to two-echelon, single-indenture problems. In a computational experiment, we show that solving the joint problem is worthwhile, since we achieve a cost reduction of over 43% at maximum (5.1% on average) compared with using a sequential approach of first solving a LORA and then the spare parts stocking problem.