Optimal Spare Parts Research Articles

Computer Systems Supporting the Management of Machines/Equipment in Hard Coal Mines. Case Study

Abstract Computer systems supporting the management of machines operation and maintenance in companies have been successfully applied in many branches of industry. Over the last few years these systems have been considerably transformed. It also seems that using them for operating mining machines in hard coal mines is inevitable, as maintenance generates high production costs. If implemented, the systems will allow for optimization of stocks, spare parts and materials, supplies, outsourced services as well as maintenance-repair works. Application of a system supporting the management of mining machines/equipment will also enable a better, more effective use of employees. As a result of these actions, the time needed for preparations and repair will be shortened, which will translate directly into the economic effects of mines.

Spare parts inventory control under a fixed-term contract with a long-down constraint

We are interested in service contracts for spare parts. We introduce a new performance measure, XLD (extreme long down), that limits the number of deliveries that are later than an agreed threshold during the contract period. We consider a single item, single location stockpoint serving multiple systems where demand is satisfied in an alternative way if the stockpoint is out of stock. Using a finite horizon Markov decision process, we derive the optimal spare parts inventory policy for meeting the contract at minimum costs. We prove that a state-dependent and time-dependent base stock policy is optimal. We also prove that the optimal base stock level is non-increasing in the number of allowed extreme long downs in the remaining contract period and that the optimal base stock level decreases at most by one unit per step. We formulate three heuristics for our finite horizon problem. These heuristics are a translation of heuristics commonly used for other service measures than the XLD measure. We assess their performance in comparison to the optimal policy. The results of our numerical study show that Heuristic 3, a myopic heuristic, is the best performing with an average optimality gap of 4.5% and an optimality gap lower than 5% in 81% of the instances. The maximum optimality gap is very high for all three heuristics, showing that important savings can be made by taking into account the actual contract performance and the remaining contract duration in stocking decisions.

Mission success evaluation of repairable phased-mission systems with spare parts

Mission success is an important criterion to measure the performance of phased-mission systems, which is the ability of systems to finish the work successfully. Activating the spare parts takes little time, which can reduce the maintenance time while performing the mission. Moreover, adding the spare parts also can ensure the mission to complete with higher success probability. This paper puts forward an integrated modeling method to evaluate the mission success probability for repairable phased-mission systems with spare parts. Some illustrative examples are introduced to verify the flexibility and effectiveness of the proposed approach with different cases. Based on the state analysis of the component group, the risk ranking of component groups or mission phases is discussed to improve the mission success probability effectively. After analyzing the effect of spare parts on mission success probability, the optimal spare parts allocation method is proposed to obtain a cost-effective solution with the maximum mission success probability.

Joint optimization of spare parts inventory and service engineers staffing with full backlogging

We consider the integrated planning of spare parts and service engineers that are needed for serving a group of systems. These systems are subject to different failure types, and for each failure, a service engineer with the necessary spare part has to be assigned to repair the system. The service provider follows a backlogging policy with part reservations. That is, a repair request is backlogged if one of the required resources is not immediately available upon demand. Moreover, a spare part is reserved if the requested spare part is in stock but no service engineer is immediately available. The spare parts are typically slow-movers and are managed according to a base-stock policy. The objective is to jointly determine the stock levels and the number of service engineers to minimize the total service costs subject to a constraint on the expected total waiting times of the repair calls. For the evaluation of a given setting, we present an exact method (computationally feasible for small problems) and an accurate approximation. For the joint optimization, we present a greedy heuristic that efficiently produces close-to-optimal results. We test how the heuristic performs compared to the optimal solution and the separate optimization of spare parts and service engineers in an extensive numerical study. In a case study with 93 types of spare parts, we show that the solution of the greedy algorithm is always within 2% of the optimal solution and is up to 20% better than a separated optimization approach encountered in practice.

Spare parts’ criticality assessment and prioritization for enhancing manufacturing systems’ availability and reliability

Manufacturing systems (MSs) have become more and more complex, due to global competition. Optimal spare parts provisioning plays a critical role in sustaining an anticipated operational competitiveness level, via efficient and effective maintenance of machinery. The forecasting of intermittent demand for spare parts is a challenge, as it is not always possible to avoid random unforeseen breakdowns, which reduce availability and increase the unreliability of manufacturing systems. The aforementioned requires a systemic perspective, in order to perform a criticality analysis and prioritization of the spare parts needed to increase a manufacturing system’s availability and reliability. This paper first demonstrates the development of an empirical model (EM), using a case study MS. The EM enables a criticality analysis to be performed, considering the system perspective of spare part management, by taking maintenance-related and logistics-related factors into account. After that, the machineries are categorized into groups, considering the factors related to the maintenance, logistics and criticality levels. The second part presents how to perform spare part prioritization within a selected group, via an analytic hierarchy process (AHP), to minimize ad hoc suboptimal assessments, together with sensitivity analyses. Finally, it presents the spare part prioritization and the subsequent sensitivity analysis results.

KEBIJAKAN PERSEDIAAN SUKU CADANG DI PT ABC MENGGUNAKAN METODE RCS (RELIABILITY CENTERED SPARES)

Based on data from the production and maintenance department, the highest downtime happen in JDK machine at ABC company. High downtime caused by spare parts lacking or replacement parts when the machine damaged, it cause the engine stop longer. This study analyze and determine spare part inventory the policy, especially critical components in JDK machine. Reliability Centered Spare (RCS) method used in this study, this method essentially determines the optimal spare parts policy for a certain period. The result of research with RCS method shows that the spare parts inventory policy for all critical components of JDK machine in one year ahead by storing or stocking the following amount: packing valve: 469 pieces; teflon: 134 pieces; bearing pump: 10 pieces; mechanical seal: 26 pieces; motor pump: 10 pieces; packing pump: 141 pieces; motor driving reel: 9 pieces; bearing driving reel: 45 pieces; mechanical seal driving reel: 163 pieces; packing heat exchanger: 70 pieces; site glass: 29 pieces; and pressure gauge: 7 pieces.

Research on optimization of spare parts inventory policy considering maintenance priority

Aiming at multi-echelon inventory optimization of spare parts, the process of equipment support under preemptive maintenance and non-preemptive maintenance were analyzed. The support time of spare parts in each maintenance sites was calculated and a model of expected backorder was established. The marginal effect algorithm was utilized to calculate the maintenance priority assignment and inventory policy of spare parts. Finally, through a numerical example, the inventory project of spare parts obtained by the proposed model can greatly reduce the cost of the spare parts while ensuring the availability of the equipment, and which can improve the military and economic benefits of the equipment support commendably.

Quantity Optimization of Spare Parts For Offshore Wind Farm Based on Component Updating

The important parts of the offshore wind turbines are expensive. The irrational allocation of spare parts will lead to low availability of wind turbines and an increase in cost. In order to reasonably determine the number of spare parts, an optimization scheme for the number of spare parts of offshore wind farms based on component updating is proposed. First, considering the complex deterioration process of the components, the time delay theory is introduced in this paper, and time delay theory is applied to the maintenance strategy of offshore wind turbine. This article optimizes the component update cycle through the component maintenance strategy. Then, considering the cycle of spare parts, the availability of the entire wind farm components is taken as the goal with maintenance strategy. The number of spare parts of the offshore wind farm is optimized through the Markov process. Finally, taking the components of the wind turbine as an example, the traditional optimization process is compared. The example shows that the method can correctly configure the number of spare parts and effectively reduce the cost of spare parts management.

Research on Simulation Modeling for Carrying Spare Parts Optimization Considering Random Common Cause Failures

It has been proved to be of great significance to determine the carrying spare parts reasonably for promoting the probability of the system successfully completing a mission. Taking into account the effects of the in-situ repair capacity and random common cause failures occurred in the mission, a novel carrying spare parts optimization model is developed. The proposed model is to maximize the probability of the system successfully completing the next mission considering storage space constraints. Additionally, a solution algorithm integrating the famous Monte Carlo simulation and marginal algorithm is presented to solve the proposed model. Finally, a real example is given to verify the validity of the model, as well as the solution algorithm, and a detailed discussion is carry out to demonstrate the effects of the storage space on carrying spare part quantity and the associated probability of the system successfully completing the mission. The results show that: (1) the proposed model can be used for carrying spare part optimization considering in-situ maintenance capacity and random common cause failure; (3) the reasonable spare part storage space and carrying spare part quantity contribute to a great probability of the system successfully completing a mission.

Optimal spare parts management for vessel maintenance scheduling

Condition-based monitoring is used as part of predictive maintenance to collect real-time information on the healthy status of a vessel engine, which allows for a more accurate estimation of the remaining life of an engine or its parts, as well as providing a warning for a potential failure of an engine part. An engine failure results in delays and down-times in the voyage of a vessel, which translates into additional cost and penalties. This paper studies a spare part management problem for maintenance scheduling of a vessel operating on a given route that is defined by a sequence of port visits. When a warning on part failure is received, the problem decides when and to which port each part should be ordered, where the latter is also the location at which the maintenance operation would be performed. The paper describes a mathematical programming model of the problem, as well as a shortest path dynamic programming formulation for a single part which solves the problem in polynomial time complexity. Simulation results are presented in which the models are tested under different scenarios.

Determination of optimal set of spare tools and supplies for national guard troops mobile workshop and choice of strategy for its replenishment

The strategy of emergency replenishment of spare parts is the most flexible. The strategy of periodic and continuous replenishment, in fact, is extreme cases. The determination of the optimal spare parts is made by using a step-by-step procedure, in which at each step the STS is added to the element selected by a criterion "reliability-cost". The procedure of forming the STS is completed.

An optimal joint maintenance and spare parts inventory model

Optimised maintenance of plant machineries is one of the important tasks to reduce plant operating cost. To carry out these maintenance tasks smoothly, development of spare parts inventory policy is the most prioritised issue. This research aims at the derivation of a joint maintenance and spare parts inventory model for a multi-component system. The decision variables are the order quantity and the ratio of ordering interval to preventive maintenance interval. We used block-based maintenance policy and periodic review inventory policy in this study. At first, a cost function is developed assuming that failure occurrences follow Poisson process with a constant failure rate and machine failures are non-repairable. Second, the model is further expanded for the case when failure occurrences follow non-homogeneous Poisson process and machine failures are repairable. The applicability of the developed models is demonstrated by numerical examples. Sensitivity analysis is also conducted to examine the effects of input parameters.

Optimization of Spare Parts Varieties Based on Stochastic DEA Model

Accurate inventory management starts with the scientific and rational classification of numerous varieties of spare parts. This paper presents a stochastic data envelopment analysis (DEA) model to address the problem of optimization of spares varieties under uncertainty. An index system is proposed in terms of product life-cycle process, which contains five design indexes, four operation indexes and five support indexes. Then, the quantification method of the index system is briefly discussed in preparation for mathematical calculation. A stochastic spares optimization model (SSOM) is proposed based on stochastic DEA with the constraints of 14 factors of the index system. The SSOM could be converted into equivalent deterministic models by probability theory, which overcomes the difficulty in solving non-linear programming. A numerical example is given to illustrate the proposed method in terms of ability to provide reasonable inventory management policies.

Spare Parts Inventory Control under a Fixed-Term Contract with a Long-Down Constraint

We are interested in service contracts for spare parts. We introduce a new performance measure, XLD (extreme long down), that limits the number of deliveries that are later than an agreed threshold during the contract period. We consider a single item, single location stockpoint serving multiple systems where demand is satisfied in an alternative way if the stockpoint is out of stock. Using a finite horizon Markov decision process, we derive the optimal spare parts inventory policy for meeting the contract at minimum costs. We prove that a state-dependent and time-dependent base stock policy is optimal. We also prove that the optimal base stock level is non-increasing in the number of allowed extreme long downs in the remaining contract period and that the optimal base stock level decreases at most by one unit per step. We formulate three heuristics for our finite horizon problem. These heuristics are a translation of heuristics commonly used for other service measures than the XLD measure. We assess their performance in comparison to the optimal policy. The results of our numerical study show that Heuristic 3, a myopic heuristic, is the best performing with an average optimality gap of 4.5% and an optimality gap lower than 5% in 81% of the instances. The maximum optimality gap is very high for all three heuristics, showing that important savings can be made by taking into account the actual contract performance and the remaining contract duration in stocking decisions.

Spare Parts Demand Analysis Method Based on Field Replaceable Unit

It is based on the reliability of spare parts optimization modeling, and the influence of failure rate, fixability, importance and availability on spare parts reserves is added to the model, and the weight of each spare part is calculated by fuzzy analytic hierarchy process.

Spare parts management for irregular demand items

Inventory optimization of high-value spare parts may generate a significant reduction of cost to allow a better allocation of resources in maintenance management. Sherbrooke's METRIC (Multi Echelon Technique for Recoverable Item Control) is the most common method to define an overall optimization process adopting a system-approach. Its main assumption consists of adopting a Poisson distribution to describe the demand pattern of the items. However, many studies proved that in high-availability systems, high-cost spare parts often follow irregular demand patterns, with very frequent zero-demand values.For this purpose, we propose an innovative model for a single site, the ZIP-METRIC, to take advantage of a distribution yet widely adopted in the healthcare and biological sciences, i.e. the Zero-Inflated Poisson. A case study of 1745 items of a European airline fleet demonstrates the model effectiveness, confirming that the ZIP-METRIC outperforms the traditional Poisson-based approach.

The Development of a Concurrent Spare-Parts Optimization Model for Weapon Systems in the South Korean Military Forces

The inventory level of concurrent spare parts (CSPs) has a significant effect on the availability of a weapon system. To improve the accuracy of CSP requirements, we developed a new version of a CSP optimization model that the South Korean military forces subsequently adopted. Our model has major improvements over its predecessor, including (1) combining failure rates from both user (i.e., the Korean military forces) field data and from predictions based on reliability handbooks; (2) modifying the shop-replaceable unit optimization logic; (3) adding an optimization process that uses genetic algorithms; (4) employing practical formulae to calculate operational availability; and (5) adding functions to increase the quality of the optimization results. Our simulation experiments identified that our model met the operational availability targets of the South Korean military forces and dramatically reduced the purchases costs of spare parts compared to the results generated by the previous version of the model.

Joint optimization of condition-based opportunistic maintenance and spare parts provisioning policy in multiunit systems

This study investigates the joint optimization of the strategy for periodic condition-based opportunistic preventive maintenance (CBOM) and a safety policy for the provision of spare parts for multiunit systems with a known number of identical units. Under this policy, maintenance activities as well as the ordering and holding activities for spare parts at each decision point are determined according to the maintenance requirement determined by the state of deterioration of the entire system and the state of the spare parts inventory. On the basis of the derivation of all possible maintenance and spare-parts-related activities at each decision point and their corresponding probabilities, an expected long-run cost rate model is formulated for a general, identical multiunit system to determine its joint optimal strategy. This is accomplished by using semiregenerative process theory that takes into account the cost of maintenance and that of the management of the spare parts inventory. In this model, all possible maintenance requirements and their probabilities are derived using the deterioration state-space partitioning method combined with the derivation of the stationary law of the system state affected by opportunistic maintenance. Further, maintenance activities, the ordering and holding actions for spare parts, and their corresponding probabilities are all determined on this basis. Finally, a case study is carried out to test the proposed optimization method. This involves a wind power farm consisting of a large number of identical main bearings in the turbines. Furthermore, a sensitivity analysis was conducted to analyze the effects of the model.

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.

Integration of Real-Time Demand Information and Spare Parts Distribution Planning for the Optimization of Spare Parts Supply in After-Sales Service Networks

Accurate spare parts demand planning and effective distribution planning is essential for providers of after-sales services in the machine and plant engineering industry to ensure high spare parts availability for maintenance and failure orders (callouts) at a reasonable cost. Low spare parts availability is primarily the result of high uncertainty in spare parts demand, leading to misallocation of parts within aftersales service networks. The lack of spare parts availability causes equipment downtime, resulting in customer dissatisfaction and possible penalty costs for after-sales service providers, if response times are contractually fixed. This paper proposes an approach and planning methods for integrating real-time status information about equipment utilization and service conditions to determine optimal spare parts stocking strategies. For this purpose, spare parts stocking strategies and ordering policies for application in after-sales service networks are analyzed. Furthermore, a binary linear optimization model is developed for the assignment of stocking strategies to spare parts based on real-time demand information of the equipment to be serviced. This method uses data provided by an internationally operating elevator company.