Repairable Spare Parts Research Articles

Prediction of Power Equipment Emergency Repair Based on Adaptive Neural Network Fuzzy Inference Method

If the emergency repair prediction of power equipment is only made from the perspective of historical spare parts inventory data, it cannot reflect the impact of disaster-causing elements and disaster evolution on the demand for emergency repair spare parts in the future. Therefore, this paper aims to propose a reliable power equipment emergency repair prediction method, and constructs a demand prediction method for emergency repair spare parts of power equipment based on scenario analysis. Constructing a power equipment repair system through intelligent reasoning methods to improve the efficiency of power equipment repair. This article comprehensively uses methods such as literature analysis, model inference, and case simulation verification, this paper innovatively combines the adaptive neural network fuzzy inference system with expert experience. This paper validates the superiority of the prediction method constructed in this paper through comparative analysis. The results show that with the increase of the amount of data, the prediction accuracy of the method proposed in this paper will be improved, which can provide a reference for the subsequent emergency repair prediction of power equipment.

A planning model for repairable spare part supply chain considering stochastic demand and backorder: an empirical investigation

Today, improving machine availability is vital for industries to compete in the global market. Spare parts play an essential role in the maintenance and repair of equipment, but planning an extensive network in strategic industries with various spare parts can be very challenging due to the existence of different decision factors. The spare parts supply chain deals with inventory management issues, which necessitates considering the related decisions such as determining the stock level and order quantity. Moreover, demand uncertainty and long supply time make decision-making more complex. This paper presents a repair and supply planning model for repairable spare parts while considering a modified formulation of demand uncertainty to minimize costs. The model determines the optimal stock level, lateral transshipment, assignment of spare part orders to suppliers, equipment to repair centers, and the number of intervals over the planning horizon used in demand estimation. This research contributes to the literature by integrating recent decisions, using demand approximation by piecewise linearization, and considering backorder in warehouses evaluated by queuing models. A hybrid approach, including heuristic and genetic algorithms, is used to optimize the model using data from an oil company. The results show that using piecewise linearization and integrated repair and supply planning decisions optimizes costs and improves performance. Also, the availability is affected by the demand estimation, which necessitates precision prediction.

A reliable and ensemble forecasting model for slow-moving and repairable spare parts: Data mining approach

Reliable prediction of spare parts is one of the most prevailing challenges for the manufacturing industry and equipment owners, which acts as a double-edged sword. If the objective function focuses on the lack of spare parts, it will cause capital sleep, and otherwise, it would make spare parts shortage. Therefore, the accuracy and reliability of this prediction have always been consequential. Howbeit, the non-uniformity of the spare parts in the real world is remarkable. Some of these parts have intermittent uses (fast-moving type), and some have less and irregular consumption (slow-moving type). Due to this uneven structure of slow-moving spare parts, the application of classical methods has low reliability and performance in forecasting. Therefore, this study concentrated on predicting slow-moving spare parts by developing a reliable and ensemble data mining approach with considering the managerial characteristics (e.g., repair ability and the irregularity of maintenance such as MTBF and MTTR…) along with their history of consumption (the amount of each order and the time interval between two charges). In the proposed approach, the amount of spare parts and the time of the following order is predicted based on an ensemble data mining model from the prediction of future consumption and the probability of scraping in the repair process. This simultaneous attention to data types (consumption and repairs) for slow-moving parts improves data adequacy. Whereas the focus of the literature has been more on improving the data mining models’ performance than on improving data adequacy; by emphasizing both issues at the same time, we can get more reliable results in predicting parts requests with more fluctuations in consumption. A clustering management model for classifying spare parts has been presented during the data preparation process to implement this model. And 51,335 slow-moving spare parts of a Steel Company have been organized and predicted. The results of this study reveal that the application of the proposed approach and ensemble model significantly increases the reliability of spare parts inventory (accuracy by up to 80% and newly defined reliability (RI) by up to 10%).

Integrated demand forecasting and planning model for repairable spare part: an empirical investigation

Efficient resource management methods are essential for spare parts used in the maintenance and repair of equipment. Forecasting plays a critical role in planning, especially under demand uncertainty. Existing works regarding spare parts with intermittent demand focus on the mere forecasting model while integrating the planning and forecasting models are not sufficiently investigated. We examine the interaction between two models to optimise planning and forecasting decisions and prevent sub-optimality. This paper presents two mathematical models, including a planning model that determines stock level, spare part order assignment to suppliers, equipment repair assignment, and the number of intervals over the planning horizon. The second model is the forecasting model by Support Vector Machine (SVM). Considering uncertainty, demand estimation is performed by piecewise linearisation considering the optimal number of intervals in the planning model used in forecasting. An interactive procedure is developed to optimise models. We use an empirical investigation from an oil company providing the spare part supply chain data. The analyses show that demand estimation by piecewise method and integrating the decisions optimises the cost, improves the forecasting accuracy, and planning performance. Moreover, we offer several insights to practitioners that shed light on spare part planning and forecasting decisions.

A forward-reverse repairable spare part network considering inventory management

A forward-reverse repairable spare part network considering inventory management

Expediting in Two-Echelon Spare Parts Inventory Systems

Problem definition: We consider dual sourcing in a distribution network for spare parts consisting of one central warehouse and multiple local warehouses. Each warehouse keeps multiple types of repairable parts to maintain several types of capital goods. The repair shop at the central warehouse has two repair options for each repairable part: a regular repair option and an expedited repair option. Irrespective of the repair option, each repairable part uses a certain resource for its repair. In the design of these inventory systems, companies need to decide on stocking levels and expedite thresholds such that total stock investments are minimized while satisfying asset availability and expediting constraints. Academic/practical relevance: Although most companies have the possibility to expedite the repair of parts in short supply, no contributions have been made that incorporate such dynamic expediting policies in repairable investment decisions. Anticipating expediting decisions that will be made later leads to substantial reductions in repairable investments. Methodology: We use queueing theory to determine the performance of the central warehouse and subsequently find the performance of all local warehouses using binomial disaggregation. For the optimization problem, we develop a greedy heuristic and a decomposition and column generation based algorithm. Results: Both solution approaches perform very well with average optimality gaps of 2.38 and 0.27%, respectively, across a large test bed of industrial size. The possibility to expedite the repair of failed parts is effective in reducing stock investments with average reductions of 7.94% and even reductions up to 19.61% relative to the state of the art. Managerial implications: Based on a case study at Netherlands Railways, we show how managers can significantly reduce the investment in repairable spare parts when dynamic repair policies are leveraged to prioritize repair of parts whose inventory is critically low.

Stockout risk estimation and expediting for repairable spare parts

Stockouts of repairable spares usually lead to significant downtime costs. Managers of Maintenance Repair Organizations (MROs) seek advance indicators of future stockouts which might allow them to take proactive actions that are beneficial for achieving target service levels with reasonable costs. Among such (proactive) actions, the most common, and the cheapest one is expediting existing repair processes.In this study, we develop an advance stockout risk estimation system for repairable spare parts. To the best of our knowledge, this is the first study to estimate the future stockout risk of a repairable part. The method considers different statistics, e.g. the number of ongoing repair processes, demand rate, repair time, etc. to estimate stockout risk of a repairable part for a given planning horizon. In our field tests with empirical data, the suggested method overperforms two heuristic approaches and achieves accuracy rates of 63% for 15 day-planning horizon and 83% for 45 days.We also suggest a repairable inventory control system including repair expediting, inspection and condemnation processes. To optimize the control parameters we suggest a simple algorithm considering two constraints: Target service level and maximum fraction of expedited demand. The algorithm is proved to be efficient for finding the optimum policy parameter in our tests with empirical data. Tests with empirical data suggest savings up to 8%. Both systems are implemented at an MRO as building blocks of a inventory control tower. The impact of the implementation is assessed with empirical simulations and verified from the financial indicators of the company.

Modern methods of motor-wheel gearboxe diagnostics of BelAZ dump trucks

The most important mining enterprise indicators are the reliability and durability of its equipment. These equipment parameters have the greatest impact not only on the mining complex efficiency, but also on the extracted minerals price, as the equipment downtime, as well as, the cost of repair works and spare parts purchase are included in the cost of coal. Thus, the main goal in improving mining production is to improve the reliability and durability of used equipment. Researches carried out in the coal company ‘Kuzbassrazrezugol’ showed that the largest number of mining equipment downtime, in particular of mining dump trucks, occurs due to unexpected failure of a motor-wheel gearbox. The introduction of the additional technological operation for diagnosing the actual technical condition of gearboxes in the maintenance procedure will allow monitoring the condition of the entire gearbox and, in particular, its components by an in-place repair method. This method consists of continuous or periodic measurement of the vibrations of the operating gearbox mechanisms. The analysis of the unit and its individual parts suitability is carried out on the bases of obtained measurements. This methodology usage at a mining enterprise will significantly reduce the number of equipment downtime and thereby increase its efficiency.

A risk-averse simulation-based approach for a joint optimization of workforce capacity, spare part stocks and scheduling priorities in maintenance planning

We model a maintenance system consisting of one repair facility, where repairables are kept on inventory to serve assets to prevent downtime and increase availability. We seek optimal values of the repairable spare parts stocks and workforce capacity in the repair facility. Further, we simultaneously search for the best repair scheduling rule that minimizes total inventory holding and backorder costs associated with the downtime of assets. The joint optimization problem under study brings about two additional challenges: (i) the difficulty of analyzing such systems due to the lack of analytical (i.e., queuing) models, and (ii) the difficulty in incorporating the decision maker’s risk attitude regarding uncertainties. We develop a risk-averse simulation-based optimization approach, in which the decision maker’s risk attitude is modeled as a trade-off between the expected and the worst-case costs in the objective function. In the developed approach, the repairable spare part supply system is analyzed with a discrete-event simulation (DES) model. The DES model is coupled with an improved reduced variable neighborhood search (IRVNS) meta-heuristic that seeks the optimal values of decision variables. We compare the performance of the proposed risk-averse simulation-based optimization approach with several plausible benchmark methods commonly used in practice and with well-known meta-heuristic algorithms.

Repairable spare parts provisioning for multiregional expanding fleets of equipment under performance-based contracting

Performance-based service is becoming a dominant business strategy, especially in the capital-intensive equipment industry. When new equipment is released into multiregional markets, managing repairable spare parts provisioning so as to maximize the service profit from an increasing number of installations becomes challenging since the demand for spare parts varies over time across multiregional markets. A two-echelon repairable spare parts service network is studied to support performance-based service under multiregional fleet expansions. This study proposes an adaptive multi-phase basestock policy that dynamically adjusts the basestock levels in the two-echelon warehouses and the repair capacity in the central depot to meet the nonstationary demands. A profit-centric model is formulated that considers linear-step revenue and availability bands. Using the structural properties of the optimization model, a greedy algorithm is developed based on marginal analysis to solve the problem. Numerical experiements are conducted using a case study of a real business problem and its variants to demonstrate the effectiveness and applicability of the proposed model and the greedy algorithm.

PREVISION AND OPTIMISATION OF REPAIRABLE SPARE PARTS: A CASE STUDY IN THE PETROLEUM INDUSTRY

This study was motivated by the high costs incurred by an energy company for repairable spare parts for faulty mission-critical items, particularly with those that operate until failure. The objective of this paper is to develop and apply a method for repairable spare part inventory management of run-to-failure equipment. To achieve a robust method that incorporates the data collected from previous failures, such as environmental factors and operating conditions, we propose an optimisation approach based on an accelerated failure time model. Accelerated failure time is used as a reliability regression model with covariates to describe different operational conditions. An algorithm is also developed to consider the repairable nature of the equipment, predicting the number of spare parts based on the expected number of failures in the period and the equipment repair cycle. The proposed method is applied using data from three different power units of electrical submersible pumps, a mission-critical item in oil production. The results show an average reduction of 60.6 per cent in the required number of spare parts, considering an average fill rate of 95.33 per cent. This reduction implies an estimated annual savings of around US$664,720 in inventory costs, considering the analysed units.

Hybrid optimisation algorithm for solving the multi‐level spare parts inventory optimisation problem

Aiming at the low quality of the solution obtained with the traditional marginal analysis algorithm for solving multi-level inventory optimisation problem of repairable spare parts, a hybrid algorithm integrating particle swarm optimisation, marginal analysis algorithm-METRIC model and tabu search is proposed. In the early stage of the hybrid algorithm, particle swarm optimisation is used to explore the global solution space and in the later stage, tabu search is used to local search, and the marginal analysis algorithm is used to constructing neighbourhood. The experimental results show that the proposed algorithm can solve the problem of spare parts multi-level inventory optimisation effectively and efficiently.

Does Quality Matter? A Study on Customer Expectation in Motorcycle Repair Provider in Surabaya, Indonesia

Motorcycle has become a common vehicle in Indonesia due to its flexibility to drive around in various geographical terrain. The number of motorcycle purchases in Indonesia is increasing annually, along with the repair services and spare part selling. Abundant repair service providers create fierce competition among themselves, so many providers maintain their service quality in order to retain their customer. The aim of this research is to investigate the impact of service quality and relationship quality to customer satisfaction in motorcycle repair providers. This research is using the quantitative approach, in which the data are collected by questionnaires distributed to samples of populations. The population is the customers who have purchased repair services and spare parts for their motorcycle, and the number of samples is 100 respondents. The data are analyzed with a SmartPLS software, and the results show a positive significant impact from service quality to customer satisfaction, a positive significant impact from service quality to relationship quality, and a positive impact from relationship quality to customer satisfaction. Therefore, for the long term competitive advantage, motorcycle repair service providers have to consider their service quality and relationship quality to retain their customers.

Research on Multi-level Inventory Optimization Algorithm of Repairable Spare Parts Based on Two Improved Differential Evolution

Research on Multi-level Inventory Optimization Algorithm of Repairable Spare Parts Based on Two Improved Differential Evolution

Classification methods for problem size reduction in spare part provisioning

Spare part provisioning for asset-intensive companies is a complicated problem due to the large number of items, low demand rates, and multi-echelon environment. A primary strategy for reducing the size and managing a large number of spare parts is using grouping techniques and data aggregation. In this paper, we address the question of how to reduce the size and complexity of large-scale, two-echelon, service part provisioning systems to benefit both inventory service levels and managerial processes while considering performance trade-offs. This paper contributes a performance-based inventory classification approach for a two-echelon inventory model by developing a novel ranking method. First, it defines the concept of the artificial stocking policy as a new classification criterion in the literature. Then, it adopts a non-subjective weighted linear scoring method for ranking items in the entire network. Finally, it presents a heuristic partitioning method, which is evaluated and compared with complete enumeration and eight alternative clustering and classification methods. The proposed model is implemented and tested in the context of the classic repairable spare part inventory model, called the VARI-METRIC. The results indicate that the proposed method is easy to apply and significantly outperforms the alternatives.

The spare part maintenance of cake breaker conveyor with reliability centered spares method

The company that this research conducted is a manufacturing industry that produces CPO. Many machines in the production section need maintenance activities so that existing machines can work without inhibiting the production process. At seed processing station, there is CBC machine that experienced frequent occurrence damage. The machine applies corrective maintenance regardless of reliability machine production components, consequently, often occur sudden machine damage. Calculation of time interval-based maintenance policy scheduled discard task and needs a replacement component (spare part) that should be available are concerned with an optimal using method of Reliability-centered Spares (RCS). Based on the results of the calculations are based on a policy scheduled discard task then obtained a policy to perform maintenance on a screw conveyor components is 91 days, hangers and bearings are 40 days, jig-drilled couplings is 21 days. The result of the method of RCS acquired number of screw conveyor parts needs with this type of repair of repairable spare is 3 units for one year, while jig-drilled couplings with this type of repair repairable, need spare part 27 units for a year, as well as the hangers and bearing with this type of repair of repairable spare part, needs 21 units for one year.

Dynamic supplier selection approach for mining equipment company

PurposeThis paper aims to address the supplier selection (SS) problem under dynamic business environments to optimize the procurement cost of spare-parts in the context of a mining equipment company (MEC). Practically, involved parameters’ value does not remain constant as planning periods due to fluctuation in the demand and their market dynamics. Therefore, dynamicity in the parameter is considered as an important factor when a company forms a responsive chain through most eligible suppliers with respect to planning periods. This area of study may be considered for their complexities to the approaches toward order-allocations with bi-products of unused and repair spare-parts.Design/methodology/approachAn integrated methodology of analytic hierarchy process (AHP) and mixed-integer non-linear programming (MILP) is implemented in the two stages during each planning periods. In the first stage, AHP is used to obtain the relative weights with respect to each spare-parts of each criterion and based on that, the ranking is evaluated in accordance with case considered. And in the second stage, MILP is formulated to find the allocations of each spare-part with two distinct approaches through Model-1 and Model-2 separately. Moreover, Model-1 and Model-2 are outlined based on the ranking and efficient parameters-value under cost, limited capacities, quality level and delay lead time respectively.FindingsThe ranking and their optimal order-allocation of potential suppliers are obtained during consecutive planning periods for both unused and repair spare-parts. Subsequently, sensitivity analysis is conducted to deduce the key nuggets with the comparison of Model-1 and Model-2 in the changing of capacity, demand and cost per spare-parts. From this analysis, it is found that suppliers who have optimal parameter settings would be better for order-allocations than ranking during the changing planning period.Practical implicationsThis paper points out the situation-specific approach for SS problem for a mining industry which often faces disruptive supplying environments. The managerial implication between ranking and parameters are highlighted through Model-1 and Model-2 by sensitivity analysis.Originality/valueIt provides useful directions for managers who are involved in the procurement of spare-parts in the mining environment. For this, suppliers are selected for order-allocation by using Model-1 and Model-2 in the dynamic business environment. The solvability of the model is presented using LINGO 17. Furthermore, the case company selected in this study can be extended to other sectors.

A clustering-based repair shop design for repairable spare part supply systems

Abstract In this study, we address the design problem of a single repair shop in a repairable multi-item spare part supply system. We propose a sequential solution heuristic to solve the joint problem of resource pooling, inventory allocation, and capacity level designation of the repair shop with stochastic failure and repair time of repairables. The pooling strategies to obtain repair shop clusters/cells are handled by a K-median algorithm by taking into account the repair time and the holding cost of each repairable spare part. We find that the decomposition of the repair shop in sub-systems by clustering reduces the complexity of the problem and enables the use of queue-theoretical approximations to optimize the inventory and capacity levels. The effectiveness of the proposed approach is analyzed with several numerical experiments. The repair shop designs suggested by the approach provide around 10% and 30% cost reductions on an average when compared to fully flexible and totally dedicated designs, respectively. We also explore the impact of several input parameters and different clustering rules on the performance of the methodology and provide managerial insights.

Cross-training policies for repair shops with spare part inventories

We study a spare part supply system for repairable spare parts where parallel repair servers may have multiple skills (can repair different failed parts). Demands for the spares occur according to Poisson processes with different rates. The failed spare parts are immediately replaced from the inventory. Otherwise, failed parts are backordered and fulfilled when a spare of the same type becomes available (repaired). The repair servers are heterogeneous and can process certain types of repairables only if they have the necessary skill. In this system, in contrast with the other skill-optimization models, there is a trade-off between adding extra skills to servers (training) or adding extra inventory. In this paper, we formulate a mathematical model to optimize the assignment of skills to servers taking into account inventories for the ready-to-use spares and backorder costs (penalties). To optimize the skill assignments and inventories, we use a hybrid approach combining a Genetic Algorithm (GA) with simulation modeling. The proposed simulation-based optimization heuristic is used for extensive analysis of optimal skill assignments where we show that partial flexibility for repair servers with limited cross-training will lead to lower total system cost.

A double-level combination approach for demand forecasting of repairable airplane spare parts based on turnover data

To address the problem that the demand forecasting methods for repairable airplane spare parts are not advanced, and that the basic forecasting data are not consistent with actual consumption, this paper proposes a double-level combination forecasting approach for repairable spare parts based on relevant data. First, we conduct an analysis for the factors that influence the demand of repairable spare parts. Second, five types of individual direct forecasting models are combined to establish a double-level combination forecast model, which is superior to both individual combination forecasting models and individual direct forecasting models. Finally, we evaluate the forecasting performance by utilizing consumption data for an aircraft fleet and turnover data for an aircraft. The forecasting results provide strong evidence that that the double-level combination forecast model is more accurate and consistent with actual demand.