Optimal Spare Parts Research Articles

Design of unreliable flow lines: How to jointly allocate buffer space and spare parts

The design of flow lines is an extensive area of research whereby most publications focus on buffer allocation to cope with random influences like breakdowns or random differences in processing times. In addition, spare parts can directly increase the machines’ availability. However, only a few papers consider the simultaneous optimization of buffer space and the possible number of spare parts in stock. The literature hardly even considers the evaluation of such systems. We are the first to introduce the joint optimization of buffer space and spare parts for flow lines of arbitrary length. First, we aim to allocate buffer capacities and spare parts efficiently. Since the buffer allocation problem is NP-hard, we can expect only to find near-optimal solutions. Second, we demonstrate the algorithmic behavior of different greedy and metaheuristics on this design problem. We illustrate how to exploit the problem structure to solve it almost optimally. Third, we generate managerial insights into allocating spare parts in manufacturing systems with buffers. We show that spare parts tend to be more effective when arranged at or near the center of a flow line, as it is already known for buffers. Moreover, we provide details on the combined buffer and spare part allocations.

An explainable artificial intelligence model for predictive maintenance and spare parts optimization

Maintenance strategies are vital for industrial and manufacturing systems. This study considers a proactive maintenance strategy and emphasizes using analytics and data science. We propose an Explainable Artificial Intelligence (XAI) methodology for predictive maintenance. The proposed method utilizes a machine learning project cycle and Python libraries to interpret the results using the Local Interpretable Model-agnostic Explanations (LIME) method. We also introduce an early concept of spare parts management, presenting insights from predictive maintenance outcomes and providing explanations for decision-makers to enhance their understanding of the influential factors behind predictions. This study demonstrates that utilizing machine learning models in predictive maintenance is highly beneficial; however, the binary outcomes of these models can be misunderstood by decision-makers. Detailed explanations provided to decision-makers will directly impact maintenance decisions and improve spare part management.

Joint Decisions of Components Replacement and Spare Parts Ordering Considering Different Supplied Product Quality

In addition to equipment maintenance decisions, spare parts ordering decisions from different suppliers play a key role in reducing related costs (e.g., maintenance, inventory and ordering costs). Since suppliers may use different production technologies and materials, spare parts (or products) from different suppliers can be different in quality. Nevertheless, in recent studies, the quality of spare parts is rarely considered to incorporate both equipment maintenance and spare parts ordering. In this paper, we investigate the joint optimization of condition-based maintenance and spare parts provisioning policy under two suppliers with different product quality. We formulate a sequential-decision problem with a Markov decision process and consequently obtain an optimal maintenance and ordering policy by an exact value iteration algorithm. To improve computation efficiency, based on the principle of sequential optimization, we develop heuristic methods. Extensive numerical experiments are conducted to assess the overall performance of the developed heuristic methods. Compared to the optimal method, results showed that the average cost gap is about 2% and computation time is reduced by 94% on average under the proposed heuristic method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the observation that automobile industries tried to integrate emergency suppliers from which spare parts have different quality into maintenance schedules to avoid stockout and reduce equipment failure during the Covid-19 pandemic. Specifically, the article focuses on balancing the trade-offs between condition-based maintenance and inventory management from two suppliers with different lead times and spare parts quality for multi-unit systems. On the one hand, effective maintenance scheduling relies on spare parts for replacement to ensure the stability of production. On the other hand, inventory management needs to select the supplier with appropriate lead time and product quality to reduce the ordering cost and avoid stockout based on the degradation states of equipment. The joint optimization of these two aspects serves to reduce the total maintenance and ordering cost. Nevertheless, most existing research aims to optimize them separately. In this paper, we formulate the joint decision problem considering the two aspects based on a Markov decision process. We obtain an optimal maintenance and ordering policy by an exact value iteration algorithm and present heuristics to improve the computation efficiency when the system contains multiple machines. Practitioners can implement the proposed methodology to make condition-based maintenance and inventory management when spare parts with different qualities are ordered from two suppliers. To balance cost and computational efficiency, it is suggested to implement the optimal policy by an exact value iteration algorithm when the number of machines is small in the system and use the heuristic methods when the number of machines is large (i.e., usually larger than 3).

Optimizing Footwear Manufacturing Through Defect Analysis and Spare Parts Optimization

This study investigates the causes of defects in the production line of inlay soles in footwear manufacturing, focusing on the impact of malfunctioning machine spare parts. Using Failure Mode and Effects Analysis (FMEA), critical machines—splitting, cutting, and moulding—are identified, along with their associated spare parts influencing defect occurrence. Process FMEA (PFMEA) prioritizes actions based on discussions with maintenance managers and quality staff. Results highlight top spare parts affecting defects, such as Fertinak, grinding stone, and V-belt for the splitting machine; hydraulic motor, hydraulic seal, and hydraulic filter for the cutting machine; and Solid State Relay, Thermocouple, and Heater for the moulding machine. These findings underscore the importance of maintaining machine performance and addressing spare part malfunctions to enhance product quality and operational efficiency in footwear manufacturing, providing actionable insights for industry practitioners. Hightligh: Critical Machines Identified: Splitting, cutting, and moulding machines analyzed for defect occurrence. Spare Parts Influence: Fertinak, Solid State Relay impact defect occurrence. Practical Industry Insights: Strategies for improving product quality and efficiency highlighted. Keywords: Defect analysis, Footwear manufacturing, Spare parts, Failure Mode and Effects Analysis (FMEA), Product quality.

Joint Optimization of Condition-Based Maintenance and Spare Parts Ordering for a Hidden Multi-State Deteriorating System

Joint Optimization of Condition-Based Maintenance and Spare Parts Ordering for a Hidden Multi-State Deteriorating System

Data-driven joint optimization of maintenance and spare parts provisioning for deep space habitats: A stochastic programming approach

The utilization of sensor data to make informed decisions on maintenance schedule and spare parts provisioning will be determinant to the development of self-aware habitable facilities–so called SmartHabs–in deep space regions. To address this novel and challenging problem, this work presents a chance constrained stochastic mixed integer program to jointly optimize maintenance and spare parts provisioning for ensuring reliable and cost-efficient operations of SmartHabs. A SmartHab is modeled as a multi-unit system with non-identical components that randomly degrade over time. The proposed optimization model is driven by telemetry sensor data as the uncertainty of the remaining lifetime of the components is estimated by data-driven prognostic algorithms. The model further incorporates characteristic operational conditions of deep space habitats such as a long planning horizon that requires multiple maintenance decisions for each component, maintenance task assignment to humans and robots, long lead times between resupply trips, and high penalties due to stock-outs of spare parts. The formulation introduces chance constraints restricting the unavailability of critical components and the number of corrective repairs with high probability under the uncertainty of component failures. The nonlinearities induced by the chance constraints are circumvented by the derivation of two linearization methods based on scenario representation and safe approximations, resulting in their tractable mixed integer linear reformulations. The proposed model is validated by simulations, showing reliable and effective plans against benchmark approaches while evidencing to be computationally scalable as the number of components increases.

Joint Optimization of Preventive Maintenance and Spare Parts Ordering Considering Imperfect Detection

The optimization of preventive maintenance and spare part ordering strategies for modern production equipment is of utmost importance, given its substantial influence on the reliability of equipment systems. Furthermore, the optimization problem discussed here has a direct impact on the reduction of maintenance expenses, thus making it a significant area of research. The optimization of preventive maintenance and spare parts ordering techniques for contemporary industrial equipment, which is a massive and complex system, faces substantial obstacles notwithstanding prior research efforts in the subject. Prior studies have typically assumed a fixed lead time for spare parts ordering, often leading to discrepancies with actual practice. When faced with a critical component failure, such as rolling bearings, it is not advisable for the decision maker to strictly adhere to the ordering strategy. Therefore, this paper presents a novel approach to the maintenance management method, which optimizes preventive maintenance and spare parts ordering strategies using a dynamic early warning period model based on different equipment states. The model incorporates two maintenance approaches, namely normal ordering and emergency ordering, and the equipment will adopt the corresponding maintenance method according to its state. Furthermore, the model takes imperfect detection of equipment states into account since equipment monitoring is not always accurate. Numerical experiments were conducted using rolling bearings, which are a crucial component in typical mechanical equipment, as a case study. The findings indicate that the improved model exhibits a unit time cost of 1.3021, whereas the original model has a unit time cost of 1.3611. Consequently, the new model effectively reduces the maintenance cost. This new method can better resolve the coordination challenge between preventive maintenance and spare parts ordering for equipment, the enhancing of equipment system reliability, and reduce maintenance expenses. In summary, the text presents a significant contribution in the form of a proposed preventive maintenance model that offers increased flexibility, aiming to effectively reduce maintenance costs.

Optimal spare parts production–distribution scheduling considering operational utility on customer equipment

Integrated scheduling of production and distribution is conducive to saving transportation cost and improving customer satisfaction. However, in previous related literature, no optimization objective has been set to intuitively reflect the influence of the scheduling scheme on the customer side, ignoring the fact that: 1) the production and distribution of spare parts directly affect the performance of operation and maintenance and further affect the operational utility of customer equipment; 2) the delivery time of spare parts has a positive effect on optimizing the operational utility of customer equipment corresponding to the delayed spare parts. To fill this gap, in the paper, we construct an optimal spare parts production scheduling model integrating logistics distribution in flowshop environment, in which equipment operation utility on the customer side is set as an optimization objective and a speed adjustment strategy is introduced, so that the manufacturer can adjust the operation strategy of the equipment corresponding to the delayed spare parts and guide the equipment operation and maintenance in the customer side. There are two objectives in our model. The first is to maximize equipment operational utility on the customer side, and the second is to minimize the total cost on the manufacturing side. To solve the problem, a three-layer hybrid coding memetic algorithm (THMA) is constructed. Two initial heuristics, respectively named Heuristics 1 and Heuristics 2, are constructed. Two local search operators operated respectively on the production and distribution stages of spare parts are constructed. By performing extensive contrast experiments, the superiority of THMA and the effectiveness of all the above operators are verified.

Developing a supplier selection model based on Markov chain and probability tree for a k-out-of-N system with different quality of spare parts

Spare parts management is one of the most important aspects in industrial systems where a large number of spare parts are stocked to replace with failed parts and reduce the system's downtime. In this study, it is supposed that the quality of spare parts can differ from the quality of the original parts since they might be supplied from different suppliers. This problem is modeled by using the probability tree and Markov chains approach to determine the optimal number of spare parts, their supplier, and appropriate quality, leading to minimization of the system's total cost and or system's total availability. Meanwhile, highly expensive parts cause large investment. In this study, a k-out-of-N redundant system is modeled in which by failing each part it is replaced by a stocked spare part if exists. Otherwise, in case of spare parts lacking the system continues to its operation with failed part(s) which reduces system performance. If the number of failed parts reaches a predetermined threshold level (N-k+1) it leads to system shutdown. All suggesting models are applied on numerical samples for to show the representation of the state and determine optimal spare parts supply strategies and inventory policies in large-scale industrial systems.

Machine learning approach for integrated maintenance and spare parts management strategies

We targeted a data mining and machine learning approach for integrated maintenance/production and spare parts management problems for components of a wind farm where the level of degradation is noticeable. The degradation is established as a function of the actual functioning mode. Spare parts are stored in a local inventory. The costs associated with the supply of spare parts and their renewal are related to the functioning mode. Our goal is to use a data mining and a machine learning approach in order to optimize the total current cost of maintenance, production and spare parts related costs over a fixed planning horizon. We formulate the problem of the optimal policy structure, which turns out to be a three-threshold policy in all operating modes. Our numerical results show that the cost reductions achieved by the integrated maintenance, production and spare parts optimization are significant.

Management Optimization of Equipment Maintenance and Spare Parts for Automobile Intelligent Manufacturing Enterprises

With the changes in the world economic environment, the vigorous development of network information and sensor technology, the combination of traditional manufacturing and advanced technology has produced tremendous changes. After decades of development, intelligent manufacturing has gradually become popular among people, and has achieved amazing results in a large number of enterprise practices. The main purpose of this paper is to study the optimization of equipment maintenance and spare parts management in automobile intelligent manufacturing. This article is mainly based on the WBS work decomposition method, combined with the product life cycle theory, ABC classification method, demand forecasting method, inventory management method, etc., described and introduced in detail and properly. Experiments show that C-type parts (1/3 of the parts are wire harness connectors) only account for 5% of the entire sales. Therefore, in the case of limited resources, C-type spare parts can be managed in a differentiated manner according to needs, such as a small amount of stock, or by referring to past experience to order C-type spare parts with less than 5 quantities.

The Demand Supply Steady-State Process-Based Multi-Level Spare Parts Optimization

Spare parts are one of the important components of the equipment comprehensive support system. Spare parts management plays a decisive role in achieving the desired availability with the minimum cost. With the equipment complexity increasing, the price of spare parts has risen sharply. The traditional spare parts management makes the contradiction between fund shortage and spare parts shortage increasingly prominent. Based on the analysis of the multi-echelon and multi-indenture spare parts support model VARI-METRIC (vary multi-echelon technology for recoverable item control, VARI-METRIC), which is widely used by troops and enterprises in various countries, the model is mainly used in high system availability scenarios. However, in the case of low equipment system availability, the accuracy and cost of model inventory prediction are not ideal. This paper proposed the multi-level spare parts optimization model, which is based on the demand-supply steady-state process. It is an analytical model, which is used to solve the low accuracy problem of the VARI-METRIC model in the low equipment system availability. The analytical model is based on the multi-level spare parts support process. The article deduces methods for solving demand rate, demand–supply rate, equipment system availability, and support system availability. The marginal analysis method is used in the model to analyze the spare parts inventory allocation strategy’s current based cost and availability optimal value. Finally, a simulation model is established to evaluate and verify the model. Then, the simulation results show that, when the low availability of equipment systems are 0.4, 0.6, the relative errors of the analytical model are 3.54%, 3.86%, and its costs are 0.52, 1.795 million ¥ RMB. The experiment proves that the inventory prediction accuracy of the analytical model is significantly higher than that of the VARI-METRIC model in low equipment system availability. Finally, the conclusion and future research directions are discussed.

Critical project planning and spare parts inventory management in shutdown maintenance

In the process industry, plant production is completely halted every so many years for large-scale maintenance comprising inspection, repair and overhaul. It is paramount that this shutdown maintenance is completed according to the planning, which requires timely availability of replacement parts. In this setting, we present a spare parts optimization model and algorithm that includes the cost-time trade-off and precedence constraints between maintenance activities. The objective is to balance between spare parts ordering cost and the expected project delay cost due to waiting for spare parts. Using two-stage stochastic programming, spare parts ordering policies are determined in the first stage and a detailed project schedule is developed in the second stage. We propose a sample average approximation with importance sampling and pruning of dominated activities to solve the problem, and demonstrate that this method solves large instances quickly. We also consider heuristics, e.g. the standard project management approach based on the widely used critical path method and two heuristics that draw from spare part inventory control literature. These heuristics give poor solutions.

Spare parts ordering decisions using age based, block based and condition based replacement policies

In the field of spare parts preparation, various models have been proposed to find the optimal order quantity for spare parts and optimal order interval. Although previous studies have concentrated primarily on classical cost targets on preparing replacement parts that do not take into account the actual deterioration state of the equipment. By combining age-based replacement, block-based replacement and condition-based replacement policies, this paper provides a multi-objective model for spare parts optimization. Spare parts which are having shorter life time, larger number of identical items and low cost items are categorized under block based replacement policy, replacement of all items will be done with fixed time interval. Spare parts which are critical are categorized in condition based replacement method, for those critical equipment remaining useful life (RUL) (i.e. Decision variable) is determined to find the actual degradation state level. Spare parts which are non-critical & having longer life time are categorized under age based replacement method; replacement will be done at its age or failure, whichever occurs first. By taking case studies in the pharmacy industry, the model and technique are demonstrated by a numerical illustration.

Pemeliharaan Mesin Hydraulic Shear Menggunakan Pendekatan Reliability Centered Maintenance dan Manajemen Suku Cadang

Production equipment that has a high operating time becomes a vital aspect in supporting the operational system, and its maintenance becomes a critical point that must be planned. This research discusses maintenance policies that focus on machine reliability and parts availability to produce proper maintenance actions, optimal maintenance scheduling and good spare-parts management. The research was conducted on a developing manufacturing company engaged the field of agricultural aids in the city of Bekasi. This study focused on hydraulic shear machines because they have the largest downtime value of 8,344.8 minutes. This problem has a negative impact on the company because it can reduce the yield. Based on these conditions, the study was conducted using the Reliability Centered Maintenance method to update the maintenance patterns and Poisson Process to determine the number of component requirements needed for the next one year. The data obtained in the form of historical damage to the machine, component cost, labor cost and production cost. This research produces an optimal preventive replacement time interval that is every 154 hours for the oil seal component, 242 hours for the shear blade component, and 324 hours for the oil hydraulic hose (1/2") component. While the number of critical components required for the next 1 year is 37 units for oil seal component, 10 units for shear blade components, and 7 units for oil hydraulic hose (1/2") component.

Integration Methodology of Spare Parts Supply Network Optimization and Decision-making

In order to optimize the spare parts supply network, a multi-objective optimization model is established with the objectives of the shortest supply time, the lowest risk, and the minimum supply cost. A decomposition-based multi-objective evolutionary algorithm with differential evolution strategy is introduced to solve the multi-objective model. A series of non-dominated solutions, that is, representing the optimal spare parts supply schemes are obtained. In order to comprehensively measure the performance of these solutions, suitable quantitative metrics are selected, and the secondary goal-based cross-efficiency Data Envelopment Analysis (DEA) model has been used to evaluate the efficiency of the obtained optimal schemes. The improved DEA model overcomes the problems that the efficient units cannot be sorted and the optimal weight is not unique in traditional DEA model. Finally, the self-evaluation efficiency and cross-evaluation efficiency of each scheme are obtained, and the optimal supply scheme is found based on their cross-evaluation efficiency.

Bayesian degradation modelling for spare parts inventory management

AbstractDecision makers in various sectors, such as manufacturing and transportation, strive to minimize downtime costs. Often, brief-planned stoppage times allow for changes in shifts and line configurations and longer periods are scheduled for major repairs. It is quite important to proactively make use of these downtimes to reduce the costs of unexpected downtimes due to failures. Among many aspects, the availability of spare parts significantly affects the operational costs of such systems. Current sensor technologies enable the condition monitoring of critical components and degradation-based spare parts management. This paper focuses on Bayesian degradation modelling for spare parts inventory management for a new system. We propose a stochastic dynamic program to minimize the expected spare parts inventory cost for a fixed planning horizon. A numerical example illustrates the value of Bayesian analysis in this management setting. The proposed methodology finds the optimal time between long stoppages and optimal spare parts order quantity when the prior information about the degradation process is accurate. The methodology can be used to analyse the sensitivity of the optimal solution to changes in the accuracy and bias of the prior distributions of the model parameters, the cost structure and the number of machines in the system.

Problems of Evaluation and Optimization of Spare Parts Units to Ensure Continuous Operation of Enterprises

The article raises the problem of increasing the competitiveness of enterprises through the introduction of automatic calculations of supplied elements. Also, the features of providing enterprises with a sufficient number of spare parts, tools, accessories, and materials are considered. For correct and General understanding the basic concepts are introduced. The main problems of non-constant supply and calculation of elements to ensure continuous operation of enterprises are formulated. The model of delivery based on a manual inspection of all elements and calculate the right amount for your next purchase. Analytical solutions are given that allow for a more accurate calculation, based on constant observation, which tracks the full life cycle of the element of interest, and the early prediction that the system may fail.&#x0D; Keywords: manufacturer, supplier, spare parts, tools, accessories, system sufficiency index, replenishment strategy, Internet of things.

Joint optimization of spare parts ordering and age-based preventive replacement

In this paper, an integrated model for the joint optimisation of preventive replacement time and spare parts ordering policy is proposed for systems with more than one identical items that operate in parallel. The operating items are preventively replaced under an item-specific age-based preventive replacement policy, while the inventory of spare parts is continuously monitored and replenished under an (s, S) continuous review policy, combined with emergency orders in case of shortages during lead time. The optimal cost of the proposed model is compared against the one of sequential optimisation, where the preventive replacement time is first selected using a traditional age-based preventive replacement model and the inventory control parameters are then optimised for the resulting value of the preventive replacement time. The numerical analysis shows that, unless a preventive replacement is not economically advisable, the proposed model significantly outperforms sequential optimisation. In addition, the proposed model is also compared against the case where the system inter-replacement times are assumed to follow the exponential distribution. The numerical analysis shows that such an approximation, although it is satisfactory when the number of items is large, can be very costly when the number of items in the system and/or the order lead time is quite small.

A System-Approach for Recoverable Spare Parts Management Using the Discrete Weibull Distribution

Optimal spare parts management strategies allow sustaining a system’s availability, while ensuring timely and effective maintenance. Following a systemic perspective, this paper starts from the Multi-Echelon Technique for Recoverable Item Control (METRIC) to investigate the potential use of a Weibull distribution for modelling items’ demand in case of failure. Adapting the analytic formulation of METRIC through a Discrete Weibull distribution, this study originally proposes a METRIC-based model (DW-METRIC) to be used for modelling the stochastic demand in multi-item systems, in order to ensure process sustainability. The DW-METRIC has been tested in a case study related to an industrial plant constituted by 98 items in a passive redundancy configuration. Comparing the results via a simulation model, the outcomes of the study allow defining applicability criteria for the DW-METRIC, in those settings where the DW-METRIC offers more accurate estimations than the traditional METRIC.