Spare Parts Inventory Control Research Articles

Joint Optimization of Spare Part Supply and Opportunistic Condition-Based Maintenance for Onshore Wind Farms Considering Maintenance Route

Maintenance and inventory control of spare parts are of great importance for efficiently managing onshore wind farms. Considering the economic and graphical dependencies among wind turbines and the stochastic degradations of the components in the turbines, this article studies the joint optimization of opportunistic condition-based maintenance and spare parts supply for onshore wind farms to minimize the operation and maintenance cost rate. For each field maintenance, the specific turbines, with failed components, are selected for replacement using a clustering method, and the shortest route for visiting the turbines with failed components is scheduled. The maintenance team departs from a maintenance center with spare parts, maintains the selected turbines with failed or degraded components, and then returns to the center. This complicated and practical problem has never been considered as a whole in the literature. An optimization model is established, and a simulation-based approach is developed to solve it. A special case, for which a solution can be obtained by directly solving a nonlinear programming model, is provided to verify the results obtained by the simulation-based approach. Examples for various scales of wind farms are provided, and an analysis is performed for determining the length of a recommended maintenance interval.

Part transformation-based spare parts inventory control model for the high-tech industries

Timely and cost-effective supply of spare parts is the main purpose of spare parts inventory management and substitution is an effective way to fulfill demand on time. However, direct substitution of spare parts is not suitable for the high-tech industries due to the ever-changing nature of the product structures. Hence, parts should be transformed to be used as substitutes. This paper provides a novel spare parts inventory control model for the high-tech industries. In the proposed model, part transformation-based substitution is considered and the near-optimal values of spare part inventory levels (s, S) that minimize total cost are determined by using a simulated annealing-based simulation optimization approach. Computational analyses are performed for a hypothetical inventory system by considering transformation and no-transformation cases. The results reveal that transformation is very useful for the companies who endure long production lead times and high penalty costs associated with backorders.

Perancangan Sistem Informasi Pengendalian Persediaan Sparepart dengan Metode Economic Order Quantity di Bengkel Mobil Sumber Jaya Probolinggo

: This research discusses the design of a spare part inventory control information system in a car workshop using the Economic Order Quantity (EOQ) method. This method functions as an optimization of inventory control by minimizing inventory and ordering costs, as well as avoiding excess or shortage of inventory. The author designed the information system using PHP programming language and MySQL as the database to facilitate the website development process. Testing was carried out by testing the functionality and performance of the system using real data. The results of this journal prove that the design of the information system can help car workshops to control spare part inventory more efficiently. By using the EOQ method, workshops can optimize the use of inventory, reduce inventory and ordering costs, and avoid excess or shortage of inventory. The designed information system also optimizes the performance of the workshop in improving its efficiency and productivity

On the Integration of Additive Manufacturing for Aircraft Spare Parts Inventory Control

On the Integration of Additive Manufacturing for Aircraft Spare Parts Inventory Control

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.

The value of installed base information for spare part inventory control

This paper analyzes the value of different sources of installed base information for spare part demand forecasting and inventory control. The installed base is defined as the set of products (or machines) in use where the part is installed. Information on the number of products still in use, the age of the products, the age of their parts, as well as the part reliability may indicate when a part will fail and trigger a demand for a new spare part. The current literature is unclear which of this installed base information adds most value – and should thus be collected – for inventory control purposes. For this reason, we evaluate the inventory performance of eight methods that include different sets of installed base information in their demand forecasts. Using a comparative simulation study we identify that knowing the size of the active installed base is most valuable, especially when the installed base changes over time. We also find that when a failure-based prediction model is used, it is important to work with the part age itself, rather than the machine age. When one is not able to collect information on the part age, a logistic regression on the machine age might be a valuable alternative to a failure-based prediction model. Our findings may support the prioritization of data collection for spare part demand forecasting and inventory control.

Control of External Tire Spare Parts 7.50-16 Inefficiency Inventory Costs Based on Economic Order Quantity

This study focuses on inventory control of spare parts outside the prohibition 7.50-16 to streamline inventory costs. The method used is Economic Order Quantity with the type of case study design at the company PT. XYZ with the object of research is the supply of Outer Tire Parts 7.50-16. In data collection, the researcher compares the Traditional or Actual Company Method with the Economic Order Quantity Method in 2019 and 2020. The data that has been collected becomes several references in the calculation so that there is a difference between the two methods. The results show that the Economic Order Quantity method can streamline inventory control beyond 7.50-16 at PT. XYZ.

A Bi-Objective Simulation-Based Optimization Approach for Optimizing Price, Warranty, and Spare Part Production Decisions Under Imperfect Repair

This paper proposes an efficient methodology based on the Monte-Carlo simulation-based bi-objective optimization, to determine base-warranty (BW) and extended warranty (EW) parameters based on the product lifecycle. The first objective, which is from the manufacturer’s perspective, maximizes the profit while the second objective minimizes the expected number of failures that occurred during the out-of-warranty period. The manufacturer can rectify failed products via minimal repair, imperfect repair and perfect repair. The optimization model has decision variables including the product price, BW length, EW length, EW price, product failure rate, imperfect repair level, and spare part production rate in each time interval. The structure of the model admits the design of a hybrid method based on the multi-objective optimization search algorithm, Monte-Carlo simulation and an exact Out-Of-Kilter (OOK) algorithm. The nondominated sorting genetic algorithm (NSGA-II) and the multi-objective particle swarm optimization (MOPSO) algorithm are used as search algorithms. The proposed approach consists of three stages, where in the first stage, product price, BW length, EW length, EW price, product failure rate, imperfect repair level are set by NSGA-II/MOPSO. In the second stage, the number of failed products is calculated by the Monte-Carlo simulation and in the third stage, we show that the spare part inventory control sub problem can be transformed to a minimum cost network flow problem which is optimized by the OOK algorithm to attain a unified solution. A Taguchi approach is used to find the optimum level of parameters. The performance of algorithms is compared based on three different metrics. Results on a real-world problem demonstrate that the NSGA-II-OOK algorithm is more effective than the MOPSO-OOK algorithm. Through a sensitivity analysis, we analyze how various levels of planning horizon can affect Pareto-set which indicates valuable managerial insight.

A spare parts inventory control model based on Prognostics and Health monitoring data under a fill rate constraint

The performance of any inventory control model depends on the quality of future demand forecasting. The better the accuracy of future demand predictions is, the lower is the safety inventory level needed to fulfill demands and meet fill rate requirements. In most real-world applications, historical demand profiles are commonly used to predict future demands. In the specific case of spare parts inventory, one way to improve the accuracy of demand prediction is to use information obtained by monitoring the degradation level of components. This approach may be especially important when demand behavior can vary over time, for instance, due to a change in the operational conditions. Thus, this paper aims at presenting a novel spare parts inventory control model for non-repairable items with periodic review. In the proposed model, Remaining Useful Life (RUL) predictions of monitored components obtained from a Prognostics and Health Monitoring (PHM) system are used to predict future demands for spare parts. It allows the reorder point, s, and the order-up-to level, S, to be dynamically adjusted as new PHM data become available. It is assumed that PHM data are updated periodically, with period R. The proposed model minimizes the total inventory cost subject to a fill rate constraint. Numerical experiments are carried out to compare the performance of the proposed [R, s, S] model with the classical [s, S] model in terms of average total cost per period and average inventory level. The results show that the proposed model yields a reduction in both the average total cost per period and the average inventory level.

Analysis of Production System Management of Ghana’s Food and Beverage Industry: Empirical evidence from Spare Parts Inventory Control, Production Quality and Maintenance Modeling

The production systems of various industries have increasingly become a strategic business management function that drives differentiation and competitive edge. Production plant maintenance is therefore an integral part of an entire production system management function, which should encapsulate spare part inventory control systems, total quality assurance, improvement measures and cost control measures. This project sought to understand the production process of firms in Ghana’s food and beverage industry, assess the spare part inventory management practices, production quality control practices and maintenance management strategies implemented in order to recommend a maintenance optimization modeling system towards ensuring an optimal operation cost and production quality. Using both the inductive and deductive research approaches, the research project was exploratory and explanatory in nature. Three major players in Ghana’s food and beverage industry: GIHOC-DCL (Ghana Industrial Holding Corporation Distillery Limited), Heathy-Life Ghana Limited (Healthy-Life) and Parlays Ghana Limited (Parlays) were purposively sampled as industrial case studies to generate data from documentary secondary data and survey based sources. The functionality tools available in MATLAB/Simulink software assisted in formulating the maintenance optimization problem, modeling, analyzing and deriving solutions. The study found that the food and beverage processing business in Ghana has a positive correlation with the main agricultural produce of the country and involves a series of activities including processing and preservation of meat, fish, fruit, vegetables, starch products, bread and sugar. Spare part inventory management and quality control tools mainly applied include Statistical Control Charts and Check Sheet rather than optimization models and software.

Spare parts inventory control based on maintenance planning

For many maintenance organizations, on-condition maintenance tasks are the most important source of spare part demand. An uneven distribution of maintenance tasks over time is an important cause for intermittency in spare parts demand, and this intermittency complicates spare parts inventory control severely. In an attempt to partially overcome these complications, we propose to use the maintenance plan, i.e. the planned maintenance tasks, as a source of advance demand information. We propose a simple forecasting mechanism to estimate the spare part demand distribution based on the maintenance plan, and develop a dynamic inventory control method based on these forecasts. The value of this approach is benchmarked against state-of-the art time series forecast methods, using data from two large maintenance organizations. We find that the proposed method can yield cost savings of 23 to 51% compared to the traditional methods.

Modelling, analysis and improvement of an integrated chance-constrained model for level of repair analysis and spare parts supply control

In traditional practices, maintenance system and spare parts inventory control are usually considered in isolation, resulting in suboptimality. In a military system, the level of repair analysis (LORA) is often employed to help operate its repair networks. In this paper, we consider an integrated LORA and inventory control problem and formulate this problem as a mixed-integer nonlinear programming problem with chance constraints. Two second-order cone constraints are proposed to approximate the chance constraints. Furthermore, we propose an outer approximation (OA) algorithm based on the OA cuts. Extensive numerical results show that the OA algorithm significantly improves the computational efficiency under various types of components and network complexity. Next, we investigate the influence of service level and resource capacity, and propose the findings. Our results indicate that a higher service level leads to steeper costs, more resources, larger storage and heavier repair burdens at operating sites. Moreover, enhancements in resource capacity from the status quo lead to improvements in repairs and shrinkage in discards, bringing direct economic benefits. The insights extend to uncertain settings. It may be initially counterintuitive for many practitioners that demand uncertainty poses relatively subtle impacts.

Optimal remanufacturing and acquisition decisions in warranty service considering part obsolescence

When a product enters the end phase of its life cycle, certain parts become obsolete and their supply is discontinued permanently. However, the manufacturer must still maintain sufficient inventory to satisfy customer warranty claims in the future. We study the spare parts inventory control problem under such a setting, where remanufacturing parts from product returns is adopted as the main approach to ensure sufficient supply. One novel feature of our model is the interrelationships among the installed base, product returns, and demand for spare parts, which lead to the endogeneity of demand. Specifically, we formulate stochastic dynamic programs for two scenarios, in which a firm either utilizes phase-out returns or proactively collects products. For the former scenario, we establish the optimality of a modified-base stock policy, and find that the inventory control target increases with the installed base size and part failure rate. For the latter scenario, we discover that a return-dependent base stock policy is optimal and conduct comparative static analyses for the acquisition and remanufacturing decisions, where their one-sided limited sensitivities in state variables (initial parts inventory and installed base size) are identified in a stationary environment. These findings are largely attributed the novel model with endogenous spare parts demand. Through extensive numerical studies, we investigate the benefits introduced by acquiring returns under a range of parameter settings. It is found that when the mismatch between warranty demand and part supply is higher, firms can enjoy more significant cost savings. Finally, the potential monetary loss when the relationships among the installed base, demand, and product returns are ignored is examined computationally. The extra cost incurred is significant and becomes higher when proactive acquisition is adopted.

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.

Maintenance Spare Parts Demand Forecasting for Automobile 4S Shop Considering Weather Data

Maintenance spare parts demand forecasting is an important foundation of maintenance spare parts inventory control, which is an essential responsibility for managers of an automobile 4S shop. Although the existence of the effect of weather conditions on maintenance spare parts demand has been verified, the study on maintenance spare parts demand forecasting for an automobile 4S shop considering weather data has not been found. In this paper, a novel method is proposed for maintenance spare parts demand forecasting for an automobile 4S shop considering weather data. By taking into account three dimensions of weather data (i.e., temperature, visibility, and slipperiness) and delayed effects of weather conditions on maintenance spare parts demand, a vector of numerical weather data with 30 weather factors is constructed to represent the related weather conditions of a given day. Then, a back propagation (BP) neural network is trained and the weights of the 30 weather factors are determined. Similar historical cases of the target case are extracted, and two forecasting models are respectively trained based on extreme learning machine (ELM) and support vector machines (SVM) using the similar historical cases. The final forecasting model is determined by comparing the fitting precisions of the two forecasting models. The experimental study is conducted based on the real data of an automobile 4S shop. The results show that weather data is critical to maintenance spare parts demand forecasting for an automobile 4S shop, and the extraction of similar historical cases is an effective approach to capture the complex effect mechanism of weather data on maintenance spare parts demand.

Optimizing under and out of warranty products’ decisions in the finite planning horizon

In this paper, we consider a manufacturer that produces products in a finite horizon time and sells products with non-renewing free replacement warranty policy. The manufacturer is responsible to provide spare parts for failed products, whether the products are under or out of warranty. Previous research on warranty optimization has focused on maximizing manufacturer profit without considering the spare part market for out-of-warranty products. This study proposes a novel nonlinear model that maximizes manufacturer profit by optimization of price, warranty length and spare part inventory for under- and out-of-warranty products in a manufacturing/remanufacturing system. Due to the model’s unique structure, we propose a new two-stage approach that combines metaheuristic and an exact method, in which the first stage is to determine product’s prices and warranty length with metaheuristic algorithm and in the second stage the remaining inventory related problem is transferred to a Minimum Cost Network Flow Problem which is solved for spare part inventory control. To illustrate the effectiveness of the suggested method, the model is solved for a case study of Iranian SANAM electronic company with two different metaheuristic algorithms and a sensitivity analysis is conducted to study the effect of various parameters on the optimal solution.

Impact of additive manufacturing on aircraft supply chain performance

PurposeThe purpose of this paper is to assess the impact of additive manufacturing (AM) implementation on aircraft supply chain (SC) networks. Additive and conventional manufacturing spare part inventory control systems are studied and compared, revealing insights into SC performance.Design/methodology/approachA leading global commercial airline’s SC network data are used to model the research problem. A system dynamics simulation approach is followed, drawing out insights for managers.FindingsA significant improvement in SC efficiency is observed through the implementation of AM, rendering it a worthwhile investment for global SCs. AM helps to balance inventory levels, and increase responsiveness while decreasing disruptions and carbon emissions in the supply networks.Practical implicationsThe paper offers guidance on the adaption of AM in aircraft SCs and AM’s impact on spare part inventory systems.Originality/valueThe study provides robust evidence for making critical managerial decisions on SC re-design driven by a new and disruptive technology. Next-generation SC and logistics will replace the current demand for fulfilling material products by AM machines.

Markov-modulated analysis of a spare parts system with random lead times and disruption risks

Spare parts supply chains are highly dependent on the dynamics of their installed bases. A decreasing number of capital products in use increases the nonstationary supply-side risk especially towards the end-of-life of capital products. This supply-side risk appears to present itself through varying lead times coupled with supply disruptions. To model the nonstationary supply-side risk, we consider an exogenous Markov chain that modulates random lead times and disruption probabilities. Assuming that order crossovers do not occur, we prove the optimality of a state-dependent base stock policy. Later, we conduct an impact study to understand the value of considering stochastic lead times and supply disruption risk in spare parts inventory control. Our results indicate that the coupled effect of random lead times and disruptions can be larger than the summation of individual effects even for moderate lead time variances. Also, the effect of nonstationarity on total cost can be as large as the summation of all risk factors combined. In addition to this managerial insight we present a procedure for supply risk mitigation based on an empirical model and our mathematical model. Experiments on a real business case indicate that the procedure is capable of reducing costs while making the inventory system more prepared for disruptions.

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.

Research on the spare parts inventory control for agriculture inter-district field operation

With the development, more and more research has begun to focus on product service systems. Product service systems link tangible products with intangible services to improve the company’s competitiveness. As an important part of product service, after-sales service directly affects the company’s image and interests. In order to ensure a good after-sales service level, the manufacturing company sets up service district for each service provider. Each service provider is only responsible for providing the required spare parts within the service district, which guarantees the service level to some extent. However, due to the instability of demand, spare parts inventory of service providers often suffers from shortages or accumulation problems. Shortage of stocks will cause excessive demand delay penalty, and the accumulation of stocks will result in a large amount of funds. How to ensure the level of service, balance the shortage and accumulation of inventory, and reduce the cost of spare parts have become an urgent problem to be solved. In order to make full use of inventory and reduce the amount of funds used, this paper do the research on spare parts inventory optimization for inter-district transport. At the same time, taking into account the need of transport vehicles in transport, this paper considers the restrictions on transport vehicles. This paper studies the optimization of spare parts inventory for inter-district transport, taking into account the limitation of the capacity of transport vehicles. Due to the limited capacity of transport vehicles, the setting of transshipment priority should not only consider the distance between service providers, but also consider the relationship between the number of transshipments and the number of vehicles required. In order to minimize the cost of spare parts, the paper establishes an inventory optimization model with the inter-district transport, and presents a mathematical model for directly transporting spare parts without considering transport vehicles and a mathematical model that does not consider inter-district transport. We find that, whether to consider the restrictions of transport vehicles, system behavior can vary drastically.