Spare Parts Inventory Management Research Articles

A new hybrid forecasting method for spare part inventory management using heuristics and bootstrapping

Spare parts are particularly challenging to forecast due to their lumpiness and representing a significant part of companies’ expenditures, so even small improvement in new approaches can considerably reduce these items’ total inventory. This paper aims to present a new hybrid forecasting method for spare part inventory management using heuristics and bootstrapping approaches to improve spare parts forecasting in normal use phase in spare parts inventory management. Our study presents an innovative methodology to model autocorrelation in demand to represent data distribution in bootstrapping in alternative to transition probabilities. The results were evaluated and validated through a case study on real data from a large iron ore corporation in Brazil, focusing on demand patterns and their impact on overall costs compared to leading-edge techniques. The mineral sector was selected due to its significant contribution to the emerging Brazilian economy and the lack of research in this field. The results revealed significant improvement in the forecasting total cost reduction up to 40% over leading-edge techniques for erratic and lumpy demand. Results suggest that relaxing autocorrelation in bootstrapping samples could lead to better deal with higher variability in demand sizes in spare parts management compared to parametric methods, as we recommend that this method should be particularly considered when dealing with spare parts with lower intermittence compared to other bootstrapping approaches. The method can be applied in any sector without restrictions, and provides managers with a systematic tool to analyze the trade-off between holding and breakage costs of spare items as well as demand parameters for the mining sector.

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.

УПРАВЛЕНИЕ ЗАПАСАМИ ЗАПАСНЫХ ЧАСТЕЙ НА МУЛЬТИБРЕНДОВЫХ АВТОСЕРВИСНЫХ ПРЕДПРИЯТИЯХ (НА ПРИМЕРЕ РЕСПУБЛИКИ КИПР)

The article presents a comprehensive study of the state and problems of spare parts inventory management at multi-brand car service enterprises on the example of the Republic of Cyprus. The general characteristics of the specifics of spare parts inventory management at multi-brand car service enterprises are presented, the foundations of the organizational model for the functioning of transport enterprises of this type, including those common to the Republic of Cyprus and the Russian Federation, are disclosed. The set of tasks for managing the inventory of spare parts at multi-brand car service enterprises has been clarified. Based on the results of a survey of executives and managers of 64 Cypriot multi-brand car service enterprises, key methods for managing spare parts stocks were identified. The results of the survey were compared with the assessment of the effectiveness of management in the subject area, typical groups of enterprises were identified according to the criterion of the main method of managing spare parts inventory and the expected management efficiency. The elements of spare parts inventory management at multi-brand car service enterprises of the Republic of Cyprus, recommended for reception into Russian practice, are identified, ways to solve the problem of improving the effectiveness of management in the subject area, common to the designated group of transport enterprises in Russia and Cyprus, including the development of automation, digitalization management, combined with the use of organizational methods (flexible supply contracts, franchising and new forms of business).

A memetic based algorithm for simultaneous preventive maintenance scheduling and spare-parts inventory management for manufacturing systems

To increase machine availability, it may be necessary to halt them to replace components before failures occur. Therefore, optimizing spare parts inventory management is a key factor in improving maintenance activities. Spare part costs are a significant contributor to total maintenance costs, including holding and ordering costs, and are often dependent on the maintenance schedule. Hence, it is important to optimize preventive maintenance scheduling and spare parts inventory management together. The first attempt to introduce a new Mixed-Integer Linear Programming (MILP) model that integrates both decisions was by Mjirda et al. (2016). Our paper is the first to extend their work in order to come up with an efficient aided decision making framework based on a mat-heuristic approach that delivers a good trade-off between computational time and solution quality. Indeed, this paper introduces a novel Hybrid Memetic Algorithm (HMA) that synergistically combines Memetic Algorithm (MA) with MILP to solve this NP-hard problem. The paper aims to provide a unified and efficient mat-heuristic based aided decision making framework that optimizes the sum of maintenance, operating, holding, and ordering costs by synchronizing preventive maintenance schedules and spare parts inventory for a set of independent machines. Its efficiency is illustrated through an extensive computational study. The HMA outperforms standalone MILP solvers, solving 36% of instances to optimality within a computational time of less than 141 s, and achieving a maximum gap of 5% for 46% of instances within less than 200 s. Our findings highlight the computational efficiency and solution quality offered by the HMA, thereby advancing the state-of-the-art in integrated spare parts inventory management and preventive maintenance scheduling.

UNJUK KERJA MESIN VERTICAL RAW MILL PLANT 10

PT X. is a cement manufacturing company. which experiences production losses due to the high value of downtime. Based on downtime data, the largest machine downtime value is found in the Raw Mill, so the Raw Mill machine becomes the object of research. This study aims to measure the effectiveness of the Raw Mill machine in the application of Total Productive Maintenance (TPM). The method used is Overall Equipment Effectiveness (OEE) to measure machine effectiveness and loss factor analysis with the Six Big Losses method. The results found that the average OEE value in 2022 was below the international OEE standard of 85%, namely 78% for Raw Mill Plant 10 in the 2019 period and 74% in the 2022 period. The low performance of the Raw Mill Plant 10 machine is caused by the breakdown loss factor and the reduced speed loss factor. The root causes found are lack of mechanical training and lack of control for spare parts. The recommendations given are the implementation of predictive maintenance, improving training facilities, and creating a spare parts inventory management system.

An improved NSGA-II with local search for multi-objective integrated production and inventory scheduling problem

In the context of collaborative manufacturing, integrated optimization of spare parts production and inventory management is practically important. This paper investigates an integrated production and inventory scheduling (IPIS) problem based on condition-based maintenance. In respect to this problem, whereby inventory and direct supply decisions are made simultaneously to achieve a better reduction in total inventory holding costs, total tardiness and total makespan, a multi-objective IPIS model is developed. An improved non-dominated sorting genetic algorithm-II with local search (INSGA-II_LS) is proposed for the multi-objective IPIS model. In INSGA-II_LS, the encoding and population initialization suited for IPIS are designed. The detailed presentation of operators of crossover, mutation, and local search that designed for the proposed IPIS problem then follows. The mathematical programming solver CPLEX and three multi-objective evolutionary algorithms called SPEA2, PESA-II, MOEA/D are designed for comparisons against INSGA-II_LS. Experimental results show the superiority of the proposed INSGA-II_LS for the IPIS problem with respect to various multi-objective performance metrics, especially for large-scale instances.

ESG concept as a tool for optimising spare parts stocks in car service centres in the Republic of Cyprus

The article proposes a comprehensive analytical study of the potential for the use of the ESG concept as a tool for optimizing spare parts stocks in car service centers of the Republic of Cyprus. The study is based on the materials of the Statistical Service of the Republic of Cyprus, on the information provided in the relevant scientific literature, as well as on the results of the empirical research. An analysis of the management accounting data of 12 car service centers of the Republic of Cyprus was carried out, including 4 dealer networks, 4 independent multi-brand companies and 4 family businesses. The directions of implementation of the environmental part of the ESG concept at car service enterprises of the Republic of Cyprus are specified. A comparative assessment of the profitability of stocks and CO2 emissions in the business processes of managing auto parts stocks has been clarified. Expert assessments of the state of ESG management in the spare parts inventory management system in the analyzed car service centers are presented. It is recommended to conduct optimization of spare parts inventory management in car service centers of the Republic of Cyprus to multiply the economic and social effects from the development of the elements of the ESG concept based on the author’s CDMD model: Systematism - Decentralization - Monitoring (measurements) - Digitalization. The introduction of the proposed CDMD model should provide economic benefits, expressed in an increase in the profitability of inventories and various ESG gains.

Spare parts inventory management at multi-brand car service enterprises

The article presents a comprehensive study of the state and problems of spare parts inventory management at multi-brand car service enterprises on the example of the Republic of Cyprus. The general characteristics of the specifics of spare parts inventory management at multi-brand car service enterprises are presented, the foundations of the organizational model for the functioning of transport enterprises of this type, including those common to the Republic of Cyprus and the Russian Fede ration, are disclosed. The set of tasks for managing the inventory of spare parts at multi-brand car service enterprises has been clarified. Based on the results of a survey of executives and managers of 64 Cypriot multi-brand car service enterprises, key methods for managing spare parts stocks were identified. The results of the survey were compared with the assessment of the effectiveness of management in the subject area, typical groups of enterprises were identified according to the criterion of the main method of managing spare parts inventory and the expected management efficiency. The elements of spare parts inventory management at multi-brand car service enterprises of the Republic of Cyprus, recommended for reception into Russian practice, are identified, ways to solve the problem of improving the effectiveness of management in the subject area, common to the designated group of transport enterprises in Russia and Cyprus, including the development of automation, digitalization management, combined with the use of organizational methods (flexible supply contracts, franchising and new forms of business).

A new procedure for spare parts inventory management in ETO production: a case study

Spare parts inventory management represents a strategic but critical process in many manufacturing companies, which have to handle a high amount of different items. This work aims at developing a simulation model to manage the inventory level of spare parts of an ETO (engineering to order) company. First, heterogeneous items have been analysed and classified into four different categories. Later, a simulation model has been developed to reproduce the inventory policy currently used to monitor and control them. Finally, a new procedure has been implemented to improve the current inventory management. Results show that the new procedure allows for reducing both the total cost and the maximum amount of space needed to stock the items. In addition, the simulation model could be used by practitioners as a practical tool to identify a proper inventory strategy according to the characteristics of the different items.

Raw Mill Machine Effectiveness Measurement through the Total Productive Maintenance (TPM) Implementation

PT X is a cement manufacturer company that experienced a loss of production caused by the high value of downtime. Based on downtime data, the raw mill machine appears to have the most significant value in machine downtime. This study aims to measure the raw mill machine's effectiveness in applying Total Productive Maintenance (TPM). The method used in this research is Overall Equipment Effectiveness (OEE). This method measures engine effectiveness and loss factor analysis using the Six Big Losses method, as well as finding the root cause through a five why and cause and effect diagram to give the most suitable recommendations. The result of this study found that the average OEE value in 2021 was below the international OEE standard of 85%, which is 72% and 67% for Raw Mill Plant 9 and Raw Mill Plant 10, respectively. The low performance of Raw Mill Plant 9 and Raw Mill Plant 10 machines is a consequence of breakdown losses and reduced speed losses that had happened. The root causes were a lack of training for mechanics and no stock control for spare parts. The recommendations are to improve training programs and facilities and create a spare parts inventory management system.

Study of emerging avenues in supply chain resilience; the case of integration of additive manufacturing with spare parts procurement

PurposeThe purpose of this research was to study the impacts of adoption of additive manufacturing (AM) for spare parts procurement, specifically in the context of supply chain resilience (SCR) especially regarding efficient spare parts inventory management. Furthermore, key narratives in the adoption of AM toward better SCR are explored.Design/methodology/approachIn-depth interviews with semi-structured open-ended questionnaire were conducted to collect primary qualitative data from 24 supply chain management (SCM) experts. Respondents consisted of experts across various industries. The data were analyzed by thematic content analysis method.FindingsThe results indicated that AM could be a suitable tool to reduce dependence on original equipment manufacturers (OEMs) for spare parts procurement. Data analysis also revealed that AM adoption might lead to significant cost and lead time reduction. Designs protected as intellectual properties (IP), substantive post-processing requirements and material compatibility were revealed to be barriers in adoption.Research limitations/implicationsThe impacts of utilizing AM for procurement of spare parts on the overall resilience of the supply chain were highlighted. Theoretical analysis of the findings was based on theoretical aspects of SCR. This was especially regarding efficient spare parts inventory management. The study results revealed the factors responsible adoption of the AM technology. A novel approach was undertaken to study the effect of AM adoption on “time-to-market” of newly launched products.Practical implicationsThe research provided insights regarding practical applications of AM adoption in spare parts procurement. The study could be beneficial to the early adopters of AM across industries for making managerial decisions. Unfamiliarity of supply chain managers with the AM technology was believed to be a major reason to adopt the technology. The study provided essential inputs regarding challenges and alternate adoption strategies of AM. Thus, the research was believed to be of potential value for creating awareness among supply chain managers regarding AM technology.Originality/valueThe research provided new insights on the impact of AM adoption in the context of SCR toward efficient spare parts inventory management. Various limiting and facilitating factors specific to Indian context were also explored.

Design and Implementation of Balanced Scorecard for Maintenance Department at Hajj Terminal, King Abdulaziz Airport

This report is about the design and implementation of a PAX system, PAX system is a Balance Scorecard (BSC) for maintenance department of PPMDC (Port Projects Management & Development Company) at Hajj Terminals, King Abdulaziz Airport. It monitors customer satisfaction, optimum service availability, the compliance of maintenance practice to GACA quality standards, maintenance department expense, spare part management, and enhancement of employees’ skills, experience, and knowledge. The research uses a descriptive design surveys and formulas to assess scores, future target and suggests crucial initiatives to attain desired goals. Applying BSC, passenger journey satisfaction score indicator is 94.4% and a two-year projection of 96% and 98%. It achieved total quality standards compliance with General Authority of Civil Aviation Service quality standards (GACA) the indicator for current estimate of 4.55/5 Civil and for two successive years are estimated to be 4.60/5 and 4.65/5. The PAX systems availability indicator is 98.8% and projected 99% (for the second year). While internal process indicator was estimated at 94% and projected at 96 % in the sconed year. Department expenses indicator was 97 % for this year, while expected to be reduced at 95 % in the second year. Spare part inventory management measured at 88.3% and projected 90% for the second year. Lastly, for learning and growth indicator, the team’ skills and experience is estimate at 95% and anticipated to grow to 96% in the short term.

A two-stage Genetic Algorithm for joint coordination of spare parts inventory and planned maintenance under uncertain failures

The main problem of spare parts management is to maintain the minimal requirement of stock keeping units kept. This research proposes a joint optimization model of multi-item multi-period spare parts inventory management and planned maintenance under uncertain failures in order to balance inventory cost and spare parts availability. This paper presents a Mixed Integer Non-linear Programming formulation of the inventory optimization model under a minimum and maximum inventory policy with stock review intervals. Some studies in the literature have considered aggregating spare parts inventory management as they assume that it will reduce the ordering cost. We consider both independent and aggregate spare parts inventory policies and then combine the formulation with the predictive maintenance interval, which is a replacement action due to uncertain failures under predefined distribution. Furthermore, a novel two-stage Genetic Algorithm is proposed as a sim-heuristic approach to deal with the non-linearity, combinations, and stochasticity of the problem and solve large-scale instances. In the end, we perform a computational study on some instances and a real-world case study to demonstrate the proposed approach’s effectiveness and efficiency. The computational study shows that the independent policy results in lower cost than the aggregate policy, and the proposed Genetic Algorithm performs efficiently for large-scale problems.

IMPROVING SPARE PART INVENTORY SYSTEM MANAGEMENT BY USING 5S PRACTICES IN CONSUMER GOODS COMPANY

The spare part inventory is an important section that must be managed and controlled in the company, primarily because this section supports the provision of spare parts for the manufacturing process. An excellent spare part inventory management system matches the records in the SAP system in the warehouse and the actual physical evidence. The problem faced by the consumer goods company was the difference between recording in the SAP system and the physical evidence for non-consumable goods. By implementing improvements to the spare part management system for receiving and picking up goods in the warehouse, data discrepancies can be eliminated on non-consumable spare parts consisting of refurbished and new spare parts. The improvement was implementing the transit area and the 5S practices in the activities conducted in the spare part warehouse. The transit area is intended for comprehensive checking between warehouse personnel and other department involved with the goods stored or picked up. Refurbished and new items are more organized, making it easier to place and pick up items.

Arena Simulation of Spare Parts Inventory Management: A Case of A Telecom Managed Service Provider

Telecommunications and utilities companies, among other large service organisations, confront a slew of decision-making challenges, many of which are connected to the management of a variety of different resources. The management of spare parts and inventories is one of the most difficult resource management concerns that an organisation has to deal with on a regular basis. Their prompt availability may have a significant influence on the overall quality of the service and the level of client satisfaction. The purpose of the study was to assess the Spare Parts Inventory Management of XYZ company operating within the Ghanaian telecommunication ecosystem. Data on demand and inventory stock level was collected from the procurement department of XYZ company. The study adopted the use of the Arena Simulation model to analyze data collected from the XYZ company. The results of the analysis indicated that the procurement department is unable to meet the spare parts demand of the Passive department where the average order received from the department in a day is 6.08. Based on the findings of the study, it is recommended that the procurement department put in measure to receive and process orders in time. The reason for the delay in processing orders could be the less poor inventory management practices of the company. The study recommends that systems be adopted to track the inventory level of the company. Due to the stochastic nature of the demand from the passive department, the company should be proactive in the management of the demand by keeping the right stock level in order to meet the demand of the department in order to reduce the long down time. Key words: Spare parts, inventory, Arena Simulation, telecommunication DOI: 10.7176/EJBM/14-12-10 Publication date: June 30 th 2022

Failure Mode and Effect Analysis Implementation for Spare Part Classification and Inventory Management in Automotive Manufacturing (Case Study of Pt. XYZ)

Failure Mode and Effect Analysis Implementation for Spare Part Classification and Inventory Management in Automotive Manufacturing (Case Study of Pt. XYZ)

A deep reinforcement learning assisted simulated annealing algorithm for a maintenance planning problem

AbstractMaintenance planning aims to improve the reliability of assets, prevent the occurrence of asset failures, and reduce maintenance costs associated with downtime of assets and maintenance resources (such as spare parts and workforce). Thus, effective maintenance planning is instrumental in ensuring high asset availability with the minimum cost. Nevertheless, to find such optimal planning is a nontrivial task due to the (i) complex and usually nonlinear inter-relationship between different planning decisions (e.g., inventory level and workforce capacity), and (ii) stochastic nature of the system (e.g., random failures of parts installed in assets). To alleviate these challenges, we study a joint maintenance planning problem by considering several decisions simultaneously, including workforce planning, workforce training, and spare parts inventory management. We develop a hybrid solution algorithm ($$\mathcal {DRLSA}$$ DRLSA ) that is a combination of Double Deep Q-Network based Deep Reinforcement Learning (DRL) and Simulated Annealing (SA) algorithms. In each episode of the proposed algorithm, the best solution found by DRL is delivered to SA to be used as an initial solution, and the best solution of SA is delivered to DRL to be used as the initial state. Different from the traditional SA algorithms where neighborhood structures are selected only randomly, the DRL part of $$\mathcal {DRLSA}$$ DRLSA learns to choose the best neighborhood structure to use based on experience gained from previous episodes. We compare the performance of the proposed solution algorithm with several well-known meta-heuristic algorithms, including Simulated Annealing, Genetic Algorithm (GA), and Variable Neighborhood Search (VNS). Further, we also develop a Machine Learning (ML) algorithm (i.e., K-Median) as another benchmark in which different properties of spare parts (e.g., failure rates, holding costs, and repair rates) are used as clustering features for the ML algorithm. Our study reveals that the $$\mathcal {DRLSA}$$ DRLSA finds the optimal solutions for relatively small-size instances, and it has the potential to outperform traditional meta-heuristic and ML algorithms.

An empirical Bayes approach to incorporating demand intermittency and irregularity into inventory control

Spare parts inventory management is complex due to the combined impact of intermittent and variable demand patterns. It becomes even more challenging if the spare parts demand distribution is highly complex due to strong interdependent demand intermittency and extremely irregular demand. The research literature proposes many analytical methods for forecasting spare parts demand. But, due to their limited flexibility in modeling complex demand patterns, existing forecasting methods may not produce satisfactory results for a spare parts portfolio displaying extremely complex demand patterns. This study proposes a novel nonparametric Bayesian forecasting approach with its roots in the empirical Bayes paradigm. The method is subject to few performance constraints and is highly flexible in dealing with a rich diversity of demand patterns, including extreme demand complexity. We assess the relative performance of this new approach with several prominent methods in the literature using an automotive parts distributor's empirical demand data for 46,272 stock-keeping units. This dataset is representative of typical spare parts portfolios that are characterized by a wide variety and extremely complex demand patterns. The experimental findings show the new Bayesian approach achieves the best overall performance in terms of inventory efficiency and minimal backorders for meeting specified target service levels. This favorable performance reflects the approach's flexibility to accommodate disparate and complex demand patterns, including interdependence of demand intermittency, irregular demand distribution, and even nonstationary demand distribution to some extent, and provide robust solutions.

Impact of ERP implementation on the Maintenance Management in the Manufacturing Sector of Pakistan

The study was conducted to understand the phenomenon of ERP implementation and its impact on the maintenance management in the manufacturing sector of Pakistan. The ERP implementation was actually operationalized through production efficiency, cost-optimization and spare parts inventory management in order to understand their impact on the maintenance management in the manufacturing sector. Moreover, the study included the scholarly work of various authors in order to understand the phenomenon in detail. Although, the research was conducted quantitatively through developing one close-ended questionnaire and then data was collected from 390 individuals presently working in the manufacturing sector of Pakistan. The researcher found through descriptive analysis that, most of the participants were between 25- 35 age bracket and belonged to either production or maintenance department. It was further found that data is considered to b e distributed normally and hence, can be used for further analysis. The researcher found ultimately that, there is a significant relationship between production efficiency, cost-optimization, spare-parts inventory management in terms of ERP implementation and maintenance management in the manufacturing sector of Pakistan. In addition to that, the strength of relationship was strong in nature in all three hypothesis and model fit suggested that, the independent variables are 84% related with the dependent variable.

An IoT-based maintenance framework for irrigation and drainage water management system at regional scale

The management of the geographically distributed assets of irrigation and drainage water grids compels a challenging alignment of operations like maintenance plans, resource allocation, and spare parts inventory management systems. We propose an IoT-based maintenance framework to optimize reliability, management costs, and logistics and increase the resilience and sustainability of the irrigation and water management network. The proposed framework provides an interface between the centralized inventory system and the maintenance and logistics operations carried out throughout the network. Information exchange is supported by bar code and RFID technologies integrated within the IT system of the company. A Maintenance Management Software (MMS) able to store assets' physical hierarchy data and inventory data is the supporting tool that fuels the framework. The framework is applied to a regional drainage water system in the Emilia-Romagna region (Italy), improving the visibility of the infrastructural network and enabling tailored and data-driven preventive maintenance plans. The combination of the components' availability, logistics and maintenance costs, failure trend, and impact on the overall network contribute to establishing new methods for assessing the threshold of service level (i.e., reliability) to guarantee for each material.