Search

In a closed-loop supply chain, the reuse of spare parts from returned systems is a recovery process referred to as spare parts harvesting. The unpredictability of the parts supply capacity from returned systems is a challenge in healthcare industry as product returns depend on several factors and regulatory and legal requirements must be respected. The focus of this paper is to provide a forecasting method of harvested parts supply capacity in healthcare industry that combines statistical methods with field information and business knowledge to provide an informed forecast. We propose a dynamic forecasting process that gets updated monthly employing TSB-Croston, 12-month moving average, ARIMA, ARIMA with seasonality, and a new business knowledge based model. A prediction method of the inventory state changes is introduced. The forecasters judgment is transformed into validation rules for an automatic forecast adjustment. This method is evaluated on more than 1400 time series with intermittent behaviour representing General Electric Healthcare spare parts harvesting history. We evaluate the performance of our method compared to each tested model using a modified MAPE, MAE, MSE, and RMSE. By means of the designed method, the forecast performance is improved compared to all tested models. [ABSTRACT FROM AUTHOR]

This paper aims to propose a Novel Condition-based maintenance (CBM) decision aid model for optimising the maintenance of complex multi-component systems. As the degradation level of each component is assumed to be independent and stochastic, it follows a specific probability distribution determined from historical data of experimental observations and inspection. The main objective is to optimise the total cost for providing maintenance actions and reducing the excess of spare parts usage. The decision support model consists of determining measurements on components with the aim of estimating the instant of time of removing predictively one or a group of components before they fail. The measurement model includes the mean residual lifetime (MRL) and some extensions developed for this purpose. For demonstrating the pertinency of the proposed model, we use a preventive maintenance strategy for one-component systems and a grouping/opportunistic maintenance for multi-component systems. Besides, a numerical comparative study performing these measurements is carried out using several examples and a case study from Electric energy distribution systems. The solution is illustrated as a decision-making optimal model for optimising the maintenance operations' costs and the total number of spare parts. The numerical results and the comparison show the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

A well-configured spare parts supply chain (SC) can reduce costs and increase the competitiveness of spare parts retailers. A structured method for configuring spare parts SCs should be used to determine whether to centralise or decentralise inventory management, also considering hybrid configurations. Moreover, such a method should define whether or not to switch the production of spare parts from Conventional Manufacturing (CM) technologies to Additive Manufacturing (AM) ones. Indeed, AM is considered the next revolution in the field of spare parts, and the adoption of AM technologies strongly affects the characteristics of SCs. However, the choice between centralisation and decentralisation is not the subject of much scientific research, and it is also not clear when AM would be the preferable manufacturing technology for spare parts. This paper aims to assist managers and practitioners in determining how to design their spare parts SCs, thus defining both the spare parts SC configuration and the manufacturing technology to adopt through the development of a decision support system (DSS). The proposed DSS is a user-friendly decision tree, and, for the first time, it allows comparison of the total costs of SCs characterised by different degrees of centralisation with both AM and CM spare parts. [ABSTRACT FROM AUTHOR]

We analyse the joint scheduling of spare parts production and service engineers in a multi-site maintenance system. In the system, for each failure, a service engineer with the required spare part needs to be allocated for on-site maintenance. If one of the required resources is not readily available, the maintenance task can be satisfied via an external channel with quick responsiveness but high involved costs. Service providers need to make an efficient plan, including production scheduling of spare parts, allocation of service engineers and outsourcing strategy. First, a mathematical model with the objectives of minimum total outsourcing costs and minimum weighted tardiness is formulated. Then, a novel and knowledge-based heuristic algorithm is developed, namely progressive Pareto algorithm based on step-size (PPAS). To verify the performance of PPAS, it is compared against other well-known algorithms, including NSGA-II, MOEA/D, NNIA, RIPG, MMOIG and MNNIA. Finally, the effectiveness of the joint scheduling mode is demonstrated by comparing it with the separate optimisation mode of spare parts production and workforce allocation. Highlights Analyse the joint scheduling of spare parts production and service engineers Construct a bi-objective optimisation model Develop a progressive Pareto algorithm based on step-size Verify the superiority of the proposed algorithm and joint scheduling mode [ABSTRACT FROM AUTHOR]

Configuring supply chains (SCs) is critical to spare parts retailers' success, entailing two key aspects: stock deployment into distribution centres (DCs) (i.e. inventory centralisation or decentralisation) and stock supply in each DC (how many spare parts to supply and how often). Given the unpredictability of spare parts demand, stock deployment and supply policies should be regularly reviewed, adapting to fluctuations in customer needs. A viable way to do this is to adopt a multi-criteria ABC criticality classification. However, the multi-criteria ABC criticality classification has often been used to plan stock supply policies in a single DC, but only once to plan spare parts deployment. Nevertheless, the available literature methodology presents major limitations, being not applicable in real companies. Therefore, this paper provides a novel methodology, called SP-LACE, which first reviews the configuration of spare parts SCs based on a multi-criteria criticality classification. Then, allows, for the first time, to evaluate the economic benefits of the reviewed SC configuration. SP-LACE was tested on two case studies and compared with the literature methodology. The results indicate that it provides economic benefits (in terms of total SC cost), overcoming the limitations of the literature methodology and ensuring high service levels. [ABSTRACT FROM AUTHOR]

The development of society has put forward higher requirements for the supply of maintenance spare parts. However, the current support index is not comprehensive enough to evaluate the system ability. Meanwhile, the influence of horizontal supply time on spare parts storage is not fully reflected. Therefore, the problem of spare parts storage configuration in two-echelon maintenance and supply support systems is analysed in the paper. First, the state transition rates of different parts are studied under the context. Then a system-level comprehensive support rate based on importance measure is proposed. To determine the number of different spare parts, an optimisation model is established to maximise system support efficiency. Furthermore, a site importance measure based on horizontal supply time is proposed to measure the site priority over spare parts storage. Next, a two-stage decision method is presented for determining the location of spare parts. Finally, the feasibility of the proposed method is verified with the help of an example for the aircraft maintenance and supply support system. [ABSTRACT FROM AUTHOR]

In capital-intensive industries, it is becoming common practice for operators to outsource the mission-critical equipment maintenance to service suppliers. Performance-based contracts (PBCs) are widely used and the equipment availability is considered as an important performance indicator. In this paper, we studied a kind of mission-critical equipment that has a major component. When a failure occurred in the major component, it is replaced by a spare part and repaired offline. We consider suppliers' risk attitudes (i.e. risk-averse, risk-neutral, and risk-seeking) and the spare parts inventory cost-sharing between the supplier and the operator to design a PBC. Specifically, from the operator's perspective, we use a principal-agent analysis framework to construct a contract model which considers different suppliers' risk attitudes. We further explore the impact of inventory cost-sharing proportion on the operator's profit and contract parameters. The results show that it is more incentive to provide larger fixed payments and performance penalties (rewards) for risk-seeking (risk-averse) suppliers as the increasing of risk coefficient under no cost-sharing. Moreover, a large cost-sharing proportion has no impact on the operator's optimal profit, but affects the contract option of different risk attitudes' suppliers. Finally, a numeric example is used to illustrate the proposed models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

3D printing technology is fundamentally transforming how companies are run and managed today. This work analyzes the problem of integrated production and transportation of automotive spare parts in the context of 3D printing. We consider two sets of customers – scheduled and breakdown maintenance – serviced by the same resource but with distinct delivery modes. We propose a mixed-integer programming (MIP) formulation for optimally solving small problem instances. We develop an exact approach based on column generation (CG) for large cases by reformulating the MIP model as a set-covering problem through Dantzig-Wolfe decomposition. We design and deploy two acceleration strategies for the quicker convergence of the CG approach. Through extensive computational experiments, we establish the superiority and suitability of the proposed solution methodology for solving real-life problems at terse computational times. [ABSTRACT FROM AUTHOR]

The sustainability of enterprises in an increasingly competitive environment is proportional to their ability to use the resources efficiently. Maintenance departments are critical to ensure that resources are ready and operational. Increasing the efficiency of maintenance departments depends on reducing the number of failures, performing planned maintenance on time and sustaining availability of spare parts needed. Therefore, it is vital that businesses consider predictive maintenance and spare parts jointly. In this study, predictive maintenance and spare parts integration studies are carried out using gearbox failure data and spare parts consumption data of a bus fleet. This study aims to contribute to the reduction of failure costs by finding failure patterns and predicting the subsequent failures and spare parts to be used. The sequential pattern mining approach was used to determine failure patterns and the traditional frequent itemset mining approach was used to predict spare parts. As a result, 45 failure patterns were found. Rules with a reliability of up to 79% were obtained. In addition, spare part clusters with a support value of approximately 40% were created. With this valuable information, businesses are able to investigate root causes, take precautions against future failures, make predictions about the spare parts that will be needed, and develop joint maintenance planning and inventory management policies. [ABSTRACT FROM AUTHOR]

The evaluation and prediction of reliability and testability of mining machinery and equipment are crucial, as advancements in mining technology have increased the importance of ensuring the safety of both the technological process and human life. This study focuses on developing a reliability model to analyze the controllability of mining equipment. The model, which examines the reliability of a mine cargo-passenger hoist, utilizes statistical methods to assess failures and diagnostic controlled parameters. It is represented as a transition graph and is supported by a system of equations. This model enables the estimation of the reliability of equipment components and the equipment as a whole through a diagnostic system designed for monitoring and controlling mining equipment. A mathematical and logical model is proposed to calculate availability and downtime coefficients for different structures within the mining equipment system. This analysis considers the probability of failure-free operation of the lifting unit based on the structural scheme, with additional redundancy for elements with lower reliability. The availability factor of the equipment for monitoring and controlling the mine hoisting plant is studied for various placements of diagnostic systems. Additionally, a logistic concept is introduced for organizing preventive maintenance systems and reducing equipment recovery time by optimizing spare parts, integrating them into strategies aimed at enhancing the reliability of mine hoisting plants. [ABSTRACT FROM AUTHOR]

Current single-view image inpainting methods often suffer from low image information utilization and suboptimal repair outcomes. To address these challenges, this paper introduces a novel image inpainting framework that leverages a structure-guided multi-scale attention pyramid network. This network consists of a structural repair network and a multi-scale attention pyramid semantic repair network. The structural repair component utilizes a dual-branch U-Net network for robust structure prediction under strong constraints. The predicted structural view then serves as auxiliary information for the semantic repair network. This latter network exploits the pyramid structure to extract multi-scale features of the image, which are further refined through an attention feature fusion module. Additionally, a separable gated convolution strategy is employed during feature extraction to minimize the impact of invalid information from missing areas, thereby enhancing the restoration quality. Experiments conducted on standard datasets such as Paris Street View and CelebA demonstrate the superiority of our approach over existing methods through quantitative and qualitative comparisons. Further ablation studies, by incrementally integrating proposed mechanisms into a baseline model, substantiate the effectiveness of our multi-view restoration strategy, separable gated convolution, and multi-scale attention feature fusion. [ABSTRACT FROM AUTHOR]

The rapid transformation from conventional vehicles to electric vehicles (EVs) holds promise for enhancing air quality, reducing CO2 emissions, revitalizing the renewable energy market, and enhancing countries' economies. This study investigated the performance of EVs in Jordan and the key challenges hindering their rapid development. It implemented a structured questionnaire of 202 EV owners/drivers and 15 interviews with technicians at many service centers. Findings revealed that Jordan leads among MENA countries in Battery Electric Vehicles(BEVs) market share and growth rate, with 52% market share of BEVs new cars in 2023, projected to surpass 80% by 2025. This rapid development is tied up with many internal and external influences; government backing, high fossil fuel prices, and tax exemptions are common causes that enhanced the market of EVs. Respondents highlighted prevalent issues of BEVs, including the rapid battery depletion, low battery capacity, electric inverter malfunctions, battery overheating, low performance in hot and cold climates, and inflating costs. Finally, this study recommends educational institutions to offer targeted training programs for EV maintenance and service, and the government should provide more incentives for this industry, and combat the influx of counterfeit charging accessories, batteries, and spare parts. [ABSTRACT FROM AUTHOR]

The present research explores the optimization of maintenance strategies for floating offshore wind (FOW) farms using nested genetic algorithms. The primary goal is to provide insights on the decision-making processes required for both immediate and strategic maintenance planning, crucial for the viability and efficiency of FOW operations. A nested genetic algorithm was coupled with discrete-event simulations in order to simulate and optimize maintenance scenarios influenced by various operational and environmental parameters. The study revealed that short-term maintenance timing is significantly influenced by wind conditions, with higher electricity prices justifying on-site spare parts storage to mitigate operational disruptions, suggesting economic incentives for maintaining on-site inventory of spare parts. Long-term strategic findings emphasized the impact of planned intervals between inspections on financial outcomes, identifying optimal strategies that balance operational costs with energy production efficiency. Ultimately, this study highlights the importance of integrating sophisticated predictive models for failure detection with real-time operational data to enhance maintenance decision-making in the evolving landscape of offshore wind energy, where future farms are likely to operate farther from onshore facilities and under potentially highly varying market conditions in terms of electricity prices. [ABSTRACT FROM AUTHOR]

This research investigates the potential of additive manufacturing (AM) to revolutionize the marine spare parts supply chain (SPSC). While AM has been widely explored in other industries, its application in the maritime sector remains limited. This study addresses this gap by comparing traditional and AM-integrated SPSC models using system dynamics simulation. By focusing on high- and low-value spare parts, this research highlights significant cost reductions and improved delivery times through strategic AM integration. Our findings demonstrate the potential of AM to enhance the efficiency and resilience of the maritime supply chain, particularly in remote environments. [ABSTRACT FROM AUTHOR]

Technological equipment in quarries that extract and deliver aggregates for different uses operates in a predetermined flow depending on the type of rocks exploited and the dimensional characteristics imposed on the final products. In this context, the interruptions in operation required to replace high-wear parts (such as the teeth of excavators and bucket loaders) must be limited as much as possible through technological solutions to increase their service life. The evolution of the wear of the teeth of the quarry equipment that come into direct contact with rocks was concretely established in the production process, in parallel with the wear values obtained by simulating the wear phenomenon in laboratory conditions, in order to validate the data collected during the operation of the machines. Preventive–repetitive maintenance within the activities of reconditioning the worn surfaces of the teeth, through the charging process by manual electric welding with covered electrodes, was applied directly to the machine, which led to the shortening of the interruptions in operation necessary to replace these spare parts. [ABSTRACT FROM AUTHOR]

To improve the spare parts support capacity of civil aircrafts and given the actual lack of evaluation methods at present, the evaluation problem of spare parts support capacity was solved in this study by proposing a multi-attribute decision method based on Type-2 hesitant Pythagorean fuzzy sets and an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). First, the basic definitions of Type-2 hesitant Pythagorean fuzzy sets were given, which were further promoted to Type-n hesitant Pythagorean fuzzy sets, and the basic order relation criterion of Type-2 hesitant Pythagorean fuzzy sets was introduced. Second, a complete evaluation system for spare parts supply support capacity was established with the spare parts of civil aircrafts as the study objects, and each evaluation indicator was introduced in detail. Then, the spare parts support solutions were preferentially sorted using the correlation coefficient formula of Type-2 hesitant Pythagorean fuzzy sets and improved TOPSIS. Finally, the reliability and reasonability of the proposed method were verified through an example calculation and comparative analysis. The experimental results indicate that the proposed method can acquire the evaluation results of spare parts support capacity more scientifically and can be referenced by relevant studies. [ABSTRACT FROM AUTHOR]

The problem of increasing passenger traffic remains acute for municipal public transport. The value of this indicator is determined by the interest of citizens in this way of making their trips and determines the feasibility of the carrier's operation. The authors conducted a study of the problems of public transport services in large- and medium-sized cities, which found that the population's interest in public urban passenger transport has generally been significantly lost. More than 40% of the city population refuses to travel on public transport, half of the population has questions about the reliability of tariff formation, and the same number of people are not satisfied with the regular route network and schedule. City residents increasingly prefer personal vehicles or taxis for their trips, which negatively affects the revenue side of carriers, as well as the level of social comfort and the quality of life of citizens. Efforts to reduce the operating costs of the carrier are aimed at correcting the current situation with urban transport so that tariffs for transportation are more acceptable for passengers. The formation of tariffs for passenger transportation for transport companies is an urgent and complex task. It is necessary to formulate the tariff in such a way as to cover your own transportation costs in the near future and, at the same time, not exceed the psychological threshold for passengers so as not to cause their negative reaction. In addition, since the transportation of passengers by urban public transport is regulated by the authorities, it is also necessary to provide an economic justification for transportation tariffs. This is difficult in the absence of substantiated indicators of consumption rates of material resources in the transport process. To solve this problem, it is necessary to carefully analyze the current costs of operating the bus fleet, as well as forecast costs for future periods. At different periods, researchers have proposed various approaches for planning the cost of operating a bus fleet. The approach we propose is to use standardization of the consumption of material resources, considering the individual operating conditions of the bus fleet and the influence of various factors. [ABSTRACT FROM AUTHOR]

Based on the monthly panel data of China's imports of agricultural machinery products from 2016–2022, this paper uses a multi-period double-difference model to assess the impact of China's imposition of counter-tariffs on China's imports of agricultural machinery in the context of U.S.–China trade friction. It is found that China's implementation of counter-tariffs significantly reduces China's imports of agricultural machinery products from the U.S. and significantly increases imports from 16 other countries, but the trade diversion effect is lower than the trade suppression effect. Mechanism analysis finds that China–U.S. trade friction affects the technological innovation capacity of agricultural machinery enterprises and the degree of uncertainty in the Chinese economy, which in turn affects China's agricultural machinery imports. Heterogeneity analysis finds that China–U.S. trade friction has a more significant inhibitory effect on seeding and planting and fertilizing machinery, drainage, irrigation, and water lifting machinery, and other machinery imported from the U.S., and a more significant diversionary effect on agricultural primary processing machinery and harvesting machinery imported from 16 other countries. The imposition of countervailing tariffs mainly affected imports of complete machinery products rather than machinery spare parts. [ABSTRACT FROM AUTHOR]

Tendons are one of the major load-bearing tissues in the body; subjected to enormous peak stresses, and thus vulnerable to injury. Cellular responses to tendon injury are complex, involving inflammatory and repair components, with the latter employing both resident and recruited exogenous cell populations. Gene expression analyses are valuable tools for investigating tendon injury, allowing assessment of repair processes and pathological responses such as fibrosis, and permitting evaluation of therapeutic pharmacological interventions. Quantitative polymerase chain reaction (qPCR) is a commonly used approach for such studies, but data obtained by this method must be normalised to reference genes: genes known to be stably expressed between the experimental conditions investigated. Establishing suitable tendon injury reference genes is thus essential. Accordingly we investigated mRNA expression stability in a rat model of tendon injury, comparing both injured and uninjured tendons, and the effects of rapamycin treatment, at 1 and 3 weeks post injury. We used 11 candidate genes (18S, ACTB, AP3D1, B2M, CSNK2A2, GAPDH, HPRT1, PAK1IP1, RPL13a, SDHA, UBC) and assessed stability via four complementary algorithms (Bestkeeper, deltaCt, geNorm, Normfinder). Our results suggests that ACTB, CSNK2A2, HPRT1 and PAK1IP1 are all stably expressed in tendon, regardless of injury or drug treatment: any three of these would serve as universally suitable reference gene panel for normalizing qPCR expression data in the rat tendon injury model. We also reveal 18S, UBC, GAPDH, and SDHA as consistently poor scoring candidates (with the latter two exhibiting rapamycin- and injury-associated changes, respectively): these genes should be avoided. [ABSTRACT FROM AUTHOR]

Point cloud registration technology, by precisely aligning repair components with the original artifacts, can accurately record the geometric shape of cultural heritage objects and generate three-dimensional models, thereby providing reliable data support for the digital preservation, virtual exhibition, and restoration of cultural relics. However, traditional point cloud registration methods face challenges when dealing with cultural heritage data, including complex morphological and structural variations, sparsity and irregularity, and cross-dataset generalization. To address these challenges, this paper introduces an innovative method called Enhancing Point Cloud Registration with Transformer (EPCRT). Firstly, we utilize local geometric perception for positional encoding and combine it with a dynamic adjustment mechanism based on local density information and geometric angle encoding, enhancing the flexibility and adaptability of positional encoding to better characterize the complex local morphology and structural variations of artifacts. Additionally, we introduce a convolutional-Transformer hybrid module to facilitate interactive learning of artifact point cloud features, effectively achieving local–global feature fusion and enhancing detail capture capabilities, thus effectively handling the sparsity and irregularity of artifact point cloud data. We conduct extensive evaluations on the 3DMatch, ModelNet, KITTI, and MVP-RG datasets, and validate our method on the Terracotta Warriors cultural heritage dataset. The results demonstrate that our method has significant performance advantages in handling the complexity of morphological and structural variations, sparsity and irregularity of relic data, and cross-dataset generalization. [ABSTRACT FROM AUTHOR]

The implementation of Industry 4.0 has integrated manufacturing, electronics, and engineering materials, leading to the creation of smart parts (SPs) that provide information on production system conditions. However, SP development faces challenges due to limitations in manufacturing processes and integrating electronic components. This systematic review synthesizes scientific articles on SP fabrication using additive manufacturing (AM), identifying the advantages and disadvantages of AM techniques in SP production and distinguishing between SPs and smart spare parts (SSPs). The methodology involves establishing a reference framework, formulating SP-related questions, and applying inclusion criteria and keywords, initially resulting in 1603 articles. After applying exclusion criteria, 70 articles remained. The results show that while SP development is advancing, widespread application of AM-manufactured SP is recent. SPs can anticipate production system failures, minimize design artifacts, and reduce manufacturing costs. Furthermore, the review highlights that SSPs, a subcategory of SPs, primarily differs by replacing conventional critical parts in the industry, offering enhanced functionality and reliability in industrial applications. The study concludes that continued research and development in this field is essential for further advancements and broader adoption of these technologies. [ABSTRACT FROM AUTHOR]

This article explores the field of industrial ecology and its focus on understanding the environmental impact of material goods throughout their lifecycle. It specifically examines the concept of product lifetimes and how they can be extended to minimize environmental harm and contribute to a circular economy. The article emphasizes the complexity of this topic and the need for further research, including factors such as repairability, user behavior, and social norms. It also discusses the importance of incorporating social and psychological factors into policy recommendations and highlights grassroots movements advocating for the right to repair. Overall, the article underscores the need for evidence-based policy recommendations and a comprehensive understanding of product lifetime extension. [Extracted from the article]

The article presents the discussion on growing use of 360-degree photography in chemical process facilities to speed up turnarounds, driven by decreasing camera costs and improved image processing. Topics include advantages of virtual tours, the efficiency of 360-degree imaging; and the accessibility of augmented and mixed-reality applications for remote inspections.

This study focuses on the successful fabrication of a cost-effective strain sensor using exclusively additive manufacturing Fused Deposition Modeling (FDM) technology, enabling fast on-site production, which is particularly advantageous in maritime settings, reducing downtime, and supporting a circular economy approach by minimizing inventory needs and environmental footprint. The principle of operation of the developed device is based on the piezoresistive characteristics of a carbon nanotube (CNT)-enriched building material, from which the main sensing element consists. The prototype exhibited reliable piezoresistive properties, and a clear correlation was observed between the thermal treatment of the printed piezoresistor and the resulting gauge factor, linearity, and hysteresis. Its robustness, simple design, and single-step manufacturing process, together with its ability to be integrated into the readout circuitry through standard soldering, enhance its reliability and durability. The key advantages of the proposed device include its low cost, simple design, and rapid remote production. [ABSTRACT FROM AUTHOR]

Suroboyo Bus Operational Risk Management System Using Fmea Method (Failure Mode Effect Analysis). Suroboyo Bus currently has several operational risks so efficient risk management planning is needed. The risk management planning mechanism used is Failure Mode and Effect Analysis (FMEA) with the hope of identifying the risks inherent in the operational business process of Suroboyo Bus. It can be seen from the survey results that 35% of the public want to add routes and 25% of the public want to add fleets. From the results of risk identification, 18 causes of the highest operational risks were obtained, including Driver absence, Minor damage or disruption to tyres, Major damage or disruption to facilities inside, Minor damage or disruption to facilities inside the bus, Traffic congestion around the bus stop, No plastic bottle checks, Damage or incomplete bus spare parts that were missed from the checking process, No helper at the passenger gathering point, Accidents on the way to the bus stop, Bus accidents, Major damage or disruption to the engine, Major damage or disruption to tyres, Unprinted receipts (out of stock), Unvalidated receipts, Lost receipts, Gears not in P position when parking at the Pool, No bus spare part checking process, accidents by cleaning staff, so SOPs, regulations, and budget policies are needed. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article discusses the challenges faced by the triple helix actors (academic institutions, organizations, and governments) during disruptive times, such as the COVID-19 pandemic. It highlights the implementation of Industry 4.0 and Quality 4.0 as ways to address these challenges. The article emphasizes the importance of Kaizen, a Japanese concept of continuous improvement, and its application at different levels and in different contexts. It also identifies research topics related to Kaizen projects and innovation frameworks in response to the pandemic. The article concludes by highlighting the contributions of various research papers in this special issue, which explore the application of Kaizen and Quality 4.0 principles in addressing the challenges of the pandemic. [Extracted from the article]

Purpose: To redesign the spare parts (MRO) inventory management at Company XYZ's warehouse, considering the conditions after the COVID-19 pandemic. Design/methodology/approach: To address this research project, the authors integrated three methodologies: action research, Lean Six Sigma (DMAIC) and Cross Industry Standard Process for Data Mining. These methodologies integrated the Lean Six Sigma (LSS) 4.0 framework applied in this project. Findings: The spare parts inventory value was reduced by 15%, and inventory turnover increased by 120% without negatively impacting the internal service level. Practical implications: Practitioners leading or participating in continuous improvement projects (CIPs) should consider data quality (data available and data trustworthiness), problem-solving approach and target area involvement to achieve CIP goals. Otherwise, the LSS 4.0 could fail or extend its duration by several weeks or months. Originality/value: This project shows the importance of controlling a target area before deciding to conduct a LSS 4.0 project. To address this problem, the LSS 4.0 team implemented 5S during the measure phase of the DMAIC. Also, this project offers significant practitioner and theoretical contributions to the body of knowledge about LSS 4.0. [ABSTRACT FROM AUTHOR]