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1 – 10 of 208José Nogueira da Mata Filho, Antonio Celio Pereira de Mesquita, Fernando Teixeira Mendes Abrahão and Guilherme C. Rocha
This paper aims to explore the optimization process involved in the aircraft maintenance allocation and packing problem. The aircraft industry misses a part of the optimization…
Abstract
Purpose
This paper aims to explore the optimization process involved in the aircraft maintenance allocation and packing problem. The aircraft industry misses a part of the optimization potential while developing maintenance plans. This research provides the modeling foundation for the missing part considering the failure behavior of components, costs involved with all maintenance tasks and opportunity costs.
Design/methodology/approach
The study models the cost-effectiveness of support against the availability to come up with an optimization problem. The mathematical problem was solved with an exact algorithm. Experiments were performed with real field and synthetically generated data, to validate the correctness of the model and its potential to provide more accurate and better engineered maintenance plans.
Findings
The solution procedure provided excellent results by enhancing the overall arrangement of the tasks, resulting in higher availability rates and a substantial decrease in total maintenance costs. In terms of situational awareness, it provides the user with the flexibility to better manage resource constraints while still achieving optimal results.
Originality/value
This is an innovative research providing a state-of-the-art mathematical model and an algorithm for efficiently solving a task allocation and packing problem by incorporating components’ due flight time, failure probability, task relationships, smart allocation of common preparation tasks, operational profile and resource limitations.
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Ahmed M. Attia, Ahmad O. Alatwi, Ahmad Al Hanbali and Omar G. Alsawafy
This research integrates maintenance planning and production scheduling from a green perspective to reduce the carbon footprint.
Abstract
Purpose
This research integrates maintenance planning and production scheduling from a green perspective to reduce the carbon footprint.
Design/methodology/approach
A mixed-integer nonlinear programming (MINLP) model is developed to study the relation between production makespan, energy consumption, maintenance actions and footprint, i.e. service level and sustainability measures. The speed scaling technique is used to control energy consumption, the capping policy is used to control CO2 footprint and preventive maintenance (PM) is used to keep the machine working in healthy conditions.
Findings
It was found that ignoring maintenance activities increases the schedule makespan by more than 21.80%, the total maintenance time required to keep the machine healthy by up to 75.33% and the CO2 footprint by 15%.
Research limitations/implications
The proposed optimization model can simultaneously be used for maintenance planning, job scheduling and footprint minimization. Furthermore, it can be extended to consider other maintenance activities and production configurations, e.g. flow shop or job shop scheduling.
Practical implications
Maintenance planning, production scheduling and greenhouse gas (GHG) emissions are intertwined in the industry. The proposed model enhances the performance of the maintenance and production systems. Furthermore, it shows the value of conducting maintenance activities on the machine's availability and CO2 footprint.
Originality/value
This work contributes to the literature by combining maintenance planning, single-machine scheduling and environmental aspects in an integrated MINLP model. In addition, the model considers several practical features, such as machine-aging rate, speed scaling technique to control emissions, minimal repair (MR) and PM.
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Maren Hinrichs, Loina Prifti and Stefan Schneegass
With production systems become more digitized, data-driven maintenance decisions can improve the performance of production systems. While manufacturers are introducing predictive…
Abstract
Purpose
With production systems become more digitized, data-driven maintenance decisions can improve the performance of production systems. While manufacturers are introducing predictive maintenance and maintenance reporting to increase maintenance operation efficiency, operational data may also be used to improve maintenance management. Research on the value of data-driven decision support to foster increased internal integration of maintenance with related functions is less explored. This paper explores the potential for further development of solutions for cross-functional responsibilities that maintenance shares with production and logistics through data-driven approaches.
Design/methodology/approach
Fifteen maintenance experts were interviewed in semi-structured interviews. The interview questions were derived based on topics identified through a structured literature analysis of 126 papers.
Findings
The main findings show that data-driven decision-making can support maintenance, asset, production and material planning to coordinate and collaborate on cross-functional responsibilities. While solutions for maintenance planning and scheduling have been explored for various operational conditions, collaborative solutions for maintenance, production and logistics offer the potential for further development. Enablers for data-driven collaboration are the internal synchronization and central definition of goals, harmonization of information systems and information visualization for decision-making.
Originality/value
This paper outlines future research directions for data-driven decision-making in maintenance management as well as the practical requirements for implementation.
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Hassan Th. Alassafi, Khalid S. Al-Gahtani, Abdulmohsen S. Almohsen and Abdullah M. Alsugair
Heating, ventilating, air-conditioning and cooling (HVAC) systems are crucial in daily health-care facility services. Design-related defects can lead to maintenance issues…
Abstract
Purpose
Heating, ventilating, air-conditioning and cooling (HVAC) systems are crucial in daily health-care facility services. Design-related defects can lead to maintenance issues, causing service disruptions and cost overruns. These defects can be avoided if a link between the early design stages and maintenance feedback is established. This study aims to use experts’ experience in HVAC maintenance in health-care facilities to list and evaluate the risk of each maintenance issue caused by a design defect, supported by the literature.
Design/methodology/approach
Following semistructured interviews with experts, 41 maintenance issues were identified as the most encountered issues. Subsequently, a survey was conducted in which 44 participants evaluated the probability and impact of each design-caused issue.
Findings
Chillers were identified as the HVAC components most prone to design defects and cost impact. However, air distribution ducts and air handling units are the most critical HVAC components for maintaining healthy conditions inside health-care facilities.
Research limitations/implications
The unavailability of comprehensive data on the cost impacts of all design-related defects from multiple health-care facilities limits the ability of HVAC designers to furnish case studies and quantitative approaches.
Originality/value
This study helps HVAC designers acquire prior knowledge of decisions that may have led to unnecessary and avoidable maintenance. These design-related maintenance issues may cause unfavorable health and cost consequences.
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Hiwa Esmaeilzadeh, Alireza Rashidi Komijan, Hamed Kazemipoor, Mohammad Fallah and Reza Tavakkoli-Moghaddam
The proposed model aims to consider the flying hours as a criterion to initiate maintenance operation. Based on this condition, aircraft must be checked before flying hours…
Abstract
Purpose
The proposed model aims to consider the flying hours as a criterion to initiate maintenance operation. Based on this condition, aircraft must be checked before flying hours threshold is met. After receiving maintenance service, the model ignores previous flying hours and the aircraft can keep on flying until the threshold value is reached again. Moreover, the model considers aircraft age and efficiency to assign them to flights.
Design/methodology/approach
The aircraft maintenance routing problem (AMRP), as one of the most important problems in the aviation industry, determines the optimal route for each aircraft along with meeting maintenance requirements. This paper presents a bi-objective mixed-integer programming model for AMRP in which several criteria such as aircraft efficiency and ferrying flights are considered.
Findings
As the solution approaches, epsilon-constraint method and a non-dominated sorting genetic algorithm (NSGA-II), including a new initializing algorithm, are used. To verify the efficiency of NSGA-II, 31 test problems in different scales are solved using NSGA-II and GAMS. The results show that the optimality gap in NSGA-II is less than 0.06%. Finally, the model was solved based on real data of American Eagle Airlines extracted from Kaggle datasets.
Originality/value
The authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.
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Nuno Miguel de Matos Torre and Andrei Bonamigo
Maintenance represents an indispensable role in the productive sector of the steel industry. The increasing use of operating with a high level of precision makes hydraulic systems…
Abstract
Purpose
Maintenance represents an indispensable role in the productive sector of the steel industry. The increasing use of operating with a high level of precision makes hydraulic systems one of the issues that require a high level of attention. This study aims to explore an empirical investigation for decreasing the occurrences of corrective maintenance of hydraulic systems in the context of Lean 4.0.
Design/methodology/approach
The maintenance model is developed based on action-research methodology through an empirical investigation, with nine stages. This approach aims to build a scenario to analyze and interpret the occurrences, seeking to implement and evaluate the actions to be performed. The undertaken initiatives demonstrate that this approach can be applied to optimize the maintenance of an organization.
Findings
The main contribution of this paper is to demonstrate that the applied method allows the overviewing results, with a qualitative approach concerning the maintenance actions and management processes to be considered, allowing a holistic understanding and contributing to the current literature. The results also indicated that Lean 4.0 has direct and mediating effects on maintenance performance.
Originality/value
This research intends to propose an evaluation framework with an interdimensional linkage between action research methodology and Lean 4.0, to explore an empirical investigation and contributing to understanding the actions to reduce the occurrences of hydraulic systems corrective maintenance in a production line in the steel industry.
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Smart manufacturing is revolutionizing the manufacturing industry by shifting the focus from traditional manufacturing to a more intelligent, interconnected and responsive system…
Abstract
Purpose
Smart manufacturing is revolutionizing the manufacturing industry by shifting the focus from traditional manufacturing to a more intelligent, interconnected and responsive system. Despite being the backbone of the economy and despite the government’s efforts in supporting and encouraging the transformation to smart manufacturing, small and medium enterprises (SMEs) have been struggling to transform their operations. This study aims to identify the challenges for SMEs’ transformation and the benefits they can get from this transformation, following a systematic review of existing literature.
Design/methodology/approach
A systematic review of existing literature has been performed to identify the peer-reviewed journal articles that focus on smart manufacturing for SMEs. First, a comprehensive list of keywords relevant to the review questions are identified. Second, Scopus and Web of Science databases were then used to search for articles, applying filters for English language and peer-reviewed status. Third, after manually assessing abstracts for relevance, 175 articles are considered for further review and analysis.
Findings
The benefits and challenges of SMEs’ transformation to smart manufacturing are identified. The identified challenges are categorized using the Smart Industry Readiness Index (SIRI) framework. Further, to address the identified challenges and initiate the SME’s transition toward smart manufacturing, a framework has been proposed that shows how SMEs can start their transition with minimum investment and existing resources.
Originality/value
Several studies have concentrated on understanding how smart manufacturing enhances sustainability, productivity and preventive maintenance. However, there is a lack of studies comprehensively analyzing the challenges for smart manufacturing adoption for SMEs. The originality of this study lies in identifying the challenges and benefits of smart manufacturing transformation and proposing a framework as a roadmap for SMEs' smart manufacturing adoption.
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The article addresses the optimization of safety stock service levels for parts in a repair kit. The work was undertaken to assist a public transit entity that stores thousands of…
Abstract
Purpose
The article addresses the optimization of safety stock service levels for parts in a repair kit. The work was undertaken to assist a public transit entity that stores thousands of parts used to repair equipment acquired over many decades. Demand is intermittent, procurement lead times are long, and the total inventory investment is significant.
Design/methodology/approach
Demand exists for repair kits, and a repair cannot start until all required parts are available. The cost model includes holding cost to carry the part being modeled as well as shortage cost that consists of the holding cost to carry all other repair kit parts for the duration of the part’s lead time. The model combines deterministic and stochastic approaches by assuming a fixed ordering cycle with Poisson demand.
Findings
The results show that optimal service levels vary as a function of repair demand rate, part lead time, and cost of the part as a percentage of the total part cost for the repair kit. Optimal service levels are higher for inexpensive parts and lower for expensive parts, although the precise levels are impacted by repair demand and part lead time.
Social implications
The proposed model can impact society by improving the operational performance and efficiency of public transit systems, by ensuring that home repair technicians will be prepared for repair tasks, and by reducing the environmental impact of electronic waste consistent with the right-to-repair movement.
Originality/value
The optimization model is unique because (1) it quantifies shortage cost as the cost of unnecessary holding other parts in the repair kit during the shortage time, and (2) it determines a unique service level for each part in a repair kit bases on its lead time, its unit cost, and the total cost of all parts in the repair kit. Results will be counter-intuitive for many inventory managers who would assume that more critical parts should have higher service levels.
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Kaisu Laitinen, Mika Luhtala, Maiju Örmä and Kalle Vaismaa
Insufficient productivity development in the global and Finnish infrastructure sectors indicates that there are challenges in genuinely achieving the goals of resource efficiency…
Abstract
Purpose
Insufficient productivity development in the global and Finnish infrastructure sectors indicates that there are challenges in genuinely achieving the goals of resource efficiency and digitalization. This study adapts the approach of capability maturity model integration (CMMI) for examining the capabilities for productivity development that reveal the enablers of improving productivity in the infrastructure sector.
Design/methodology/approach
Civil engineering in Finland was selected as the study area, and a qualitative research approach was adopted. A novel maturity model was constructed deductively through a three-step analytical process. Previous research literature was adapted to form a framework with maturity levels and key process areas (KPAs). KPA attributes and their maturity criteria were formed through a thematic analysis of interview data from 12 semi-structured group interviews. Finally, validation and refinement of the model were performed with an expert panel.
Findings
This paper provides a novel maturity model for examining and enhancing the infrastructure sector’s maturity in productivity development. The model brings into discussion the current business logics, relevance of lifecycle-thinking, binding targets and outcomes of limited activities in the surrounding infrastructure system.
Originality/value
This paper provides a new approach for pursuing productivity development in the infrastructure sector by constructing a maturity model that adapts the concepts of CMMI and change management. The model and findings benefit all actors in the sector and provide an understanding of the required elements and means to achieve a more sustainable built environment and effective operations.
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Ahmad Ebrahimi and Sara Mojtahedi
Warranty-based big data analysis has attracted a great deal of attention because of its key capabilities and role in improving product quality while minimizing costs. Information…
Abstract
Purpose
Warranty-based big data analysis has attracted a great deal of attention because of its key capabilities and role in improving product quality while minimizing costs. Information and details about particular parts (components) repair and replacement during the warranty term, usually stored in the after-sales service database, can be used to solve problems in a variety of sectors. Due to the small number of studies related to the complete analysis of parts failure patterns in the automotive industry in the literature, this paper focuses on discovering and assessing the impact of lesser-studied factors on the failure of auto parts in the warranty period from the after-sales data of an automotive manufacturer.
Design/methodology/approach
The interconnected method used in this study for analyzing failure patterns is formed by combining association rules (AR) mining and Bayesian networks (BNs).
Findings
This research utilized AR analysis to extract valuable information from warranty data, exploring the relationship between component failure, time and location. Additionally, BNs were employed to investigate other potential factors influencing component failure, which could not be identified using Association Rules alone. This approach provided a more comprehensive evaluation of the data and valuable insights for decision-making in relevant industries.
Originality/value
This study's findings are believed to be practical in achieving a better dissection and providing a comprehensive package that can be utilized to increase component quality and overcome cross-sectional solutions. The integration of these methods allowed for a wider exploration of potential factors influencing component failure, enhancing the validity and depth of the research findings.
Details