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The demand for contracts on assets availability has increased. Recently published papers show that the use of asset health monitoring technologies is being encouraged to improve the asset performance. This is based on reason rather than analysis. This paper aims to understand and assess the effect of different types of business processes for maintenance resource levels on the behaviour of the maintenance operations and asset availability located at different customer locations using different asset monitoring levels.

A discrete event simulation (DES) model was developed to mimic complex maintenance operations with different monitoring levels (reactive, diagnostics, and prognostics). The model was created to understand and assess the influence of resources (labour and spare parts) on a particular maintenance operation. The model was created to represent different levels of asset monitoring to be applied in a case study. Subsequently, different levels of spare parts (ranging from deficient inventory to a plentiful spares inventory) and labour were applied to show the effects of those resources on the asset availability.

This research has found that the DES was able to discern different processes for asset monitoring levels in complex maintenance operations. It also provided numerical evidence about applying such asset monitoring levels and proved that the higher asset monitoring level does not always guarantee higher asset availability.

The developed model is a unique model that can provide the decision makers of maintenance operations with numerical evidence to select an appropriate asset monitoring level based on their particular maintenance operations.

A novel DES model was developed to support maintenance operations decision makers in selecting the appropriate asset monitoring level for their particular operations. This unique approach provides numerical evidence rather than reasoning, and also proves that the higher asset monitoring level does not always guarantee higher asset availability.

The purpose of this paper is to examine how certain limitations of the current approaches to planning and scheduling of aircraft heavy maintenance can be addressed using a single integrated framework supported by unified data structures.

The “unitary structuring technique”, originally developed within the context of manufacturing planning and control, is further enhanced for aircraft heavy maintenance applications, taking into account the uncertainty associated with condition‐based maintenance. The proposed framework delivers the advanced functionalities required for simultaneous and dynamic forward planning of maintenance operations, as well as finite loading of resources, towards optimising the overall maintenance performance.

Execution of maintenance operations under uncertainty involves materials changes, rectification and re‐assembly. It is shown that re‐scheduling of materials (spare‐parts), resources and operations can be taken care of by simultaneous and dynamic forward planning of materials and operations with finite loading of resources, using the integrated framework.

As part of adopting the proposed framework in practice, it needs to be guided by an overall methodology appropriate for application‐specific contexts.

The potential direct benefits of adopting the proposed framework include on‐time project completion, reduced inventory levels of spare‐parts and reduced overtime costs.

Existing approaches to aircraft maintenance planning and scheduling are limited in their capacity to deal with contingencies arising out of inspections carried out during the execution phase of large maintenance projects. The proposed integrated approach is, capable of handling uncertainty associated with condition‐based maintenance, due to the added functionalities referred to above.

Scheduling of aircraft maintenance operations is a gap in the literature. Maintenance times should be determined close to the real-life to schedule aircraft maintenance operations effectively. The learning effect, which has been studied extensively in the machine scheduling literature, has not been investigated on aircraft maintenance times. In the literature, the production times under the learning effect have been examined in numerous studies but for merely manufacturing and assembly lines. A model for determining base and line maintenance times in civil aviation under the learning effect has not been proposed yet. It is pretty challenging to determine aircraft maintenance times due to the various aircraft configurations, extended maintenance periods, different worker shifts and workers with diverse experience and education levels. The purpose of this study is to determine accurate aircraft maintenance times rigorously with a new model which includes the group learning effect with the multi-products and shifts, plateau effect, multi sub-operations and labour firings/rotations.

Aircraft maintenance operations are carried out in shifts. Each maintenance operation consists of many sub-operations that are performed by groups of workers. Thus, various models, e.g. learning curve for maintenance line (MLC), MLC with plateau factor (MPLC), MLC with group factor (MGLC) were developed and used in this study. The performance and efficiency of the models were compared with the current models in the literature, such as the Yelle Learning model (Yelle), single learning curve (SLC) model and SLC with plateau factor model (SLC-P). Estimations of all these models were compared with actual aircraft maintenance times in terms of mean absolute deviation (MAD), mean absolute percentage error (MAPE) and mean square of the error (MSE) values. Seven years (2014–2020) maintenance data of one of the top ten maintenance companies in civil aviation were analysed for the application and comparison of learning curve models.

The best estimations in terms of MAD, MAPE and MSE values are, respectively, gathered by MGLC, SLC-P, MPLC, MLC, SLC and YELLE models. This study revealed that the models (MGLC, SLC-P, MPLC), including the plateau factor, are more efficient in estimating accurate aircraft maintenance times. Furthermore, MGLC always made the closest estimations to the actual aircraft maintenance times. The results show that the MGLC model is more accurate than all of the other models for all sub-operations. The MGLC model is promising for the aviation industry in determining aircraft maintenance times under the learning effect.

In this study, learning curve models, considering groups of workers working in shifts, have been developed and employed for the first time for estimating more realistic maintenance times in aircraft maintenance. To the best of the authors’ knowledge, the effect of group learning on maintenance times in aircraft maintenance operations has not been studied. The novelty of the models are their applicability for groups of workers with different education and experience levels working in the same shift where they can learn in accordance with their proportion of contribution to the work and learning continues throughout shifts. The validity of the proposed models has been proved by comparing actual aircraft maintenance data. In practice, the MGLC model could efficiently be used for aircraft maintenance planning, certifying staff performance evaluations and maintenance trainings. Moreover, aircraft maintenance activities can be scheduled under the learning effect and a more realistic maintenance plan could be gathered in that way.

The aim of this paper is to describe the most important occupational risks in maintenance operations.

This study analyzed all maintenance‐related fatalities since 1985 together with one group of severe accidents in Finland. In connection with the study, risk assessments were carried out in companies with the aim of charting the risks on sites.

The results indicate that the typical risks in maintenance operations involve poor ergonomics and that the most severe risks among these can lead to direct injury. Severe or even fatal injuries are mainly caused by crushing or falling.

To manage the risks, maintenance operations should be taken more carefully into account when designing and reconstructing machinery and work environments. It should be ensured that workers have relevant safety knowledge by means of risk assessments and instruction in safe working practice. In addition, safety cooperation with the customer is essential.

The findings indicate that maintenance operations include certain occupational risks. Among such risks, subcontracting offers a specific challenge to the service provider's safety management. The findings of this study provide advice on the safety measures necessary for accident prevention and how to execute them.

Several options exist for operations managers to control maintenance intensity. The effects of the maintenance department capability and the redundancy options within the context of capacity strategy are examined. In viewing the operations manager as a monitoring specialist, the firm′s choice is shown to depend on the economic, social, and technological implications inherent in the design of maintenance float policy.

Asset management has recently gained significance due to emerging business models such as Product Service Systems where the sale of asset use, rather than the sale of the asset itself, is applied. This leaves the responsibility of the maintenance tasks to fall on the shoulders of the manufacturer/supplier to provide high asset availability. The use of asset monitoring assists in providing high availability but the level of monitoring and maintenance needs to be assessed for cost effectiveness. There is a lack of available tools and understanding of their value in assessing monitoring levels. The paper aims to discuss these issues.

This research aims to develop a dynamic modelling approach using Discrete Event Simulation (DES) to assess such maintenance systems in order to provide a better understanding of the behaviour of complex maintenance operations. Interviews were conducted and literature was analysed to gather modelling requirements. Generic models were created, followed by simulation models, to examine how maintenance operation systems behave regarding different levels of asset monitoring.

This research indicates that DES discerns varying levels of complexity of maintenance operations but that more sophisticated asset monitoring levels will not necessarily result in a higher asset performance. The paper shows that it is possible to assess the impact of monitoring levels as well as make other changes to system operation that may be more or less effective.

The proposed tool supports the maintenance operations decision makers to select the appropriate asset monitoring level that suits their operational needs.

A novel DES approach was developed to assess asset monitoring levels for maintenance operations. In applying this quantitative approach, it was demonstrated that higher asset monitoring levels do not necessarily result in higher asset availability. The work provides a means of evaluating the constraints in the system that an asset is part of rather than focusing on the asset in isolation.

Traction substations are an integral part of the railway infrastructure. In order to ensure that machinery and equipment is available and utilised at full capacity, intelligent maintenance methods should be implemented. The purpose of this paper is to focus on the development of lean maintenance strategies implemented on traction substations and offer the maintenance manager an alternative maintenance method to be utilised during optimisation initiatives.

The rail company under consideration has various improvement options, the maintenance supply chain is a priority. The existing maintenance process of 3 kV_DC traction substations is examined and all operations and activities directly and indirectly associated with maintenance are analysed with lean methodologies. The analysis of maintenance operations and activities reveals that some of these operations and activities are non-value-adding and, therefore, regarded as waste. These activities only prolong the maintenance process without adding value to the process. Alternative operations and activities are suggested and simulated.

The application of lean indicates that the maintenance process could be optimised to deliver on a more efficient process, which would improve the quality of maintenance by ensuring that maintenance is timeously carried out. Through reducing the downtime caused by maintenance, the availability of the system is preserved and the railway lines could be used to capacity.

The research is limited to 3 kV_DC traction substations and the associated maintenance process, as this was the area of opportunity for the company. The track and overhead traction equipment maintenance is not included.

The practical application of this research is in optimisation of the value chain for maintenance, specifically in the rail industry.

This research could be applied by maintenance managers to effectively implement if a run-to-failure (RTF) maintenance philosophy is being utilised. The research offers maintenance techniques within the RTF maintenance scope which reduces the maintenance cycle and equipment downtime.

The purpose of this paper is to present a modified failure mode and effects analysis (FMEA) in order to make the assignment of the scores for the occurrence factor more robust, and to link the FMEA chart directly to the maintenance activities.

A well-known clustering algorithm (i.e. K-means), along with a normalisation approach, are applied and compared for the assignment of the occurrence scores. Subsequently, the relationship between failures and maintenance operations is made explicit by a correlation matrix. Finally, the K-means algorithm is applied to the maintenance operations again in order to sort them into priority classes.

It is found that this revised FMEA approach improves the standard one due to its more rigorous mathematical formulation and lean applicability in real operating environments.

The novel approach may be improved by a deeper statistical analysis and/or applying the fuzzy theory.

A real case study is introduced in order to show the applicability of this approach to the quality control of a blow moulding process. It is found that this approach reveals a high potentiality for dealing with real issues.

The paper provides a further step towards bridging the gap between theory and practical application of the FMEA approach.

In the present context of globalization, maintenance of production systems is very important. A lot of organizations are facing many problems in maintenance management. Therefore, the purpose of this paper is to identify the main problems in maintenance operations and compare these problems with those in manufacturing operations as found in the literature for effective maintenance.

To identify the main problems in maintenance operations and to compare them with those in manufacturing operations, a large amount of published literature was studied. The paper systematically categorizes the published literature and then analyzes and reviews it theoretically.

Lack of top management support, lack of measurement of overall equipment effectiveness (OEE), lack of strategic planning and implementation and many more problems are biggest problems in the maintenance operations as well as manufacturing operations. These have emerged as top problems in the implementation of effective maintenance strategies in industries.

From the findings, the authors can conclude that for good maintenance, top management is supposed to be supportive in taking different initiatives. Industrial organizations should focus on improving overall performance of machines identified as OEE rather than only productivity of machines. This paper will be extremely useful for the researchers, maintenance professionals and others concerned with maintenance to understand the significance of maintenance problems in industries.

These findings will be highly useful for professionals from the manufacturing sector in implementing effective maintenance strategy in the maintenance management system.

There are about 600 million accommodation units in the world. The maintenance of the accommodation is essentially similar in every country. Research work in accommodation maintenance has been minimal so far. This study deals with some fundamental models of building maintenance. The paper gives the most important development projects in the following groups: maintenance group, maintenance area, city, and state. Special attention is concentrated on the strategic and normative planning of area maintenance system in a city connected with other related urban operations. The running costs of housing accommodation by the methods mentioned in the paper can be reduced by about 30%.