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The purpose of this paper is to present an approach for measuring the ability of oil and gas production plants to utilize shutdowns opportunistically for maintenance.

Key performance indicators have been developed from case studies with two offshore oil and gas installations on the Norwegian Continental Shelf. The key performance indicators measure the quality of the work preparations and the ability to utilize shutdowns opportunistically. Shutdowns may provide opportunities for execution of maintenance, but it is hardly possible to undertake any maintenance work requiring shutdown if the organization is not well prepared and the work is not well planned.

The results from testing of the indicators on two oil and gas installations shows that several of the indicators are relevant for determining the quality of preparations, whereas more effort needs to be put into gathering data applicable for monitoring the actual utilization of the shutdowns.

Production losses, due to turnarounds and unforeseen shutdowns in oil and gas operations, are significant, and the improvement potential is large. The indicators may assist maintenance managers in planning and improving the plant's utilization of shutdowns and may contribute to substantial cost savings.

The approach in the paper adds important knowledge on how to actually measure the quality of maintenance work planning and execution.

The purpose of this paper is to develop a decision support tool for risk-based maintenance scheduling for a large heavily equipped gas sweetening unit in a Liquefied Natural Gas (LNG) plant. Two conflicting objectives, i.e., total maintenance cost and the reliability, are considered in the tool. The tool is tested with the real plant data and suggests several Pareto-optimal schedules for a decision maker to choose from. The financial impacts are assessed.

A bi-objective scheduling optimization model is developed for maintenance scheduling using a risk-based framework. The model is developed integrating genetic algorithm and simulation-based optimization to find Pareto-optimal schedules. The model delivered true Pareto front optimal solutions for given plant-specific data. The two conflicting objectives: the minimization of total expenditures incurred on maintenance-related activities and improving the total reliability are considered.

For large and complex processing facilities such as LNG plant, a shutdown of facility generates a significant financial impact, resulting in millions of dollars in production loss. The developed risk-based equipment selection strategy helps to minimize such an event of production loss by generating a thorough maintenance strategy for inspection, repair, overhaul or replacement schedule of the unit without initiating the shutdown. The proposed model has been successfully applied to obtain an optimize maintenance schedule for a gas sweetening unit.

A future work may consider the state-dependent models for various failure modes that will result in obtaining a better representation of the model. The proposed scheduling can further be extended to multi-criteria scheduling including availability, resource limitation and inflationary condition. A comparative analysis with other meta-heuristic techniques such as harmony search algorithm, tabu search, and simulated annealing will further help in confirming the schedule obtained from this application.

Maintenance scheduling using a conventional approach for special equipment generally does not consider the conflicting objectives. This research addresses this aspect using a bi-objective model. The usefulness of risk-based method is to assist in minimizing the financial and safety risk exposure to the operating companies, but some variation in results is expected due to varying risk matrix for different organizations.

Managing two objectives, i.e., minimizing the cost of maintenance-related activities, while at the same time maximizing the overall reliability dramatically, helps in mitigating adverse safety and financial risk due to fires, explosions, fatality and excessive maintenance cost.

Research develops a decision support tool for managing conflicting objectives for an LNG process. This research highlights the impact of utilizing the simulation-based approach coupled with risk-based equipment selection for complex processing unit or plant maintenance scheduling optimization.

The aim of this study is to establish a framework with which process plants can evaluate the performance of their turnaround maintenance (TAM) projects at the end of the event without making reference to previous events.

This is a case study. The study involved the case studies of six process plants in the UK. Three sources of evidence were used in this study including; interviews, direct observation and official documents.

The case studies show that TAM projects can be evaluated by assessment of the management performance and the resultant benefits realised. The uses of generic project success measures are not very adequate for TAM.

The study developed success measurement criteria for TAM projects. Instead of comparing performance of TAM projects with previous ones to assess how successful the outcome is, organisations can evaluate their TAM project's performance based on the current TAM using the criteria developed in the framework. In addition, these success criteria will enable the organisation to evaluate the benefits expected from the TAM project outcome at hand‐over of the plant from the TAM project management to the operations management.

Operators of process plants and hence engineering facilities still measure the outcome of TAM using measures that are generic for EPC projects. Considering the peculiar nature of TAM, this study has developed success measurement criteria for TAM projects specifically.

Turnaround maintenance (TAM) is a planned stoppage of production for conducting a comprehensive maintenance of equipment or plant with the purpose of improving plant availability and performance. The purpose of this paper is to investigate trends in the operation and management of TAM, as reported in the literature, and identify gaps, in the context of a system approach that views a plant as part of a network of a supply chain.

This literature review is based on over 80 subject-relevant papers and uses content analysis. The literature subjects are classified into several managerial areas that include organization, planning, scope and risk analysis, execution, performance measurement and learning. The gap in the literature is identified in light of the proposed system view for TAM.

The system view of TAM opens new opportunities for new research areas for improving the operation and management of TAM. These areas include optimizing TAM scheduling and developing methods for managing risks along the entire business supply chain. In addition, new approaches for collaboration, sharing knowledge, best practices and expertise within the supply chain become necessary for effective TAM planning and control.

This paper reviews the literature and provides a new classification of TAM. It adopts the system view for TAM that has brought new insights in the operation and management of TAM. New trends for research in the area of TAM are identified.

Output from the Irish Dairy Industry has grown rapidly since the abolition of quotas in 2015, with processors investing heavily in capacity expansion to deal with the extra milk volumes. Further capacity gains may be achieved by extending the processing season into the winter, a key enabler for which being the reduction of duration of the winter maintenance overhaul period. This paper aims to investigate if Lean Six Sigma tools and techniques can be used to enhance operational maintenance performance, thereby releasing additional processing capacity.

Combining the Six-Sigma Define, Measure, Analyse, Improve, Control (DMAIC) methodology and the structured approach of Turnaround Maintenance (TAM) widely used in process industries creates a novel hybrid model that promises substantial improvement in maintenance overhaul execution. This paper presents a case study applying the DMAIC/TAM model to Ireland’s largest dairy processing site to optimise the annual maintenance shutdown. The objective was to deliver a 30% reduction in the duration of the overhaul, enabling an extension of the processing season.

Application of the DMAIC/TAM hybrid resulted in process enhancements, employee engagement and a clear roadmap for the operations team. Project goals were delivered, and original objectives exceeded, resulting in €8.9m additional value to the business and a reduction of 36% in the duration of the overhaul.

The results demonstrate that the model provides a structure that promotes systematic working and a continuous improvement focus that can have substantial benefits for wider industry. Opportunities for further model refinement were identified and will enhance performance in subsequent overhauls.

To the best of the authors’ knowledge, this is the first time that the structure and tools of DMAIC and TAM have been combined into a hybrid methodology and applied in an Irish industrial setting.

The purpose of this study is to optimize short‐term maintenance scheduling of utility systems satisfying network constraints.

A mathematical programming model with network constraints is presented.

There are some cases in which the maintenance of a certain unit affects the operations of non‐maintenance units. The schedule should be evaluated by labor cost, material cost and opportunity costs. However, most utility systems contain dual‐directional flows, making the interdependency of the units an unstable element. In such systems, the dependency between one unit and the other should be adjusted depending on the conditions.

Power, steam and water are distributed by utility systems. Unit maintenance affects the operation of non‐maintenance units within the networks. Effective short‐term maintenance scheduling of utility systems must work within these network constraints. Unlike conventional scheduling methods, the excessive concentration of maintenance tasks should be controlled to reduce supply losses. A mixed integer linear programming model was utilized for identifying the solution of the problem. The present model can be applied to various utility systems.

The model can address alternative flows to reflect current conditions. Binary variables are applied to preserve the consistency of flow balances. The logic tree analysis expressed the current flow balances using linear constraints. The effectiveness of the model was supported by case examples.

This paper aims to synthesize and categorize the published literatures related to maintenance strategies formulation, selection and implementation in various industries. The purpose of this paper is to develop a conceptual framework based on literature review for formulation of maintenance strategies, selection and the implementation of selected strategies. Further, to study on impact of maintenance strategies implementation in maintenance function.

A literature review has been carried out to identify the existing frameworks related to maintenance strategies formulation, selection of maintenance strategy and implementation of maintenance strategy in the industry. Literature support for all the conceptual constructs referred in the framework has been discussed to establish a logical sequence.

A conceptual framework for maintenance strategies formulation, selection and implementation and its impact in maintenance function has been developed. Further, constructs and sub-constructs which form the basis for maintenance strategies formulation, selection and implementation have been identified from the literatures. In addition, propositions have also been formulated to support the conceptual framework and these propositions provide the logical relationship among the maintenance strategies formulation, selection among the formulated strategies and the implementation of these strategies.

The conceptual framework developed in this paper for maintenance strategy formulation and selection is yet to be empirically tested. The proposed framework can be tested in various industries.

Literature study on maintenance strategy formulation and selection has so far been very limited. Maintenance strategy selection is a critical decision-making problem for the maintenance managers working in the process plant, manufacturing companies, etc. The conceptual framework proposed in this paper will help maintenance managers to asses, formulate, select suitable maintenance strategy and implement for their organization.

The paper provides comprehensive study on maintenance strategy problem which will be useful to researchers, maintenance managers and other professionals in various industries such as process industry, manufacturing industry, etc., to understand maintenance strategy selection problem and implementation of maintenance strategy.

The purpose of this paper is to select the best maintenance policy for different types of equipment of a manufacturer integrating the fuzzy analytic hierarchy process (FAHP) and the technique for order of preference by similarity to ideal solution (TOPSIS) models.

The decision hierarchy of this research includes three levels. The first level aims to choose the best maintenance policy for different types of equipment of an acid manufacturer. These equipment pieces include molten sulfur ponds, boiler, absorption tower, cooling towers, converter, heat exchanger and sulfur fuel furnace. The second level includes decision criteria of added-value, risk level and the cost. Lastly, the third level comprises time-based maintenance (TBM), corrective maintenance (CM), shutdown maintenance and condition-based maintenance (CBM) as four maintenance policies.

The best maintenance policy for different types of equipment of a manufacturer is the main finding of this research. Based on the obtained results, CBM policy is suggested for absorption tower, boiler, cooling tower and molten sulfur ponds, TBM policy is suggested for converters and heat exchanger and CM policy is suggested for a sulfur fuel furnace.

This research develops a novel model by integrating FAHP and an interval TOPSIS with concurrent consideration of added-value, risk level and cost to select the best maintenance policy. According to the highlights of the previous studies conducted on maintenance policy selection and related tools and techniques, an operative integrated approach to combine risk, added-value and cost with integrated fuzzy models is not developed yet. The majority of the previous studies have considered classic fuzzy approaches such as FAHP, FANP, Fuzzy TOPSIS, etc., which are not completely capable to reflect the decision makers’ viewpoints.

The purpose of this paper is to develop a fuzzy analytic network process (FANP) model to select the maintenance policy of an acid manufacturing company.

Four maintenance strategies of Corrective Maintenance (CM), Time-Based Maintenance (TBM), Condition-Based Maintenance (CBM) and Shutdown Maintenance (SM) are investigated to be considered for seven equipment of the case study. These equipment are almost new and include boiler, molten sulfur ponds, cooling towers, absorption tower, converter, sulfur fuel furnace and heat exchanger. Chang’s extended analysis has been employed to deal with fuzzy data and analyze the fuzzy decision matrices. The proposed approach is applied to a sulfuric acid production plant and the suitable maintenance policy is found for all seven equipment of the company.

Based on the obtained results, the CBM policy is appropriate for high-risk (cooling tower) and high added value equipment (absorption tower). In addition, TBM is selected for boiler and converters while SM is selected for molten sulfur ponds. Finally, high-cost, low-risk and low added value equipment (sulfur fuel furnace and heat exchanger) are more appropriate with CM policy.

This research presents a novel idea to consider cost, risk and added value in the context of maintenance policy selection. From the methodological and theoretical features, this research offers new insights in this area since, to the best of the authors’ knowledge, no comparable study has been conducted before.

The aim of this study is to identify and establish management skills/knowledge required for the successful management of turnaround maintenance (TAM) projects.

A mixed‐method research approach was adopted for this study involving questionnaire survey and case studies of major continuous process plants in the UK. Data were collected through questionnaires from 160 process plants and the case studies of six process plants in the UK. These data were triangulated to improve on the findings.

The findings show that there are specific management skills very specific towards ensuring the successful management of TAM projects. The findings also show that TAM managers are mostly at present appointed without assessing them with any specific skills set.

This study developed a set of management skills required for turnaround maintenance projects. These specific appropriate management skills established for TAM management should help to reduce mismatching of skills and the job. This is of paramount importance in the selection of the TAM manager to ensure the individual with the right skills is given the responsibility of managing the project.

Operators of process plants and hence engineering facilities still appoint their TAM manager without reference to any skill set. Most of the time, maintenance managers or project managers are appointed to manage TAM without considering any set of requirements. This study which is the first of its kind in this area is a major contribution to the field.