Search results

The self-cleaning properties of nanostructured titanium dioxide facade coatings are useful in Singapore's tropical climate. However, its potential maintenance issues need to be determined right at the design stage. The purpose of this paper is to highlight the development of the design for maintainability tool which is a multicriteria design decision score sheet that evaluates the maintainability potential of nano-facade coating applications on high-rise façades with concrete and stonemasonry finishes and curtain walls.

Quantitative methods (expert and practitioner surveys) are conducted in this research study. Analytic hierarchy process (AHP) and sensitivity analysis were used to develop a robust Design for Maintainability tool.

Safety measures indicator received the highest weighted score by experts, while the maximizing performance, minimizing risk, minimizing negative environmental impact and minimizing consumption of matter and energy were the top ranking main criteria by both experts and practitioners. The top ranked design for maintainability sub-criteria identified by practitioners and experts were risk management, maintenance considerations, climatic conditions, safety measures, lifecycle cost and maintenance access, sun's path, rainfall intensity, biological growth measures and building age profile.

Most researches on the maintainability of nano-façade coatings uses experimentation to test the durability of nano-façade coatings, while this study focuses on design based empirical data such as establishing and ranking the list of design for maintainability criteria or indicators to minimize future defects and maintenance issues. The design for maintainability tool contributes to the maintainability of nano-façade coatings leading to maximizing its performance while minimizing cost, risks, resource consumption and negative environmental impact.

The purpose of this paper is to identify the productivity improvement potentials from maintenance planning practices in manufacturing companies. In particular, the paper aims at understanding the connection between machine criticality assessment and maintenance prioritization in industrial practice, as well as providing the improvement potentials.

An explanatory mixed method research design was used in this study. Data from literature analysis, a web-based questionnaire survey, and semi-structured interviews were gathered and triangulated. Additionally, simulation experimentation was used to evaluate the productivity potential.

The connection between machine criticality and maintenance prioritization is assessed in an industrial set-up. The empirical findings show that maintenance prioritization is not based on machine criticality, as criticality assessment is non-factual, static, and lacks system view. It is with respect to these finding that the ways to increase system productivity and future directions are charted.

In addition to the empirical results showing productivity improvement potentials, the paper emphasizes on the need for a systems view for solving maintenance problems, i.e. solving maintenance problems for the whole factory. This contribution is equally important for both industry and academics, as the maintenance organization needs to solve this problem with the help of the right decision support.

This paper proposes a heuristic, data-driven approach to the rapid performance evaluation of periodic maintenance on complex production plants. Through grouping, maintenance interval (MI)-based evaluation and performance assessment, potential nonvalue-adding maintenance elements can be identified in the current maintenance structure. The framework reduces management complexity and supports the decision-making process for further maintenance improvement.

The evaluation framework follows a prescriptive research approach. The framework is structured in three steps, which are further illustrated in the case study. The case study utilizes real-life data to verify the feasibility and effectiveness of the proposed framework.

Through a case study conducted on 9,538 pieces of equipment from eight offshore oil and gas production platforms, the results show considerable potential for maintenance performance improvement, including up to a 23% reduction in periodic maintenance hours.

The problem of performance evaluation under limited data availability has barely been addressed in the literature on the plant level. The proposed framework aims to provide a quantitative approach to reducing the structural complexity of the periodic maintenance evaluation process and can help maintenance professionals prioritize the focus on maintenance improvement among current strategies.

The proposed framework is especially suitable for initial performance assessment in systems with a complex structure, limited maintenance records and imperfect data, as it reduces management complexity and supports the decision-making process for further maintenance improvement. A similar application has not been identified in the literature.

Decisions taken during the early design of adaptive façades involving kinetic, active and responsive envelope for complex commercial buildings have a substantial effect on inclusive building functioning and the comfort level of inhabitants. This study aims to present the application of an analytic network process (ANP) model indicating the order of priority for high performance criteria that must be taken into account in the assessment of the performance of adaptive façade systems for complex commercial buildings.

The nominal group technique (NGT) stimulating and refining group judgments are used to find and categorize relevant high performance attributes of the adaptive façade systems and their relative pair-wise significance scores. An ANP model is applied to prioritize these high performance objectives and criteria for the adaptive façade systems.

Embodied energy and CO₂ emission, sustainability, energy saving, daylight and operation maintenance were as the most likely and crucial high performance criteria. The criteria and the weights presented in this study could be used as guidelines for evaluating the performance of adaptive façade systems for commercial buildings in planning and design phases.

This research primarily provides the required actions and evaluations for design managers in accomplishing a high performance adaptive façade system, with the support of an ANP method. Before beginning the adaptive façade system of a building design process, the design manager must determine the significance of each of these attributes as high performance primacies will affect the results all through the entire design process.

In this research, a relatively innovative, systematic and practical approach is proposed to sustain the decision-making procedure for evaluation of the high performance criteria of adaptive façade systems in complex commercial buildings.

This paper proposes a progressive, multi-level framework for diagnosing maintenance performance: rapid performance health checks of key performance for different equipment groups and end-to-end process diagnostics to further locate potential performance issues. A question-based performance evaluation approach is introduced to support the selection and derivation of case-specific indicators based on diagnostic aspects.

The case research method is used to develop the proposed framework. The generic parts of the framework are built on existing maintenance performance measurement theories through a literature review. In the case study, empirical maintenance data of 196 emergency shutdown valves (ESDVs) are collected over a two-year period to support the development and validation of the proposed approach.

To improve processes, companies need a separate performance measurement structure. This paper suggests a hierarchical model in four layers (objective, domain, aspect and performance measurement) to facilitate the selection and derivation of indicators, which could potentially reduce management complexity and help prioritize continuous performance improvement. Examples of new indicators are derived from a case study that includes 196 ESDVs at an offshore oil and gas production plant.

Methodological approaches to deriving various performance indicators have rarely been addressed in the maintenance field. The proposed diagnostic framework provides a structured way to identify and locate process performance issues by creating indicators that can bridge generic evaluation aspects and maintenance data. The framework is highly adaptive as data availability functions are used as inputs to generate indicators instead of passively filtering out non-applicable existing indicators.

The purpose of this paper is to increase productivity through smart maintenance planning by including productivity as one of the objectives of the maintenance organization. Therefore, the goals of the paper are to investigate existing machine criticality assessment and identify components of the criticality assessment tool to increase productivity.

An embedded multiple case study research design was adopted in this paper. Six different cases were chosen from six different production sites operated by three multi-national manufacturing companies. Data collection was carried out in the form of interviews, focus groups and archival records. More than one source of data was collected in each of the cases. The cases included different production layouts such as machining, assembly and foundry, which ensured data variety.

The main finding of the paper is a deeper understanding of how manufacturing companies assess machine criticality and plan maintenance activities. The empirical findings showed that there is a lack of trust regarding existing criticality assessment tools. As a result, necessary changes within the maintenance organizations in order to increase productivity were identified. These are technological advancements, i.e. a dynamic and data-driven approach and organizational changes, i.e. approaching with a systems perspective when performing maintenance prioritization.

Machine criticality assessment studies are rare, especially empirical research. The originality of this paper lies in the empirical research conducted on smart maintenance planning for productivity improvement. In addition, identifying the components for machine criticality assessment is equally important for research and industries to efficient planning of maintenance activities.

The shipper selects a suitable shipping route and plans for a voyage in order to import and export cargo on the basis of published sailing schedules. The reliability of the sailing schedule will influence the shipper’s logistics expense, which means that the logistics costs will depend on the reliability of schedules published by container shipping companies. Therefore, it is important to consider factors which can cause delays would for container ships sailing on sea routes. The reliability of published sailing schedules can be affected by a number of different factors. This study adopts the multi-criteria decision making (MCDM) method to estimate the importance of the delaying factors in a sailing schedule. In addition, the consistent fuzzy preference relations (CFPR) method is applied to identify the subjective importance (weights) of the delaying factors. The entropy weight method combined with the actual performance of the container shipping company are both used when estimating the objective importance (weights) of the delaying factors. According to the analysis results, the criteria can be divided into four quadrants with different management implications, which indicate that instructions for chase strategy, sailing schedule control, fleet allocation, transship operation arrangement and planning for ports in routes are often ignored by container shipping companies. Container shipping companies should consider adjusting their operational strategies, which would greatly improve their operational performance.

This paper aims to present a dual-perspective framework for maintenance service delivery that should be used by manufacturing companies to structure and manage their maintenance service delivery process, using aggregated historical and real-time data to improve operational decision-making. The framework, built for continuous improvement, allows the exploitation of maintenance data to improve the knowledge of service processes and machines.

The Dual-perspective, data-based decision-making process for maintenance delivery (D3M) framework development and test followed a qualitative approach based on literature reviews and semi-structured interviews. The pool of companies interviewed was expanded from the development to the test stage to increase its applicability and present additional perspectives.

The interviews confirmed that manufacturing companies are interested in exploiting the data generated in the use phase to improve operational decision-making in maintenance service delivery. Feedback to improve the framework methods and tools was collected, as well as suggestions for the introduction of new ones according to the companies' necessities.

The paper presents a novel framework addressing the data-based decision-making process for maintenance service delivery. The D3M framework can be used by manufacturing companies to structure their maintenance service delivery process and improve their knowledge of machines and service processes.

The objective of this study is to investigate the barriers hindering the integration of lean manufacturing (LM) practices within the furniture industry of Bangladesh. The traditional operational paradigms in this sector have posed substantial challenges to the effective implementation of LM. In this study, the barriers of implementing LM in the furniture business are examined, aiming to provide a systematic understanding of the barriers that must be addressed for a successful transition.

The research reveals that “Fragmented Industry Structure,” “Resistance to Lean Practices” and “Inadequate Plant Layout and Maintenance”, emerged as the foremost barriers to LM implementation in the furniture industry. Additionally, “Insufficient Expert Management,” “Limited Technical Resources” and “Lack of Capital Investment” play significant roles.

The outcomes of this study provide valuable insights into the furniture industry, enabling the development of strategies for effective LM implementation. One notable challenge in lean implementation is the tendency to revert to established practices when confronted with barriers. Therefore, this transition necessitates informed guidance and leadership. In addition to addressing these internal challenges, the scope of lean implementation should be broadened.

This study represents one of the initial efforts to systematically identify and assess the barriers to LM implementation within the furniture industry of Bangladesh, contributing to the emerging body of knowledge in this area.