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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.

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.

To be able to continuously provide affordable services to consumers, managers of critical infrastructure (CI) maintenance supply networks have to balance investments in resilience with costs. At the same time, CI providers need to consider factors that influence resilience such as the geographical spread or the location of the network. This study aims to contextualize supply chain resilience knowledge by exploring how maintenance resource configurations impact resilience and costs in CI supply networks.

An in-depth longitudinal single case study of a representative CI provider that has centralized its maintenance supply network is used. Data were collected before and after the change to evaluate the effect of the changes on the maintenance supply network.

This study shows that in this specific CI maintenance context, structural resource choices such as the quantity or location of spare parts and tools, the creation and exploitation of tacit knowledge and staff motivation impact both resilience and costs due to geographical spread, network location and other network properties.

This study extends general supply chain resilience knowledge to a new setting (i.e. CI) and shows how existing insights apply in this context. More specifically, it is shown that even in engineered supply networks there is a need to consider the effect of human agency on resilience as the creation and exploitation of tacit knowledge are of immense importance in managing the network. In addition, the relationship between normal accidents theory and high reliability theory (HRT) is revisited as findings indicate that HRT is also important after a disruption has taken place.

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 investigate the impact of early-stage maintenance clustering. Few researchers have previously studied early-stage maintenance clustering. Experience from product and service development has shown that early stages are critical to the development process, as most decisions are made during these stages. Similarly, most maintenance decisions are made during the early stages of maintenance development. Developing maintenance for clustering is expected to increase the potential of clustering.

A literature study and three case studies using the same data set were performed. The case studies simulate three stages of maintenance development by clustering based on the changes available at each given stage.

The study indicates an increased impact of maintenance clustering when clustering already in the first maintenance development stage. By performing clustering during the identification phase, 4.6% of the planned work hours can be saved. When clustering is done in the planning phase, 2.7% of the planned work hours can be saved. When planning is done in the scheduling phase, 2.4% of the planned work hours can be saved. The major difference in potential from the identification to the scheduling phase came from avoiding duplicate, unnecessary and erroneous work.

The findings from this study indicate a need for more studies on early-stage maintenance clustering, as few others have studied this.

Many youths are out-of-school with few having the basic sustainable skills to earn a living. Some of the engaged ones have interpersonal relationship and other problems that can sustain the successes of their business. Therefore, this study is set to investigate the relationship between affective behaviour, emotional intelligence and success of out-of-school youths in cell phone maintenance enterprise.

Purposive sampling technique was used to select the sample. Of the total, 350 out-of-school youths who are engaged in cell phone maintenance enterprise in computer village Ikeja, Lagos State, Nigeria, were used as a sample, but 292 samples with completely filled research instrument were used for the study. Data collected were validated through the principal component analysis and the hypothesis tested through the confirmatory factor analysis using AMOS and SPSS.

The result of the study showed that affective behaviour and social skills do not necessarily predict but self-motivation predicts the career success of out-of-school youths in cell phone maintenance enterprise. Self-awareness, emotional regulation, social awareness and emotional receptivity significantly influence affective behaviour towards success in their chosen career.

This study enhances the cell phone maintenance association or group to adopt the participation of on-the-job training of their members to help them build good relationship and self-esteem. The training will improve their emotional intelligence and further enhance the creation of a formidable emotional intelligent workplace team.

The study affirms that the constructs of emotional intelligence are predictors of career success among out-of-school cell phone maintenance. It boosts their moral and psychological behaviours towards building good customer relationship which culminates into success in their career area. This study also motivates the out-of-school youths that success is multifaceted that involves building adequate personal and social relationship within the circle of their co-maintenance personnel and customers.

This study showed that success in any chosen career involves adequate training, inter- and intra-personal relationship and building adequate emotional intelligence to overcome the varying challenges that may be encountered. Also it indicated that personal development in a chosen career is essential and career successes can be built around personal goal orientation rather than building it in circle of people around. The study does not totally neglect social relationship because no man can live and succeed in isolation.

The purpose of this paper is to explore the interdependencies between temporary and permanent aspects of project organizing and how they affect the management of public infrastructure operation and maintenance (O&M) activities.

The paper applies a case study approach and uses Lundin and Söderholm's (1995) framework of the temporary organization (with the themes of time, task, team and transition) to distinguish between temporary and permanent aspects of organizing two infrastructure O&M projects.

This paper adds to the literature on temporary organizations by recognizing a mixture of temporary and permanent aspects of project organizing in an empirical project-level example. In line with previous research, the themes of time, task, team and transition were shown to be interdependent. Furthermore, the paper broadens the theory of temporary organizations by presenting a project organization with significant permanent aspects.

Project managers of public sector projects need to be aware of the possible mixture of temporary and permanent aspects of project organizing. Management of projects that are found to have a mixture of temporary and permanent aspects should combine the perspectives and management practices of both temporary and permanent organizing. Not acknowledging permanent aspects could lead to management that is not adapted to the prerequisites of project organizing in this context.

The findings further develop the literature on temporary organizations by recognizing that there is not only a mixture of temporary and permanent aspects between the temporary organization and its permanent environment but there is also a mixture of temporary and permanent aspects of organizing within project organizations.

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.

To proactively draw efficient maintenance plans, road agencies should be able to forecast main road distress parameters, such as cracking, rutting, deflection and International Roughness Index (IRI). Nonetheless, the behavior of those parameters throughout pavement life cycles is associated with high uncertainty, resulting from various interrelated factors that fluctuate over time. This study aims to propose the use of dynamic Bayesian belief networks for the development of time-series prediction models to probabilistically forecast road distress parameters.

While Bayesian belief network (BBN) has the merit of capturing uncertainty associated with variables in a domain, dynamic BBNs, in particular, are deemed ideal for forecasting road distress over time due to its Markovian and invariant transition probability properties. Four dynamic BBN models are developed to represent rutting, deflection, cracking and IRI, using pavement data collected from 32 major road sections in the United Arab Emirates between 2013 and 2019. Those models are based on several factors affecting pavement deterioration, which are classified into three categories traffic factors, environmental factors and road-specific factors.

The four developed performance prediction models achieved an overall precision and reliability rate of over 80%.

The proposed approach provides flexibility to illustrate road conditions under various scenarios, which is beneficial for pavement maintainers in obtaining a realistic representation of expected future road conditions, where maintenance efforts could be prioritized and optimized.

Asset-intensive organizations rely heavily on physical assets that are often expensive, complex and have a significant impact on organizational performance. Past introductions of critical assets in various industries showed that despite many preparations in maintenance and operations, shortcomings were identified after deployment resulting in unreliable performance. The main purpose of this qualitative study is to explore the factors that determine how asset-intensive organizations can achieve reliable outcomes in critical asset introductions despite random failures as a result of increasing complexity and infant mortalities.

To gain a detailed understanding of the issues and challenges of critical asset introductions, a case study in railways (rolling stock introductions) was conducted and analyzed using qualitative analysis.

The case showed that organizational factors were perceived as decisive factors for a reliable performance of the introduction, while the main focus of the introduction was on the asset and its technical systems. This suggests that more consideration toward organizational factors is needed. Therefore, a critical asset introduction framework was proposed based on 15 identified factors.

Reliable performance is often associated with technical systems only. This empirical study emphasizes the need for a more holistic perspective and the inclusion of organizational factors when introducing critical assets seeking reliable performance. This study demonstrated the application of the affinity diagramming technique in collectively analyzing the data adopting a multidisciplinary orientation.