Search results

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.

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 study consists of a literature study and a case study. The need for a method via which to handle instruction complexity was identified in both studies. The proposed method was developed based on methods from the literature and experience from the case company.

The purpose of the study presented in this paper is to investigate how linking different maintenance domains in a modular maintenance instruction architecture can help reduce the complexity of maintenance instructions.

The proposed method combines knowledge from the operational and physical domains to reduce the number of instruction task variants. In a case study, the number of instruction task modules was reduced from 224 to 20, covering 83% of the maintenance performed on emergency shutdown valves.

The study showed that the other methods proposed within the body of maintenance literature mainly focus on the development of modular instructions, without the reduction of complexity and non-value-adding variation observed in the product architecture literature.

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.

The purpose of this study is to ensure productive, robust and sustainable production systems and realise digitalised manufacturing trough implementation of Smart Maintenance – “an organizational design for managing maintenance of manufacturing plants in environments with pervasive digital technologies”. This paper aims to support industry practitioners in selecting performance indicators (PIs) to measure the effects of Smart Maintenance, and thus facilitate its implementation.

Intercoder reliability and negotiated agreement were used to analyse 170 maintenance PIs. The PIs were structurally categorised according to the anticipated effects of Smart Maintenance.

Companies need to revise their set of PIs when changing manufacturing and/or maintenance strategy (e.g. reshape the maintenance organisation towards Smart Maintenance). This paper suggests 13 categories of PIs to facilitate the selection of PIs for Smart Maintenance. The categories are based on 170 PIs, which were analysed according to the anticipated effects of Smart Maintenance.

The 13 suggested categories bring clarity to the measuring potential of the PIs and their relation to the Smart Maintenance concept. Thereby, this paper serves as a guide for industry practitioners to select PIs for measuring the effects of Smart Maintenance.

This is the first study evaluating how maintenance PIs measure the anticipated effects of maintenance in digitalised manufacturing. The methods intercoder reliability and negotiated agreement were used to ensure the trustworthiness of the categorisation of PIs. Such methods are rare in maintenance research.

This study aims to examine the role that four distinct bundles (developmental, utilisation, maintenance and accommodative) of HRM practices play in enhancing work engagement among chronically ill employees, and to analyse whether perceptions of discrimination on the grounds of illness can affect these relationships.

The data were collected through a quantitative survey using a sample of 669 chronically ill employees of a major Italian company.

This study's findings confirm the importance of discerning between positive, insignificant and negative effects of distinct HR bundles on chronically ill employees' work engagement. Furthermore, this study's results suggest that the positive effect of utilisation practices (i.e. practices aimed at enabling employees to make full use of existing, but not yet necessarily utilised, individual resources) on engagement is greater when chronically ill employees perceive a discrimination-free work environment.

This study's findings confirm the importance of discerning between positive, no, and negative effects of distinct HR bundles on chronically ill employees' work engagement. Furthermore, this study's results suggest that the positive effect of utilisation practices (i.e. practices aimed at enabling employees to make full use of existing but not necessarily applied individual resources) on engagement is greater when chronically ill employees perceive a discrimination-free work environment.

The study highlights those HR bundles that have the capacity to positively affect the work engagement of chronically ill employees, a minority group rarely considered in HRM studies. Furthermore, the research identifies perceived discrimination on the grounds of illness as a contextual condition that may hinder the otherwise positive effect of HRM practices on the engagement of workers suffering from a chronic illness.

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.

Many organizations expect their service engineers, or frontline employees (FLEs), to behave as brand advocates by engaging in favorable communication about the brand and its offerings toward customers. However, this approach is not without risk as customers may be disappointed or even frustrated with brand advocacy behavior in many service encounters. The purpose of this paper is to study the impact of FLEs’ brand advocacy on customer satisfaction with the service encounter, and identify the conditions under which the effects are detrimental. This paper specifically considers service issue severity and product newness as contingency conditions.

Building on social identification theory, the paper builds a conceptual model, which is empirically tested using a data set that matches data from service engineers, customers, and archival records from the after-sales service department of a globally operating business-to-business print and document management solutions provider.

This paper finds that brand advocacy behavior harms customer satisfaction especially in service encounters that involve simple service issues (e.g. maintenance) for products that are new to the market. Fortunately, brand identification can compensate this negative effect under many service conditions. While the joint effect of brand identification and advocacy is most beneficial for severe service issues of new products, no effect on customer satisfaction was found for established products.

This paper identifies those service situations in which brand advocacy is advisable and guides managers toward achieving more favorable customer evaluations.

Past research has considered several FLE branding activities in the frontline but the effects of brand advocacy have not been isolated. In addition, most studies have assumed the effects of employee brand-related behaviors on customer satisfaction to be universally positive rather than negative and focused on antecedents and not on moderators and consequences.

This study aims to contribute to a better understanding of organizational strategies, attitudes and supports that can help people with intellectual disabilities to access competitive jobs through supported employment.

A multiple case study was carried out based on an intentional sampling involving the coworker mentors of six people with intellectual disabilities currently working in standardized environments. Semi-structured interviews were performed with the participants, and an inductive thematic analysis was used for data analysis.

The study identified five critical factors in the work of people with intellectual disabilities in standardized work environments, which potentially could act as facilitators or as obstacles, depending on how they were managed. The study also identified two key factors that acted mainly as facilitators and one as an obstacle.

The study reveals the existence of factors that sometimes act as facilitators and sometimes as obstacles, depending on how they are managed by the company leaders or the disabled worker himself.

This paper seeks to define a theoretical model for the urban regeneration of mass housing areas based on citizen initiative, self-management and self-financing in the form of the neighbourhood cooperative. This paper aims to identify mechanisms for economic resource generation that enable the improvement of the urban surroundings and its buildings without assuming disproportionate economic burdens by the local residents based on two principles, the economies of scale and service provision.

The research is structured in three phases: a literature review of the different trends in self-financing for urban regeneration and the conceptual framework for the definition of a cooperative model; the definition of theoretical model by analysing community ecosystem, neighbourhood-based services and the requirements for its economic equilibrium; and the discussion of the results and the conclusions.

The results show the potential of the cooperative model to generate a social economy capable of reducing costs and producing additional resources to finance the rehabilitation process. The findings show not only the extent of economic advantages but also multiple social, physical and environmental benefits. Its implementation involves the participation of multiple actors, which is one of its significant advantages.

The main contribution is to approach comprehensive urban rehabilitation from a collaborative understanding, overcoming the main financing difficulties of the current practices based on public subsidy policies. The model also allows an ethical relationship to be built with supplier companies by means of corporate social responsibility.