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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.

Technological advancements are reshaping the manufacturing industry toward digitalized manufacturing. Despite the importance of top-class maintenance in such systems, many industrial companies lack a clear strategy for maintenance in digitalized manufacturing. The purpose of this paper is to facilitate the implementation of maintenance in digitalized manufacturing by proposing a strategy development process for the Smart Maintenance concept.

This study is designed as a multiple-case study, where the strategy development in three industrial cases is analyzed. Several methods were used to collect data on the case companies' development of smart maintenance strategies. The data were analyzed with an inductive approach.

A process of strategy development for smart maintenance is proposed, including six steps: benchmarking, setting clear goals, setting strategic priority, planning key activities, elevating implementation and follow-up.

The proposed process provides industry practitioners with a step-by-step guide for the development of a clear smart maintenance strategy, based on the current state of their maintenance organization. This creates employee engagement and is a new way of developing maintenance strategies.

Maintenance strategies are traditionally regarded as a selection of corrective/reactive and preventive maintenance actions using a top-down approach. By contrast, the proposed process is starting from the current state of the maintenance organization and allows a mixture of top-down and bottom-up approaches, supporting organizational development. This is a rare perspective of maintenance strategies and will make maintenance organizations ready for the demands of digitalized manufacturing.

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 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 investigates a mediational model between legitimated elements, financial resource mobilisation and subsequent early firm growth among New Technology-Based Firms (NTBFs) using conformity and control perspectives of legitimacy.

To test the hypotheses, a longitudinal database of 303 NTBFs from Sweden, Finland and France is used. The ordinary least square regression analysis method is applied, and the proposed mediation relationships are studied by employing the four-step approach developed by Baron and Kenny (1986).

This study finds that based on the conformity principle, two out of three legitimated elements (business plan and incubator relationship, but not start-up experience) have an impact on financial resource mobilisation, which in turn, is associated with early growth in NTBFs based on the control principle. Thus, financial resource mobilisation positively mediates the relationships among the two legitimated elements and early growth in NTBFs.

This study has several limitations, which also generate promising pathways for future research. Future research should study the relationship between the three legitimacy elements and financial resource mobilisation and early growth across a wider range of firms and settings. The questionnaire was also based on a single point in time and could not capture the evolving nature of the legitimacy elements and fundraising. Hence, future research can examine the multidimensionality of these processes; longitudinal qualitative studies can be a complement, allowing for a better understanding of the impact of legitimacy on NTBFs.

The findings offer implications for managers of NTBFs because developing legitimacy is critical to NTBFs early growth and development. The findings indicate that NTBFs' founders must systematically develop business plans and that incubators help enhance legitimacy through a signalling.

It is believed that the study meaningfully contributes to the collective understanding of the role of legitimacy in driving the development of NTBFs. Given the importance of NTBFs in our economies, coupled with the lack of attention given to the role of mobilisation of external resources in explaining NTBF early growth, it is believed that the study is both timely and important.

The findings meaningfully contribute to the collective understanding of NTBF growth. While there are studies that have examined the antecedents of growth and finance separately, this study proposes a novel mediational model that integrates both and tests it empirically.

The purpose of this study is to consolidate existing research on ‘service networks’ and to frame this literature as a new ‘context for learning’. Research from inter-organizational learning is used to qualify this consolidation and advances from inter-organizational learning are used to operationalize how service network actors in this new context can organize for inter-organizational learning to create more value for themselves and their customers.

By conceptualizing the learning context of a service network and the interrelated dimensions, an overview of the learning challenges for improved service performance is provided.

Inspired by the service triangle, the proposed framework highlights the learning challenges among two or more actors and the knowledge and skills needed for them to organize the service network. To build a collaboration characterized by trust, behaviors associated with transparency and receptivity are argued to be imperative.

The framework can increase the opportunities for inter-organizational learning in a service network. Knowing the learning context and the challenges associated with this learning allows for a more accurate intervention and allocation of resources to improve service network performance.

The novelty lies in the consolidation of the literature of service networks and the extension of the literature on inter-organizational learning hereto.