With the trends of industry 4.0 and increased degree of digitalization in production plants, it is expected that production plants in future is much more adaptive where they can both self-optimize production parameters as well as self-maintain of standard activities. All though this would reduce manual operations, new work activities are expected in a cyber-physical production plant. For instance, the establishment of digital twins in cloud solutions enabled with Internet of Things (IoT) can result in crafts in maintenance analytics as well as more guided maintenance for the maintenance operator with augmented reality. In addition, more service from external personnel such as the machine builder is expected to be offered in Industry 4.0. In overall, it will be of interest to identify and recommend qualification criteria relevant for a cyber physical production plant that would be implemented in the organisation. The aim of this article is to evaluate the role of operator as well as other relevant job categories in a cyber physical production plant. The result in this paper is a recommended framework with qualification criteria of these job categories. Further research will require more case studies of this framework.

Possessing an efficient production line relies heavily on the availability of the production equipment. Thus, to ensure that the required function for critical equipment is in compliance, and unplanned downtime is minimized, succeeding with the field of maintenance is essential for industrialists. With the emergence of advanced manufacturing processes, incorporating predictive maintenance capabilities is seen as a necessity. Another field of interest is how modern value chains can support the maintenance function in a company. Accessibility to data from processes, equipment and products have increased significantly with the introduction of sensors and Industry 4.0 technologies. However, how to gather and utilize these data for enabling improved decision making within maintenance and value chain is still a challenge. Thus, the aim of this paper is to investigate on how maintenance and value chain data can collectively be used to improve value chain performance through prediction. The research approach includes both theoretical testing and industrial testing. The paper presents a novel concept for a predictive maintenance platform, and an artificial neural network (ANN) model with sensor data input. Further, a case of a company that has chosen to apply the platform, with the implications and determinants of this decision, is also provided. Results show that the platform can be used as an entry-level solution to enable Industry 4.0 and sensor data based predictive maintenance.

To stay competitive in the future, industrialists must be prepared to adopt the imminent changes and new technologies associated with Industrie 4.0. These changes apply equally to the field of maintenance, which is also developing quickly. Sensors, along with analyses and competence, are one of the most critical factors for Industrie 4.0 as they are the connectors between the digital and physical world. Utilization of these sensors within maintenance is a relatively unexplored field. Thus, the aim of this paper is to present a novel concept for ways sensor management can be linked to maintenance and thereby improve operational availability. The paper also presents an overview of sensor management and trends within maintenance.

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To have a maintenance function in the company that ensures a competitive advantage in the world market requires the world class maintenance (WCM). Though several different periods in history, maintenance has shifted from reactive maintenance fixing it when it breaks towards more systematic analysis techniques in terms of root cause analysis. With the onset of digitalisation and the breakthrough technologies in from Industry 4.0 more advanced analytics are expected in WCM. In particular the indicator profit loss indicator (PLI) has shown promising results in measuring e.g. time losses in production in a monetary term. Further, this indicator has also been proposed to be included in predictive maintenance. However, it is not pointed out clearly which role PLI will have in WCM. The aim of this article is therefore to investigate the trends of WCM as well as how PLI can be included in this journey.