With the emergence of Industry 4.0, maintenance is considered to be a specific area of action that is needed to successfully sustain a competitive advantage. For instance, predictive maintenance will be central for asset utilization, service, and after-sales in realizing Industry 4.0. Moreover, artificial intelligence (AI) is also central for Industry 4.0, and offers data-driven methods. The aim of this article is to develop a new maintenance model called deep digital maintenance (DDM). With the support of theoretical foundations in cyber-physical systems (CPS) and maintenance, a concept for DDM is proposed. In this paper, the planning module of DDM is investigated in more detail with realistic industrial data from earlier case studies. It is expected that this planning module will enable integrated planning (IPL) where maintenance and production planning can be more integrated. The result of the testing shows that both the remaining useful life (RUL) and the expected profit loss indicator (PLI) of ignoring the failure can be calculated for the planning module. The article concludes that further research is needed in testing the accuracy of RUL, classifying PLI for different failure modes, and testing of other DDM modules with industrial case studies.
In production environment today, ''silo thinking'' is a challenge where controlling an asset associated with several disciplines and departments can lead to a suboptimal result. This requires a more integrated approach with an integrated planning (IPL) framework. In this framework novel maintenance key performance indicators (KPIs) are needed. The purpose of this article is to develop the novel maintenance KPI profit loss indicator (PLI). This indicator is based on measuring both on the ''hidden factory'' and waste in production, presenting it as a financial measure. The notion ''hidden factory'' is used as a metaphor for measuring the time losses in industry through the maintenance KPI overall equipment effectiveness (OEE). This indicator divides the time losses into availability losses, performance losses and quality losses. In addition, a financial measure for waste based on literature from Toyota production system (TPS) and waste treatment and disposal is also included in PLI. Through a case study in the saw mill industry PLI is demonstrated and evaluated. It is expected that this indicator will be demonstrated in several industry branches in the future.
This abstract is an introduction to the next generation of High Performance Industrial Manufacturing, through the Industry standard 4.0. The fourth Industrial standard is based on advanced Automation and Robotics, sensor based computer technology, interconnected by wireless communication, and supported by BIG Data solutions. Effective management and human cooperation i.e. teamwork and SUM© processes will be increasingly important in the future. This paper also focuses on the new industrial standard in relation to OEE (Overall Equipment Effectiveness), Predictive Maintenance and total performance related to all equipment in the industrial factory processes. Not a few highly automated machines, but the system as whole. OEE is a well-known standardized tool for performance measurement throughout the industry. In order to utilize data systems as ERP (Enterprise Resource Planning) and PLM (engineering systems), they must be integrated with business systems. Management normally acts on the bases of facts and financial performance. In most companies increased profit is the overall goal. Future management systems aim for a fully integrated and automated data flow based on advanced sensor technology. Increased use of digital systems enables quicker and better decisions. Refers to previous paper "High Performance Innovative teams (IWAMA 2010).
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.
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