## 2 Literature Review
### Maintenance under the Framework of Industry 4.0  
The modern manufacturing world is undergoing a digitalization phase under the framework of Industry 4.0. During the current industrial revolution, digital technologies have tremendously advanced, which enables the improvement of other scientific fields, including that of technical maintenance. With the recent development in the ICT, and the integration of Internet of Things (IoT), the manufacturing domain has passed in a whole new phase by relying on the utilization of data, i.e., Big Data Analytics. For the successful completion of MRO besides the technical knowledge/expertise of the stuff carrying out the operations, it is crucial that all the needed components are acquired. Therefore, companies are maintaining an inventory of components. However, under the framework of Industry 4.0, and with the utilization of digital technologies, the existing inventory practices can be further improved through proper classification of the MRO components while additive manufacturing can facilitate in reducing lead times to produce several components [6; 7; 8; 9; 10; 26; 27; 28].  
For Industry 4.0 applications, two key objectives are to ensure maximum uptime throughout the production chain, and to increase productivity while reducing production costs. In Reference [29], a systematic approach to analyze the strengths and weaknesses of current open-source Big Data and stream processing technologies and assess their use for Industry 4.0 use cases is presented. In addition,the model of Industry 4.0 is gradually being implemented in worldwide development, distribution, and marketing chains. As such, the design and integration of a stainless-steel predictive maintenance system is presented in Reference [29]. This case study uses data from machinery involved in producing high-quality steel sheets. Following the predictive maintenance field, the use of real-time detection and prediction algorithms regarding future failures has significantly benefited from the technological advances of Industry 4.0 era. There's also growing interest in decision-making algorithms triggered by predictions of failure over the past few years. a literature review on decision making in predictive maintenance in the context of smart manufacturing is done in References [30; 31]. Additionally, a review on predictive maintenance as an integrated predictive platform for production systems, focusing on maintenance approaches, methods, and tools is described in Reference [32].  
Another significant issue is the estimation of the maintenance time for a new maintenance project, which is among the main maintenance offerings, is based solely on the experience and knowledge of the engineer. The research work presented in Reference [33] proposes a framework for knowledge-based calculation of maintenance time based on Key Performance Indicators (KPI) monitoring to support the capture and reuse of information in maintenance activities as well as to improve the performance of the given maintenance PSS. Next, an extendable and reusable scheduling approach in the context of predictive maintenance, which supports multiple heterogeneous inputs and outputs, is described in Reference [33]. In accordance with the current schedule, predictive maintenance indicators from the tracked equipment are used for scheduling maintenance operations. a web service architecture is implemented to accommodate highly different use scenarios such as suppliers of equipment.