# Transformation towards a Smart Maintenance Factory: The Case of a Vessel Maintenance Depot
## 5 Discussion and Conclusions  
In terms of the entire lifecycle, smart factories have been mainly studied in the building phase, and the need to expand research to the service phase has been suggested [6; 7; 32]. This study emphasized the service phase of the smart factory for a vessel maintenance depot. Due to the variability of repair and maintenance work, there was a limit to applying the smart factory concept in the manufacturing field as it is. The procedures and methods were proposed for the smart transformation of maintenance factory by applying Industrial 4.0 technology. In order to respond to the variability of maintenance works, methods for securing on-site agility and predicting maintenance were presented. For an agile response to the field, we mainly set the direction of visualization on-site to control the processes in line through the visual management and to secure the linkage between each job through the  
Figure 9: Future image of a transformed smart depot. on-site operation support system. For predictive maintenance, data of the status of the currently operating vessels was collected in real-time and analyzed by the big data system. The entire maintenance depot was built as a mobile workplace with IoT and LTE enabling data-based factory management. In data-driven factories, data standardization should be implemented through data governance. Although the goal is to achieve efficient management with big data analysis, the reality is that factories neither collect/manage data nor have a modeling concept for the data. Since the IT system has stored data without considering the data format or type, it has been difficult to utilize the stored data. The technology-oriented automation was implemented rather than equipment-oriented automation. For automation, semi-automation was proposed rather than full automation considering the work characteristics. Instead of the investment-oriented levels of a smart factory, effect-driven automation should be given the top priority in consideration of the difficulty of learning for operators and the work environment of the factory. While IT innovation improves the process by applying the necessary technologies and systems, smart transformation changes existing processes through the application of the fourth industrial technologies of data, systems, automation, and infrastructure. In other words, the approach of smart transformation is the opposite perspective to the legacy IT approach. The concept of a smart factory in the ship building phase needed to be changed for the application of the smart maintenance factory, but most of the technologies including 3D, AR, big data, IoT, and mobile could be accommodated. The applied enablers in the study were IoT system, wireless system, operation support IT system, big data system, remote support system, semi-automation, and infrastructure improvement. Among them, the contribution to improvement was evaluated to be the highest through the operating support system that supports the connection and visualization on-site. Through the linkage between the mission, KPIs, and expected effect, the goals of smart transformation for the plant rather than the individual work could be carried out. This linkage is considered as a basic strategic direction, enabling the performance-oriented smart maintenance factory operation. In summary, we conducted a study to expand the smart factory concept, which was limited to the ship building phase and to the ship servicing phase. The smart transformation procedure, framework, and architecture for a smart maintenance factory were proposed with a practical case. The designed practical case was embodied as a data-driven and technology-driven smart factory. The results of this paper will be a reference model for building a smart maintenance factory in the service area. Future studies should verify the actual increases of MTTFs and reductions of MTTR through smart maintenance factory operation.