# PrimaVera: Synergising Predictive Maintenance
## 2 State of the Art
### Asset Management and Organisational Factors  
Predictive maintenance is an active research area that has seen significant progress over the past decade, both in industry and in academia. Progress is much related to advancements in the area of big data analytics [59]. While many core building blocks of predictive maintenance (such as sensor technology, failure prediction methods, and optimisation techniques) exist, current solutions focus on individual steps in the predictive maintenance cycle and only work for very specific settings as discussed in the introductory chapter of this paper. Developing advanced maintenance techniques is therefore only useful if they are well integrated into an organisation [60]. A quote of a Maintenance Engineer at the Netherlands Railways who recently studied the use of predictive strategies illustrates these organisational challenges:  
_\"Preentive water filling based on real-time water level data and a predictive model seems to be an appropriate maintenance strategy; however, this requires the dynamic usage of human resources and filling stations... Trains move, making the logistic puzzle more complicated... Our overall goal is to maximise the availability of trains with functioning toilets in a cost-effective way.\"_  
Traditionally organisational aspects regarding the implementation of data-driven maintenance have been mentioned by other authors and have often been neglected [48; 60; 61; 62; 63]. Therefore, the PrimaVera project specifically studies the impact of data-driven maintenance on organisational processes where data-driven maintenance is being introduced. Procedures for the effective implementation of data-driven maintenance systems within organisations need to be designed in a timely way to allow effective use of its predictions in operational maintenance planning processes. Furthermore, earlier research shows that the implementation of predictive maintenance should include ambition levels, available data [64] and a fit of predictive maintenance with the organisational maturity of the organisation [60]. The following organisational interfaces have been identified by [60]: strategy and goals, decisions, structure, budget and capacity, and documentation. It can be debated that early integrated decision making is needed to evaluate the impact on these interfaces. Because asset management is a multi-disciplinary discipline, the organisational impacts expected by the introduction of data-driven maintenance systems on the aforementioned interfaces should therefore be approached from multiple perspectives. The perspectives mentioned by [65], e.g., technical, economic, commercial, compliance, and organisational aspects seem relevant to be used here, especially because asset management aspects are rarely limited to a one-dimensional perspective. The most critical organisational impacts should therefore ideally be identified and assessed before the introduction of data-driven maintenance by studying the use of the aforementioned perspectives in this specific asset management area. Because of the complexity of the associated systems, processes, and people there will always remain a number of organisational decisions that need to be identified and addressed before data-driven maintenance of individual components can actually be implemented. As [66] pointed out there are always trade-offs between maintenance costs, availability and efficiency in (multi-component) systems. It can be argued based on the outcomes of the work of [PERSON] [66] that organisational processes need to become more flexible to make data-driven maintenance on a multi-component system more feasible. Therefore, the PrimaVera project will also investigate how organisational readiness and resilience in processes can be developed before or during the introduction of these systems. The use of high-reliability theory and anti-fragility in organisations can be seen as emerging fields [67] besides the needed attention for cultural aspects [60].