With the onset the digitalization and Industry 4.0, the maintenance function and asset management in a company is forming towards Smart Maintenance. An essential application in smart maintenance is to improve the maintenance planning function with better criticality assessment. With the aid from artificial intelligence it is considered that maintenance planning will provide better and faster decision making in maintenance management. The aim of this article is to develop smart maintenance planning based on principles both from asset management and machine learning. The result demonstrates a use case of criticality assessment for maintenance planning and comprise computation of anomaly degree (AD) as well as calculation of profit loss indicator (PLI). The risk matrix in the criticality assessment is then constructed by both AD and PLI and will then aid the maintenance planner in better and faster decision making. It is concluded that more industrial use cases should be conducted representing different industry branches
