Competitiveness and customer loyalty represent essential pillars of corporate success for production companies. To achieve this goal, reliable assets are essential over the entire asset-life-cycle, especially in the utilization phase, as required by an asset management. Due to the constantly changing production conditions, fluctuating demands and changing legal requirements, the assets identified as critical also change continuously. It is therefore necessary to identify the greatest risk factors at an early stage and to derive timely measures for risk reduction and cost savings to secure competitive advantages and to adapt the maintenance strategy of the assets accordingly in order to be able to survive on the market in the long term. To evaluate these critical assets dynamically and to adapt the maintenance strategy accordingly, a procedure and decision model was developed. Based on a company analysis and the determination of the complexity level of the considered entity, as well as the prevailing data maturity, the model specifies a set of tools for performing the criticality assessment. The dynamization of the assessment is especially essential in times of increasing asset intensity and productivity, as well as the ever-increasing scarcity of resources. The system analysis to be carried out in the first step of the assessment aims to identify relevant input factors that affect the asset and accordingly provide a set of criteria for carrying out the criticality assessment. It is important to note, that the criteria set follows a holistic approach in the sense of the Generic Management and encompasses all success factors of the unit under consideration to achieve the greatest possible output through system optimization. By considering the existing asset park and the success factors affecting it, a fully comprehensive criticality assessment can be carried out with the aim of increasing efficiency and effectiveness of the assets in the long term, as well as being able to react flexibly to the changing environmental conditions. The developed model was validated on the basis of three application examples.