The paper investigates how resilience can be understood as an operational paradigm for system management from a multistage decision making perspective. Specifically, agents involved in the resilience management aim at an optimum process of sequential preparedness/protection, emergency response, recovery, and adaptation activities, trading off the loss due to lack of system functionality with the needed investment. Agents' decisions are made sequentially aiming at minimizing the overall system loss in the presence of numerous uncertainties in the intensity of the disruption, post-disruption demand, repair durations, etc. We restrict the actions to only depend on uncertain parameters realized up to the current decision period. This process can be formulated as a multi-stage optimization problem. We discuss how the linear decision rule approximation can be used to overcome the curse of the computational complexity of the problem and to derive operationally implementable policies. By referring to a simple example of a notional infrastructure, we demonstrate that the proposed approach helps providing a holistic view of resilience management and deriving optimal actions and policies for resilience enhancement.