<![CDATA[Water resilience is still big problem in Indonesia. In border and underdeveloped areas in Indonesia, the use of water sources is still considered not resilience. Especially in military context, where water needs are bigger and also more fundamental, this water resilience problem demanding a comprehensive solution. To address this issue, this research proposes the use of ordinary differential equations as a mathematical tool to model the dynamics of system damage over time, consumption, maintenance scheme, water crisis scheme, and other factors affecting water distribution resilience in military facilities. This journal presents a conceptual model of failure risk management water distribution system using a differential equation model approach to support water resilience. Specifically, the derivation of failure equation in the “reliability and maintenance system technical” textbook will be the basic reference for generating mathematical model. It is used because our model will be focused in improving failure risk management. By using the model, there are a lot of problem will be tackled such as Identify and manage failure risks in the water supply system, design an efficient water distribution maintenance scheme, and predict how strong the system to face water crisis. But before the model applied, the prediction of model will be tested by applying it in form of computer program. The case study of this research will be focused in testing the model in form of computer program with some simplicity and assumption. Through this approach, it is expected to find solutions that improve water usage efficiency, support the well-being of military personnel, and contribute to national water resilience to bolster national defense especially in case of water crisis happened. This research holds significant benefits for scientific advancement by providing a conceptual model that can serve as a reference for future research. It has the potential to make a tangible contribution but also still need so much development especially for application in real data, adding others variables that can be included for next research, conducting the interpretation, and better defining the measurement boundaries.]]>
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