This dissertation supports human decision-making with a Model-Based Systems Engineering methodology enabling engineering analysis, and in particular Operations Research analysis of discrete-event logistics systems, to be more widely used in a cost-effective and correct manner. A methodology is a collection of related processes, methods, and tools, and the process of interest is posing a question about a system model and then identifying and building answering analysis models. Methods and tools are the novelty of this dissertation, which when applied to the process will enable the dissertation's goal. One method which directly enables the goal is adding automation to analysis model-building. Another method is abstraction, to make explicit a frequently-used bridge to analysis and also expose analysis model-building repetition to justify automation. A third method is formalization, to capture knowledge for reuse and also enable automation without human interpreters. The methodology, which is itself a contribution, also includes two supporting tool contributions. A tool to support the abstraction method is a definition of a token-flow network, an abstract concept which generalizes many aspects of discrete-event logistics systems and underlies many analyses of them. Another tool to support the formalization method is a definition of a well-formed question, the result of an initial study of semantics, categories, and patterns in questions about models which induce engineering analysis. This is more general than queries about models in any specific modeling language, and also more general than queries answerable by navigating through a model and retrieving recorded information. A final contribution follows from investigating tools for the automation method. Analysis model-building is a model-to-model transformation, and languages and tools for model-to-model transformation already exist in Model-Driven Architecture of software. The contribution considers if and how these tools can be re-purposed by contrasting software object-oriented code generation and engineering analysis model-building. It is argued that both use cases share a common transformation paradigm but executed at different relative levels of abstraction, and the argument is supported by showing how several Operations Research analyses can be defined in an object-oriented way across multiple layered instance-of abstraction levels. Enabling Operations Research analysis of discrete-event logistics systems to be more widely used in a cost-effective and correct manner requires considering fundamental questions about what knowledge is required to answer a question about a system, how to formally capture that knowledge, and what that capture enables. Developments here are promising, but provide only limited answers and leave much room for future work.