Towards a Methodology for the Definition and Evaluation of Enterprises Through Modeling and Simulation: Application to Product Manufacturing

As manufacturing systems adopt increasingly digital technologies, stakeholders need methods to understand how these changes affect operational performance. Existing enterprise modeling and simulation efforts rarely provide a structured way to evaluate digital transformation choices at the enterprise level. This paper proposes an integrated methodology that links enterprise models with discrete-event simulation to assess how digital attributes influence factory outcomes. The enterprise is modeled using the Unified Architecture Framework to capture goals, activities, resources, and roles in a simulation-agnostic form. The model is then mapped to a SimPy-based discrete-event simulation of a notional drone manufacturing line. Digital enablers, such as IoT-enabled maintenance, digital thread maturity, and workforce digital literacy, are represented as configurable levers that affect failure and repair behavior, rework and learning, inventory buffering, and supplier reliability. A Latin Hypercube Sampling design and surrogate modeling are used to explore a wide space of configurations efficiently. Results show non-linear relationships between workforce size and throughput, diminishing returns from inventory buffering, and conditional benefits from digital thread integration that depend on workforce and system constraints. A multi-constraint demonstration illustrates how surrogate-based tools can help tune workforce, inventory, reliability, and digital levers to meet concurrent targets on throughput, quality, cost, and downtime. The approach offers a reusable framework for structured, model-based exploration of digital strategies in manufacturing.