This paper describes the design and development of a discrete event simulation based, dynamic, capacity analysis tool. Manufacturing capacity analysis, traditionally approached through the prescribed tools of mathematical programming, is presently devoid of a dynamic simulation framework that delivers multi-period asset evaluations based on a real-time performance estimate. A suggested framework for this class of decisions involves periodic (quarterly), deterministic, constrained evaluation models to specify resource allocation decisions. Capacity evaluation models are inherently non-stationary due to the structural modifications that occur when adding new production resources. Homogeneity is employed here in the uniform description of heterogeneous resources as general mathematical objects, or discrete event models (DEVS). Each object represents both the individual production resource's dynamic state and static parameters. Exercising these objects via simulation optimizes resources through Return on Operating Assets (ROOA), a fixed and variable cost roll-up metric that results in a balance between capital assets (machines) and work-in-process (WIP) allocation for a given demand level. Global optimization is achieved through distributed DEVS/CORBA administrators that monitor and constrain asset investment in the sequential concatenation of time periods. While the production model captures the flow behaviour of manufacturing operations and its performance scale, strategic scope is built into the administrator via alternative rule bases, or specialized management decision sets. Using System Entity Structure Alternative Evaluation (SEAE), these specialized alternatives reconfigure the plant model to produce the non-intuitive results for the different production ramp-up scenarios evaluated.
ASJC Scopus subject areas
- Strategy and Management
- Management Science and Operations Research
- Industrial and Manufacturing Engineering