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Dynamic-Data-Driven Application Systems (DDDAS) is a new modeling and control paradigm which adaptively adjusts the fidelity of a simulation model. The fidelity of the simulation model is adjusted against available computational resources by incorporating dynamic data into the executing model, which then steers the measurement process for selective date update. To this end, comprehensive system architecture and methodologies are first proposed, where the components include a real-time DDDAS simulation, grid modules, a web service communication server, databases, various sensors and a real system. Abnormality detection, fidelity selection, fidelity assignment, and prediction and task generation are enabled through the embedded algorithms developed in this work. Grid computing is used for computational resources management and web services are used for inter-operable communications among distributed software components. The proposed DDDAS is demonstrated on an example of preventive maintenance scheduling in a semiconductor supply chain.

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Simulation is one of the most valuable and powerful analytical tools for process improvement, irrespective of whether the process is one of manufacturing, transportation, health care, or general service. Its value typically increases as the process to be modeled becomes more complex. Furthermore, discrete-event simulation analyses combine synergistically, and become more powerful, when conjoined with other industrial engineering techniques such as bottleneck (constraint) analysis, work measurement, floor space requirements and facility layout analysis, and value-stream mapping. In this case study, we describe the application of simulation, in concert with these other techniques, to improving the efficiency, and hence the reliability and profitability, of steel-mill manufacture, in a decidedly international context, of a wide variety of pipes for generic use in a variety of industrial applications.