Description:
Global competition requires manufacturers to assess the likely effects of decisions about design and configuration of products, processes, and resources. The complexity of modern manufacturing requires advanced analysis, simulation in particular, to answer questions such as: What will be the capacity of this proposed line? What throughput, cycle time, and other metrics can be achieved for this product with this line configuration? Where should capacity be added if demand increases? Large enterprises usually employ experts to build simulation models to answer these questions, although the process can be lengthy and expensive. Most small- and medium-sized enterprises (SMEs) simply do not have access to the necessary analytical capabilities.
Discrete-event simulation analysis is increasingly critical to analyzing, designing, evaluating, and controlling large-scale, complex, uncertain systems [1]. However, it currently takes too long to design, collect information/data, build, execute, and analyze simulation models, leading to insufficient input to decision-making [2]. These barriers are particularly formidable to SMEs, leaving them with little ability to quantitatively assess the effect of potential decisions. An ability to automate the development of simulations would have significant benefits for manufacturing enterprises.
The goal is to develop tools that significantly reduce the time, cost, and expertise required to use simulation analysis to answer questions about designing and operating manufacturing systems. Tools are sought that can answer a flexible and general set of questions about manufacturing systems, scenarios within them, and alternatives to them.
The tools will leverage models of manufacturing systems that provide the necessary input to analysis.
Phase I expected results:
Demonstrate an architecture with widely understood semantics for manufacturing system modeling, appropriate model-to-model transformations, and well-structured manufacturing system simulations. This demonstration should start with at least one frequently-asked manufacturing question or use case, answer the question using simulation analysis, and elucidate how a proper software implementation would significantly reduce the time, cost, and expertise requirements to answer the question as compared with the status quo.
Phase II expected results:
Deploy the Phase I architecture in a form usable by SMEs who currently have no access to manufacturing system simulations or other advanced analytics. Expand and mature the Phase I implementation to additional questions, use cases, and classes of manufacturing systems. The result should be a prototype of a commercially viable software solution.
NIST staff may be available for consultation, input, and discussion, as needed.
