Abstract
Virtual factory models can help improve manufacturing decision making when augmented with data analytics applications. Virtual factory models provide the capability of simulating real factories and generating realistic data streams at the desired level of resolution. Deeper insights can be gained and underlying relationships quantified by channeling the simulation output data to an external analytics tool. This paper describes integration of a virtual factory prototype with a neural network analytics application. The combined capability is used to create a neural network capable of predicting the expected cycle times for a small job shop. The capability can adapt by retraining the neural network whenever the production circumstances change significantly. The trained neural network can be used for functions such as order promising and can support factory management. The analytical and adaptive combination represented by the virtual factory integrated with the neural network thus supports the move towards smart manufacturing.
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