Comparison of data analytics approaches using simulation

Abstract

Manufacturers need to quickly estimate cycle times for incoming orders for promising delivery dates. This can be achieved by using data analytics (DA) / machine learning (ML) approaches. Selecting the right DA/ML approach for an application is rather complex. Obtaining sufficient and right type of data for evaluating these approaches is a challenge. Simulation models can support this process by generating synthetic data. Simulation models can also be used to validate DA models by generating new data under varying conditions. This can help in the evaluation of alternative DA approaches across expected range of operational scenarios. This paper reports on use of simulation to select an approach to support the order promising function in manufacturing. Two DA approaches, Neural Networks and Gaussian Process Regression, are evaluated using data generated by a manufacturing simulation model. The applicability of the two approaches is discussed in the context of the selected application.