Data analytics-based decision support workflow for high-mix low-volume production systems

In order to answer the ever-fluctuating demand of high-mix low-volume production environments, reconfiguring the production systems and improving their performance rely heavily on the application of advanced decision support tools. Estimating the expected values of the performance measures (KPIs) in the face of these decisions, however, is even more challenging in such an environment as the complex structure, behavior and input demand creates an enormously large variable domain restraining the analysis. The paper introduces a novel workflow for providing simulation-based decision support for improving KPIs of high-mix low-volume production systems by reducing the size of the input domain with the application of unsupervised machine learning techniques.