Dynamics and performance modeling of multi-stage manufacturing systems using nonlinear stochastic differential equations Conference Paper uri icon


  • Modern manufacturing enterprises have invested in a variety of sensors and IT infrastructure to increase plant floor information visibility. This offers an unprecedented opportunity to track performances of manufacturing systems from a dynamic, as opposed to static, sense. Conventional static models are inadequate to model manufacturing system performance variations in real-time from these large non-stationary data sources. This paper addresses a physics-based approach to model the performance outputs (e.g., throughputs, uptimes, and yield rates) from a multi-stage manufacturing system. Unlike previous methods, degradation and repair dynamics that influence downtime distributions in such manufacturing systems are explicitly considered. Sigmoid function theory is used to remove discontinuities in the models. The resulting model is validated using real-world datasets acquired from the General Motor's assembly lines, and it is found to capture dynamic s of downtime better than traditional exponential distribution based simulation models.

name of conference

  • 2008 IEEE International Conference on Automation Science and Engineering (CASE 2008)

published proceedings

  • 2008 IEEE International Conference on Automation Science and Engineering

author list (cited authors)

  • Mittal, U., Hui Yang, .., Bukkapatnam, S., & Barajas, L. G

citation count

  • 6

complete list of authors

  • Mittal, Utkarsh||Bukkapatnam, Satish TS||Barajas, Leandro G

publication date

  • August 2008