Generating Large Data Sets for Simulation of Electronics Manufacturing

Ping Zhang, James B. Pick

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Very often the data sets needed for large-scale system simulation and testing aren't available. Even when it's possible to collect and use the real-world data, they're not always suitable. In some situations, only a small portion of the data sets is actually needed for system testing. In others, the sets may involve many data variables and extensive data elements in each data variable, creating high complexity and difficulty. This is especially true in manufac turing production planning, where many fac tors must be considered, and the scope of the data sets is often very large. Here we introduce the procedure and methods we developed for generating large data sets in manufacturing using Monte Carlo techniques combined with the Extended Entity Relationship modeling method. We introduce an approach that can deal with complicated relationships and order ing among random variates. We generate the data sets for an IBM electronics manufactur ing facility. We examine use of the sets to test an information visualization system for pro duction planning. We discuss the goals of ran dom sample generation and the verification of the generation of the random variates.

Original languageEnglish (US)
Pages (from-to)231-246
Number of pages16
Issue number4
StatePublished - Apr 1998


  • Manufacturing
  • Monte Carlo
  • data modeling
  • verification

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Graphics and Computer-Aided Design

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