Production log data analysis for reject rate prediction and workload estimation

András Pfeiffer, Dávid Gyulai, Ádám Szaller, L. Monostori

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The main focus of the research presented in this paper is to propose new methods for filtering and cleaning large-scale production log data by applying statistical learning models. Successful application of the methods in consideration of a production optimization and a simulation-based prediction framework for decision support is presented through an industrial case study. Key parameters analysed in the computational experiments are fluctuating reject rates that make capacity estimations on a shift basis difficult to cope with. The most relevant features of simulation-based workload estimation are extracted from the products' final test log, which process has the greatest impact on the variance of workload parameters.

Original languageEnglish
Title of host publicationWSC 2018 - 2018 Winter Simulation Conference
Subtitle of host publicationSimulation for a Noble Cause
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages11
ISBN (Electronic)9781538665725
Publication statusPublished - Jan 31 2019
Event2018 Winter Simulation Conference, WSC 2018 - Gothenburg, Sweden
Duration: Dec 9 2018Dec 12 2018

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Conference2018 Winter Simulation Conference, WSC 2018


ASJC Scopus subject areas

  • Software
  • Modelling and Simulation
  • Computer Science Applications

Cite this

Pfeiffer, A., Gyulai, D., Szaller, Á., & Monostori, L. (2019). Production log data analysis for reject rate prediction and workload estimation. In WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause (pp. 3364-3374). [8632482] (Proceedings - Winter Simulation Conference; Vol. 2018-December). Institute of Electrical and Electronics Engineers Inc..