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

Abstract

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.
Pages3364-3374
Number of pages11
ISBN (Electronic)9781538665725
DOIs
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
Volume2018-December
ISSN (Print)0891-7736

Conference

Conference2018 Winter Simulation Conference, WSC 2018
CountrySweden
CityGothenburg
Period12/9/1812/12/18

Fingerprint

Workload
Data analysis
Estimation Capacity
Statistical Learning
Datalog
Prediction
Cleaning
Decision Support
Computational Experiments
Simulation
Filtering
Optimization
Experiments
Model
Framework

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.. https://doi.org/10.1109/WSC.2018.8632482

Production log data analysis for reject rate prediction and workload estimation. / Pfeiffer, András; Gyulai, Dávid; Szaller, Ádám; Monostori, L.

WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause. Institute of Electrical and Electronics Engineers Inc., 2019. p. 3364-3374 8632482 (Proceedings - Winter Simulation Conference; Vol. 2018-December).

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

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., 8632482, Proceedings - Winter Simulation Conference, vol. 2018-December, Institute of Electrical and Electronics Engineers Inc., pp. 3364-3374, 2018 Winter Simulation Conference, WSC 2018, Gothenburg, Sweden, 12/9/18. https://doi.org/10.1109/WSC.2018.8632482
Pfeiffer A, Gyulai D, Szaller Á, Monostori L. Production log data analysis for reject rate prediction and workload estimation. In WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause. Institute of Electrical and Electronics Engineers Inc. 2019. p. 3364-3374. 8632482. (Proceedings - Winter Simulation Conference). https://doi.org/10.1109/WSC.2018.8632482
Pfeiffer, András ; Gyulai, Dávid ; Szaller, Ádám ; Monostori, L. / Production log data analysis for reject rate prediction and workload estimation. WSC 2018 - 2018 Winter Simulation Conference: Simulation for a Noble Cause. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 3364-3374 (Proceedings - Winter Simulation Conference).
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