Manufacturing Lead Time Estimation with the Combination of Simulation and Statistical Learning Methods

András Pfeiffer, Dávid Gyulai, B. Kádár, L. Monostori

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

12 Citations (Scopus)

Abstract

In the paper, a novel method is introduced for selecting tuning parameters improving accuracy and robustness for multi-model based prediction of manufacturing lead times. Prediction is made by setting up models using statistical learning methods (multivariate regression); trained, validated and tested on log data gathered by manufacturing execution systems (MES). Relevant features, i.e.; the predictors most contributing to the response, are selected from a wider range of system parameters. The proposed method is tested on data provided by a discrete event simulation model (as a part of a simulation-based prediction framework) of a small-sized flow-shop system. Accordingly, log data are generated by simulation experiments, substituting the function of a MES system, while considering several different system settings (e.g.; job arrival rate, test rejection rate). By inserting the prediction models into a simulation-based decision support system, prospective simulations anticipating near-future deviations and/or disturbances, could be supported. Consequently, simulation could be applied for reactive, disturbance-handling purposes, and, moreover, for training the prediction models.

Original languageEnglish
Title of host publicationProcedia CIRP
PublisherElsevier
Pages75-80
Number of pages6
Volume41
DOIs
Publication statusPublished - 2016
Event48th CIRP International Conference on Manufacturing Systems, CIRP CMS 2015 - Ischia, Italy
Duration: Jun 24 2015Jun 26 2015

Other

Other48th CIRP International Conference on Manufacturing Systems, CIRP CMS 2015
CountryItaly
CityIschia
Period6/24/156/26/15

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Keywords

  • Lead time
  • Robust prediction
  • Simulation
  • Statistical learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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