PRODUCTION UNCERTAINTIES MODELLING BY BAYESIAN INFERENCE USING GIBBS SAMPLING

Amir Azizi, Amir Yazid b. Ali, Loh Wei Ping, Mohsen Mohammadzadeh

Abstract


Analysis by modelling production throughput is an efficient way to provide information for production decision-making. Observation and investigation based on a real-life tile production line revealed that the five main uncertain variables are demand rate, breakdown time, scrap rate, setup time, and lead time. The volatile nature of these random variables was observed over a specific period of 104 weeks. The processes were sequential and multi-stage. These five uncertain variables of production were modelled to reflect the performance of overall production by applying Bayesian inference using Gibbs sampling. The application of Bayesian inference for handling production uncertainties showed a robust model with 2.5 per cent mean absolute percentage error. It is recommended to consider the five main uncertain variables that are introduced in this study for production decision-making. The study proposes the use of Bayesian inference for superior accuracy in production decision-making. 


Keywords


(Uncertainty; Bayesian; Throughput).

Full Text:

PDF


DOI: https://doi.org/10.7166/26-3-572

Refbacks

  • There are currently no refbacks.




Copyright (c) 2015


ISSN 2224-7890 (on-line) ; ISSN 1012-277X (print)


Powered by OJS and hosted by Stellenbosch University Library and Information Service since 2011.


Disclaimer:

This journal is hosted by the SU LIS on request of the journal owner/editor. The SU LIS takes no responsibility for the content published within this journal, and disclaim all liability arising out of the use of or inability to use the information contained herein. We assume no responsibility, and shall not be liable for any breaches of agreement with other publishers/hosts.

SUNJournals Help