Integration of Seasonal Autoregressive Integrated Moving Average and Bayesian Methods to Predict Production Throughput Under Random Variables

Integration of Seasonal Autoregressive Integrated Moving Average and Bayesian Methods to Predict Production Throughput Under Random Variables

Analysing and modelling efforts on production throughput are getting more complex due to random variables in today’s dynamic production systems. The objective of this study is to take multiple random variables of production into account when aiming for production throughput with higher accuracy of...

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Bibliographic Details
Main Author: Amir, Azizi
Format: Article
Language:English
Published: Faculty Mechanical Engineering, UMP 2014
Subjects:
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/8238/
http://umpir.ump.edu.my/id/eprint/8238/
http://umpir.ump.edu.my/id/eprint/8238/
http://umpir.ump.edu.my/id/eprint/8238/1/Integration_of_Seasonal_Autoregressive_Integrated_Moving_Average_and_Bayesian_Methods_to_Predict_Production_Throughput_Under_Random_Variables.pdf
id oai:umpir.ump.edu.my:8238
recordtype eprints
spelling oai:umpir.ump.edu.my:82382018-02-23T02:42:58Z http://umpir.ump.edu.my/id/eprint/8238/ Integration of Seasonal Autoregressive Integrated Moving Average and Bayesian Methods to Predict Production Throughput Under Random Variables Amir, Azizi TS Manufactures Analysing and modelling efforts on production throughput are getting more complex due to random variables in today’s dynamic production systems. The objective of this study is to take multiple random variables of production into account when aiming for production throughput with higher accuracy of prediction. In the dynamic manufacturing environment, production lines have to cope with changes in set-up time, machinery breakdown, lead time of manufacturing, demand, and scrap. This study applied a Bayesian method to tackle the problem. Later, the prediction of production throughput under random variables is improved by the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. The integrated Bayesian-SARIMA model consists of multiple random parameters with multiple random variables. A statistical index, R-squared, is used to measure the performance of the integrated model. A real case study on tile and ceramic production is considered. The Bayesian model is validated with respect to the convergence and efficiency of its outputs. The results of the analyses indicate that the Bayesian-SARIMA method produces a higher R-squared value, at 98.8%, compared with previous studies on Bayesian methods where the value was 90.68% and the ARIMA method where it was 97.38%. Consequently a robust approach in terms of the degree of prediction accuracy is proposed. This integrated method may be applied for the estimation of other production performance factors like lead time and cycle time in different types of dynamic manufacturing environment. Faculty Mechanical Engineering, UMP 2014 Article PeerReviewed application/pdf en cc_by http://umpir.ump.edu.my/id/eprint/8238/1/Integration_of_Seasonal_Autoregressive_Integrated_Moving_Average_and_Bayesian_Methods_to_Predict_Production_Throughput_Under_Random_Variables.pdf Amir, Azizi (2014) Integration of Seasonal Autoregressive Integrated Moving Average and Bayesian Methods to Predict Production Throughput Under Random Variables. Journal of Mechanical Engineering and Sciences (JMES) , 7. pp. 1236-1250. ISSN 2289-4659 (print); 2231-8380 (online) http://dx.doi.org/10.15282/jmes.7.2014.23.0121 DOI: 10.15282/jmes.7.2014.23.0121
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic TS Manufactures
spellingShingle TS Manufactures
Amir, Azizi
Integration of Seasonal Autoregressive Integrated Moving Average and Bayesian Methods to Predict Production Throughput Under Random Variables
description Analysing and modelling efforts on production throughput are getting more complex due to random variables in today’s dynamic production systems. The objective of this study is to take multiple random variables of production into account when aiming for production throughput with higher accuracy of prediction. In the dynamic manufacturing environment, production lines have to cope with changes in set-up time, machinery breakdown, lead time of manufacturing, demand, and scrap. This study applied a Bayesian method to tackle the problem. Later, the prediction of production throughput under random variables is improved by the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. The integrated Bayesian-SARIMA model consists of multiple random parameters with multiple random variables. A statistical index, R-squared, is used to measure the performance of the integrated model. A real case study on tile and ceramic production is considered. The Bayesian model is validated with respect to the convergence and efficiency of its outputs. The results of the analyses indicate that the Bayesian-SARIMA method produces a higher R-squared value, at 98.8%, compared with previous studies on Bayesian methods where the value was 90.68% and the ARIMA method where it was 97.38%. Consequently a robust approach in terms of the degree of prediction accuracy is proposed. This integrated method may be applied for the estimation of other production performance factors like lead time and cycle time in different types of dynamic manufacturing environment.
format Article
author Amir, Azizi
author_facet Amir, Azizi
author_sort Amir, Azizi
title Integration of Seasonal Autoregressive Integrated Moving Average and Bayesian Methods to Predict Production Throughput Under Random Variables
title_short Integration of Seasonal Autoregressive Integrated Moving Average and Bayesian Methods to Predict Production Throughput Under Random Variables
title_full Integration of Seasonal Autoregressive Integrated Moving Average and Bayesian Methods to Predict Production Throughput Under Random Variables
title_fullStr Integration of Seasonal Autoregressive Integrated Moving Average and Bayesian Methods to Predict Production Throughput Under Random Variables
title_full_unstemmed Integration of Seasonal Autoregressive Integrated Moving Average and Bayesian Methods to Predict Production Throughput Under Random Variables
title_sort integration of seasonal autoregressive integrated moving average and bayesian methods to predict production throughput under random variables
publisher Faculty Mechanical Engineering, UMP
publishDate 2014
url http://umpir.ump.edu.my/id/eprint/8238/
http://umpir.ump.edu.my/id/eprint/8238/
http://umpir.ump.edu.my/id/eprint/8238/
http://umpir.ump.edu.my/id/eprint/8238/1/Integration_of_Seasonal_Autoregressive_Integrated_Moving_Average_and_Bayesian_Methods_to_Predict_Production_Throughput_Under_Random_Variables.pdf
first_indexed 2018-09-07T01:05:54Z
last_indexed 2018-09-07T01:05:54Z
_version_ 1610908806163726336

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