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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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	ump-8238
recordtype	eprints
spelling	ump-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	2023-09-18T22:05:35Z
last_indexed	2023-09-18T22:05:35Z
_version_	1777414686360207360

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

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