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An Integrated Approach Based on SARIMA and Bayesian to Estimate Production Throughput under Five Random Variables

Azizi, Amir (2013) An Integrated Approach Based on SARIMA and Bayesian to Estimate Production Throughput under Five Random Variables. In: Proceedings of Malaysian Technical Universities Conference on Engineering & Technology (MUCET 2013), 3-4 December 2013 , Kuantan, Pahang. pp. 1-12..

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Abstract

Analyzing and modeling efforts on production throughput are getting more complex due to random variables in todays dynamic production systems. The production line faces the changes in setup time, machinery break down, lead time of manufacturing, demand, and scraps. Bayesian approach is applied to tackle the problem. Later, it is developed by Seasonal Autoregressive Integrated Moving Average (SARIMA) approach. The integrated BayesianSARIMA model consists of multiple random parameters with multiple random variables. A statistical index, Rsquared, is used to measure the performance of the developed model. A real case study on tile and ceramic production is considered. The Bayesian model is validated with respect to convergence and efficiency of its outputs. The results of the analyses present that the Bayesian-SARIMA produces higher R-squared value indicated by 98.8% compared with previous studies on Bayesian and ARIMA approaches individually.

Item Type: Conference or Workshop Item (Speech)
Uncontrolled Keywords: Production throughput; Demand; Scrap; Setup time; Machinery break down; Lead time of manufacturing
Subjects: T Technology > TJ Mechanical engineering and machinery
Faculty/Division: Faculty of Manufacturing Engineering
Depositing User: Noorul Farina Arifin
Date Deposited: 30 Jun 2014 02:31
Last Modified: 23 Feb 2018 03:10
URI: http://umpir.ump.edu.my/id/eprint/6050
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