An Integrated Approach Based on SARIMA and Bayesian to Estimate Production Throughput under Five Random Variables
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 ta...
| Main Author: | |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/6050/ http://umpir.ump.edu.my/id/eprint/6050/1/fkp-2013-Amir-Integrated_Approach_BasedMUCET.pdf |
| Summary: | 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. |
|---|