Modelling and analysing soy sauce production using discrete event simulation
The stochastic demand of a product in a competitive market causes manufacturing industries struggle to control their productions. This situation has been worsened by various factors complicating the production processes, such as process bottlenecks, imbalance employees' working time and unoptim...
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| Format: | Thesis |
| Language: | eng eng |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://etd.uum.edu.my/10143/1/s822279_01.pdf https://etd.uum.edu.my/10143/2/s822279_02.pdf |
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| Summary: | The stochastic demand of a product in a competitive market causes manufacturing industries struggle to control their productions. This situation has been worsened by various factors complicating the production processes, such as process bottlenecks, imbalance employees' working time and unoptimised production schedules. However, these factors have rarely been considered in previous discrete event simulation studies, which typically examine the impact of changing the capacity of available resources on system throughput. This research simulates the production processes of a soy sauce manufacturing company by considering all the factors. The current processes and their processing time, observed and collected from a real system, were modelled using Arena software. Then, model was run, verified and validated to measure the current performance of the system, especially on the utilization of available resources. Various 'what-if analysis' was performed to propose strategies to improve the current production processes. The developed model identified some problems in the production processes, which had not been realized by the company. The occurring bottleneck at the Moromi and Koji processes imposed the company to extend their operations hours, which ignite the issue of unplanned overtime working hours. The research helps the manufacturing company understand the problems occuring in their production processes without interrupting the real processes. In addition, it helps them identify the pain point in the production flow and analyse potential actions and its impact on production throughput. |
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