Ang, Chew Woon (2024) Optimizing machine capacity for meeting volatile demands: A simulation-based decision-making approach. Masters thesis, Universiti Teknikal Malaysia Melaka.
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Abstract
This thesis aims to optimize machine capacity in food processing industry to meet volatile demands. The objectives of the study include identifying current capacity problems, constructing a simulation model to evaluate different blender capacities, validate the model and recommend the best blender configuration. The four objectives are successfully fulfilled following the eight steps of simulation technique framework. AnyLogic software is used to construct a simulation model, for model validation and scenarios testing. Nice scenario testing are performed and the simulated output are examined, an optimum blender configuration is determined through the elimination approach in the selection process. An option of addition of 3000 litre blender is appears to be the best choice among all option as it is able to accommodate from small to extreme demand growth, while creating several advantages on the flexibility in term of resource allocation and operational hours. The study contributes to the existing body of knowledge in the field of capacity utilization and demand volatility management within the food processing industry. Extended study should be continued to evaluate blender configuration effectiveness considering investment, production, labor and refurbishment costs, along with incorporating additional metrics like service level and sales lead time.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Machine capacity optimization, Food processing industry, Volatile demand |
| Subjects: | T Technology > T Technology (General) T Technology > TP Chemical technology |
| Divisions: | Library > Tesis > FTKIP |
| Depositing User: | Muhamad Hafeez Zainudin |
| Date Deposited: | 04 Feb 2025 16:03 |
| Last Modified: | 04 Feb 2025 16:03 |
| URI: | http://eprints.utem.edu.my/id/eprint/28441 |
| Statistic Details: | View Download Statistic |
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