Productivity Improvement of the Coconut Water Conveying System Using Simulation Modeling: A Case Study

Phoometh Sangrayub; Jirawat Jirachatwongchai; Pasuree Lumsakul; Parinya Kaweegitbundit

doi:10.55003/ETH.430102

Authors

Phoometh Sangrayub Faculty of Engineering, Rajamangala University of Technology Rattanakosin WangKlai Kangwon Campus, Thailand
Jirawat Jirachatwongchai Faculty of Engineering, Rajamangala University of Technology Rattanakosin WangKlai Kangwon Campus, Thailand
Pasuree Lumsakul Faculty of Engineering, Rajamangala University of Technology Rattanakosin WangKlai Kangwon Campus, Thailand
Parinya Kaweegitbundit Faculty of Engineering at Kamphaeng Saen, Kasetsart University, Thailand

DOI:

https://doi.org/10.55003/ETH.430102

Keywords:

Coconut water, Conveying, Productivity, Simulation

Abstract

This study examines the coconut water conveying system through a case study of the coconut company. The applicability of Discrete-Event Simulation (DES), implemented using FlexSim, is demonstrated as a decision-support tool for analyzing and improving the system. The existing operation is characterized by inefficiencies in manual handling and unbalanced workloads, which negatively affect system throughput. Various scenarios were evaluated to determine optimal staffing levels at each workstation. Simulation results indicated that scenario 6 achieved an optimal allocation of labor at each workstation, thereby maximizing average throughput and reducing average work-in-process compared with the current system. Furthermore, a comprehensive simulation model was developed to evaluate potential process improvements, identifying the shell cracking and separation operations as the primary bottleneck constraining overall system throughput. The optimized conveyor-based configuration increases average throughput by 45.89%, reduces average work-in-process by 59.70%, and decreases average waiting time by 49.86% compared with the current system.

References

Y. R. Wang and A. N. Chen, “Production Logistics Simulation and Optimization of Industrial Enterprise Based on Flexsim,” International Journal of Simulation Modeling, vol. 15, no.4, pp. 732–741, 2016.

D. Leks and A. Gwiazda, “Application of FlexSim for modelling and simulation of the production process,” Selected Engineering Problems, vol. 6, 2015, pp. 51–56.

J. Yuan and R. Zhang, “Analysis and optimization of bottlenecks via simulation,” in 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Bangkok, Thailand, Dec. 16–19, 2018, pp. 1879–1883, doi: 10.1109/IEEM.2018.8607413.

B. Santhosh Kumar, D.V. Mahesh and B. Satish Kumar, “Modeling and analysis of flexible manufacturing system with FlexSim,” International Journal of Computer Engineering Research (IJCER), vol. 5, no. 10, pp.1–6, 2015.

S. Chawla and R. M Singari, “Modelling and Simulation of Crankcase Cover Manufacturing in the Automobile Industry,” Journal of Scientific & Industrial Research, vol. 82, no.6, pp. 597–602, 2023, doi: 10.56042/jsir.v82i06.1816.

G. Jidong, L. Yuyan, Z. Kaibin, J. Junhao, F. Caiping, M. Yuwei, Z. Yongyang and Z. Dawei, “Improvement of a Furniture Production Line Based on Flexsim” in Proc. International Conference on Industrial Engineering and Operations Management, Harbin, China, July 9–11, 2021, pp. 133–143.

D. Krenczyk, W. M. Kempa, K. Kalinowski, C. Grabowik and I. Paprocka, “Integration of manufacturing operations management tools and discrete event simulation,” IOP Conference Series: Materials Science and Engineering, vol. 400, 2018, Art. no. 022037, doi: 10.1088/1757-899x/400/2/022037.

A. M. Shihab Mahin, M. S. Solaiman and L. B. Mahmud Shahriar, “Optimization of Inbound Logistics of Automobile Industry Using Flexsim,” in 7th Bangladesh Conference on Industrial Engineering and Operations Management, Dhaka, Bangladesh, Dec. 21–22, 2024, pp. 660–672, doi: 10.46254/ba07.20240083.

R. Poloczek and B. Oleksiak, “Management of a simulation project in a manufacturing company,” Scientific Papers of Silesian University of Technology. Organization and Management Series, vol. 2023, no. 182, pp. 377–390, 2023, doi: 10.29119/1641-3466.2023.182.22.

S. Wang, S. Wang and N. Zhang, “Flexsim-based Simulation and Optimization of Green Logistics Distribution Center,” in 2022 the 14th International Conference on Computer Modeling and Simulation, Chongqing, China, June 24–26, 2022, pp. 76–82. doi: 10.1145/3547578.3547590.

D. Liu, Y. Pan and L. Li, “Logistics Engineering Simulation Using Computer 3D Modeling Technology,” Journal of Physics: Conference Series, vol. 2143, no. 1, 2021, Art. no. 012018, doi: 10.1088/1742-6596/2143/1/012018.

T. Berlec, B. Tansek, and J. Kusar, “Selection of the Most Suitable Material Handling System in Production,” International Journal of Simulation Modelling, vol. 20, no. 1, pp. 64–75, 2021, doi: 10.2507/ijsimm20-1-542.

R. Aliyu and A. A. Mokhtar, “Research Advances in the Application of Flexsim: A Perspective on Machine Reliability, Availability, and Maintainability Optimization,” Journal of Hunan University Natural Sciences, vol. 48, no. 9, pp.517–564, 2021.

Q. Cheng, H. Shen, H. Chu, Z. Liu, C. Zhang and J. Ren, “Research on logistics simulation and optimization of die forging production line based on FlexSim,” Journal of Physics: Conference Series, vol. 1624, no. 2, 2020, Art. no. 022063, doi: 10.1088/1742-6596/1624/2/022063.

N. Chemkomnerd, W. Pannakkong, T. Tanantong, V. Huynh and J. Karnjana “A scenario-driven simulation approach to sustainable hospital resource management: aging society, pandemic preparedness and referral enhancement,” BMC Health Services Research, vol. 25, no. 1, 2025, Art. no. 982, doi: 10.1186/s12913-025-13221-7.

Y. Ge, M. E. H. Ong and S. S. W. Lam, “Efficient design of automated guided vehicle systems in operating theatres via discrete events simulation,” Scientific Reports, vol. 15, no. 1, 2025, Art. no. 20766, doi: 10.1038/s41598-025-05934-w.

A. A. Alhaider, N. Lau, O. Alotaik and P. B. Davenport, “Discrete event simulation and agent-based modelling of distributed situation awareness in patient flow management,” Scientific Reports, vol. 15, 2025, Art. no. 30068, doi: 10.1038/s41598-025-15344-7.

J. F. Leon, P. Marone, M. Peyman, Y. Li, L. Calvet, M. Dehghanimohammadabadi and A. A. Juan, “A Tutorial on Combining Flexsim with Python for Developing Discrete-Event Simheuristics,” in 2022 Winter Simulation Conference (WSC), Singapore, Dec. 11–14, 2022, pp. 1386–1400, doi: 10.1109/WSC57314.2022.10015309.

A. M. Law, “How the ExpertFit distribution-fitting software can make your simulation models more valid,” in Proc. 2011 Winter Simulation Conference (WSC), Phoenix, AZ, USA, Dec. 11–14, 2011, pp. 63–69, doi: 10.1109/WSC.2011.6147740.

N. Samattapapong, “Productivity improvement of tapioca packing process through simulation modeling analysis” in 2018 5th International Conference on Industrial Engineering and Applications (ICIEA), Singapore, April 26–28, 2018, pp. 453–457, doi: 10.1109/IEA.2018.8387143.

P. Lumsakul, P. Saengkhiao and P. Kaweegitbundit, “Improvement of inbound logistics process in coconut manufacturing using FlexSim simulation: A case study,” Journal of Engineering and Digital Technology (JEDT), vol. 12, no. 1, pp. 1–11, 2024.

J. Chen, Z. Liu, Z. Yin, X. Liu, X. Li, L. Yin and W. Zheng, “Predict the effect of meteorological factors on haze using BP neural network,” Urban Climate, vol. 51, 2023, Art. no. 101630, doi: 10.1016/j.uclim.2023.101630.