Evaluation and Optimization of Costs for Agricultural Machinery Management System in Arjo Diddessa Sugar Factory

Articles in Press

Document Type : Research Article-en

Authors

• S. K. Busse ¹
• T. K. Hurisa ²
• E. A. Esleman ¹

¹ Department of Mechanical Engineering, School of Mechanical, Chemical, and Materials Engineering, Adama Science and Technology University, Oromia, Ethiopia

² Department of Agricultural Engineering, Faculty of Technology, Wallaga University, Ethiopia

10.22067/jam.2025.90012.1288

Abstract

Efficient control of agricultural machinery is crucial in sugar plants for maintaining product quality, managing operational costs, and improving productivity. The Ethiopian sugar industry is vital to the country's economy; however, issues with machinery management can lead to higher maintenance costs and poor operational efficiency. This study aims to evaluate the agricultural machinery management system at the Arjo Diddessa sugar factory and optimize operational costs. Between 2016 and 2022, data were collected through surveys, interviews, and observations. To improve machinery running costs, a linear programming model was studied using Linear Interactive and Discrete Optimizer )LINDO( software. The findings revealed that 49% of non-operational machinery required minor repair, whereas 14% required disposal. The anticipated work rate exceeded the actual rate by 35.33%. Among the tasks, uprooting exhibited the smallest variance at 5.73%, while inter-row cultivation displayed the greatest discrepancy at 67.21%. Initial repair expenses were minimal but increased as the equipment aged. The optimization model achieved a maximum reduction of 10.60% in operational costs during 2021-22, highlighting the importance of accurate machinery work rate estimation and performance analysis for enhancing efficiency. The study identified critical inefficiencies in machinery management and emphasized the need for robust maintenance systems and strategic replacement plans for aging equipment. Optimizing operational efficiency is essential for improving productivity and reducing costs in sugar production processes.

Keywords

• Machinery field capacity
• Maintenance performance
• Machinery management system
• Operational efficiency

Main Subjects

• Design and Construction

©2025 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0).

References

1. Abbasi, R., Martinez, P., & Ahmad, R. (2022). The digitization of agricultural industry– a systematic literature review on agriculture 4.0. Smart Agricultural Technology, 2(February), 100042. https://doi.org/10.1016/j.atech.2022.100042
2. Ambo, Y. (2024). Assessment of Maintenance Activities and Strategies for Tractor and Operator Care in Gurage Zone, Wolkite Town, Ethiopia. Engineering Science, 9(2), 39-46. https://doi.org/10.11648/j.es.20240902.12
3. Ashine, E. T., Tilahun Ashine, E., Yesuf, M. B., & Bokke, A. S. (2022). Estimation and Mapping Spatiotemporal Variability of Crop Water Requirement for Sugarcane in Arjo Dedessa Sugar Factory and its Surrounding. Journal of Remote Sensing and GIS, 11(5), 1000232. https://doi.org/10.35248/2469-4134.22.11.232
4. Ayele Zikargie, Y., & Cochrane, L. (2024). From developmentalism to the homegrown economy of Ethiopia: The narratives and the reality. Heliyon, 10(15), e35021. https://doi.org/10.1016/j.heliyon.2024.e35021
5. Bhamare, D. M. A. (2023). Optimizing Crop Selection and Production Planning in Agriculture: Applications of Linear Programming for Profit Maximization and Sustainability. International Journal of Research Publication and Reviews, 4(6), 3281-3287. https://doi.org/10.55248/gengpi.4.623.46630
6. Boninsenha, I., Mantovani, E. C., Costa, M. H., & da Silva Júnior, A. G. (2022). A Linear Programming Model for Operational Optimization of Agricultural Activity Considering a Hydroclimatic Forecast—Case Studies for Western Bahia, Brazil. Water (Switzerland), 14(22). https://doi.org/10.3390/w14223625
7. Diez de Bonilla-Jiménez, O. I., Chávez-Mejía, A. C., Navarro-González, M. I., Ruiz-Velázquez, I. E., & Molina-Valencia, U. (2024). Optimization of the Disinfection Process in Potabilization Systems in Cuenca Alto Atoyac, Mexico. Water (Switzerland), 16(23). https://doi.org/10.3390/w16233451
8. Etikan, I., & Babatope, O. (2019). A basic approach in sampling methodology and sample size calculation. MedLife Clinics, 1, 1006.
9. Fahmida, M., Mursalin, M. S., Huda, M. N., Shah, M. F., Hossain, M. J., & Haque, M. J. (2024). Design, fabrication and performance evaluation of a rotary power weeder for maize cultivation. Agricultural Engineering International: CIGR Journal, 26(3), 71-78.
10. Firoozi, A. A., Tshambane, M., Firoozi, A. A., & Sheikh, S. M. (2024). Strategic load management: Enhancing eco-efficiency in mining operations through automated technologies. Results in Engineering, 24(September). https://doi.org/10.1016/j.rineng.2024.102890
11. Gebeyehu, A. K., & Abbink, J. (2022). Land, sugar and pastoralism in Ethiopia: Comparing the impact of the Omo-Kuraz sugar projects on local livelihoods and food (in) security in the lower Omo Valley. Pastoralism, 12(1). https://doi.org/10.1186/s13570-022-00242-8
12. Gebreeyessus, G. D., Mekonnen, A., Chebude, Y., & Alemayehu, E. (2021). Quantitative characterization and environmental techno-legal issues on products and byproducts of sugar and ethanol industries in Ethiopia. Renewable and Sustainable Energy Reviews, 145(April), 111168. https://doi.org/10.1016/j.rser.2021.111168
13. Jupiara, B., Schardong, F., Ribeiro, R. P., Guilherme, N., Pinto, M., & Fagundes, P. D. M. (2024). Optimization in Agricultural Management : Exploring Linear Programming for Rural Properties. IOSR Journal of Business and Management, 26(4), 57-65. https://doi.org/10.9790/487X-2604015765
14. Kalwar, M. A., Khan, M. A., Shahzad, M. F., Wadho, M. H., & Marri, H. B. (2022). Development of linear programming model for optimization of product mix and maximization of profit: case of leather industry. Journal of Applied Research in Technology & Engineering, 3(1), 67-78. https://doi.org/10.4995/jarte.2022.16391
15. Kolhe, K. P., Lemi, D. G., & Busse, S. K. (2024). Studies of tractor maintenance and replacement strategies of Wonji Shoa Factory, Ethiopia. Journal of Agricultural Engineering, 55(1). https://doi.org/10.4081/jae.2024.1552
16. Nunes, P., Santos, J., & Rocha, E. (2023). Challenges in predictive maintenance– A review. CIRP Journal of Manufacturing Science and Technology, 40, 53-67. https://doi.org/10.1016/j.cirpj.2022.11.004
17. Oladejo, N. K., Abolarinwa, A., & Salawu, S. O. (2020). Linear Programming and Its Application Techniques in Optimizing Portfolio Selection of a Firm. Journal of Applied Mathematics, 2020. https://doi.org/10.1155/2020/8817909
18. Papageorgiou, A. (2015). Agricultural Equipment in Greece: Farm Machinery Management in the Era of Economic Crisis. Agriculture and Agricultural Science Procedia, 7, 198-202. https://doi.org/10.1016/j.aaspro.2015.12.017
19. Pejić Bach, M., Topalović, A., Krstić, Ž., & Ivec, A. (2023). Predictive Maintenance in Industry 4.0 for the SMEs: A Decision Support System Case Study Using Open-Source Software. Designs, 7(4). https://doi.org/10.3390/designs7040098
20. Rahman, M. M., Ali, M. R., Oliver, M. M. H., Hanif, M. A., Uddin, M. Z., Tamim-Ul-Hasan, Saha, K. K., Islam, M. H., & Moniruzzaman, M. (2021). Farm mechanization in Bangladesh: A review of the status, roles, policy, and potentials. Journal of Agriculture and Food Research, 6, 100225. https://doi.org/10.1016/j.jafr.2021.100225
21. Salawu, E. Y., Awoyemi, O. O., Akerekan, O. E., Afolalu, S. A., Kayode, J. F., Ongbali, S. O., Airewa, I., & Edun, B. M. (2023). Impact of Maintenance on Machine Reliability: A Review. E3S Web of Conferences, 430, 1-12. https://doi.org/10.1051/e3sconf/202343001226
22. Shaheb, M. R., Venkatesh, R., & Shearer, S. A. (2021). A Review on the Effect of Soil Compaction and its Management for Sustainable Crop Production. Journal of Biosystems Engineering, 46(4), 417-439. https://doi.org/10.1007/s42853-021-00117-7
23. Sun, J., Zhang, Y., Chen, H., & Qiao, J. (2023). Optimization Model and Application for Agricultural Machinery Systems Based on Timeliness Losses of Multiple Operations. Agriculture (Switzerland), 13(10), 1-19. https://doi.org/10.3390/agriculture13101969
24. Yaseen, M. U., Ahmad, S., Ahmad, M., Long, J. M., Raza, H. A., Iftekhar, H., Ameer, S., & Ogunbiyi, D. (2024). A Multi-Function Novel Crop Seeder for the Management of Residues and Mechanized Sowing of Wheat in a Single Path. AgriEngineering, 6(3), 2445-2469. https://doi.org/10.3390/agriengineering6030143
25. Zhang, H., Yang, Z., Wang, Y., & Twumasi, M. A. (2023). Impact of Agricultural Mechanization Level on Farmers ’ Health Status in Western China : Analysis Based on CHARLS Data. International Journal of Environmental Research and Public Health.
26. Zhang, Q., Zhao, J., Yang, X., Wang, L., Su, G., Liu, X., Shan, C., Rahim, O., Yang, B., & Liao, J. (2024). Design and Testing of an Offset Straw-Returning Machine for Green Manures in Orchards. Agriculture (Switzerland), 14(11). https://doi.org/10.3390/agriculture14111932
27. Zikargie, Y. A., Wisborg, P., & Cochrane, L. (2022). State-led modernization of the Ethiopian sugar industry: questions of power and agency in lowland transformation. Journal of Eastern African Studies, 16(3), 434-454. https://doi.org/10.1080/17531055.2023.2166449
28. Zikargie, Y. A., Wisborg, P., & Cochrane, L. (2023). State-led modernization of the Ethiopian sugar industry: questions of power and agency in lowland transformation. Journal of Eastern African Studies, 16(3), 434-454. https://doi.org/10.1080/17531055.2023.2166449