Enhancing Marketing Performance Of Coconut Farmers Through AI-Enabled Supply Chain Optimization

Gopalakrishnan S; Guru P

doi:10.29284/wrh5yp03

Authors

Gopalakrishnan S Research Scholar Department of Management Studies Periyar Maniammai Institute of Science & Technology (Deemed to be University), Tamil Nadu, India.
Guru P Professor Department of Management Studies Periyar Maniammai Institute of Science & Technology (Deemed to be University), Tamil Nadu, India.

DOI:

https://doi.org/10.29284/wrh5yp03

Keywords:

Artificial Intelligence, Supply Chain Optimization, Marketing Performance, Coconut Farming, Agricultural Supply Chains.

Abstract

Coconut farming is an important sector of rural livelihoods and agri-based economies, though inefficiencies within conventional supply chain systems tend to curtails the marketing performance of farmers, their income stability, and access to the market. This paper explores how the use of artificial intelligence (AI)-based supply chain optimization can be used to boost the marketing performance of coconut farmers. The study investigates how AI-based applications, including demand forecasting, price prediction, inventory optimization, and logistics planning, can be used to deal with the issues associated with price fluctuations, losses after harvesting, and information asymmetry. The proposed research CocoSMART-ML is examined within the framework of mixed-method research, the primary data were gathered with the help of coconut farmers, the middlemen and the marketing agencies, and the secondary data served as the supportive tool of revealing the main flaws of the supply chain. The machine learning models were used to study the demand patterns, optimize the distribution routes, and predict the prices of markets. Indicators that were used to measure the performance of marketing were market reach, growth in sales, price realization, and profitability of the farmers. The results indicate that optimization of the supply chain with AI can affect the efficiency of decisions, decrease operational expenses, and increase the level of transparency throughout the coconut value chain. The farmers who embraced the use of AI in farming practices reported better market access, enhancement in price realization, and less reliance on intermediaries. Moreover, the implementation of AI can help in sustainable supply chain management through the reduction of wastage and enhancing the use of resources. The paper identifies the opportunities of revolutionizing agricultural supply chains and enhancing marketing performance of small and marginal coconut farmers enabled by AI technologies. The findings give useful information to policy-makers, agri-tech developers, and cooperatives to encourage the use of AI as a strategic instrument to improve marketing performance and guarantee long-term sustainability in the coconut farming industry.

References

1. Ciampi, F., Demi, S., Magrini, A., Marzi, G., & Papa, A. (2021). Exploring the impact of big data analytics capabilities on business model innovation: The mediating role of entrepreneurial orientation. Journal of Business Research,123(NA), 1-13.

2. Goralski, M. A., & Tan, T. K. (2020). Artificial intelligence and sustainable development. The International Journal of Management Education,18(1), 100330-NA

3. Kitsios, F., & Kamariotou, M. (2021). Artificial Intelligence and Business Strategy towards Digital Transformation: A Research Agenda. Sustainability,13(4), 2025.

4. V. Pasupuleti, B. Thuraka, C. S. Kodete, and S. Malisetty, “Enhancing supply chain agility and sustainability through machine learning: Optimization techniques for logistics and inventory management,” Logistics, vol. 8, no. 3, p. 73, 2024.

5. P. Kumar, D. Choubey, O. R. Amosu, and Y. M. Ogunsuji, “AI-enhanced inventory and demand forecasting: Using AI to optimize inventory management and predict customer demand,” World J. Adv. Res. Rev., vol. 23, no. 1, pp. 1931–1944, 2024.

6. Anjanadevi, B, B S Nagakishore, T Sujith, V Nagesh (2024). “Smart Supply Chain Management Using Machine Learning Algorithms”, Journal of Electrical Systems, 20-3 (2024): 2501-2509.

7. Santoso, I., Purnomo, M., Sulianto, A.A., & Choirun, A. (2021). Machine learning application for sustainable agri-food supply chain performance: a review. IOP Conference Series: Earth and Environmental Science, 924.

8. Sadaphal, S., Wagh, G., Pawase, S., Andhale, A., Nirmal, P.D., & of, A.P. (2021). “Agri-hub smart agriculture marketing using machine learning and cloud”, International Journal of Creative Research Thoughts (IJCRT), | Volume 9, Issue 6 June 2021 | ISSN: 2320-2882.

9. Judijanto, L., Metyopandi, V., & Sumarni, S. (2024). Influence of Machine Learning Algorithm, Demand Prediction, and Automation System in Responsive Inventory Management in Retail Industry in Central Java. West Science Social and Humanities Studies.

10. Kumari, S., Venkatesh, V.G., Tan, F.T., Bharathi, S.V., Ramasubramanian, M., & Shi, Y. (2023). Application of machine learning and artificial intelligence on agriculture supply chain: a comprehensive review and future research directions. Annals of Operations Research, 348, 1573 - 1617.

11. Benítez-García, I., Davizón, Y.A., Hernández-Santos, C., de la Cruz, N., Hernández, A., Quiñonez-Ruiz, A., Smith, E.D., Sánchez-Leal, J., & Smith, N.R. (2025). Mathematical Modeling and Stability Analysis of Agri-Food Tomato Supply Chains via Compartmental Analysis. World.

12. Islam, M. A., Hasan, M. A. R., Juthi, S., & Haque, S. (2025), “A Systematic Review of Business Strategy Transformation Using AI, Machine Learning, And Deep Learning” Innovatech Engineering Journal, 2(01), 31–46. https://doi.org/10.70937/itej.v2i01.56.

a. A. P. Arnaiz, L. S. Cristal, A. O. Fernandez, and M. R. Flores, “Optimizing inventory management and demand forecasting system using time series algorithm,” World J. Adv. Res. Rev., vol. 20, no. 3, pp. 21–27, 2023.

13. X. Ma, W. Zeyu, X. Ni, and G. Ping, “Artificial intelligence-based inventory management for retail supply chain optimization: a case study of customer retention and revenue growth,” J. Knowl. Learn. Sci. Technol. ISSN 2959-6386, vol. 3, no. 4, pp. 260–273, 2024.

14. R. Pareschi, V. Piantadosi, S. Pullo, and F. Salzano, “Revolutionizing Agri-Food Sustainability: An Overview and Future Outlook: Integrating IoT, DLT, and Machine Learning for Enhanced Farming Practices,” in 2024 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4. 0 & IoT), IEEE, 2024, pp. 139–144.

15. S. Darshan M, “Integrating data mining and predictive modeling techniques for enhanced retail optimization,” arXiv e-prints, p. arXiv-2409, 2024.

16. S. Holloway, “Impact of Digital Transformation on Inventory Management: An Exploration of Supply Chain Practices,” 2024.

17. Theissler, J. Pérez-Velázquez, M. Kettelgerdes, and G. Elger, “Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry,” Reliab. Eng. Syst. Saf., vol. 215, p. 107864, 2021.

18. V. Pasupuleti, B. Thuraka, C. S. Kodete, and S. Malisetty, “Enhancing supply chain agility and sustainability through machine learning: Optimization techniques for logistics and inventory management,” Logistics, vol. 8, no. 3, p. 73, 2024.

19. D. Fogarty and X. Cui, “Machine learning and AI in marketing analytics: Leveraging the survey data to find customers,” Appl. Mark. Anal., vol. 10, no. 2, pp. 158–175, 2024.