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Abstract

Food self-sufficiency is an essential foundation in supporting national resilience. Food distribution in Indonesia still experiences serious obstacles, such as slow delivery, fuel waste, and inefficient distribution routes. AI technology has the potential to be a strategic solution to optimize the distribution system through route efficiency, energy savings, and increased delivery speed. This study aims to: (a) Test the effect of supply chain strategic fit, performance, and AI adoption in food distributors; (b) Dig deeper into the implementation, obstacles, challenges, readiness and opportunities, as well as driving factors for AI implementation in the food supply chain in Indonesia. The method used is a mixed-method, namely a quantitative approach with SEM-PLS and a qualitative approach using N-Vivo. The results of the quantitative analysis show that AI adoption mediates the relationship between supply chain strategic fit and rice distributor performance. Furthermore, the results of the qualitative analysis also support the idea that the distribution system using AI is considered to have great potential in optimizing food distribution in Indonesia. However, this adoption has several challenges, including costs, human resources, infrastructure, regulations, differences in regional topology, privacy and security, and ethics.

Keywords

Food Independence Supply Chain Strategic Fit Artificial Intelligence Food Distributor Performance Mix Method

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How to Cite
Viyani, A. O., Zahara, I., Tassa, A., & Dika, L. (2025). Towards Smart Food Distribution: Integrating Supply Chain Strategic Fit and Artificial Intelligence for National Food Independence. Amkop Management Accounting Review (AMAR), 5(2), 936–946. https://doi.org/10.37531/amar.v5i2.3165
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References

  1. Albert, Y., & Alijoyo, A. (2024). Systematical review: How artificial intelligence impact supply chain capability and capacity in emerging markets. INOBIS: Jurnal Inovasi Bisnis dan Manajemen Indonesia, 7(4), 429–441. https://doi.org/10.31842/jurnalinobis.v7i4.333
  2. Al-Shboul, M. A. (2024). Do artificial intelligence system adoptions foster production management supply chain performance in pharmaceutical manufacturing firms? An empirical exploring study from the MENA region. Business Process Management Journal, 30(7), 2427–2455.
  3. Angayarkanni, A., Sheshasaayee, A., YuJie, Z., & Ohmsakthi, V. (2025). Agricultural process optimization and supply chain management enhancing sustainable agriculture through AI technology. Journal of Information Systems Engineering and Management, 10(32s). https://jisem-journal.com/index.php/journal/article/view/5319
  4. Artificial intelligence in supply chain management. (2024). Nanotechnology Perceptions.
  5. Badan Pangan Nasional. (2024). Jumlah penduduk yang mengalami ketidakcukupan konsumsi pangan nasional update tahun 2024 [Dataset]. https://satudata.badanpangan.go.id/datasetpublications/a9m/penduduk-pou-nasional-2024
  6. Borah, A. J., Das, B. K., Thomas, S. A., Saravanan, R., Salim, S. S. S., & Dhande, K. K. (2024). Artificial intelligence in supply chain management. Nanotechnology Perceptions, 820–833.
  7. Chan, L., Zhang, P., Kowtha, R., & Lee, C. M. (2024). Artificial intelligence for supply chain management (SCM): A thematic literature review. International Journal of Business & Management Studies.
  8. Dhal, S. B., & Kar, D. (2024). Transforming agricultural productivity with AI-driven forecasting: Innovations in food security and supply chain optimization. Forecasting.
  9. Fagan, M. L. (2023). Using AI in supply chains. Supply Chain Management Journal.
  10. Firmansyah, H. R., Purba, E. M., Purba, R. E., & Rosmiati, R. (2024). Household food insecurity and its association with nutritional status of under five children in Indonesia. Journal of Nutrition and Food Security, 12(1), 55–67.
  11. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Los Angeles, CA: SAGE Publications.
  12. Hamadi, K., & Ekambaram, S. (2024). Supply chain management with AI.
  13. Hibatullah, F. H., Raidasari, F., Triana, A. P., Siagian, V. K. L., & Simarmata, T. (2024). Revealing food fulfillment threads and innovative technology for enhancing rice productivity and ensuring the food security in Indonesia. International Journal on Food, Agriculture and Natural Resources, 5(3), 45–51.
  14. Hirsch, K., Niemann, W., & Swart, B. (2024). Artificial intelligence and information systems capabilities for supply chain resilience: A study in the South African fast-moving consumer goods industry. Journal of Transport and Supply Chain Management, 18, 1–17.
  15. Ivanka, R., Atalla, F., Limbong, A. D., & Simarmata, T. (2024, April 12). Assessing the current state and future trends of land use conversion: Implications for food security in Indonesia. Journal of Agricultural Development Studies, 10(2), 88–103.
  16. Jones, J. (2025). Exploring the role of artificial intelligence in optimizing supply chain operations.
  17. Kim, W., Fang, M., Pang, Q., & Su, M. (2023). SME innovation, supply chain strategy fit and business performance: The moderating role of environmental uncertainty. Technology Analysis & Strategic Management.
  18. Kumar, P. (2024). AI for optimizing supply chain management. International Journal for Science, Technology and Engineering, 12, 549–560.
  19. Kumar, S., Gupta, R., & Sharma, P. (2023). Challenges facing artificial intelligence adoption during COVID-19 pandemic: An investigation into the agriculture and agri-food supply chain in India. Sustainability, 15(8), 6377. https://doi.org/10.3390/su15086377
  20. Larasati, P. F., Ratnayake, R. M. C., & Mulyono, N. B. (2023). Risk assessment of agri-food supply chain to minimise food insecurity in developing economies: A case study of poultry chain in Indonesia. Journal of Supply Chain Risk Management, 7(4), 133–150.
  21. Sarfraz, M., Khawaja, K. F., Han, H., Ariza-Montes, A., & Arjona-Fuentes, J. M. (2023). Sustainable supply chain, digital transformation, and blockchain technology adoption in the tourism sector. Humanities and Social Sciences Communications, 10(1), 1–14.
  22. Sharma, J., Tyagi, M., Panchal, D., & Bhardwaj, A. (2021). Dimensions modelling for reliable Indian food supply chains. Journal of Supply Chain Management, 133–150.
  23. Sugiyono. (2019). Metodologi penelitian kuantitatif, kualitatif, dan R&D. Bandung: Alfabeta.
  24. Uly, Y. A. (2022, October 24). Distribusi yang tidak merata bikin harga pangan jomplang. Kompas.com. https://money.kompas.com/read/2022/10/24/172242326/distribusi-yang-tidak-merata-bikin-harga-pangan-jomplang
  25. Viyani, A. O. I. Z. (2024). Artificial intelligence untuk bisnis. PT Penamuda Media.
  26. Viyani, A. O. I. Z. (2024). The role of AI Leonardo, Discord, and Rytr in business enhancement and digital economic growth in Indonesia. Indonesian Interdisciplinary Journal, 8(1), 616–638.
  27. Woods, M., Paulus, T., Atkins, D. P., & Macklin, R. (2016). Advancing qualitative research using qualitative data analysis software (QDAS)? Reviewing potential versus practice in published studies using ATLAS.ti and NVivo, 1994–2013. Social Science Computer Review, 34(5), 597–617.
  28. Zhang, Y., Wang, L., & Chen, J. (2024). Artificial intelligence in supply chain management: A systematic literature review of empirical studies and research directions. Computers in Industry, 162, 104132. https://doi.org/10.1016/j.compind.2024.104132

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