Material Flow Cost Accounting (MFCA)-Driven Smart Goat Livestock Management System

Muhammad Pringgo Prayetno; Nicholas Renaldo; Umar Faruq; Achmad Tavip Junaedi; Marice Br Hutahuruk; Suhardjo Suhardjo; Arih Dwi Prihastomo; Nyoto Nyoto; Harry Patuan Panjaitan; Luciana Fransisca

doi:10.61230/luxury.v4i1.148

Authors

Muhammad Pringgo Prayetno Institut Bisnis dan Teknologi Pelita Indonesia, Indonesia
Nicholas Renaldo Institut Bisnis dan Teknologi Pelita Indonesia, Indonesia
Umar Faruq Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Achmad Tavip Junaedi Institut Bisnis dan Teknologi Pelita Indonesia, Indonesia
Marice Br Hutahuruk AMIK Mahaputra Riau, Indonesia
Suhardjo Suhardjo Institut Bisnis dan Teknologi Pelita Indonesia, Indonesia
Arih Dwi Prihastomo Institut Bisnis dan Teknologi Pelita Indonesia, Indonesia
Nyoto Nyoto Institut Bisnis dan Teknologi Pelita Indonesia, Indonesia
Harry Patuan Panjaitan Institut Bisnis dan Teknologi Pelita Indonesia, Indonesia
Luciana Fransisca Institut Bisnis dan Teknologi Pelita Indonesia, Indonesia

DOI:

https://doi.org/10.61230/luxury.v4i1.148

Keywords:

Material Flow Cost Accounting (MFCA), Environmental Management Accounting, Smart Farming, IoT-Based Livestock Management, Eco-Efficiency, Emission Accounting, Artificial Intelligence, Digital Green Accounting

Abstract

The livestock sector plays a crucial role in food security and rural economic resilience; however, goat farming management in developing economies remains largely traditional and weakly integrated with structured environmental accounting systems. This study develops and validates a Material Flow Cost Accounting (MFCA)-Driven Smart Goat Livestock Management System, which integrates environmental management accounting, Internet of Things (IoT) monitoring, emission estimation, and artificial intelligence (AI)-based decision support within a unified digital platform. Using a design science research approach combined with field validation, the system was implemented in a medium-scale goat farm over a two-month period. The MFCA model quantified material inputs and outputs in both physical and monetary terms, including feed conversion, waste generation, and methane (CH₄) and nitrous oxide (N₂O) emissions based on IPCC Tier 1 guidelines. The results demonstrate improvements in feed efficiency (from 74% to 84%), mortality reduction (from 8% to 4%), increased data accuracy (from 60% to 92%), and a 22% improvement in eco-efficiency ratios. The AI module achieved 87% accuracy in estrus detection and 84% accuracy in early disease classification. The study extends MFCA application from manufacturing to biological production systems and introduces the concept of accounting-driven smart farming, where environmental accounting is embedded within digital infrastructure. The findings contribute to the advancement of Digital Environmental Management Accounting (Digital EMA) and provide a scalable model for sustainable livestock transformation in emerging economies.

References

Arlia, Massaguni, M., Ambarwati, E. R., Multazam, A. M., & Renaldo, N. (2025). Community Health Promotion Program: Evaluating the Impact of Health Interventions in the Community. International Journal of Community Service, 4(01), 145–158. https://doi.org/10.55299/ijcs.v4i1.1436

Junaedi, A. T., Suhardjo, S., Andi, A., Putri, N. Y., Hutahuruk, M. B., Renaldo, N., Musa, S., & Cecilia, C. (2024). Use of AI-based Banking Applications for Customer Service. Luxury: Landscape of Business Administration, 2(2), 127–136. https://doi.org/https://doi.org/10.61230/luxury.v2i2.100

Macdonald, K., Bahruddin, Hartoto, A. S., Unger, C., Cisneros, P., Pugley, D. D., Herrera Salazar, D., Winanti, P. S., & Kurniawan, N. I. (2024). The politics of accountability in global sustainable commodity governance: Dilemmas of institutional competition and convergence. Global Policy, 15(5), 838–854. https://doi.org/10.1111/1758-5899.13426

Nassar, F. S. (2026). Strategic role of poultry production sciences in shaping the future of global food security and strengthen sustainability. Poultry Science, 105(5), 106617. https://doi.org/10.1016/J.PSJ.2026.106617

Pence, I., Kumas, K., Siseci Cesmeli, M., & Akyüz, A. (2024). Animal-based CO2, CH4, and N2O emissions analysis: Machine learning predictions by agricultural regions and climate dynamics in varied scenarios. Computers and Electronics in Agriculture, 226, 109423. https://doi.org/10.1016/J.COMPAG.2024.109423

Renaldo, N., Chandra, T., Junaedi, A. T., Panjaitan, H. P., & Jahrizal, J. (2025). A Social Accounting Perspective for Institutional Transformation and Resource Management in the AI-Based Financial Sector. Jurnal Ekonomi Dan Bisnis Jagaditha, 12(2), 230–238. https://doi.org/10.22225/jj.12.2.2025.230-238

Renaldo, N., Fadrul, Andi, Sevendy, T., & Simatupang, H. (2022). The Role of Environmental Accounting in Improving Environmental Performance through CSR Disclosure. International Conference on Business Management and Accounting (ICOBIMA), 1(1), 17–23. https://doi.org/https://doi.org/10.35145/icobima.v1i1.2743

Renaldo, N., Fransisca, L., Junaedi, A. T., Nyoto, N., Suhardjo, S., Putra, R., Putri, N. Y., Purnama, I., Jahrizal, J., & Yovita, I. (2024). Etika dalam Membuat Kuisioner bagi Ikatan Cendekiawan Muda Akuntansi. 4(2), 101–107.

Renaldo, N., Fransisca, L., Junaedi, A. T., Tanjung, A. R., Chandra, T., Suharti, S., Andi, A., Suhardjo, S., Augustine, Y., & Musa, S. (2024). Real-time Value Creation Metrics in Manufacturing Through Blue Innovation and IoT-Based Accounting. International Conference on Business Management and Accounting, 3(1), 108–118. https://doi.org/10.35145/icobima.v3i1.4903

Renaldo, N., Hafni, L., Hocky, A., Suhardjo, & Junaedi, A. T. (2022). The Influence of Digital Technology and Efficiency Strategy on Business Sustainability with Quality Management as Moderating Variables. 2nd International Conference on Business & Social Sciences, 1464.

Renaldo, N., Suyono, Andi, Putri, N. Y., & Cecilia. (2023). How Business Intelligence, Intellectual Capital, and Company Performance Increase Company Value? Leverage as Moderation. Journal of Applied Business and Technology, 4(1), 93–99. https://doi.org/https://doi.org/10.35145/jabt.v4i1.123

Renaldo, N., & Veronica, K. (2024). The Role of Artificial Intelligence in Early Detection of Financial Statement Fraud in Digital Financial Institutions, AI-Fraud Behavior Integration Model. Business Management and Accounting (ICOBIMA), 3(1), 135–144. https://doi.org/10.35145/icobima.v3i1.5093

Said Mohamed, E., Belal, A. A., Kotb Abd-Elmabod, S., El-Shirbeny, M. A., Gad, A., & Zahran, M. B. (2021). Smart farming for improving agricultural management. The Egyptian Journal of Remote Sensing and Space Science, 24(3), 971–981. https://doi.org/10.1016/J.EJRS.2021.08.007

Swalih, M. M., Ram, R., & Tew, E. (2024). Environmental management accounting for strategic decision‐making: A systematic literature review. Business Strategy and the Environment, 33(7), 6335–6367. https://doi.org/10.1002/bse.3828

Tran, T. T., & Herzig, C. (2020). Material flow cost accounting in developing countries: A systematic review. In Sustainability (Switzerland) (Vol. 12, Number 13). MDPI. https://doi.org/10.3390/su12135413

Usul, H., & Olgun, E. B. (2025). An analysis of material flow cost accounting in companies using different cost accounting systems. Heliyon, 11(4), e42555. https://doi.org/10.1016/J.HELIYON.2025.E42555

Walls, C., Putri, A. R. K., & Beck, G. (2023). Material Flow Cost Accounting as a Resource-Saving Tool for Emerging Recycling Technologies. Clean Technologies, 5(2), 652–674. https://doi.org/10.3390/cleantechnol5020033

Weggeman, M., & Cauffman, L. (2024). Using design science research to develop and validate the application of client-oriented psychological approaches illustrated by the design of a solution-focused approach. Methods in Psychology, 11, 100151. https://doi.org/10.1016/J.METIP.2024.100151

Zhu, L., Yang, X., Xian, Y., Jiang, W., Pu, X., Wang, S., Cheng, J., Niu, L., Zhao, Y., Chen, L., Zhou, X., Wang, Y., Gan, M., Zhu, L., & Shen, L. (2025). Empowering precision livestock farming: Artificial intelligence applications in animal genomic breeding and multi-dimensional phenotypic measurement. Smart Agricultural Technology, 12, 101655. https://doi.org/10.1016/J.ATECH.2025.101655