Transforming Poultry Supply Chains through Big Data Harmonization: Global comparisons, barriers, and lessons for Malaysia

Siti Hajar Baharin; Abdul Rahman Ahmad; Ahmad Fuzi Md Ajis

doi:10.21834/e-bpj.v10iSI27.6812

Authors

Siti Hajar Baharin Universiti Teknologi MARA Cawangan Johor Kampus Segamat, Johor, Malaysia.
Abdul Rahman Ahmad Universiti Teknologi MARA Puncak Perdana, Shah Alam, Malaysia
Ahmad Fuzi Md Ajis Universiti Teknologi MARA Cawangan Johor Kampus Segamat, Johor, Malaysia

DOI:

https://doi.org/10.21834/e-bpj.v10iSI27.6812

Keywords:

Big Data Harmonization (BDH), poultry operators, blockchain, poultry supply chain

Abstract

Big Data Harmonization (BDH) is a disruptive technology that enhances decision-making in poultry supply chains by integrating and consolidating fragmented data to improve sustainability, reduce costs, and enhance data quality. This paper uses a systematic Literature Review (SLR) method to analyze BDH usage trends in six countries and the review reveals Malaysia's delay in adopting the technology due to fragmented systems, limited infrastructure, and low digital literacy. These barriers limit the progress of Malaysia's poultry industry in achieving data-driven efficiency. This paper offers solutions to accelerate BDH use, improve cost-effectiveness, and increase the resilience of Malaysia's poultry supply chain.

References

Ahmad, M. S., Yusof, M. M., & Mohamed, H. (2018). Barriers to ICT adoption among SMEs in Malaysia: A qualitative study. International Journal of Business and Management, 13(7), 117–127. https://doi.org/10.5539/ijbm.v13n7p117

Chen, X., Ma, J., & Wang, H. (2018). Big data management in agro-industries: The case of China. Computers and Electronics in Agriculture, 144, 310–318.

Department of Veterinary Services Malaysia. (2022). Annual report: Poultry industry statistics. Retrieved from https://www.dvs.gov.my

Doughman, E. (2023). Big data can streamline the poultry supply chain. WATT Poultry, 18 January. Retrieved from https://www.wattagnet.com/poultry-future/new-technologies/article/15537210/big

Ferlito, C. (2020). The poultry industry and its supply chain in Malaysia: Challenges from the COVID-19 emergency. ResearchGate. Retrieved from https://www.researchgate.net/publication/341440161_The_Poultry_Industry_and_Its_Supply_Chain_in_Malaysia_Challenges_from_the_Covid-19_Emergency

Govindan, K., Soleimani, H., & Kannan, D. (2018). Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. European Journal of Operational Research, 240(3), 603–626. https://doi.org/10.1016/j.ejor.2014.07.012 DOI: https://doi.org/10.1016/j.ejor.2014.07.012

Ismail, N., & Masron, T. A. (2020). Digital transformation and its impact on SMEs' performance: Insights from Malaysia. Journal of Small Business and Enterprise Development, 27(6), 1025–1043.

Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37. https://doi.org/10.1016/j.compag.2017.09.037 DOI: https://doi.org/10.1016/j.compag.2017.09.037

Khan, A., Zhang, Z., & Ahvanooey, M. T. (2022). Blockchain for digital library traceability: Challenges and future trends. Journal of Digital Innovation, 19(3), 145–156.

Kim, D. H., & Lee, J. H. (2020). Smart farming in Korea: Trends and future perspectives. Journal of Agricultural Innovation and Development, 12(4), 34–49.

Kim, S. H., & Jung, J. Y. (2020). Blockchain applications in agriculture: Trends and future directions. Journal of Agricultural Innovation, 22(3), 88–105.

Lee, C. Y., & Khalid, H. (2019). Policy frameworks for big data adoption in Malaysia’s agriculture sector. Malaysian Policy Journal, 25(3), 32–50.

Lim, J. S., & Mohd, R. (2020). Adoption of blockchain in the poultry industry: Implications for food safety. Food Supply Chain Review, 8(2), 45–60.

Malaysia Global Business Forum. (2022). Malaysia’s poultry ecosystem: An analysis of business data. Retrieved from https://www.malaysiaglobalbusinessforum.com/wp-content/uploads/2022/07/MGBF-Report_Malaysia-Poultry-Ecosystem-An-Analysis-of-Business-Data.pdf

Ministry of Agriculture and Food Industries Malaysia. (2022). National Agriculture Policy 2.0. Retrieved from https://www.mafi.gov.my

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097 DOI: https://doi.org/10.1371/journal.pmed.1000097

Mustafa, M., Halim, M. A. A., & Majid, M. R. A. (2017). Digital infrastructure and its impact on Malaysia's economic growth: A review. Journal of Economics and Sustainable Development, 8(8), 112–118.

Perçin, S. (2023). Identifying barriers to big data analytics adoption in circular agri-food supply chains: A case study in Turkey. Environmental Science and Pollution Research, 30, 52304–52320. https://doi.org/10.1007/s11356-023-26091-5 DOI: https://doi.org/10.1007/s11356-023-26091-5

Rahman, A. A., Yaakob, Z., & Zakaria, Z. (2021). Public-private partnerships in Malaysia: The challenges of implementing big data initiatives. Malaysian Journal of Economic Studies, 58(1), 1–15. https://doi.org/10.22452/MJES.vol58no1. DOI: https://doi.org/10.22452/MJES.vol58no1

Sheng, Z., Yang, S., Yu, Y., & Vasilakos, A. V. (2020). A survey on smart agriculture: Development modes, technologies, and security and privacy challenges. IEEE/CAA Journal of Automatica Sinica, 8(2), 273–302. DOI: https://doi.org/10.1109/JAS.2020.1003536

Singh, R., & Chandra, S. (2019). Barriers to adopting AI in emerging markets: A case of India.

Smith, A. (2018). Government policies for the adoption of big data analytics in agriculture. Journal of Agricultural & Food Information, 19(4), 308–325.

Verdouw, C. N., Wolfert, J., Beulens, A. J. M., & Rialland, A. (2016). Virtualization of food supply chains with the Internet of Things. Journal of Food Engineering, 176, 128–136. DOI: https://doi.org/10.1016/j.jfoodeng.2015.11.009

Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming. A review. Agricultural Systems, 153, 69–80. DOI: https://doi.org/10.1016/j.agsy.2017.01.023

Yusoff, N. H., Omar, M. W., & Yusop, Z. (2018). The need for big data governance in Malaysian public sector. Journal of Theoretical and Applied Information Technology, 96(21), 7135–7144.