Influence of Edge Computing Adoption on Real-Time Data Processing Efficiency in Smart Manufacturing Systems in Brazil
DOI:
https://doi.org/10.47941/ijce.3276Keywords:
Edge Computing Adoption, Real-Time Data Processing Efficiency, Smart Manufacturing SystemsAbstract
Purpose: The purpose of this article was to analyze influence of edge computing adoption on real-time data processing efficiency in smart manufacturing systems.
Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.
Findings: The adoption of edge computing in smart manufacturing systems significantly enhances real-time data processing efficiency by reducing latency, improving data throughput, and enabling faster decision-making. Edge computing facilitates the processing of data closer to the source, minimizing reliance on centralized cloud systems and accelerating response times in mission-critical applications. As a result, manufacturing operations benefit from increased system reliability, optimized resource allocation, and enhanced operational agility.
Unique Contribution to Theory, Practice and Policy: Technology-organization-environment (TOE) framework, diffusion of innovation (DOI) theory & socio-technical systems (STS) theory may be used to anchor future studies on the influence of edge computing adoption on real-time data processing efficiency in smart manufacturing systems. Practically, manufacturing firms should conduct comprehensive infrastructure readiness assessments to ensure compatibility between edge technologies and existing operational systems. From a policy standpoint, governments should offer targeted incentives such as tax credits, grants, or digital transformation subsidies to accelerate edge computing adoption in manufacturing sectors.
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References
Al-Okaily, A., Lutfi, A., Alsyouf, A., & Abualigah, L. (2022). Factors influencing the adoption of Industry 4.0 technologies in manufacturing SMEs: A diffusion of innovation theory approach. Technology in Society, 70, 102001. https://doi.org/10.1016/j.techsoc.2022.102001
Culot, G., Nassimbeni, G., Podrecca, M., & Sartor, M. (2023). The role of socio-technical alignment in Industry 4.0 adoption. Journal of Manufacturing Technology Management, 34(1), 150–170. https://doi.org/10.1108/JMTM-09-2021-0374
da Silva, T. R., Costa, M. F., & de Almeida, R. C. (2021). Streaming data analytics in precision agriculture: Applications and impact in Brazil. Computers and Electronics in Agriculture, 186, 106177. https://doi.org/10.1016/j.compag.2021.106177
Garcia Lopez, P., Montresor, A., Epema, D., Datta, A., Higashino, T., Iamnitchi, A., ... & Voulgaris, S. (2021). Edge-centric computing: Vision and challenges. ACM Transactions on Internet Technology (TOIT), 21(2), 1–34. https://doi.org/10.1145/3434770
Garcia Lopez, P., Montresor, A., Epema, D., Datta, A., Higashino, T., Iamnitchi, A., ... & Voulgaris, S. (2021). Edge-centric computing: Vision and challenges. ACM Transactions on Internet Technology (TOIT), 21(2), 1–34. https://doi.org/10.1145/3434770
Kumar, R., Singh, A., & Sharma, S. (2021). Evaluating the real-time analytics of digital financial services in India: A UPI case study. Journal of Financial Services Research, 59(3), 435–452. https://doi.org/10.1007/s10693-021-00347-2
Munyua, H., & Kamau, P. (2020). Leveraging real-time data for service delivery in sub-Saharan Africa: Challenges and prospects. Information Technology for Development, 26(4), 768–786. https://doi.org/10.1080/02681102.2020.1774667
Nguyen, T. H., Ngo, L. V., & Ruël, H. (2022). A TOE framework perspective of smart manufacturing technology adoption. Technological Forecasting and Social Change, 176, 121464. https://doi.org/10.1016/j.techfore.2022.121464
Owusu, E. A., Yeboah, F. K., & Tutu, R. A. (2022). Real-time climate data and agricultural decision-making in Ghana: A spatial intelligence approach. Climate and Development, 14(4), 320–333. https://doi.org/10.1080/17565529.2021.1936062
Sarker, V. K., Kayes, A. S. M., Badsha, S., & Watters, P. A. (2021). Edge computing in industrial IoT: Insights, challenges, and future directions. Journal of Network and Computer Applications, 179, 102975. https://doi.org/10.1016/j.jnca.2020.102975
Satyanarayanan, M., Simoens, P., Xiao, Y., Pillai, P., Chen, Z., Ha, K., & Hu, Y. C. (2019). Edge analytics in the Internet of Things. IEEE Pervasive Computing, 18(2), 24–31. https://doi.org/10.1109/MPRV.2019.2914052
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2020). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646. https://doi.org/10.1109/JIOT.2016.2579198
Smith, J. D., Thompson, R., & Nguyen, M. (2020). Real-time data systems in US healthcare: Performance, challenges, and future trends. Health Information Science and Systems, 8(1), 21. https://doi.org/10.1007/s13755-020-00106-6
Yamamoto, H., & Kato, M. (2021). 5G-enabled edge computing for industrial IoT in Japan: A case of smart manufacturing. International Journal of Advanced Manufacturing Technology, 114(5–6), 1587–1599. https://doi.org/10.1007/s00170-021-07010-0
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