Internet of things (IOT) Enabled Sustainable Energy MANAGEMENT (SEM) for SMEs – A Review

Abstract

Australian Small and Medium Enterprises (SMEs) are increasingly burdened by rising energy costs and operational inefficiencies. This study explores the role of IoT-enabled energy management systems in addressing these challenges. Using quantitative analysis of data from 20 Australian SMEs across four sectors i.e., Manufacturing, Retail, Healthcare, and Hospitality, the research examines the impact of IoT adoption on energy savings, investment costs, and security concerns. Key findings show that IoT systems can lead to energy savings between 15% and 22%, with Manufacturing SMEs achieving the highest savings, averaging 20%. A moderate positive correlation (r = 0.56, p = 0.01) was found between IoT investment cost and energy efficiency, indicating that greater investment is generally associated with higher savings. However, major barriers remain; 65% of SMEs identified high costs and 30% cited security risks as significant hurdles to adoption. These concerns were particularly notable in the Hospitality sector, which reported the highest number of security incidents. Visual tools such as bar charts, scatter plots, and pie charts were used to illustrate trends in savings, cost-to-benefit ratios, and industry specific barriers. Statistical tests including t-tests and ANOVA confirmed that differences in energy savings across industries are significant, with Manufacturing clearly outperforming others.  The findings highlight the need for affordable, secure and scalable IoT frameworks like SIoTCF and Industry 4.0. Targeted support through subsidies, training, and sector specific strategies will be essential to unlock the full potential of IoT for sustainable SME operations in Australia.

To strengthen the practical relevance of the findings of this study, it provides real-world SME case studies and a five-step SCOPE-IoT framework which supports secure, affordable implementation. Sensitivity analysis was carried out to assess the robustness of the results under alternate assumptions, allowing SMEs to have better informed planning decisions and lower risk.