Small and medium-sized enterprises (SMEs) face unique challenges that can be effectively addressed through the adoption of data mining and business intelligence (BI) tools. This systematic literature review scrutinizes the deployment and efficacy of BI and data mining technologies across SME sectors, assessing their impact on operational efficiency, strategic decision-making, and market competitiveness. Therefore, drawing from a methodologically rigorous analysis of 93 scholarly articles published between 2014 and 2024, the review elucidates the evolving landscape of BI tools and techniques that have shaped SME practices. It reveals that advanced analytics such as predictive modeling and machine learning are increasingly being adopted, though significant gaps remain, particularly shaped by economic factors. The utilization of BI and data mining enhances decision-making processes and enables SMEs to adapt effectively to market dynamics. Despite these advancements, SMEs encounter barriers such as technological complexity, high implementation costs, and substantial skills gaps, impeding effective utilization. Our review, grounded in the analysis of business intelligence tools used indicates that dashboards (31.18%) and clustering techniques (10.75%) are predominantly utilized, highlighting their strategic importance in operational settings. However, a considerable number of studies (66.67%) do not specify the BI tools or data mining techniques employed, pointing to a need for more detailed methodological transparency in future research. The predominant focus on the ICT and manufacturing sectors underscores the industrial context sector specific applicability of these technologies, with ICT accounting for 45.16% and manufacturing 22.58% of the studies. We advocate for targeted educational programs, development of user-friendly and cost-effective BI solutions, and strategic partnerships to facilitate knowledge transfer and technological empowerment in SMEs. Empirical research validating the impacts of BI and data mining on SME performance is crucial, providing a directional pathway for future academic inquiries and policy formulation.

Big Data (BD) has emerged as a pivotal tool for small and medium-sized enterprises (SMEs), offering substantial benefits in enhancing business performance and growth. This review investigates the impact of BD on SMEs, specifically focusing on business improvement, economic performance, and revenue growth. The objective of this systematic review is to evaluate the drivers and barriers of BD adoption in SMEs and assess its overall impact on operational efficiency and business outcomes. A comprehensive systematic review of 93 research papers published between 2014 and 2024 was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The methodology included detailed analysis of research approaches, addressing biases and gaps in the literature. BD adoption in SMEs led to significant improvements in operational efficiency, revenue generation, and competitiveness. However, the studies reveal persistent challenges, such as limited financial resources and technical expertise. The review identified a reporting bias, with 47% of studies using quantitative methods, 28% employing case studies, and mixed-method and qualitative studies underrepresented (22% and 17%, respectively). This imbalance highlights a potential overreliance on quantitative approaches, which may limit the depth of insights gained. While BD offers considerable potential for driving innovation and enhancing competitiveness in SMEs, addressing the current methodological biases and resource-related barriers is crucial to fully harness its benefits. Future research should focus on diverse approaches to provide a holistic understanding of BD’s impact on SMEs.

Information technology (IT) strategic planning plays a pivotal role in enhancing the performance of small and medium-sized enterprises (SMEs), improving operational efficiency and decision-making, and fostering innovation. This systematic review aims to synthesize the impacts of IT strategic planning on SME performance, focusing on key performance indicators such as operational efficiency, cost reduction, and customer satisfaction. A total of 80 studies were rigorously analyzed. Quantitative methods were used in 45% of the studies, while 25% applied thematic analysis. The adoption of cloud-based solutions (11.25%) and hybrid IT setups (17.5%) were also evaluated as critical enablers of flexibility and cost-efficiency. SMEs aligning their IT strategies with business objectives demonstrated up to a 20% reduction in operational costs and a 15% increase in customer satisfaction. Resource constraints were identified as a significant challenge in 60% of the studies. Nevertheless, effective IT planning contributed to long-term sustainability and competitive advantage in 60% of the reviewed studies. This review provides actionable insights and strategic frameworks to guide SMEs in optimizing their IT investments, overcoming resource limitations, and ensuring sustainable growth.

Small and medium-sized enterprises (SMEs) face substantial barriers in accessing reliable and cost-effective IT infrastructure, primarily due to economic constraints and limited technical resources. Hybrid cloud computing solutions, including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS), offer potential to overcome these barriers by providing scalable, flexible IT services. This study systematically reviews the impact of cloud computing on SME performance, focusing on key performance metrics such as cost-efficiency, operational reliability, and competitive advantage. A systematic literature review was conducted using the PRISMA 2020 framework. Inclusion criteria included studies published in English from 2014 to 2024, focusing on cloud computing for SMEs and presenting clear analytical frameworks for evaluating performance. Out of an initial pool of 18,570 studies, 90 met the criteria for detailed analysis. Findings show that cloud computing adoption enhances SME performance, with approximately 82% of studies reporting improvements in operational efficiency and 76% noting cost savings. Competitive advantage was identified as a key benefit in 64% of studies, driven by cloud-enabled scalability and access to advanced technology. Key adoption drivers include management support (cited by 68% of studies), service quality (56%), and perceived risks (54%), while barriers such as initial cost concerns and data security risks were also prevalent, affecting 48% and 45% of SMEs, respectively. This review provides strategic insights for SMEs and policymakers, emphasizing the importance of tailored cloud strategies that align with specific operational needs and budget constraints. By addressing key predictors and challenges, this study offers a roadmap for SMEs to leverage cloud computing to improve performance, sustainability, and competitiveness.

In electrical power systems, from generation power stations down to distribution substations, power transformers play a key role in ensuring reliable electricity transfer in the correct range from the generating source to the end-users. Over time, due to their operational demands and other various factors, transformers become susceptible to failures which threaten their reliability and life span. To address this issue, various transformer fault diagnosis methods are employed to detect and monitor the state of transformers, such as the dissolved gas analysis (DGA) method. In this paper, a systematic literature review (SLR) is conducted using the Preferred Reporting Items for Systematic Reviews (PRISMA) framework to record and screen current research work pertaining to the application of machine learning algorithms for DGA-based transformer fault classification. This study intends to assess and identify potential literature and methodology gaps that must be explored in this research field. In the assessment of the literature, a total of 124 screened papers published between 2014 and 2024 were surveyed using the developed PRISMA framework. The survey results show that the majority of the research conducted for transformer fault classification using DGA employs the support vector machine (32%), artificial neural network (17%), and k-Nearest Neighbor (12%) algorithms. The survey also reveals the countries at the forefront of transformer fault diagnosis and a classification based on DGA using machine learning algorithms. Furthermore, the survey shows that the majority of research conducted revolves around fault diagnosis with an emphasis on improving the accuracy of techniques such as SVM and ANN. At the same time, limited effort is put into other key metrics such as precision, Mean Squared Error, and R-Squared, and also, current works surveyed do not explore regularization techniques for preventing overfitting and underfitting of the proposed models.

The challenge of providing reliable electricity during power interruptions, especially in rural and remote regions, has prompted the exploration of Hybrid Renewable Energy Systems (HRESs). This systematic review employs the PRISMA framework to conduct a comparative analysis of HRES configurations, specifically those integrating rooftop solar photovoltaic (PV), diesel generators (DGs), converters, and battery energy storage systems (BESSs). This review assesses the techno-economic performance of these systems in various countries, highlighting the cost efficiency, reliability, and environmental impact compared to traditional single-resource systems. The analysis reveals that HRESs offer significant advantages in managing energy supply during power interruptions, particularly in regions with high solar potential but unreliable grid access. A comparative analysis with other countries demonstrates that while HRES configurations are tailored to local conditions, the integration of solar PV with diesel generators is a consistently effective strategy across different contexts. This review provides essential insights for policymakers and stakeholders, facilitating the optimization of energy solutions tailored to regional needs.