Advancing Trending Statistical Techniques to Examine Growth and Variability in Scottish Sustainable Business Enterprises

[Purpose] This study aims to explore the growth and variability of enterprises in four key Scottish industrial sectors between 2008 and 2021, emphasizing the importance of sustainable business practices in today's evolving economic landscape. [Design/methodology/approach] The research employs a quantitative and testing approach, using advanced statistical methods by ORANGE data mining, namely Compound Annual Growth Rate (CAGR), Mean, Standard Deviation (SD), Coefficient of Variation (CoV), Root Mean Square Error (RMSE), and Average Growth Rate (AGR). Data from sectors like Electronics, Information Technology, Information and communication technology (ICT), and Telecommunications form the basis of this analysis. [Findings] The study reveals varied growth patterns across sectors. Information Technology displayed a steady growth (CAGR of 1.03%), while the Electronics sector exhibited more variability (Coefficient of Variation of 5.79%). These findings highlight the differing dynamics and stability of enterprises in the context of economic and technological changes. [Research limitations/implications] The analysis is limited to Scottish enterprises and may not reflect trends in other geographical contexts. Further research expands to compare with global trends, or utilize machine learning-based analysis for regression and future probabilistic forecasts. [Practical implications] The insights are valuable for business strategists and policymakers, aiding in informed decision-making and strategic planning for sustainable business development. [Social implications] The study contributes to understanding the sustainability of business practices, which is critical in the current socio-economic climate. [Originality/value] This paper enriches the discourse in business administration by integrating modern statistical analyses, offering a novel perspective on the management of sustainable enterprises during significant global economic shifts.