Enhancing Energy Management in Industries Through MIS and Data Analytics Integration

Abstract

Energy management in industries has gained prominence due to rising energy costs and the need for sustainable practices. Recent advancements in Management Information Systems (MIS) and data analytics present significant opportunities for optimizing energy use. This study aims to analyze the effectiveness of integrating MIS and data analytics in enhancing energy management in industrial settings, focusing on cost reduction, energy efficiency, and sustainability. A sample of 52 industries from various sectors was surveyed between January 2023 and December 2024 at International American University, California, USA. Data were collected using both energy consumption records and MIS-integrated data analytics tools. Statistical methods such as regression analysis, ANOVA, and hypothesis testing were employed to evaluate energy savings and efficiency improvements. Standard deviation, p-values, and correlation coefficients were used for in-depth analysis of variables. The integration of MIS and data analytics resulted in a 27% average reduction in energy consumption across the study sample. A significant decrease in operational costs was observed, averaging a 21% reduction. Statistical analysis showed a mean energy efficiency improvement of 35%, with a standard deviation of 5.4%. The p-value of the regression model was 0.04, indicating statistical significance at a 95% confidence level. Correlation coefficients between energy savings and operational efficiency were found to be 0.88, suggesting a strong positive relationship. Predictive analytics models contributed to a 30% improvement in energy forecasting accuracy. Furthermore, an analysis of operational load management revealed a 16% improvement in peak load reduction, while the energy consumption per unit of output decreased by 23%, with a p-value of 0.03, suggesting a highly significant correlation between energy usage and production output. The adjusted R-squared value was 0.72, demonstrating that the model explains 72% of the variance in energy efficiency outcomes. The study concludes that the integration of MIS and data analytics significantly enhances energy management, driving cost reductions, operational efficiency, and sustainability in industrial operations.