Enhancing Business Operations Efficiency thorough Predictive Analytics - Saleh, Haifa Hadi; Chyad, Azzam Khalid; Barakat, Maha; Naamo, Ghazwan Salim (Journal of Ecohumanism, 2024)
Background: In today's competitive world, organizations constantly seek innovative ways to improve operational efficiency and maintain a competitive advantage. Introducing big data and advanced analytics techniques has created new opportunities for optimizing corporate processes. Objective: The article aims to investigate the potential of predictive analytics in improving business operations efficiency, emphasizing cost savings, process optimization, and better decision-making. Methods: We used a mixed-methods research design, integrating quantitative analysis of operational data from 30 organizations in several industries with qualitative interviews with industry experts. Predictive analytics models, such as regression analysis, time series forecasting, and machine learning algorithms, were created and applied to past data to discover patterns, forecast future trends, and make operational recommendations. Results: Using predictive analytics significantly increased operational efficiency in the participating organizations. On average, operating costs were reduced by 20%, process efficiency increased by 15%, and decision-making speed and accuracy improved significantly. Furthermore, the study highlighted critical elements that contribute to the successful use of predictive analytics in corporate operations. Conclusion: Predictive analytics is a powerful tool for firms looking to improve operational efficiency. Companies may use historical data and advanced analytics to not only predict future patterns but also make educated decisions that significantly increase efficiency and competitiveness. According to the findings, organizations that want to prosper in the digital era should prioritize predictive analytics integration.