Predictive analytics for business.

Parmar, A. S., Sharma, P., & Agarwal, L. (2022). Predictive analytics for business. International Journal of Food and Nutritional Sciences, 11(7), 1184-1191. https://ijfans.org/uploads/paper/8a7828c5f1a7f2e402798f27fa790f4f.pdf

Published in the International Journal of Food and Nutritional Sciences, this scholarly article explores the critical role of predictive analytics in modern business environments. The authors examine how organizations utilize historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes. The primary argument is that predictive analytics has moved beyond a niche technical tool to become a core component of strategic business planning. By forecasting market trends and consumer behaviors, businesses can mitigate risks and optimize their supply chains. The study provides a comprehensive overview of the methodologies involved in data modeling and highlights the importance of data quality in ensuring the accuracy of predictions. Furthermore, the researchers discuss the ethical considerations and potential biases inherent in automated decision-making systems, urging business leaders to maintain human oversight. This work is intended for researchers and practitioners looking to integrate analytical frameworks into their operational models for improved competitive advantage.