Business Analytics Models in the Tech Corporates from Automative Sectors to Semiconductor Sectors

Abstract

The integration of business analytics models in technology-driven corporations across automotive and semiconductor sectors is redefining decision-making, enhancing operational efficiency, and driving innovation. These models leverage advanced analytics techniques, including predictive modeling, machine learning, and optimization algorithms, to process complex datasets and generate actionable insights. In the automotive sector, analytics models facilitate advancements in supply chain optimization, predictive maintenance, and autonomous vehicle development. Similarly, in the semiconductor industry, analytics plays a pivotal role in yield optimization, defect detection, and demand forecasting. This research examines the diverse applications of business analytics across these sectors, highlighting how data-driven strategies address industry-specific challenges such as volatile demand, technological complexities, and competitive pressures. It also explores the interplay between analytics and emerging trends, such as Industry 4.0 and AI-driven manufacturing. By evaluating case studies and frameworks, the study provides insights into the strategic adoption of analytics to enhance decision-making and operational agility. The findings emphasize the transformative potential of business analytics in tech-driven industries, paving the way for smarter, more resilient corporate strategies.