AUTOMATION IN MANUFACTURING: A SYSTEMATIC REVIEW OF ADVANCED TIME MANAGEMENT TECHNIQUES TO BOOST PRODUCTIVITY

Abstract

The increasing demand for efficiency and agility in manufacturing has driven the adoption of advanced automation and data-driven decision-making strategies. This study systematically reviews 20 peer-reviewed articles published before 2023, examining key technologies that optimize manufacturing time management, including real-time analytics, robotic process automation (RPA), predictive maintenance, human-robot collaboration (HRC), cybersecurity, and digital twins. The review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, ensuring a rigorous and transparent selection process. The findings indicate that real-time scheduling and predictive analytics reduce production delays by 20% to 40%, while RPA enhances workflow efficiency by 30% to 50%, significantly minimizing manual errors. The study further reveals that predictive maintenance reduces machine failure rates by 40% to 60%, lowering operational disruptions and maintenance costs by 20%. Additionally, collaborative robots (cobots) increase production efficiency by 25% to 35%, improving labor productivity while ensuring worker safety. However, the expansion of cloud-based manufacturing and IoT-enabled automation has introduced cybersecurity risks, with cyberattacks causing up to 30% operational downtime in compromised facilities, necessitating AI-driven security measures. The integration of digital twin technology enhances manufacturing agility by 30% to 45% and improves production accuracy by 25%, enabling real-time process adjustments and predictive optimization. Compared to earlier studies that emphasized static, rule-based automation, recent advancements demonstrate that AI-enhanced, adaptive systems provide superior responsiveness and efficiency. The results underscore the necessity of combining automation, data-driven analytics, and cybersecurity frameworks to achieve sustainable time optimization in smart manufacturing. This review provides valuable insights for industry leaders, researchers, and policymakers seeking to enhance operational efficiency, cost-effectiveness, and resilience in the evolving landscape of industrial automation.