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