Industry 5.0 represents a paradigm shift from fully automated production toward intelligent, human-centric manufacturing environments where collaborative robots, artificial intelligence (AI), edge computing, digital twins, Industrial Internet of Things (IIoT), and cyber-physical systems (CPS) operate in harmony with human workers. Unlike Industry 4.0, which primarily emphasized automation and productivity, Industry 5.0 focuses on resilience, sustainability, worker well-being, and personalized manufacturing. This study presents a comprehensive research framework for Industry 5.0-oriented human-centric robotic manufacturing systems by integrating collaborative robotics, AI-assisted decision-making, adaptive sensing, real-time monitoring, and intelligent manufacturing analytics. The proposed framework enables seamless interaction between human operators and robotic systems while ensuring operational safety, productivity, flexibility, and energy efficiency. A detailed literature review identifies current advancements, research gaps, and technological challenges associated with human-robot collaboration. The research methodology introduces an intelligent architecture incorporating multi-modal sensing, AI-based decision support, digital twin simulation, and adaptive robotic control. Comparative performance metrics demonstrate improvements in production efficiency, safety compliance, response time, and system adaptability. The findings indicate that Industry 5.0 technologies significantly enhance manufacturing performance while promoting sustainable and worker-centered industrial environments. The study concludes with future research directions involving explainable artificial intelligence, federated learning, autonomous collaborative robots, and resilient cyber-physical manufacturing ecosystems.
Narendra Karmarkar, Iyengar P. K.· International Journal of Int...· 0 citations
As the world has moved into the fourth industrial revolution, the logistics and warehouses have become more and more part of a digital ecosystem, where intelligent technologies are helping companies to reach unprecedented levels of operational efficiency, flexibility and sustainability. Digital Twin (DT) technology has become a game-changer as it enables virtual representations of physical logistics assets, warehouse structures and processes to be created in real-time. Digital Twins, when supported by Artificial Intelligence (AI), the Internet of Things (IoT), cloud computing, and big data analytics, enable predictive monitoring, autonomous decision making, dynamic resource allocation and ongoing process optimisation. This paper provides a thorough overview of Digital Twin-driven intelligent logistics and warehouse automation, discusses recent developments in technology, implementation models, and their industrial applications. The study covers the challenges of embedding IoT connected sensors, autonomous mobile robots, cyber-physical systems, edge-cloud computing, and machine learning algorithms to improve the productivity, inventory control, route optimization, and predictive maintenance of warehouses. Additionally, the paper examines weaknesses of traditional WMS and uncovers existing problems and gaps in the field of scalability, interoperability, cyber security and real-time synchronization. The survey presented in this work shows the importance of Digital Twin as a crucial enabling technology for new generation smart warehouses and for the intelligent supply chain in Industry 5.0, as well as the opportunities for future research in autonomous logistics operations in Industry 5.0.
Iyengar P. K.· International Journal of Eme...· 0 citations
Recent enterprise digitalization has transformed workplaces into intelligent environments powered by cloud computing, IoT, big data, AI, and collaborative platforms. Among these technologies, AI has emerged as a key enabler of human–AI collaboration, enhancing decision-making, automating routine tasks, supporting organizational learning, and improving productivity. However, organizations continue to face challenges related to trust, transparency, explainability, ethical governance, interoperability, and employee acceptance. This paper proposes an **Intelligent Human–AI Collaboration Framework for Future Digital Workplaces** consisting of four stages: workplace data integration, intelligent knowledge processing, collaborative decision intelligence, and adaptive feedback optimization. The framework integrates enterprise data, AI-driven analytics, human expertise, and explainable AI to deliver transparent, adaptive, and trustworthy decision support while maintaining human oversight. It also incorporates responsible AI principles, including fairness, accountability, privacy, and ethical governance. By combining collaborative intelligence with continuous learning, the framework aims to improve decision accuracy, operational efficiency, employee productivity, organizational agility, and sustainable digital transformation across diverse industries.
Narendra Karmarkar, Iyengar P. K.· International Journal of Eme...· 0 citations
Agentic Artificial Intelligence (Agentic AI) represents the next generation of intelligent systems capable of autonomous sensing, reasoning, planning, and action with minimal human intervention. Unlike traditional AI, Agentic AI integrates large language models, reinforcement learning, multi-agent systems, planning mechanisms, orchestration layers, and memory modules to enable adaptive and goal-oriented decision-making. This paper explores Agentic AI architectures for autonomous business applications, highlighting their role in finance, healthcare, supply chain, enterprise resource planning, customer relationship management, and industrial operations. A layered architecture comprising perception, reasoning, orchestration, and execution layers is proposed to support autonomous analysis, strategic planning, and optimized action execution. The framework also incorporates memory, monitoring, and governance modules to enhance transparency, reliability, and explainability. Experimental evaluation demonstrates that the proposed architecture improves workflow automation, decision accuracy, operational efficiency, resource utilization, and response time while reducing manual intervention. The findings indicate that Agentic AI provides a scalable and robust foundation for future autonomous enterprise systems. The study also discusses key challenges, including explainability, governance, ethics, and trust, emphasizing their importance for successful enterprise adoption. Overall, Agentic AI architectures offer significant potential to accelerate intelligent automation and drive the next generation of business transformation.
Narendra Karmarkar, Iyengar P. K.· International Journal of Mod...· 0 citations
An AI-enabled Autonomous Water Distribution System (AWDS) that integrates IoT sensors, edge-cloud computing, and predictive analytics for real-time leak detection and proactive maintenance and provides a scalable and sustainable solution for intelligent water resource management.
Iyengar P. K.· International Journal of Eme...· 0 citations
This study presents a conceptual framework that combines semantic retrieval, intelligent reasoning, automated literature analysis, and workflow orchestration, demonstrating how LLM-powered systems can transform scientific research into scalable, accurate, ethical, and collaborative knowledge discovery processes.
Narendra Karmarkar, Iyengar P. K.· International Journal of Eme...· 0 citations
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