• Deep Manishkumar Dave

Neural Manufacturing represents an emerging paradigm in the manufacturing industry that aims to create intelligent, adaptive, and resilient production systems. Inspired by biological neural networks, Neural Manufacturing entails integrating various elements of the manufacturing value chain into an interconnected, collaborative ecosystem. Core concepts include creating cognitive enterprises through artificial intelligence and machine learning, connecting entire value chains for end-to-end visibility and coordination, and building purpose-driven collaborative ecosystems. The origins of Neural Manufacturing stem from advancements in digital technologies like cloud computing, predictive analytics, and automation. Key benefits include heightened agility, sustainability, productivity, and human-machine collaboration. While promising, realizing the potential of Neural Manufacturing requires strategic planning, investment in capabilities building, and managing organizational change. With diligent adoption, Neural Manufacturing is poised to usher in a new era defined by optimized, innovative, and adaptive manufacturing operations. This literature review explores the conceptual origins, technological drivers, implementation roadmap, benefits, and challenges associated with this transformative shift in the manufacturing paradigm.

Artificial Intelligence, Big Data Analytics, Connected Value Chains, Digital Transformation, Internet of Things, Resilience, Robotics, Sustainability

"Neural Manufacturing: The Future of Intelligent Production", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.b674-b687, September-2023, Available :http://www.jetir.org/papers/JETIR2309172.pdf

"Neural Manufacturing: The Future of Intelligent Production", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppb674-b687, September-2023, Available at : http://www.jetir.org/papers/JETIR2309172.pdf