This study analyzes and optimizes production systems in the Industry 4.0 context, examining the fundamental shift from centralized, push-based production models to decentralized, adaptive, pull-based approaches. The research employs a mixed-method approach combining comprehensive literature review and multiple case studies across manufacturing sectors. Findings reveal that integration of Internet of Things (IoT), cyber-physical systems, artificial intelligence, and big data analytics enables real-time communication between production components, product personalization, and faster decision-making. Despite significant benefits in efficiency, flexibility, and competitiveness, implementation challenges persist, including high initial investment, employee resistance, technical expertise limitations, and integration complexity. Optimization approaches such as mixed-integer linear programming, digitally-integrated Lean Six Sigma, and digital twin simulations effectively enhance performance indicators including flexibility, reliability, and energy efficiency. The study concludes that successful production system transformation requires an integrated strategy encompassing process engineering, digital competency development, change management, and continuous evaluation to ensure sustainable optimization in the digital era

Industry 4.0 has driven significant transformation in manufacturing maintenance strategies, particularly in the implementation of Total Productive Maintenance (TPM). This research examines the paradigm shift from conventional TPM methods to Artificial Intelligence (AI)-based approaches, utilizing a qualitative methodology with phenomenological design and participatory observation in manufacturing environments. Results indicate that integrating AI with TPM has transformed maintenance approaches from reactive to predictive, enabling real-time anomaly detection, predictive maintenance, and maintenance schedule optimization that significantly improve Overall Equipment Effectiveness (OEE). Despite providing benefits such as reduced downtime and maintenance costs, AI-based TPM implementation faces challenges including data fragmentation, skilled human resource requirements, and organizational culture resistance. The development of collaboration between industry and academic institutions, as well as investment in edge computing infrastructure, are identified as key to maximizing the potential of AI-based TPM in enhancing operational sustainability in manufacturing.

Digital technological advancements have driven a paradigm shift towards Industry 4.0, characterized by the integration of cyber-physical systems and artificial intelligence in manufacturing processes. This study analyzes the role of Internet of Things (IoT) in accelerating Manufacturing 4.0 transformation, focusing on production efficiency optimization. Through a systematic literature review of 45 Scopus-indexed journals published between 2019-2024, complemented by experimental analysis on a laboratory-scale smart factory prototype, this research examines the implementation of IoT-based technologies such as predictive maintenance and digital twins. Results demonstrate that IoT integration significantly enhances production efficiency through real-time monitoring (30% downtime reduction), predictive maintenance (20% maintenance cost decrease), and data-driven supply chain optimization (15% energy savings). Case studies from Siemens and PT. Unilever Indonesia confirm that IoT sensor integration with ERP systems improves demand forecasting accuracy by 25% and reduces production errors to 0.001%. Despite challenges including high initial infrastructure costs and cybersecurity vulnerabilities, IoT implementation offers sustainable competitive advantages through enhanced operational efficiency, quality control, and resource management.