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
