<![CDATA[Sustainable manufacturing faces new difficulties and opportunities in the post-pandemic economy. Given the changing manufacturing scene, this research addresses production planning resource allocation and efficiency. The study incorporates sophisticated technology, lean manufacturing, data analytics, and supply chain collaboration. A mathematical model for resource allocation and efficiency optimization starts the investigation. The model incorporates production capacity, demand fulfillment, unit costs, and resource efficiencies. An objective function minimizes production cost while meeting demand and resource capacity limits. The model supports optimization and decision-making. Research shows that modern technologies improve resource allocation and efficiency. Automation, robots, and AI algorithms improve operations, minimize errors, and enable data-driven decision-making. Lean manufacturing, including waste reduction and just-in-time inventory management, improves resource usage and efficiency. Data analytics aids production planning decision-making. Real-time production bottleneck, energy consumption, and resource utilization data informs proactive modifications. Data analytics improve decision-making. Sustainable manufacturing requires supply chain collaboration. Stakeholder collaboration, synchronized planning, and information exchange align production plans, reduce disruptions, and boost supply chain efficiency. The study promotes collaboration for long-term sustainability. The study admits numerous limitations but offers significant observations and recommendations. Context-specific application, data availability and quality, and real-world production system complexity are examples. These limitations should be addressed in future study. This research optimizes resource allocation and production planning for sustainable post-pandemic manufacturing. Advanced technology, lean manufacturing, data analytics, and supply chain collaboration can improve resource utilization, cut costs, and contribute to environmental sustainability. The findings support real-world industrial strategy development.]]>
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