Resilience, sustainability, and human-centric are viewpoints of Industry 5.0. Addressing the prevailing societal challenges requires the collaborative efforts of stakeholders involved in supply chains. This study explores a manufacturing supply chain system aided by Additive Manufacturing (AM) and data space. A method to identify optimal conditions to maximize parts manufacturing efficiency under a limited material supply is derived using integer linear programming optimization. The findings indicate that utilizing AM with a small buy-to-fly ratio is expected to maximize the number of parts that can be produced. On the other hand, a conventional manufacturing approach is capable of producing a higher number of parts when cost constraints are imposed. The findings of this study also indicate that utilizing AM results in a reduction of CO2 emissions per part. In summary, the manufacturing supply chain system that incorporates AM and data space can provide viable solutions that consider both manufacturing volume and CO2 emissions under material supply constraints by sharing data about the processing conditions of parts and cost constraints.