To design and deploy their supply chains, companies must naturally take quite different decisions, some being strategic or tactical, and others of an operational nature. This work resulted in a decision support system for optimising a biomass supply chain in Portugal, allowing a more efficient operations management, and enhancing the design process. Uncertainty and variability in the biomass supply chain is a critical issue that needs to be considered in the production planning of bioenergy plants. A simulation/optimisation framework was developed to support decision-making, by combining plans generated by a resource allocation optimisation model with the simulation of disruptive wildfire scenarios in the forest biomass supply chain. Different scenarios have been generated to address uncertainty and variability in the quantity and quality of raw materials in the different supply nodes. Computational results show that this simulation/optimisation approach can have a significant impact in the operations efficiency, particularly when disruptions occur closer to the end of the planning horizon. The approach seems to be easily scalable and easy to extend to other sectors.

The transition towards a Circular Economy has positioned remanufacturing as a critical sustainable industrial strategy. However, the inherent uncertainties of End-of-Life products make traditional, mainly deterministic, manufacturing approaches insufficient. While technologies from both Industry 4.0 and 5.0, such as Digital Twins, Discrete Event Simulation, and Human-Robot Collaboration, offer promising solutions, current literature reveals two major gaps. Firstly, these technologies are typically applied in a fragmented way, rarely applied within a unified approach. Secondly, human operators are usually modelled as deterministic resources, ignoring both physical ergonomics and cognitive variability. To address these limitations, this paper proposes a conceptual framework architecture for a human-centric digital twin framework for remanufacturing processes. The proposed approach seamlessly integrates, in two different abstraction levels, process-level system dynamics using Discrete Event Simulation with execution-level interactions through physics-based human-robotics simulation within a unified Digital Twin environment. This approach enables dynamic task allocation between human operators and collaborative robots, continuously evaluating operational efficiency and operator ergonomics. Ultimately, the proposed detailed architecture intends to provide a scalable blueprint to help industrial sectors adapt to the high uncertainties of remanufacturing.