A process system can be generally decomposed into hierarchical levels or scales at which different physical and/or chemical phenomena take place. The first step of multiscale process modeling is to connect the molecular level with the phase level, where the main task is to model and predict the properties of fluid mixtures based on the atomic- or molecular-level information. Typically, quantum chemical (QC) computation, molecular simulation, and equations of state are used to provide such predictions. Recently, due to the ever-increasing number of available data and fast development of cheminformatics and machine learning tools, data-driven descriptor models have been developed and widely used for property predictions. With the properties of the system, it is then possible to derive the constitutive relations (e.g., kinetics and phase equilibria) and implement them into the mass, energy, and momentum conservations of each process unit. Taking into account the connections between different units, one can finally scale the system up toward the process level where process modeling, intensification, and optimization are performed to maximize the economic and environmental performances of the process. At a higher scale, supply chain design has a great importance in which different elements of a supply chain (raw material, processing, storage, and transportation) are considered and analyzed simultaneously.