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Mapping material flows in an economy is crucial to identifying strategies for resource management toward lowering the waste and environmental impacts of society, a key objective of research in industrial ecology. However, constructing models for mapping material flows at a sectoral level, such as in physical input–output tables (PIOTs) at highly disaggregated levels, is tedious and relies on a large amount of empirical data. To overcome this challenge, a novel collaborative cloud platform PIOT-Hub is developed in this work. This platform utilizes a Python-based simulation system for extracting material flow data from mechanistic models, thus semi-automating the generation of PIOTs. The simulation system implements a bottom-up approach of utilizing scaled engineering models to generate physical supply tables (PSTs) and physical use tables (PUTs) which are converted to PIOTs (described in (Vunnava & Singh, 2021)). Mechanistic models can be uploaded by users for sectors on PIOT-Hub to develop PIOTs for any region. Both models and resulting PST/PUT/PIOTs can be shared with other users utilizing the collaborative platform. The automation and sharing features provided by PIOT-Hub will help to significantly reduce the time required to develop PIOT and improve the reproducibility/continuity of PIOT generation, thus allowing the study of the changing nature of material flows in regional economy. In this paper, we describe the simulation system MFDES and PIOT-Hub architecture/functionality through a demo example for creating PIOT in agro-based sectors for Illinois. Future work includes scaling up the cloud infrastructure for large scale PIOT generation and enhancing the tool compatibility for different sectors in economy.