Integrating Multimodal AI for Smart Inventory Management and Sustainable Material Optimisation in Offsite Manufacturing Construction

The project’s main objective is to automate and create smart inventory management, gain a comprehensive understanding and minimise material wastage, produce informed decision-making, and improve the efficiency of manufacturing processes. Additionally, we aim to incorporate the lifecycle assessment (LCA) to evaluate the environmental impacts of the optimised designs, ensuring sustainability and compliance with environmental standards, ultimately reducing carbon footprint.
The technical approach uses machine learning (ML) to optimise inventory management and integrates time-series data on material off-cuts. The idea is to leverage existing data associated with barcodes for each material off-cut, collect and incorporate additional data sources, open-domain of linked data (Lidar, high-resolution images, etc.) through sensors to capture real-time information on material type, profile, and length of the off-cuts. By combining temporal and spatial information in a comprehensive dataset, the project can leverage a multimodal AI framework to gain a comprehensive understanding and optimise material usage, enhance design decisions, minimise material wastage, and improve efficiency in the Norscot manufacturing processes.

The project’s expected outcomes are to i) automate and implement smart inventory management, ii) reduce material wastage and optimise material usage through the data-driven multimodal AI framework; iii) generate a comprehensive LCA report demonstrating the environmental benefits of the optimised inventory management system. The project aligns with the SMDH objectives by collecting and generating reliable data to be shared on the SMDH Manufacturing Data Exchange Platform (MDEP), thus contributing to the collective knowledge and best practices within the UK manufacturing community. Furthermore, the project supports Norscot on its digitisation journey by providing innovative, data-driven solutions that are cost-effective and scalable, thereby enhancing competitiveness and promoting growth in the manufacturing sector.