This proposed research aims to improve the construction supply chain's efficiency, resilience, and agility by providing a data-driven forecast solution for small equipment, tools, and consumables throughout an industrial construction project's lifecycle. The proposed research is expected to provide insight into the historical demand for small equipment, tools, and consumables before and during the construction project. A variety of data modelling techniques (e.g., machine learning, statistical learning) will be used to develop an AI-enabled data-driven forecast solution on the demand of small equipment, tools, and consumables. Partnered with our industry organizations – PCL Industrial Management (general contractor) and Vallen Inc (industrial supplier), the proposed research is expected to develop solutions that can be leveraged among multiple stakeholders within the construction supply chain to improve transparency and predictability. Once applied in practice, the proposed solutions are expected to enhance the efficiency, productivity, agility, and capital allocation of the construction supply chain in small equipment, tools, and consumables, resulting in reduced waste, improved project performance and increased competitiveness.