Can be used in a variety of ways to enhance traceability in supply chains, including conducting risk and predictive analysis of supply chain data and automating traceability processes.
Artificial intelligence (AI) and machine learning can be used in many different ways – from data analysis to automation of traceability processes – to support supply chain traceability. For example, AI (specifically natural language processing) and machine learning have been used in the context of Supply Chain Mapping to surface previously unknown relationships among supply chain actors beyond the first tier by analyzing open-source information such as business reports and media articles. AI analysis has been used to support Forensic Tracing, specifically with microbiome analysis. AI has also been used alongside Satellite Imaging to detect supply chain risk.
AI and machine learning can play an important role in supporting traceability by moving paper-based, manual processes to a digital platform. These technologies support automated and large-scale ingestion of the traceability data found in documents such as certificates or purchase orders. AI-enabled tools can be used to extract relevant data from such documents and validate it by evaluating it against other sources. Data resulting from these and other traceability processes can be analyzed using AI tools to provide insight into supply chain risk and greater visibility into operations.
Artificial intelligence (AI) and machine learning require a sophisticated approach to technology. No matter the context in which AI and machine learning are used, supply chains that seek to benefit from this technology will need to have the ability to operate in a high-tech environment.