The Elephant in the Boardroom Is Unstructured Supply Chain Data
Most supply chain data is unstructured, and regulators don't care — here's what that means for your compliance, costs, and brand reputation.
The Problem Nobody Wants to Talk About
At a recent Customs and Border Protection conference on supply chain compliance technology, the uncomfortable truth came up: most supply chain data is unstructured, and what CBP is requiring for compliance is not in sync with where most businesses actually are today. Unstructured supply chain data refers to data that is not organized in a consistent or logical manner — anything from unstructured text in emails and documents to unformatted invoices and purchase orders. When supply chain data is unstructured, it becomes difficult to process and analyze, causing companies to miss critical information that impacts their operations. Put plainly: a company could unknowingly be supporting forced labor activity in their supply chain and have no way of knowing it. More than ever, companies are required to map their supply chains to comply with laws like the UFLPA or Germany's LkSG — yet most large companies have their data spread across multiple ERP systems accumulated through years of M&A, with data structuring never having been a priority.
Unstructured data doesn't just create compliance risk — it quietly erodes profitability and brand trust.
Supply chain costs can account for up to 80% of a company's total costs, according to McKinsey. When data is unstructured, it becomes nearly impossible to identify cost-saving opportunities such as optimizing inventory, reducing transportation costs, or consolidating suppliers — leading to unnecessary expenses that erode profitability. During the COVID-19 pandemic, companies that lacked structured data had no visibility or agility to respond to sudden shifts in demand and transportation restrictions, causing severe delays in the delivery of critical goods. Beyond costs, unstructured data also threatens brand reputation. In today's socially conscious environment, consumers are increasingly focused on ethical and sustainable business practices. When supply chain data is unstructured, companies cannot reliably verify that suppliers are meeting ethical standards — and may unknowingly become associated with violations, as Nike discovered in 2017 when reports surfaced about labor abuses among its Southeast Asian suppliers despite having codes of conduct in place.
Structure your data, adopt the right technology, and automate your supplier screenings — in that order.
First, implement structured data management practices. This means organizing and standardizing data across the supply chain so it can actually be processed and analyzed. Structured data enables companies to identify cost-saving opportunities and verify that suppliers are meeting ethical and sustainability standards. Second, adopt technology solutions. AI and machine learning can help companies manage and analyze supply chain data far more effectively than manual processes — identifying patterns and trends that surface hidden risks and operational efficiencies. Third, conduct automated supplier screenings on a regular basis. Automated screenings help identify potential risks such as human rights violations or environmental damage before they become reputational crises, enabling companies to take corrective action proactively.