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I recently spoke at a Customs and Border Protection conference which focused on supply chain technologies that help companies comply with new trade enforcement regulations around forced labor in supply chains. I mentioned to a high ranking Department Of Labor employee that the elephant in the room is that most supply chain data is unstructured, and that what CBP is requiring for compliance is not in sync with where most businesses are today. His response was, ‘Too Bad’. 


We all know that in our fast-paced business world, data plays a crucial role in making informed decisions. Supply chain data, in particular, helps companies keep track of their operations and improve their performance. However, when supply chain data is unstructured, it can have a significant impact on a company's bottom line and brand reputation. In this article, we will explore how unstructured supply chain data affects a company's bottom line and brand, and offer three next steps to mitigate these impacts.

Unstructured supply chain data refers to data that is not organized in a consistent and logical manner typically found in large ERP (Enterprise Resource Planning) software like SAP. This data can include anything from unstructured text data in emails and documents to unstructured invoices and POs. When supply chain data is unstructured, it can be difficult to process and analyze. As a result, companies may miss critical information that could impact their operations, leading to supply chain disruptions, increased costs, and decreased profitability. Or put more plainly, a company could unknowingly be supporting forced labor activity in their supply chain.

For example, in 2017, Nike faced criticism when reports surfaced that some of its suppliers in Southeast Asia violated the company's ethical and sustainability standards (The Guardian, 2017). While Nike had implemented supplier codes of conduct, the company struggled to enforce them due to unstructured supply chain data. The incident highlighted the importance of having structured supply chain data to ensure that suppliers are meeting ethical and sustainability standards.

More than ever before companies are required to map their supply chain to comply with trade laws like the Uyghur Forced Labor Prevention Act (UFLPA) or the German Supply Chain Due Diligence Act (LkSG). Most large companies have their supply chain data spread across multiple ERP systems due to M&A activity over many years. Structuring, cleansing and hydrating data has not been a priority for most companies. 

One way that unstructured supply chain data can affect a company's bottom line is through increased costs. According to a study by McKinsey, supply chain costs can account for up to 80% of a company's total costs (McKinsey, 2018). When data is unstructured, it can be challenging to identify cost-saving opportunities, such as optimizing inventory levels, reducing transportation costs, or consolidating suppliers. By missing these opportunities, companies may incur unnecessary costs that can erode their profitability.

During the COVID-19 pandemic, unstructured supply chain data played a significant role in causing delays and disruptions. With sudden changes in consumer demand and restrictions on transportation and manufacturing, companies faced unprecedented challenges in managing their supply chains. According to a report by Gartner, many companies lacked the visibility and agility to respond quickly to changing conditions (Gartner, 2020). Unstructured data made it difficult to anticipate and manage disruptions, leading to delays in the delivery of critical goods such as medical supplies and personal protective equipment. 

Unstructured supply chain data can also impact a company's brand reputation. In today's socially conscious world, consumers are increasingly concerned about ethical and sustainable business practices. When supply chain data is unstructured, it can be difficult to track and verify that suppliers are meeting ethical and sustainable standards. As a result, companies may unknowingly be associated with unethical practices, leading to negative brand perception and reputational damage.

So what can you do?

  1. Implement structured data management practices - Companies can mitigate the impacts of unstructured supply chain data by implementing structured data management practices. This involves organizing and standardizing data across the supply chain, making it easier to process and analyze. By having structured data, companies can identify cost-saving opportunities and ensure that suppliers are meeting ethical and sustainability standards.
  1. Adopt technology solutions - Technology solutions such as artificial intelligence and machine learning can help companies manage and analyze supply chain data more effectively. These solutions can identify patterns and trends in data that may be missed by manual analysis, providing insights that can help companies optimize their operations and reduce costs.
  1. Conduct automated supplier screenings - Conducting regular screening of suppliers can help companies ensure that they are meeting ethical and sustainability standards. Audits can also help identify potential risks in the supply chain, such as human rights violations or environmental damage, and allow companies to take corrective action before they become reputational issues.

Unstructured supply chain data can have a significant impact on a company's bottom line and brand reputation. By implementing structured data management practices, adopting technology solutions, and conducting regular screenings, companies can mitigate the impacts of unstructured supply chain data and ensure that they are making informed decisions that support their bottom line and brand reputation.


McKinsey. (2018). Supply Chain 4.0 in consumer goods. Retrieved from

The Guardian. (2017). Nike under fire over labour abuses in Asia factories. Retrieved from

Gartner. (2020). COVID-19 Exposes the Weaknesses of Traditional Supply Chain Strategies. Retrieved from

Marketing Team