Supplier Vetting: A Numbers Game?

Author: Jared Smith, Co-Founder


A mere 20 years ago, the internet was a relatively barren place with only one million websites. When a company sought necessary, tangible data on a potential supplier, spreadsheets and word of mouth were still the primary means of determining whether or not the supplier was qualified and trustworthy enough for hire.  

Fast forward to today. The internet – with more than one billion websites worldwide - has become an invaluable tool for finding supplier statistics that would otherwise likely remain undiscovered. In fact, a ubiquitous amount of information is now available to anyone, anytime, anywhere. Putting it together in one database – big data— offers never before insight that companies can use to:

  • Increase supply chain visibility
  • Reduce business risks
  • Maintain compliance  

However, even with the unprecedented technological strides made in the last 20 years, where companies get data and whether they can rely on those sources remains one of the biggest challenges facing supply chain risk management today. And with the globalization of supply chains and the continual flood of information inundating everyone in our digital world, numbers without clear context are simply useless.

SupplyChainBrain recently reported a high-level break-down of which numbers matter most when it comes to supplier vetting:

  • Primary data – Self-reported and, therefore, the most easily accessible type of data by a large margin. There's simply too much of this data to use, so companies should prioritize what they think is reliable and most relevant, targeting a reliance of not much more than 50 percent of their vetting decisions on primary data.
  • Secondary data – In general, more accurate than primary data since it does not come from the actual supplier. This source includes public record, such as contractor licenses, and relevant third parties, such as trade unions who hold training records. Unfortunately, we’re still years away from having regular access to good, reliable secondary data, as more investment from trade organizations, government entities and companies willing to collaborate with other companies is needed before this will occur.
  • Algorithms – Advanced algorithms can accurately score data and rank suppliers by checking patterns of bad information. Supplier information found in online databases should not be taken at face-value, if it has not been scrubbed using algorithm and ideally, machine learning to constantly improve itself. 

Although Software as a Service (SaaS)-based solutions can vet suppliers better than any person could by making their best guess or manually inputting data, hard numbers still need to be supplemented by human insight. With more data available now than ever before, it often takes a final evaluation from a flesh-and-blood individual to catch anomalies that might otherwise have been misinterpreted by a computer in the same way that you want a driver behind the wheel of a self-driving car to avoid accidents until it's been proven safe over and over. In both scenarios, lives are at stake.

When it comes to fine-tuning critical intelligence, especially in the case of supplier relations, it’s clear mathematics offers insights efficiently and unbiasedly and will continue to improve the vetting process. In the meantime, don’t discredit a second glance at the numbers from a human expert. 

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