Rooting out ‘Fake News’ in Supply Chain Risk Management
by Kelly Barner
I have always asserted that anyone with procurement or supply chain responsibilities needs to be immersed in global news on a daily basis. It has always been important to apply a healthy skepticism to news, but today this discipline has become a greater challenge to perform. The explosion of media outlets and content channels means that finding information is no longer the primary challenge. Today the obstacle for anyone looking to inform themselves or their team on current events is to determine the relevance and veracity of the content in question. I’m talking about ‘Fake news’.
‘Fake News’ is the term du jour for content presented as fact without the proper substantiation or containing an implicit bias. But despite the attention it receives, the problem is not new. Any information that will be used to motivate a decision must be triangulated, or validated, in more than one source. In addition, the sources themselves must be quality checked for reliability and editorial rigor.
All news, whether covering business or entertainment, sports or local issues, competes for the scarce discretionary time of readers. In some cases, this leads to ‘click bait’ style tactics, where the title or headline leads readers to make an assumption that is not supported by the body of the article.
Modern attention spans are decreasing because there is so much content competing for our eyes, ears, and minds. It is tempting to stop reading after the title or leading paragraphs, but this does not meet the burden of quality assurance. Given that most supply chain risk managers are already spread very thin, any approach to separating real news from fake must be realistic given the expectations of efficient decision making.
First: Scan for Data
Don’t even try to read the article word for word at first. If the title or social media tease suggests a fact based theory, there should be actual data in the content. If there is no data – meaning numbers, figures, or charts – move on. If there is data, it should be clear what the source of that data is. Did the author do research? Was it a third party survey? An internet poll? Three guys in a garage making up numbers? If you are going to make a recommendation to the business based on this information, you deserve to know the source of any facts presented in support of a thesis statement. In addition, supply chain professionals should expect to be asked for the source of specific, relevant facts.
Second: Opinion or Fact?
In a value-oriented business environment, we know that numbers can not hope to tell the whole story. But is the author communicating facts or just sharing their opinion? A common approach is to make a statement, support it with a fact from a reputable third party, and then connect the statement to the main point of the article. Know the author and look up the attributed sources – authors and sites. Check out the logos or ads in the margins – could this content be connected to the publishing source’s revenue model beyond just drawing traffic?
Third: White list or black list?
If you’ve ever read anything about Internet security, you know that there are two philosophies: black listing (where sites are assumed to be harmless until they demonstrate otherwise) and white listing (where sites must demonstrate that they deserve to be trusted before they are allowed through). Although AI is not yet ready to completely take over the contextual evaluation of news sources as real or fake, we can find an effective approach using systems logic. Each source must have earned authority and influence based on their reporting accuracy and data veracity. In a world where we are accustomed to searching the Internet by topic rather than starting with a trusted source and then searching for relevant information, this may bring about a real change in approach when it comes to supply chain news.
Supply chain risk management requires fact-based decision making as well as an appreciation of context. The relevance of news can be determined through automation based on a set of pre-determined criteria. As of today, however, it is still up to the humans involved in the effort to determine whether the data, fact-basis, and reputation are present to justify making a new decision or advocating for a strategy shift in the face of risk.