Nate Silver, author of The Signal and the Noise: Why So Many Predictions Fail – But Some Don’t, sets forth a very cogent warning. “[H]uman judgment,” Silver says, “is intrinsically fallible. It’s hard for any of us… to recognize how much our relatively narrow range of experience can color our perception of the evidence.”


TMIBook cover: The Signal and the Noise

I learned recently that there is texting shorthand for “too much information” and it’s “TMI.” There are lots of ways to end up getting TMI. We have all probably been in such circumstances—both socially and in our jobs.


There are many, many companies and supply chains undertaking very large scale projects—and some at huge expense—in attempt to gather more and more data. These “big data” projects may end up backfiring if there is no clear understanding of the dangers associated with TMI.


Nate Silver explains how this goes beyond merely have our experience coloring our perceptions of the data we gather:


The instinctual shortcut that we take when we have ‘too much information’ is to engage with it selectively, picking out the parts we like and ignoring the remainder, making allies with those who have made the same choices and enemies of the rest.


Therefore, Silver has concluded: “We face danger whenever information growth outpaces our understanding of how to process it.


Here is the problem

“The numbers have no way of speaking for themselves,” Silver explains. So, instead, “[w]e speak for them. We imbue them with meaning.”


Unfortunately, “we may construe them in self-serving ways that are detached from their objective reality.”


Because sales and marketing folks may have a self-serving desire more sales, they may interpret certain factors in the data as “signals” when, in fact, these factors are not signals, but noise.


And, further, because there is no hard evidence found in the data to refute the interpretation made by sales and marketing, the entire supply chain may be affected by this interpretation of the data.


“Unless we work actively to become aware of the biases we introduce, the returns to additional information may be minimal—or diminishing.”


Silver wisely reminds us, “Unless we work actively to become aware of the biases we introduce, the returns [read: benefits, returns on investment] to additional information may be minimal—or diminishing.”


If we are not careful, we will invest heavily in attempts improve and increase our data collection effort; or spend significant amounts of time, energy and money attempting to slice-and-dice large volumes of data in (what may prove to be) vain attempts to make sense of it all.


But, “[w]hile the quantity of information is increasing [vastly, every day], the amount of useful [or relevant] information almost certainly isn’t. Most of it is just noise, and the noise is increasing faster than the signal. There are so many hypotheses to test, so many data sets to mine—but a relatively constant amount of objective truth.” This is Nate Silver’s assessment, and we find this is never more true than in supply chains.


Only relevant information matters

What supply chains really need to know can be boiled down to a few simple things:

  1. Are the supply chain’s buffers (i.e., stock buffers, time buffers, capacity buffers) strategically positioned?
  2. Are the supply chain’s buffers strategically sized?
  3. Are the supply chain’s buffers dynamically managed?
  4. How likely is each buffer to protect FLOW in the supply chain at this present time?
  5. What are the supply chain’s priorities for actions at this moment?


If you are not able to find the answers to these questions without digging through screen-after-screen, or the review of one or more (potentially large) reports, then your access to relevant information needs to be dramatically improved.


We can help.


Your turn

How are you and your supply chain? Too little information? Too much information? Can you answer the five questions in five minutes or less?


If not, we can help. Leave your comments below, or feel free to contact us directly, if you prefer.



Read more: Silver, Nate. The Signal and the Noise: Why So Many Predictions Fail - But Some Don't. New York, NY: Penguin Books, 2015.


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