What hurricanes teach us about the consumers and the economy…
By: John Horst, Cissp, Issap
Tags: Weather, Hurricane, Dorian, Central Banking, Federal Reserve

What hurricanes teach us about the consumers and the economy…

Deflation is the monster in the central banker’s closet

What if I told you Amazon.com was in the business of predicting hurricanes?

Seeing as they are getting into just about every business, it might not be a surprise. But that’s not the point here.

What matters is the technology which produces this hurricane forecast map. It is exactly the same technology which enables Amazon to pull off same-day delivery. Let me explain.

Knowledge Management and Storm Path Prediction

The field of “Knowledge Management” (KM) breaks things down into three basic groups. There is “data” — which in meteorology would be a reading like temperature, atmospheric pressure, dew point, etc. The data originates from an instrument at a point in time. If we are looking at consumer purchases, a product would be a point of data (let’s use hot chocolate and marshmallows — more on that in a moment).

When you take various points of data and bring them into context with each other you have what KM calls “information” — or data in context with other data. For hurricane forecasting, various meteorological data points are brought into a three dimensional information set called a “cube.” The first two dimensions would be like a spreadsheet. You might have latitude/longitude/altitude positions as rows and the various readings as columns. The spreadsheet itself would represent these readings and their locations at a single point in time. Time, then, becomes the third dimension of the cube. You basically have time slice spreadsheets stacked front-to-back as if in a filing cabinet.

The third element of KM is Knowledge. In order to elicit knowledge from the cube you employ a sophisticated statistical algorithm. There can be any number of such algorithms — when you hear the weather reporter refer to “the models,” they are referring to various algorithms which analyze the information in the cube.

These algorithms are fine tuned over time. We can go back to previous storm seasons and the information cube from a storm at its beginning (say, when it becomes a named storm), and we can take that “time slice” from the cube and run it through our model today to see what our model will “predict.” Of course, since we are taking information from the past, we already “know” how the storm will proceed. We can compare that with our model’s calculation and use the results to fine tune the model’s math.

As a result, when a storm like Dorian forms, we have numerous finely tuned models. We take today’s time slice from the information cube we are building in real time and run it through the models. The models then create future time slices for our information cube. Those future time slices (think of the spreadsheet of rows and columns) is then the underlying data which produces the map we see on the news.

Meteorology and Consumer Purchasing

Now let’s go from one weather extreme to another. It’s snowing outside and very cold, so you settle down to a book and a cup of hot chocolate and marshmallows.

Let’s say I own a hot chocolate company and I get from Big Data a comprehensive data set of consumer purchases of products like mine. I create an information cube, each slice showing me the store locations (rows) and the volume of sales of various products (columns — let’s just limit this to the hot chocolate and the marshmallows.)

I can now create a time-based “heat map” — this is a lot like the storm path map in that I can hit “play” and see sales volume (think storm intensity) increase and dissipate. On this heat map, the colors will reflect the volume of sales — dark red will show me when and where hot chocolate and marshmallows are flying off the shelves.

Now imagine we get meteorological data sets from the same period in the past. We bring the weather information into context with the consumer purchase information and create an entirely new set of information. When we press play on our heat map we see red and green ebb and flow to represent sales volume as it goes up and down. We also see weather data for the same times and places.

Now what I am looking for is a statistically significant correlation between weather data (as it gets colder and stormier in the winter) and sales volume of hot chocolate and marshmallows.

This was originally a heat map showing where fitness centers were located. Imagine the red shows us where our hot chocolate and marshmallows are flying off the shelves.

Weather models are remarkably accurate — and getting better

There is some consternation today about hurricane Dorian originally being forecast to hit the Florida coast, only now to be forecast to take a sharp turn north right before making landfall. Everyone is up in arms over the alarm because it was disruptive.

But when we look at this strictly as an exercise in Knowledge Management the models were remarkably accurate, especially if we just look at the predictions made a day or so into the future. (This is why the maps show us a cone surrounding the path — the cone gets wider the further out the map goes in time, reflecting the decreased certainty about the prediction.)

If we step back from the “breaking news” of a dangerous storm, we note that the run-of-the-mill weather forecasts we listen to on the news are really no different. The models are spitting out time slice spreadsheets a few days into the future and the guy or gal on TV is telling us what that data means for our commute or that picnic we had planned for Labor Day.

But back to my hot chocolate and marshmallow sales…

If I find a statistically significant correlation between weather data and sales volume, I can see where and under what weather conditions my product is selling best. I can then go to the weather models in the winter and I will “know” about a week in advance where the weather is going to be cold and stormy. I will also “know” this is likely to mean my sales volume will spike. What I cannot have, as a business owner, is for a potential customer to come to the shelf looking for my products, and not find them because I did not supply it to the store in the volume necessary to meet the demand.

This is basically how Amazon pulls off same-day delivery — but only in certain markets. These markets have enough of the right kinds of data Amazon’s models require to produce highly reliable predictions. Amazon’s “map” shows them when and where various kinds of products will be in demand in a few days. This allows them to move those products into regional warehouses so, when they are ordered, the products are in a close-enough proximity to the customer that Amazon can deliver on the same day.

And now a word from our sponsors…or not

You might have found my subtitle odd: “Deflation is the monster in the central banker’s closet.”

Janet Yellen as former Fed Chair?

If you are my hot chocolate and marshmallow customer, and you have reason to believe I will lower my price tomorrow, why would you buy today? This “deflationary” mindset, if we extrapolate it across the economy, means supply chains start to back up with an excess of supply. If my hot chocolate and marshmallow supply chain starts backing up, in the minds of central bankers I will have to lower my price. This will only confirm my customer’s suspicions and they will decide to wait a little longer, hoping to get a better price tomorrow. Writ large, at least as the conventional wisdom goes, a depression-causing spiral begins. Expectations of lower prices mean transactions are delayed. That delay prompts producers to lower their prices to move product. That confirms the expectations of lower price, so that expectation persists and transactions are further delayed — causing prices to drop… Wash, rinse, repeat.

Central bankers think this is a monetary problem. If my consumer has more money, they will be able to pay today’s price and my supply chain starts moving again. When the only tool you have is a hammer, every problem looks like a nail. So the central banker says we need to add money to the economy.

But what if deflation is not, nor has ever been, a monetary problem? What if deflation is a supply-chain management problem? What if the deficit is not in the money supply, but in the “knowledge” of where certain products are likely to have enough demand to support today’s price. What if the real tool needed here is Knowledge Management?

The central banker is worried about why people are not buying. As a businessman, I don’t care why people are not buying — I want to know where they are buying at today’s price and why they are buying at today’s price so I can supply the market at those places and times.

It seems the central banking crowd has remained unaware that there is this thing in today’s economy that was not around during our grandparents’ time. This thing is the computer, and the fatal flaw of central banking (and academic economics) is they haven’t the foggiest idea of how real people in the real economy of today actually use computers to manage their supply chains. There are two kinds of people in the economy — those who do real things, and those who talk and write about those who do real things… Central banks and academic economists are clearly the latter.

I actually learned to code on one of these!

If they would simply stop intervening in what they clearly do not understand, the utility of today’s Knowledge Management technology would be allowed to come to the forefront of the economy. Instead, by injecting more and more money, businesses are basically excused from having to compete against each other to see who can best use today’s technology to manage their supply chains.

Left alone, those who can, will. Those who can’t will go out of business (and probably end up teaching economics).