Strategy Rethought

You can’t always get what you want/But if you try sometimes, well, you just might find/You get what you need
The Rolling Stones

There’s a trend in defense matters to want the absolute best always. We tried to get 132 bombers with long range, massive payloads, and very low radar signatures. We got 21 B-2s. The USAF wanted 750 frontline stealth fighters. It received 187 F-22s. Nobody, it seemed, ever paid close attention to the budgets allocated to missile-carrying aircraft.

We see the same thing now in the US hypersonic missile market. The Congressional Budget Office (CBO) thinks it can buy 100 of the Lockheed Martin AGM-183 ARRWs (or a like device) for an average price of $14M (2016$), with a range of 1000 miles. For an agency with “Budget” in its title, you might think they would have done the analysis below. If they had, they’d find their projection is 108 standard deviations past that market’s highly correlated Demand Frontier.

We’ve paid heavily to make our frontline bombers invisible to radar – don’t imagine they need to use missiles with stand-off ranges to accomplish hypersonic missions.

The solution is clear: make any hypersonic missile smaller and cheaper with less range and payload and fly it closer to the target.

China claims to fly them. Russia wants them, too. It makes sense that the USA must also have these hypersonic weapons.

But, as in all budget matters, there are limits. At the current CBO projection for the average cost of the 100th missile of that ilk ($14M in 2016$)1,2 that price point is 108 std devs past the budget limit. The solution is to make a much smaller missile with far less range and fly it closer to its targets.

Discovery, Invention, and Stocks

There is a difference between discovery and invention. A discovery brings to light what existed before, but what was not known; an invention is the contrivance of something that did not exist before.
Sir William Ramsay

There was a hidden discipline lying about, unseen from view. I unearthed it. When I did, I discovered HypernomicsTM. Its foundation, the Law of Value and Demand, states that

  1. Features determine Value
  2. Value drives Price
  3. Price limits Quantity Sold
  4. Quantity Sold is a Feature.

Useful by itself, it needed an invention to get results quickly.

That came as HypernomicaTM (formerly MEE4DTM) software, built by Shad Torgerson, Kent Joris, and me. It speeds up the analysis of complex markets.

Just over 44 months ago, we set it on the most complicated market we could find — that of stocks. Using only stocks from the S&P 500, our HypernomicsTM Fund (private, not open to the public) managed to beat it by 2.35X. The likelihood of that happening by chance is very much less than one in a trillion. At the same time, our fund outperformed Berkshire Hathaway A by a factor of 1.39X.

HypernomicaTM is available now; soon, we will begin classes on it. Be among the first to benefit from this discovery and its companion invention.

There’s a preferred way to compare the means of two groups and verify if their differences came about randomly.That is the Student’s t-test. William Sealy Gosset conceived it in 1908 (published under the pen name “Student”). That test (the two-tailed version) reveals that the likelihood that HypernomicsTM beat the S&P 500 by 2.35X over 44+ months was due to chance was 2.01 E-237.That test for HypernomicsTM against Berkshire Hathaway A, where we beat it by 1.39X in the same period, calculates the probability that the result was due to chance as 1.57E-152. In short, our algorithm, built using the HypernomicaTM Software, works. It will work for you, too, whatever your market might be.

Ants, Airbus, And Avoidance

Hit ’em where they ain’t.
Wee Willie Keeler

I finished a run the other day and stretched at the trail’s end. I looked down, and an ant caught my eye. Starting from position A1, it reached A2 and seemed to be going in a circle for a moment. But then, as its path widened to A3, A4, and A5, I realized it was doing reconnaissance! I raced home and found out that ants do that to “avoid hostile conspecific neighbours,” when considering where to set up camp, they use “a weighted additive strategy, the most comprehensive of consumer evaluations, to choose nests with the best combination of attributes.”*

People, of course, do the same thing.

In B, during 2009-2018, the business jet market had a lot of competitors offering various cabin and price combinations. Noticeably, though, there were a few prominent gaps in the market, akin to how the ants saw regions away from their neighbors. In Q1 of 2019, Airbus launched its A220-100 business jet in the most prominent open region (C). Along with the base airliner from which it came, this vehicle has several hundred orders, helping ensure its long-term viability.

When considering where to place your next product, work to avoid local opposition.

Introducing Hypernomics

“Everything should be made as simple as possible, but not simpler.”
Albert Einstein

The world oversimplifies.

Want more government revenue? Raise taxes. That’ll work. But why did Nevada make more cannabis tax revenue in 2019 than California with a tax rate of less than half of CA’s?

You’d like the safest helicopter in the world for the president? Put all the widgets on it. Oops, too many, it’s too expensive, Obama cancels it.

Do you like cool? I give you the DeLorean. But it’s under-powered. It won’t sell. The company goes bankrupt.

These are but a few cases where thin study led to bad outcomes. The solution for them and many more is to expand the analysis.

Don’t suppose you can get by with two dimensions when the problem begins with four.

Earlier, I called the field I found Multidimensional Economics. The research revealed that the forces within it exist beyond markets.

Thus, its new name: HYPERNOMICS.

Hyper-: Existing in more than three dimensions: hyperspace

-nomy-: A system of laws governing or a body of knowledge about a specified field: agronomy

-nomic: adj combining form

HYPERNOMICS studies forces working with and against each other in four or more dimensions.

#entrepreneurship #innovation #strategy #success #design

Demand By Proxy

You can’t always get what you want…

Finding data can be hard. Say you needed to model demand for Japanese bullet train travel, or the London to Paris run. Ideally, both firms would post these figures, and you could reduce that to insight. While you can locate that information for NYC cab data (see one of my previous posts), you won’t for The Shinkansen or the Chunnel. What to do?

Let’s suppose train operators match the number of seats offered by class to their demand. Why wouldn’t they? If they had too many high-priced seats open, they’d either drop the price or change the seating arrangement. Eventually, they would come to a configuration that works most of the time.

Below, we see the number of seats by price for Japanese (A) and European (B) high-speed rail services. By themselves, neither has enough data to form a viable study. Together, in (C), they reveal collective thinking from different sides of the planet – and it’s much the same. Yes, that’s only two routes. More would be better. But this shows us we can gain an understanding of a market using the files we have instead of the ones we want.

…If you try sometimes, you just might find…you get what you need (Mick Jagger/Keith Richards).

#innovation #demand #markets #marketanalysis #strategy

Fast Trains

“I knew I was going to take the wrong train, so I left early.”
Yogi Berra

So, what’s the right train?

Specifically, what are the most appropriate specifications for new trains we need to move people about here in the United States? Happily, the world already has some blueprints for success.

With over 50 years of experience and a perfect safety record, Japan has been using high-speed rail for some time. They’ve blazed a trail followed by Europe and later by China. Any new railroad project should consider the Shinkansen. In 2011, I had a look.

I found the Value (sustainable price) of a Japanese train ticket as a function of its trip length, MPH, and seat area (all P-values < 0.001) with a 91.3% adjusted R2. Tripling the distance traveled and speed pushed prices up by 88% and 27%, respectively. The added fare for distance made sense, but the speed change seems low. I used net speed, however, which likely understated the Value of the train going fast when it could.

Interestingly, as seat square area increased 47% from unreserved (A) past reserved (B) and green (C) to gran (D), ticket prices went up 88%.

What is the demand for this service compared to its Value? See the next post for some insights.

#innovation #technology #travel #strategy #tips