How To Lose $1B

Irony is wasted on the stupid.
Oscar Wilde

In his 9/1/2021 article, Jon Hemmerdinger discovered Aerion, a company tied to its AS-2 supersonic business jet (C), was going to put its assets up for sale (E).  He further found Aerion had hired Development Specialists (DS) to manage the process, and DS had “not set a sale price, saying, ‘The market will tell them what their assets are worth.’”

I nearly choked on the irony – Now they knew market reactions counted.  They sure didn’t previously.

Had they studied their Demand Frontier (D) before they started, they would have known while there was a possibility of hitting their targeted sale figure of 300 units in a decade, those chances were slim.

The US Presidential Helicopter program had a similar fate, failing to realize its Demand limits (A, B), and lost over $4B. The USG sold it for parts at four cents on each dollar spent.

Aerion will sell intellectual property; its AS2 didn’t go into production.  Maybe they can get a dime on the dollar.  Based on their $4B development cost estimate, they likely spent $1B by the time they stopped.

Next time, model the market first.  It costs a tiny fraction of the losses suffered forgoing market analysis.

#hypernomics #innovation #technology #management #markets

Hypernomics Observations: 1st In A Series

“It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so.” Josh Billings

Paul Samuelson (A) wrote, “the equilibrium price, i.e., the only price that can last, is that at which the amount willingly supplied and the amount willingly demanded are equal.  Competitive equilibrium must be at this intersection point of supply and demand curves (Economics, 9th Ed., p. 63).”  We see an example of this phenomenon in B, as iron mines with progressively more costs form an upward-sloping supply curve intersecting a Demand curve at a single point.  For single-feature markets such as this one, Samuelson’s argument makes sense.

But Hypernomics researcher twins Cristina (C) and Sheila (D) (both played by my daughter, Meagan Swanson) observe dozens of prices for scores of flat screen TV models (E).  Cristina, who studies Value, suspects their sustainable prices and costs rise with desired features.  Sister Sheila, a Demand analyst, has a hunch that as quantities sold go up, prices and attendant costs must fall.  They both agree that the markets’ multiple and frequently changing prices negate a single-point equilibrium.  What takes its place?

#hypernomics #innovation #markets #management #economy #wsu

Dude, Where’s My Geometry?

Let me tell you how it will be/There’s one for you, nineteen for me
The Beatles, Taxman

Hypernomics loves geometry. It touches markets in many ways, including the Demand Frontiers, notably as they mature. Ignoring market geometry can lead to unwelcome multimillion-dollar surprises.

In 2014, CO and WA legalized recreational marijuana. CO taxed it 30%. Seeking vast riches from this new revenue source, WA taxed it about 108% (it varied by county). At years-end, WA, with a third more people than CO, made $51.7M in recreational pot tax monies. CO made $375.4M.

What WA didn’t understand because they didn’t study it was the cannabis demand curve slope. While we don’t have the WA pot data on hand (they do), we have a like curve for cars. If we apply another 100% tax on vehicles, the number sold, given the demand slope, falls by about 70%-80%. As steep as these reductions are, they still don’t match the WA experience in 2014, suggesting the actual slope for recreational cannabis in that year was even flatter than that for automobiles.

But there is no need to guess about this. If you study it, within statistical bounds, market geometry will “tell you how it will be.”

#innovation #hypernomics #business #technology #management

Two Trillion Dollars; One Hard Edge

If you inquire what the people are like here, I must answer, “The same as everywhere!”
Wolfgang Goethe

How did $2T in the 2018 new car market worldwide behave?

When it comes to market limits, it was in much the same way.

Figure A studies all 36 electric car models in 2018 for which there were sales quantities and prices, depicted by green dots. Added to them are 43 like figures for selected gasoline-powered models in the same year, shown by blue points. Note that while several models of each type do not sell well, those that make the most sales form a wall extending from the upper left to the lower right. The line described by the overlaid small red points is the 2018 Car Demand Frontier. Of all the similar curves we’ve studied, this one is the sharpest, with a minuscule Mean Absolute Percentage Error of 6.0%.

We see the differences between each sub-market in Figure B, where the prices paid for horsepower change between types and as sales grow.

Taking only 43 of the 100s of models of gas-powered cars, trucks, and SUVs offered represents a short cut. But, if we take the most popular (Toyota Corrolla) and expensive of them (Bugatti Chiron), it’s likely viable.

#innovation #market #future #economy #startups #management

Problem? What Problem?

A mathematical problem should be difficult to entice us, yet not completely inaccessible. It should be a guidepost on the mazy paths to hidden truths. – David Hilbert

In a space-limited outdoor diner we visited a while ago, we observed the seating arrangement in A. They had two tables for two and ten for four. Seven of the four-place tables had parties of two. So, I wondered – is the setup they had the best for the crowd they faced?

A report I found (see below) noted that restaurant parties of two outnumber four-person parties by over two to one. On average, there should be more tables set up for couples than for larger groups.

But the average condition may not be the usual one. Or the one they faced.

What to do?

Suppose the four left-most tables were modular. The establishment could separate them into eight two-place setups. Then they could seat all seven of their two-person parties and put a two-top in storage. Their capacity would go down by two (at least temporarily), but, in the case shown, occupancy could go up by 30%, as we see in B.

Restaurants make money through occupancy, not capacity. It’s important to know what problem you need to solve.

#sales #demand #restaurants #business #success #management #problemsolving

Restaurant Math – Thin Odds

You spend your life waiting for a moment that just don’t come/Well, don’t waste your time waiting – Bruce Springsteen, Badlands

You know the feeling you get when you walk up to a roulette wheel in a casino, place a $100 on 00, it comes up, you win $3,500, and then you let it ride on 00, hit it again, and walk out with $122,500? No? Me either.

The reason I don’t is that while it’s possible to come up with that combination, the chance of that happening on a 38-pocket wheel is 1/38 * 1/38 = 1/1,444 = 0.00069, about 7 times in 10,000 tries.

But that is more than twice as likely as the probability of a restaurant result I recently witnessed.

As we left a brewery, it had four open tables; each sat eight, B. Four parties waited, C, held in place by their policy not to seat parties of four or fewer in tables for eight. But, the chance of filling them up according to their plan and the data, A, is 0.13^4 = 0.00028, less than half that of our roulette gambit.

Meanwhile, those people stayed hungry. They and the restaurant both suffered.

We are, all of us, always playing games of chance. It pays to know the odds. Anybody up for blackjack?

#business #success #management #probability #sales #restaurant

Solve Profit First

Suppliers make products and see what markets will bear for them.  That’s precisely backward.

Instead, we can solve for profit potential first and discover product specifications second.

Suppose a market has products for which there are particular quantities, and prices demanded, as shown by the red dots.  We want to avoid competition, so we choose a Target Price, 1, that exploits a price gap.  Given a Demand Frontier, this sets a quantity limit, 2.

With some work (not shown), we find the market supports Features A & B with a green Value Surface (supportable prices based on those features), and that there’s an area of interest with no competition.  Linked to that region are the costs for 1 and 200 units of our new product.  If we constrain the problem (orange planes), we form an enclosure.

We then run Financial Catscans through this region.  Much like brain scans, they are virtual market section cuts.  At the optimum, we solve for the specs of Features A (3) and B (4), and the per-unit profit (5).  Per unit profit (5) times the demand limit quantity (2) yields max potential profit.

In the process, we’ve solved a 4D problem (Feature A, Feature B, Price, Quantity) from a 1D goal (profit).

#innovation #price #value #markets #profit #sales #manangement