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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

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A Sufficient Condition

For a successful technology, reality must take precedence over public relations, for Nature cannot be fooled
Richard P. Feynman

Feynman’s observation about truth is one that can move past physics, where he won the Nobel prize.

It happens that in all markets, we collectively form relationships about how we respond to product features and prices. It is our Nature.

As we build, say, a helicopter for the President of the United States (A), we might be inclined to keep adding to it: unprecedented speed, a larger cabin, extra defensive systems. After all, we might tell ourselves; there won’t be a more important thing in the sky. It is a necessary condition to protect it and everyone inside.

The bonus features drive an increase in Value – and Price.
But at a group-induced limit we call the Demand Frontier, sales stop. We do not have any more monies to buy more of the product in question past the points on that line. Trying to exceed the Demand Frontier is a sufficient condition to stop any program.

In the case of the VH-71, USG requirements creep let it become untenable. All of that could have been avoided by analyzing the market as in B.

Doing a market analysis is hard.

Losing $4.2 billion is harder.

#innovation #demand #marketanalysis #business

 

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Finding Your Niche

Wee Willie Keeler knew a thing or two about baseball. The Hall of Famer still holds the National League hitting streak record, 45 games over two seasons. He summed up his approach with “Hit ‘em where they ain’t.” It turns out that’s sound advice for entering a market, too.

In the early 1970s, the airline industry embraced the then-new Boeing 747 wide-body. Lockheed and McDonnell Douglas both wanted some twin-aisle profits as well. They came up with the L-1011 and DC-10, respectively. While they had obvious design differences, from the standpoint of their customer airlines, they were virtually identical, with highly similar specifications and prices. Neither had a corner in the market – they shared the same spot, and would have to split the sales.

Lockheed only sold 250 L-1011s; its break-even point was 500 units. With a lower target, McDonnell-Douglas managed to squeak past its break-even value of 438 planes, as it sold 446 DC-10s, eventually offering engine options and added range to distinguish it from the L-1011.

But neither model was a financial triumph.

Nothing guarantees success in the market.

But mimicking the competition reduces your chances.

#innovation #newproduct #business #success #branding #competition #entrepreneurship

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Steep Learning Curves

A 2016 EPA paper called “Cost Reduction through Learning in Manufacturing Industries and the Manufacture of Mobile Sources” estimated automobile learning curves at 84% to 88%.

Some think “steep learning curves” are a bad thing. Used in their first sense, i.e., how quickly someone learns, it’s the opposite. Learning or experience curves measure time reductions to do a task as repetitions of it double, as percentages. If it took you 10 hours to do a job the 1st time you did it, 8.4 hours the 2nd and 84% of 8.4 hours or 7.1 hours the 4th time, you’re on an 84% learning curve. If you took 10 hours for the 1st job and 8.8 hours for the 2nd one, and 88% of 8.8 hours or 7.7 hours for the 4th, an 88% curve described your experience.

Our 1981 auto industry work found that Product Demand fell at the equivalent of an 87.2% curve. As shown below, eventually, a flatter 88% learning curve for a car could intersect its Product Demand Curve. Then, production ceases, as its cost exceeds its sustainable price.

A steeper cost curve of 84% never approaches the Product Demand Curve; instead, it diverges from it. In such cases, Demand Frontiers limit sales.

Moral of the story: Steep learning curves are good.

#innovation #learningcurve #sales #steeplearningcurve

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Bounding Problems

In countless games, the parameters, once set, never vary. There are only a given number of spaces on the chessboard. American football always uses an elliptical spheroid built to strict specifications. All NBA basketball rims are the same diameter, ten feet in the air.

Markets seem different. They don’t have instruction manuals. At first glance, it would seem you could do anything you want in them.

Still, they have rules. Not understanding them can sink a project.

In addition to DeLorean not understanding the value of horsepower (see the last post), they also failed to appreciate the Demand Frontier they faced in 1981. As A shows us, DeLorean thought they could exceed that limit by nearly two standard deviations, despite no one else beating it by half that much.

In this and many other markets, Product Market Demand Curves form. Always flatter than the overall Demand Frontier they build, they describe the product price limits as quantities sold increase. These curves set boundaries producers must consider before they enter any market. Ignoring them can have disastrous consequences.

How do Product Market Demand Curves compare to their Learning Curves? Look to the next post for answers.

#markets #demand #pricing #boundary #innovation #business

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DeLorean CSI

In October 1982, the US government charged John DeLorean with cocaine trafficking in a deal he thought would stave off bankruptcy for his self-named company.

How did it all go wrong?

His DeLorean Motor Company sports car (A) came with several sales features. It had a rear-mounted engine, brushed stainless steel body panels, and its iconic gull-wing doors. Its original designation was the DMC-12, the “12,” reflecting its price, in thousands. But, when it came time to start taking orders, DeLorean dropped the name and raised the price.

The renamed DeLorean entered the market with 130 horsepower, priced at $25,000. As we see in B, no car with that amount of power came close to its price. The 1981 Audi 5000 Turbo, with the same horsepower, sold for $7,000 less.
Statistics reveal the sustainable prices for 1981 cars were a function of their horsepower and units sold (both P-values < 0.01).

As shown in C, the DeLorean’s predicted sustainable price was $15,500; its posted price was nearly three standard deviations too high.

To sell all 7,500 units it produced for $25K, D shows us its installed horsepower should have doubled to 262. As 1981 ended, it only sold 3,000.

Moral of the story: do market math.

#innovation #marketanalysis #valueanalysis #pricing #cars

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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

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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

 

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Electrifying

Helicopters are highly versatile machines able to take off and land in tiny areas. They’re perfect for landing on buildings in dense urban areas. But since they beat the air to death with thunderous rotors turned by noisy jet fuel turboshaft engines, they’ve been banned from many a city. What to do?

Let’s go electric.

Nine years since the first manned all-electric helicopter flew, https://lnkd.in/eXi3bV8, there’s a race to build versions that can take passengers. With battery energy densities tripling since 2010 and prices falling simultaneously, there’s a vast potential market. Several firms are in it.

Perhaps the one closest to putting an electric rotorcraft up for sale is newcomer Volocopter, with its 2X (in A). It uses 16 motors, one to a rotor, and carries a pilot and one passenger.

Longtime manufacturer Bell offers its Nexus 4EX (B). More capable, it transports four passengers and a pilot. An entirely different configuration, it has four ducted, rotating propellers mounted atop wings.

If flights in these new machines are substitutes for cab rides, given Yellow Cab data for NYC in January 2017 (C & D), which part of the market would you address first?

#innovation #technology #entrepreneurship #travel #business

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Economic Entanglement

Inventors need manufacturers who require operators. The fate of each depends largely on the others. If just one player fails, an entire enterprise could sink; this is economic entanglement.

Such arrangements are highly intermeshed. Revenue to the inventor, 2, is a cost to the manufacturer, who pays license, technical development, and royalty fees to gain access to technology, 1. After a manufacturer finishes development and begins production, it wins sales, and their revenue from that, 3, is a cost to operators who buy it.

An operator may agree to pay for part of the manufacturer’s development costs to be among the first to gain access to the latest tech. Once it begins to receive and use the new products, it earns revenue from its users, 4.

Key to the success of such operations is satisfactory financial metrics, 5 and 6, as determined by each participant’s performance and Return On Investment (ROI) parameters to which the parties agree, 1. In this case, under the Manufacturer Costs & Loan, we see their build costs have ballooned to 200% of their original prediction, which might sink the program, if not for exercising a significant portion (87.3%) of an available USG grant.

#innovation #entrepreneurship #economicentanglement #partnerships