Optimizing Supply and Demand with Narrow Artificial Intelligence (AI)

Many a marketer has lamented, “I wish I knew exactly what my competitors are selling and where they’re successful.” The good news is that in many instances, the information to answer the query exists. The bad news; data sources needed for the query are often strewn across the internet, in trade association figures and sometimes third party databases.  Not to mention, the data could be siloed within your own company in various departmental datamarts.

A challenge like this calls for some really smart thinking and compute power. It’s also begging for an algorithm.

But what is an algorithm? Simply stated, an algorithm is a set of rules specifying how to solve a problem.  And while some algorithms incorporate randomness, they are usually instructions intended to move in steps. For example, a cake recipe is an algorithm of sorts, as performing steps out of order, or skipping steps won’t result in a very tasty fare.

And while some algorithms are quite simple in design, the real “beauty” of an algorithm emerges where complexity and scale predominate.  An article from the Financial Times, “Supply and Demand in the Sky” illustrates this point.

The Financial Times article mentions that in Europe, each airline knows how many passengers are flying its planes at any one time. This is, of course, because each airline has data from its own source systems such as reservations, resource scheduling, departure control etc. However, airlines have no information on how many passengers are flying competitor planes.  They can guess the answer based on routes and the maximum capacity (seats) of competitor airplanes, but they really don’t know.

Why all the fuss in knowing what the competition is up to? Rest assured, if you are an airline marketing executive trying to decide where to add another route, it’s definitely helpful to know if your competitor is flying full or nearly full airplanes.

But with multiple millions of investment dollars riding on your decision, an uneducated “guess” isn’t going to cut it. In this case, an algorithm designed by Phillipp Goedeking of Airconomy rides to the rescue!

Phillipp Goedeking has a PhD in biology, but his real love is taking a mathematical approach to complexity. To help airlines determine how many people fly from one city to another, Dr. Goedeking looked at flight frequencies and type of aircraft used and easily came up with the total number of seats available at any one time. So this number is the available “supply”, but what about “demand”?

To determine demand, Goedeking’s algorithm uses computational power from a bank of 150 computers to produce various demand estimates –essentially an educated guess. Then it matches these “guestimates” against thousands of sets of quantifiable transport data. The algorithm then refines its estimates by “comparing data through a very sophisticated trial and error process.”

Now, since airlines don’t share passenger data, it’s hard to know how close the algorithm’s “guestimates” are to truth. However airline interest in Goedeking’s algorithm suggests that he’s very close to the right answer.

Some key takeaways (there may be more) from the Financial Times article include:

  • Some business challenges exist that are too complex—or too time consuming for human intelligence
  • Well designed algorithms (with compute power) can help sort through complex data sets and choose best options from millions of opportunities
  • Some algorithms are designed to “learn”, improve and evolve over time
  • Some decisions are too costly to be left to educated guesses or gut decisions.  If you don’t know the answer, chances are someone like Phillipp Goedeking does

No algorithm, by itself, is sufficient to claim competitive advantage. Smart people and smarter processes also must be added to the mix. That said, data management capabilities and “intelligent” algorithms are a ticket towards helping some companies write their own future.

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