Applying machine learning to the freight industry

Applying machine learning to any industry.

Here's a checklist that I just came up with:

  Step 1. Pick an industry. Any industry.
  Step 2. Find a problem that can be formulated as a function.
  Step 3. Is that function non-trivial? If not, go back to Step 1.
  Step 4. List all the input parameters for that function.
  Step 5. Is any of them accurately observable? If not, focus on that parameter and go back to Step 2.
  Step 6. Apply some ML to it. (Choice of the tech wouldn't matter that much.)
  Step 7. Had some improvement?

A lot of the statements made in the article are rather questionable.

Applying "digital learning" to saying schedules and saying that is a data culture? If the input is bad, improve the input of the data. What is stated regarding outdated schedules highly depends on the company. Some companies might be bad, but why try to improve bad data?

It seems more that they don't know about the different schedules you usually have. One is the proforma. It tells you that there should be a weekly call on some weekday at some time. But then for practical purposes you look at the estimated times the vessel arrives.

In the article they pretend that a vessel suddenly departed a day early without anyone knowing. That's really not how that works. Cargo needs to be delivered to the terminal. You're not going to silently advance such a vessel and not be able to fill it up with cargo. Cargo which then stands at the terminal for 6-7 days and causing problems for the next call (too much cargo).

Then this one: > We’re still pretty much the only company that tries digitalization of an end to end shipment.

What about https://www.inttra.com/ ?

That the shipping industry as a whole is very inefficient is known. But it still seems like a lot of statements made in this article are rather questionable.

Flexport, which is a YC company, seems to be doing the same thing. Their guy is on here regularly and should have something to say about this.

Flexport, though, is an real freight forwarder. They take responsibility for end to end delivery as a common carrier. If you shipped through Flexport and it's stuck on a bankrupt Hanjin ship somewhere, it's Flexport's job to get it unstuck. Does Kontainer do that? They seem to be more like a price and schedule comparison site / lead generation system.

That was an interesting article. In the past I have worked with bulk commodity shipping (i.e ore + coal) rather than commercial freight. In my case the company owned the berth the and the unloaders. So it was a similar but slightly different problem space. The biggest issue was obtaining a reliable schedule from the ships. When I worked there the harbor master communicated directly with ships captain about expected arrival date etc and it was all stored on spreadsheets.

There is probably room in this space for some ML smarts similar to what is in the article (extrapolating arrival times based on weather, ships condition, Panama Canal data etc.) and forecasting ahead for a more accurate arrival date.

The benefit for doing this is because of something called "Demurrage" which comes into play. Demmurage is a special type of charge that is incurred when there are unloading delays. It wasn't unheard of because of the absence of accurate scheduling for multiple ships to arrive within close proximity of each other. When this happened the ships would be forced to lined up outside of the harbor either waiting for tug boat availability to tow them into berth or waiting for actual space on the berth. They'd be sitting there racking up demurrage charges. There is a pay off in optimising berth scheduling, whether its large enough to build a company around I'm not sure...

What happened to the gold standard of logistics industry, which is Operations Research (OR)?

OR has essentially been customized to this domain long before generalized ML appeared on the scene - I can't imagine some of-the-self sci-kit learn libraries have improved on this much.

Freight Industry is not that simple, though. In some ports, it envolves a huge infrastructure, country laws (inspections from health authorities, customs and even navy sometimes) and so on.

In that case, 20 calls are just the beginning. You can't ship a matchbox without hiring a company to do the (sometimes dirty) job for you. Data culture worth nothing in places like that.

A potential problem with this business model is that small-medium enterprises who lack logistics departments (target market for the product) tend to buy their goods Delivered Duty Paid, which means the seller arranges the transportation. If the seller is in China, they are required by law to appoint a licensed freight forwarder agent to place the ocean booking for them. So you really cannot play ball in the eastbound transpacific trade until you have a licensed operation in the PRC--which requires forming a Joint Venture corporation with a Chinese company.

And it sounds like this company wants to streamline online booking, SLI and B/L creation and shipment tracking, but I don't think I saw anything about handling ordering of or payment for the goods themselves, so it's still up to the buyer and seller to arrange purchase orders, commercial invoices, letters of credit, etc.

"We’re still pretty much the only company that tries digitalization of an end to end shipment."

Bullshit

HN interface comment: This list is displayed badly in Android Chrome. I get an invisible 24-char-wide horizontally scrolling box to see the items in. Definitely suboptimal for reading. fovc's comment below in this thread also demonstrates this issue in the list.

Does anyone else have this issue?