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Pyramid_3375x4875_lineShaft.indd 1 10/23/2017 3:05:16PM
toring its transit so the end customer has visibility into the
shipment’s arrival. But the real problems occur before the
vessel leaves the dock.
Shippers, freight forwarders, and non-vessel–operating
common carriers (NVOCCs) will reserve slots, only to
abandon the booking. Maybe they’ve found cheaper rates
elsewhere, the forwarder couldn’t get the box to the carrier
on time, or there wasn’t sufficient freight to be stuffed in
the box to justify the cost of tender. To compensate for the
lost business, carriers use a practice called “rolling,” where
a shipper’s cargo is abruptly moved to another sailing in
favor of a more-profitable customer. Shippers respond by
double-booking their shipments, reserving slots, sometimes on two sailing strings, just to get space aboard one.
Carriers aid and abet the process by overbooking their
capacity.
About one-quarter of all ship bookings never material-
ize because users find cheaper rates elsewhere, according
to the New York Shipping Exchange (NYSHEX), which
has created a digital capacity-allocation platform sup-
ported by real-time market data and binding contracts
with incentives for shippers to ship on the contracted
vessel and for carriers to make the contracted capacity
available. No-shows cost carriers about $23 billion a year,
NYSHEX has estimated. (The group did not respond to
requests for comment.) The cost of repositioning empty
containers to locations where they can be filled with
cargo represents another $15 billion to $20 billion
hit, according to consultancy BCG (formerly Boston
Consulting Group).
Much container volume moves under contract.
However, contracts have proved difficult to enforce,
and as a result, there are no repercussions for violating
them. Though steamship lines may be convinced their
customers are at fault, suing them and risking the loss
of future business is another matter. “No one wants to
end up in court to live up to the contractual obligations,”
said Craig Fuller, founder of TransRisk, a digital platform
expected to be rolled out later this year that would allow
participants to trade futures contracts for spot truckload
pricing. Like the liner trade, the U.S. truckload market
suffers from delivery variability caused by shippers and
truckers kicking one another to the curb in search of lower
or higher rates.
COMMON-SENSE STUFF
One obvious approach to ending the chaos is to develop
ironclad and enforceable contracts that hold shippers and
carriers financially accountable for failing to live up to
their obligations. At a recent industry conference, Patrick
McGrath, a senior vice president at German liner Hapag-Lloyd A.G., said that financial incentives should exist, but
that carriers must first be in a stronger position to insist
on them.
A tailwind might be found in the development of
blockchain technology, a distributed ledger that creates
a transparent and indelible trail of each transaction. At
the core of the blockchain concept is so-called smart or
self-executing contracts that are converted to computer
code, stored, and supervised by a network of computers
running the blockchain. A smart contract has binding
enforceability and has a built-in financial escrow that pays
out to the damaged party whether it be shipper or carrier,
according to Fuller of TransRisk, who also co-found-ed the “Blockchain in Transport Alliance” (BiTA), a
multi-industry group tasked with developing blockchain
standards.
BiTA members are working to perfect smart contracts
that would govern the penalties and commitments from
ship lines and NVOCCs, Fuller said. A first draft of the
language should be published sometime in the third quarter, he said.
Liners could take a page from other industries like airlines and hotels and offer discounts in return for shipper
flexibility on sailing times, said Philip Damas, head of
supply chain advisers at U.K.-based consultancy Drewry,
who spoke at the conference along with McGrath. At
the same time, users could also pay more for guaranteed
space, Damas said.
Artificial intelligence (AI), machine learning, and pre-