How Big Data Influences Logistics
Big Data
The most-often cited definition of Big Data according to supplychaindive.com
is the one from Gartner that says “Big Data is high-volume, high-velocity and/
or high-variety information assets that demand cost-effective, innovative forms
of information processing that enable enhanced insight, decision making, and
process automation.”
How Big Data influences logistics
Here are 5 examples of how Big Data influences logistics:
-
Last-mile of shipping
Almost 30% of the delivery cost of a package comes from the last mile. This
is because of different challenges during the delivery.
Trucks have to park near the destination, they need to walk to the final
address and have to take stairs or an elevator to get to the right floor. Also
some items have to be signed for and when the customer is not there, nobody can
receive the package. Also the delivery personnel must be very careful not to
damage the package during the last part of the delivery.
Big Data can make this whole process easier. By tracking the delivery guy
at every moment, the logistics companies can see patterns in how long it takes
to deliver… and they can change some things. For example, Dr. Winkenbach said
that his data showed that “deliveries in big cities are almost always improved
by creating multi-tiered systems with smaller distribution centres spread out
in several neighbourhoods, or simply pre-designated parking spots in garages or
lots where smaller vehicles can take packages the rest of the way.”
-
Reliability is more transparent
It can be interesting for the shippers, carriers, and costumers when the
whole delivery process is tracked. When a shipment is going to be late, the
carrier has to know how he can prevent the other packages to be late at
destination. With the data the carrier companies can negotiate with shippers by
showing how often they deliver on time.
-
Optimized routes
Why optimization? Optimization helps to save money and to avoid that
shipments come in late.
The Big Data can keep in mind fuel costs that change, highways and roads
that can be temporarily shut down, the number of vehicles at your disposal, the
weather conditions and with this the routes can be optimized so that the costs
are the lowest, but everyone gets their package on time.
An example:
In 2004 UPS implemented a right-turn policy. With this policy UPS says that
the company uses 10 million gallons of fuel less and delivers 350,000 more
packages every day. The drivers only turn left 10% of the time. The left turns
are only used when absolutely necessary.
-
Higher quality shipping for sensitive goods
For perishable goods Big Data can also be very interesting. For example, a
truck is transporting ice cream and desserts. If you have a temperature sensor
inside, the state of the goods is monitored the whole time. The computer can
give the driver alternative ways to drive if it sees that the original route
would make the ice cream melt because of traffic, etc.
-
Automation of Warehouses and the Supply Chain
Because of Big Data automated systems can function through intelligently
routing.
Recources
(2016). How
your company can use Big Data analysis to improve last mile deliveries.
Retrieved from https://www.beetrack.com/en/blog/big-data-analysis-in-last-mile-deliveries
Lebied, M.
(2017). 5 examples of how Big Data in Logistics can transform the supply chain.
Retrieved from https://www.datapine.com/blog/5-examples-how-big-data-logistics-transform-supply-chain/
Rands, K.
(2017). How Big Data is disrupting the Logistics Industry. Retrieved from https://dzone.com/articles/how-big-data-is-disrupting-the-logistics-industry
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