Big data - Modelling World 2015, London

892 by benmschmidt on Flickr (C) 19th century shipping visualized through the logs of Matthew Fontaine Maury (1806-1873), US Navy

Shipping

movements in the

19th century

Things are happening outside the freight industry

(and have been for some time)

Stage Coach Wheel by arbyreed on Flickr

<<<<<<<<<

Development of transportation technology has been

fairly linear

…for the last 5500 years

We are in the middle of a gigantic exponential development curve

beginning

Measure real-time

system behaviour

Emil Johansson - EJOH.SE

Manage complex systems

Image from: http://www.as-coa.org/watchlisten/ascoa-visits-rios-operations-center

Predict future events

Avoid unpleasant surprises

Domain knowledge critical!

See for instance: Waller, M. A. and Fawcett, S. E. (2013), Data Science, Predictive Analytics, and Big Data: A Revolution

That Will Transform Supply Chain Design and Management. JOURNAL OF BUSINESS LOGISTICS, 34: 77–84

Data scientists - the new superstars

"Data Science Venn Diagram" by Drew Conway - Own work. Licensed under Creative Commons Attribution-Share Alike 3.0 via Wikimedia Commons - http://commons.wikimedia.org/wiki/File:Data_Science_Venn_Diagram.png#mediaviewer/File:Data_Science_Venn_Diagram.png

Human resources

Reduction in driver turnover, driver

assignment, using sentiment data

analysis

Real-time capacity availability

Inventory management

Examples of applications in freight (Waller and Fawcett, 2013)

Transportation management

Optimal routing, taking into account weather, traffic congestion, and driver characteristics

Time of delivery, factoring in weather,

driver characteristics, time of day and date

Forecasting

Waller, M. A. and Fawcett, S. E. (2013), Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. JOURNAL OF BUSINESS LOGISTICS, 34: 77–84

DHL 2013: ”Big Data in Logistics”

Jawbone measures sleep interruption during earthquake

https://jawbone.com/blog/napa-earthquake-effect-on-sleep/

http://www.scdigest.com/ontarget/14-01-21-1.php?cid=7767

Package item(s) as a package for eventual shipment to a delivery address

Associate unique ID with package

Select destination geographic area for package

Ship package to selected distribution geographic area without completely

specifying delivery address

Orders satisfied by item(s) received?

Package redirected? Determine package location

Convey delivery address, package ID to delivery location

Assign delivery address to package

Deliver package to delivery address

Convey indication of new destination geographic area and package ID to

current location

Yes

Yes

No

No

smile! by Judy van der Velden (CC-BY,NC,SA)

Predictive shipping

Image: Alain Delorme, alaindelorme.com

The current model is focused on economy of scale and standardization

But the biggest problem in transportation is time.

There is not enough of it. Ever.

In S

ea

rch

Of

Lo

st T

ime

by

bo

ge

nfr

eu

nd

on

Flic

kr

The transport industry does not

like real-time decisions.

At all.

Batch-handling

Zip codes Zones

Time-tables

DSC_9073.jpg by James England on Flickr (CC-BY)

Strategic Tactical Operational Predictive

Time horizons Freight industry

Most (preferably all) decisions in the

transportation industry are made here. At the latest.

Uninformed, ad-hoc, and

probably non optimal,

decisions

Science fiction

Multicolour Jelly Belly beans in Sugar! by MsSaraKelly on Flickr (CC-BY)

Requirements on Big data specific to

freight transport

Geocoded data

Decentralised data Flows

Goods Resources

Value

Information

Products

Multiple perspectives

Strategic Tactical

Operative Predictive

http://dashburst.com/infographic/big-data-volume-variety-velocity/

En la cima! by Alejandro Juárez on Flickr (CC-BY)

3 mountaintops to climb…


3 data types

Mountaintop #1

Collection of data in real-time

Fixed Historical Snapshot


Mountaintop #1

Collecting data in real-time

5 data domains Vehicle Cargo Driver Company

Infrastructure/facility

at lea

st…

Length Weight Width Height

Capacity + other PBS-criteria

Emissions Fuel consumption

Route

Position Speed

Direction

Weight Origin

Destination Accepted ETA

Temperature + other state variables

Temperature + other state variables

Education/training

Speed (ISA) Rest/break schedule

Traffic behaviour Belt usage

Alco lock history

Schedule status (time to next break etc.)

Contracts/ agreements Previous interactions Backoffice support


Vehicle

Cargo

Driver

Company

Infrastructure/facility

Map + fixed data layers Traffic history

Current traffic Queue

Availability

DATA MATRIX

Say hi to the new sensors

…but they are still not enough

(Freight) companies want to share as little data as possible,

with as little friction as possible, to get the highest utility possible

Private Property by Nathan O'Nions on Flickr (CC-BY)

Mountaintop #2

Processing data in real-time


Locals and Tourists #1 (GTWA #2): London by Eric Fischer on Flickr

Mountaintop #2

Processing data in real-time


Mountaintop #3

Exploiting data in real-time


Real-time decision making not always successful…

Requirement


Transport 1


Transport 2


Requirement


Transport 1


Transport 2


No access!

Full access!

Smart access/guidance control

Fixed

Historic

al

Snapshot

Fixed

Historic

al

Snapshot

Requirements. Different.

Fixed

Historic

al

Snapshot

Port area

City centre

Freight terminal

Bridge

CASES (MANY)

CASES (MANY MORE)

Challenges

The Challenger by Martín Vinacur on Flickr (CC-BY)

Cross-disciplinary

Cross-industries

Cross-borders

It’s not business as usual.

This is the internet happening to freight

transport.

There is no ’usual’ anymore.

Hello Kitty Darth Vader by JD Hancock on Flickr (CC-BY)

It’s not business as usual.

Get used to it.

This is the internet happening to freight

transport.

There is no ’usual’ anymore.

Hello Kitty Darth Vader by JD Hancock on Flickr (CC-BY)

Big data - Modelling World 2015, London

Technology

Transcript of Big data - Modelling World 2015, London