big data in freight transport

Big data from a freight company perspective

Per Olof Arnäs, PhD

Chalmers University of Technology Gothenburg, Sweden

about.me/perolofarnas

Slides online: slideshare.net/poar

Film by Waze

Demographic and social

change

Shift in economic

power

Rapid urbanisation

Technological breakthroughsClimate

change and resource scarcity

5 GLOBAL TRENDS

Source: PWC (google: pwc megatrends 2014)

Things are happening outside the freight industry

(and have been for some time)



1957



Image: Richard Hancock, twitter.com/CanaryWorf

2015

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

beginni

ng

A new global eco system where new types of, knowledge based,

industries compete with traditional ones

http://jaysimons.deviantart.com/art/Map-of-the-Internet-1-0-427143215

355:365:2015BWH by hermitsmoores on Flickr (CC-BY,NC,SA)

Make analogue information digital

Digitization:

Mob

ile W

orld

Con

gres

s 201

6 by

Kār

lis D

ambrān

s on

Flic

kr (C

C-BY

)

Increased use of digital technology

Digitalization:

Mob

ile W

orld

Con

gres

s 201

6 by

Kār

lis D

ambrān

s on

Flic

kr (C

C-BY

)

Increased use of digital technology

Digitalization:

Make analogue information digital

Digitization:

Both are important! (and interesting)

Ominous Windmill by Conrad Kuiper on Flickr (CC-BY,NC,SA)

Digit(al)ization is not a trend

Ominous Windmill by Conrad Kuiper on Flickr (CC-BY,NC,SA)

Digit(al)ization is not a trend

It is a force of nature

Process improvement

Servic

e

developm

entInfrastructure

development

Customer controls last

mile

Faster and better

returns

Better delivery

experience

Secure identification on pickup/delivery

Distribution of food

Home delivery

Support companies that want to add E-commerce to their business

Collect-in-store

Local same-day delivery

Improved delivery note

Delivery and pickup during

weekends

Marketing of the E-channel

Sustainable and climate friendly

3PL targeted at E-commerce

Faster, more reliable and secure

deliveries in Europe

Better infrastructure on

consumer side

Better security

Source: Svensk Digital Handel 2014 Bo Zetterqvist

Areas of development for logistics companies in relation to e-commerce

Process improvement

Servic

e

developm

entInfrastructure

development

Customer controls last mile

Faster and better

returns

Better delivery

experience

Secure identification on pickup/delivery

Distribution of food

Home delivery

Support companies that want to add E-commerce to their business

Collect-in-store

Local same-day delivery


Delivery and pickup during

weekends

Marketing of the E-channel

Sustainable and climate

friendly

3PL targeted at E-commerce

Faster, more reliable and

secure deliveries in Europe

Better infrastructure on

consumer side

Better security


Areas of development for logistics companies in relation to e-commerce

Digital development

needed in freight

transport

Customer controls last mile

Faster and better

returns

Better delivery

experience

Secure identification

on pickup/delivery

Collect-in-store


Sustainable and climate

friendly3PL targeted at

E-commerce

Faster, more reliable and

secure deliveries in

Europe

Better security


Digital development needed in freight transport

Process improvement Use ICT to make the system more efficient

Real-time decision making, footprinting, better digital interaction between stakeholders

Service development Use ICT to create new services

Digital information enables new business models

Infrastructure development Use ICT to interact with infrastructure

Location Based Intelligence etc.

Challenges

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

Low profit margins Social issues

Fragmented industry

Data all over the place, but

not where most needed

Large investments

Image: Alain Delorme, alaindelorme.com

The current model is focused on economy of scale and standardization

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

Business processes Infrastructure

Paper based Phone

Papers

Road signsAnalogue

tools

RDS

Monitor fuel

cosnumption

Digitalisation version 0 0.5 1.0 1.5 2.0

E-m

ail

Fax

TMS

-

systems

Excel

Route planning

GPS for n

avigatio

n

Electro

nically

genera

ted

freig

ht docum

ents

Barcodes

RFI

D-t

ags

Simple order handling

Advanced order handling

Open interface

Web

based UI

Platform based

systems

Hardw

are-

oriented

Data collection

systems

(prop

rietary)

Com

munication w

ith

vehicles

E-invoice

Web based

booking

Route optimisation

Th

e so

cia

l web

Open connectivity

Integrated

prognosis

Data collection

systems (open)

Tolling

systems

Webservices with

traffic data

Dyn

amic

ro

utin

g sy

stem

s

Pe

rform

an

ce

Ba

sed

ac

ce

ss

Pe

rfo

rma

nc

e

Ba

sed

ac

ce

ss

Mas

hups

Mul

tiple

dat

a so

urce

s

Pro

be

dat

a

Individual

routin

g

inform

ation

Platooning

PlatooningExceptions handling

Sm

art g

ood

s

Manual

Computers

Software

Functions

Dis

trib

uted

deci

sion

m

akin

g

Goods as bi-

directio

nal

hyperlink

Paper based

CC-BY Per Olof Arnäs, Chalmers

Goods VehicleBarcodes

RFIDSensors

ERP systemsTMS systems

E-invoicesCloudbased

services

Order handlingDriver supportVehicle economics

RDS-TMCRoad taxesActive traffic support

Predictive

maintenance

2014-10-15

Smart access/guidance control

• Data amounts increase greatly

• There are data gaps/silos preventing development

• Lack of standards

• Personal data privacy is a long-term threat

• Lack of talent/capacity to handle foreseen need

https://ts.catapult.org.uk/documents/10631/169582/The+Transport+Data

+Revolution/99e9d52f-08a7-402d-b726-90c4622bf09d

Gartners Hype Cycle for Emerging Technologies

Augmenting humans with technology

Machines replacing humans

Humans and machines working

alongside each other

Machines better

understanding humans and

the environment

Humans better understanding

machines

Machines and humans

becoming smarter

Gartners Hype Cycle for Emerging Technologies

Source: Gartner July 2015

Could affect transportation and logistics

http://www.dhl.com/en/about_us/logistics_insights/dhl_trend_research/trendradar.html

2011 2013 2015

”Big data is an all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process using on-hand data management tools or traditional data processing applications.”

- Wikipedia

2015

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

Jawbone measures sleep interruption during earthquake

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

Not statistics

Exhausted by Adrian Sampson on Flickr (CC-BY)

just

Not Business

Intelligence

Basingstoke Office Staff Desk "No computer" by John Sheldon on Flickr (CC-BY,NC,SA)

just

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

Varela Rozdos, I and Tjahjono, B, 2014 ”BIG DATA ANALYTICS IN SUPPLY CHAIN MANAGEMENT: TRENDS AND RELATED RESEARCH”, 6th International Conference on Operations and Supply Chain Management, Bali, 2014

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

Requirements on Big data specific to

freight transport

Geocoded data

Decentralised data

FlowsGoods

Resources

Value

Information

Products

Multiple perspectives

StrategicTactical

Operative Predictive

Bitcoin, bitcoin coin, physical bitcoin, bitcoin photo by Antana on Flickr (CC-BY,SA)


Block chain technology

Records transactions and data among actors that do not trust each other

Fully decentralized

https://news.bitcoin.com/nimber-disrupts-logistics-system-blockchain-matters/

http://www.economist.com/news/leaders/21677198-technology-behind-bitcoin-could-transform-how-economy-works-trust-machine


http://www.coindesk.com/how-bitcoins-technology-could-make-supply-chains-more-transparent/

https://news.bitcoin.com/future-use-cases-blockchain-technology-parcel-tracking-regardless-courier/

Block chain technology

Records transactions and data among actors that do not trust each other

Fully decentralized

Strategic Tactical Operational Predictive

Time horizons

We are approaching this boundary

…and we are starting to move past it!

Real-time!

The Action of New York City by Trey Ratcliff on Flickr (CC-BY,NC,SA)

Real-time (data driven) decision making

Data collection Data processingData exploitation

http://mindconnect.se/http://waze.com

https://mydrive.tomtom.com/

http://waze.com

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

Collection of data in real-time

5 data domainsVehicle CargoDriver Company

Infrastructure/facility

at leas

t…

LengthWeightWidthHeight

Capacity+ other PBS-criteria

EmissionsFuel consumption

Route

PositionSpeed

Direction

WeightOrigin

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

Fixed Historical Snapshot

Vehicle

Cargo

Driver

Company

Infrastructure/facility

Map + fixed data layers Traffic history

Current traffic Queue

Availability

DATA MATRIX

Say hi to the new sensors

http://mobsentech.com

Mountaintop #2

Processing of data in real-time


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

Mountaintop #2

Processing of data in real-time


Mountaintop #3

Exploiting data in real-time


IFTTT.com

IF This, Then That

Connects unrelated services

Real-time decision making not always successful…

CASES (MANY)

CASES (MANY MORE)

Smart access/guidance control

Requirement

Transport 1

Transport 2

Requirement

Transport 1

Transport 2

No access!

Full access!

Requirements. Different.

Port area

City center

Freight terminal

Bridge

7Big Data Best Practice Across Industries

Usage of data in order to:Increase Level of TransparencyOptimize ResourceConsumption Improve Process Qualityand Performance

Increase customersloyalty and retentionPerforming precisecustomer segmentationand targetingOptimize customerinteraction and service

Expanding revenuestreams from existingproductsCreating new revenuestreams from entirelynew (data) products

Exploit data for: Capitalize on data by:

New Business Models

Customer Experience

OperationalEfficiency

Use data to: • Increase level of

transparency• Optimize resource

consumption • Improve process quality

and performance

Exploit data to: • Increase customer

loyalty and retention• Perform precise customer

segmentation and targeting • Optimize customer interaction

and service

Capitalize on data by: • Expanding revenue streams

from existing products • Creating new revenue

streams from entirely new (data) products

New Business ModelsCustomer ExperienceOperational Efficiency

Figure 4: Value dimensions for Big Data use cases; Source: DPDHL / Detecon

2.1 Operational Efficiency

For metropolitan police departments, the task of tracking down criminals to preserve public safety can sometimes be tedious. With many siloed information repositories, casework often involves making manual connection of many data points. This takes times and dramatically slows case resolution. Moreover, road policing resources are deployed reactively, making it very difficult to catch criminals in the act. In most cases, it is not possible to resolve these challenges by increasing police staffing, as government budgets are limited.

One authority that is leveraging its various data sources is the New York Police Department (NYPD). By capturing and connecting pieces of crime-related information, it hopes to stay one step ahead of the perpetrators of crime.6 Long before the term Big Data was coined, the NYPD made an effort to break up the compartmentalization of its data ingests (e.g., data from 911 calls, investigation reports, and more). With a single view of all the informa-

tion related to one particular crime, officers achieve a more coherent, real-time picture of their cases. This shift has significantly sped up retrospective analysis and allows the NYPD to take action earlier in tracking down individual criminals.

The steadily decreasing rates of violent crime in New York7 have been attributed not only to this more effective streamlining of the many data items required to perform casework but also to a fundamental change in policing practice.8 By introducing statistical analysis and georaphical mapping of crime spots, the NYPD has been able to create a “bigger picture” to guide resource deployment and patrol practice.

Now the department can recognize crime patterns using computational analysis, and this delivers insights enabling each commanding officer to proactively identify hot spots of criminal activity.

6 “NYPD changes the crime control equation by the way it uses information”, IBM; cf. https://www-01.ibm.com/software/success/cssdb.nsf/CS/JSTS-6PFJAZ7 “Index Crimes By Region”, New York State Division of Criminal Justice Services, May 2013, cf. http://www.criminaljustice.ny.gov/crimnet/ojsa/stats.htm8 “Compstat and Organizational Change in the Lowell Police Department”, Willis et. al., Police Foundation, 2004; cf. http://www.policefoundation.org/

content/compstat-and-organizational-change-lowell-police-department

2.1.1 Utilizing data to predict crime hotspots

DHL 2013: ”Big Data in Logistics”

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

Integration of digital and physical worlds

http://www.sygic.com/gps-navigation/addon/head-up-display

The sharing economy hits freight transport (again and again…)

Servitization

Move up in the value chain

Upgrade drop points

Consumer services

Expose data

Mall of Scandinavia

http://www.smartcompany.com.au/growth/innovation/41765-online-retailer-offers-a-courier-that-waits-at-your-door-fashion-advice-not-included.html

https://www.amazon.com/dashbutton

https://www.shyp.com

https://www.shyp.com

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

Anticipatory shipping

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

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

Anticipatory shipping 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)

Curated services made possible with data

Mindconnect Sendify

http://blog.digital.telefonica.com/?press-release=telefonica-dynamic-insights-launches-smart-steps-in-the-uk

Vizualisation

Created by Oliver O'Brien (UCL Geography/UCL CASA)

Vizualisation/combination

Measure real-time

system behaviour

Emil Johansson - EJOH.SE

Manage complex systems

Avoid unpleasant surprises

Predict future events

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

Create teams

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 from a freight company perspective

Per Olof Arnäs, PhD

Chalmers University of Technology Gothenburg, Sweden

about.me/perolofarnas

Slides online: slideshare.net/poar

Film by Waze