The Blue Yonder StoryFrom science to business

SaaS solution for predictive analytics

2

At CERN1, near Geneva, thousands of physicists and engi-

neers from around the world are engaged in the largest

scientific endeavour in history. Buried a hundred meters

underground is the Large Hadron Collider (LHC), a 27-kilo-

meter long particle accelerator, built to probe the funda-

mental constituents of our universe.

But the LHC is not just engineering on a huge scale, it also

produces vast quantities of raw data, up to 600 terabytes

every second. This is Big Data on an unprecedented scale.

This paper will show how Blue Yonder’s products, which

have their roots in particle physics, are helping CERN sci-

entists and business alike to meet the opportunities and

challenges presented by Big Data.

PaRTiClE PhYSiCS aND Big DaTa

1 CERN, the European Organization for Nuclear Research, currently has 20 member states. With approximately 3,200 employees

(as of: 31 December 2011), CERN is the largest research center in the world in the area of particle physics. Over 10,000 visiting scientists from

85 nations work on CERN experiments. The yearly budget at CERN was 850 million euros in 2010.

The Blue Yonder Story

Blue Yonder 4

NeuroBayes at CERN 5

Recognizing particles 8

Big Data is big business 9

The applications of Blue Yonder 10

Case study: SportScheck 12

Case study: BGV/Badische Versicherungen 13

Predicting the future with Blue Yonder 14

Contents

3

4

Many businesses possess an incredibly valuable but often under-

exploited resource – their data. Blue Yonder is Europe’s leader in

forecasting and data pattern recognition, helping companies con-

vert their data resources into profit with its award-winning Predictive

Analytics Suite. Blue Yonder’s products have found applications in

a wide array of sectors including manufacturing, insurance, finance

and retail, enabling businesses to make accurate predictions, in-

crease profits and plan for the future with confidence.

Despite the many practical applications of their products, Blue

Yonder has its roots in fundamental blue-sky research. The compa-

ny was founded in 2008 by Professor Dr. Michael Feindt, a particle

physicist from the University of Karlsruhe in Germany. In the 1990s,

Michael Feindt was working on the DELPHI2 experiment at CERN, a

particle detector at the Large Electron Positron Collider, which was at

the time the world’s largest and most powerful particle accelerator.

Michael Feindt had begun to use computer programs with machine-

learning capabilities to analyze data from the DELPHI experiment.

Machine-learning programs mimic the way the human brain works

and can be trained to recognize patterns in large amounts of

data. He found that they could be used to help distinguish one

particle type from another, which can be a big challenge for parti-

cle physics experiments where hundreds of different particles are

produced in each collision.

While working on the DELPHI experiment, Dr Feindt revealed a

number of different applications for machine learning. Much to his

surprise, he occasionally found that there were mixed sentiments

about the value of machine learning. Not everyone was convinced

that machine learning was more effective than traditional tech-

niques. After further investigation, Michael Feindt discovered that the

less than satisfactory results were primarily a result of human error.

In response, Dr. Feindt began to build a machine-learning package

that was resistant to human error, and that was professional and

robust. The result was NeuroBayes for Science, a sophisticated soft-

ware package that proved itself extremely useful in particle physics

analysis. This discovery found applications both in other CERN experi-

ments and international laboratories such as Fermilab in Chicago.

However, in Feindt’s own words “after looking outside the ivory

tower, I realized that these methods are not only applicable in

physics”3. In 2008, he founded Blue Yonder, a company that is now

demonstrating the value of NeuroBayes in a wide range of commer-

cial sectors.

Blue Yonder

2 DElPhi stands for: Detector with lepton, photon and hadron identification. The construction and assembly of the DELPHI detector took

seven years. The entire development was carried out by 550 physicists from 56 universities and institutes from 22 countries.

3 From DELPHI to Phi-T: Spin-off from physics research to business, Professor Dr. Michael Feindt, CERN, 29 May 2009.

55

UNDERSTaNDiNg OUR UNivERSEThe LHC was built to answer profound questions about the nature

of matter and the origins of our universe. It has already demonstrat-

ed stunning success with the discovery in 2012 of the long-sought

Higgs boson, a particle that explains the origin of mass. Scientists

across the globe are waiting with bated breath for the next discovery

to come from the LHC. They hope that we may soon learn the nature

of the mysterious dark matter that makes up 80% of the universe and

perhaps even glimpse the process that brought matter into exist-

ence after the big bang.

aN ExTREmE maChiNE

Almost every statistic associated with the LHC is extraordinary,

whether it be its sheer size, the fantastically low temperatures at

which it operates or the number of scientists working on this remark-

able machine.

ThE laRgE haDRON COlliDER

CERN’s flagship experiment is the Large Hadron Collider (LHC), a

ring-shaped machine, 27 kilometers in circumference, buried 100

meters below the countryside on the Franco-Swiss border. The LHC

is the largest scientific device ever constructed and one of the most

ambitious projects of the 21st century. This enormous device accel-

erates particles to 99.9999991% of the speed of light before colliding

them into each other. The vast energy of the collisions produces new

particles that are studied by four huge detectors, situated around the

ring.

NEUROBaYES aT CERN

Blue Yonder can trace its roots to fundamental particle physics, and

today NeuroBayes for Science is still being applied at CERN, the Euro-

pean particle physics lab outside geneva.

CiRCUmfERENCE 27 kilOmETERS

TEmPERaTURE –271° CElSiUS

COlliSiONS 600 milliON PER SECOND

SPEED Of PaRTiClES 99.9999991% SPEED Of lighT

Raw DaTa 600 TERaBYTES PER SECOND

RECORDED DaTa 1 gigaBYTE PER SECOND

COSTS 3 BilliON EUROS

maNPOwER 10,000 aPPROx.

One of the most incredible facts about the lhC is the enormous quan-

tity of data that it generates. handling and analyzing this data is one

of the key challenges faced by CERN scientists and an area where Blue

Yonder’s Predictive analytics Suite is proving invaluable.

6

QUaliTY, NOT QUaNTiTY

This vast quantity of raw data presents a number of serious chal-

lenges. The first obvious problem is that it is completely impossible

to store anywhere near all the data produced by the detectors. How-

ever, the vast majority of collisions are “boring”; they only contain

particles that physicists are already familiar with. Only in very rare

cases does something interesting happen, like the production of a

Higgs boson. To get around this, physicists employ sophisticated

computer algorithms known as “triggers” which make an extremely

fast analysis of a collision, and decide whether or not something in-

teresting has happened; if not, the data is never recorded. The trig-

ger reduces the data rate from hundreds of millions of collisions per

second to just a few thousand per second. In other words, only

0.0002% of the raw data actually ends up being recorded.

A novel application of NeuroBayes for Science was discovered in the

trigger of the LHCb detector at CERN. NeuroBayes for Science used its

machine-learning capabilities to learn to distinguish “beauty” particles

from uninteresting background, or signal from noise, in the parlance

of high-energy physics. The performance was superior. NeuroBayes

for Science was able to run as part of the trigger, keeping pace with

the phenomenal data rate, while filtering out the rare beauty parti-

cles from the huge number of uninteresting collisions.

vERY Big DaTa

Particles collide inside the LHC detectors 600 million times per

second. Each collision produces hundreds of particles, all of which

create signals in the detectors’ sensors. Each collision produces

around one megabyte of information, meaning that the LHC

generates around 600 terabytes of raw data every second. If all that

data could be recorded, it would amount to 11,000 exabytes

(1 exabyte = 1 million terabytes) per year. 11,000 exabytes is an

unprecedented amount of information. A study in 2011 estimated

that the total amount of information stored in all the computers,

newspapers and books on Earth came to a grand total of 295 exa-

bytes. In other words, the LHC generates more than 30-times the total

data in existence, every year!

To put this in perspective, if all that information was printed on paper,

you could cover the entire land area of the earth with seven layers of

books.

7

detector

100 millionper second

hardwaretrigger 1 million

per second

softwaretrigger

1,000per second

datastorage

1 eventper hour

physicist’scomputer

NeuroBayes is used in both

the software trigger and on

the physicist’s computer to

filter out the small number of

interesting particles from the

huge number of collisions.

THE ROLE OF NEUROBAYES FOR SCIENCE IN PROCESSING DATA AT LHCB

fiNDiNg ThE NEEDlE iN ThE haYSTaCk wiTh NEUROBaYES fOR SCiENCE

8

In addition to helping physicists handle the huge rate of data flow at

the LHC, NeuroBayes for Science is also proving crucial in analyzing

the data once it has been filtered. A collision produces many different

types of particles and it is essential to be able to distinguish one type

from another.

NeuroBayes for Science mimics the way the human brain works, al-

lowing it to learn to spot patters in data, or in the case of physics,

to be able to tell different types of particle from one another. Using

simulated data, physicists at the LHCb experiment were able to train

NeuroBayes for Science to recognize the different signatures left by

different particles in their detector.

The software was able to take a wide range of complex inputs, figure

out which ones were most important in separating different particle

types, and then provide a probability that a given particle was of a

particular type. Physicists used the probabilities to analyze the data

for real, obtaining a substantial boost in performance compared to

the traditional method.

NeuroBayes for Science began in high energy particle physics and is

still finding wider applications at CERN and beyond. In total, more

than 200 scientific publications, PhD dissertations and master’s theses

have been produced using NeuroBayes for Science and physicists

are using it at almost all large particle accelerator centers world-

wide: DESY4 in Germany, Fermilab in the US and KEK5 in Japan and

even at the antimatter-search-experiment, AMS, on the International

Space Station. Important discoveries have been made, such as the

RECOgNiZiNg PaRTiClES

first measurement of the particle-antiparticle oscillation frequency of

the Bs-meson, the discovery of orbitally-excited Bs-mesons and the

discovery of the single top quark production process.

A large range of spectacular applications have been carried out by

the Belle Collaboration at the KEK accelerator in Japan. KIT-scientists

working with Professor Feindt have created a hierarchical artificial

intelligence system that simulated the work of research physicists in

the reconstruction of more than 1,200 particle reactions. The result

was stunning: the system could reconstruct twice as many events

as all of the 400 physicists in the collaboration over the 10 years of

operation.

For the successor experiment Belle II, to begin operation in 2016,

physicists are tackling another Big Data problem with NeuroBayes

for Science. The new detector will contain so many sensors that it will

not even be possible to read out the detector completely after each

trigger. Engineers are working to implement NeuroBayes for Science

in hardware so that it will be able to intelligently decide which parts

of the detector are important even before the sensor data reach a

computer.

The impressive ability of Blue Yonder’s solution to take a wide range

of information from large data sets and learn to spot patterns and

make predictions makes it an invaluable tool for physicists. But the

applications are not just limited to academic research. NeuroBayes

now helps companies in many areas increase profitability and plan

for the future.

4 German Electron Synchotron: The research center at the Helmholtz Association is one of the world’s leading accelerator centers. Every year,

more than 3,000 visiting researchers from 40 nations work there to promote new technology that is relevant for society, and to promote

innovation.

5 KEK works with research in the area of particle and nuclear physics, as well as in material and biological science, making use of several large

particle accelerators. The KEK also develops components for the ATLAS detector of the LHC at CERN, as well as the Crab Cavities for the KEKB.

With the help of this equipment, it was possible to reach the current 2013 luminosity world record, with the Belle experiment from 2009.

Approximately 700 employees work at KEK. Between 2008 and 2011, they spent approximately 80,000 person days on one or more of the

1,000 experiments. Of that number, one-quarter were of experiments carried out by foreign scientists. During that time, KEK had expendi-

tures of approximately 340 million euros per year.

9

Similar to experiments at CERN, businesses also collect vast quantities

of data from a wide range of sources; customer mobile devices, cash

registers, social media, vehicle GPS sensors, manufacturing facilities;

the list goes on. Their data represents a resource that, if exploited,

can result in significant increases in efficiency, competitiveness and

ultimately profitability.

However, many businesses under-exploit their data resources. The

sheer quantity of data amassed and its rapidly evolving nature makes

traditional data-mining techniques unsuitable. In addition, businesses

do not have the expertise or resources to fully utilize their data.

Similar to what is being done at CERN, Blue Yonder is using

predictive analytics to help their clients make the most of big data.

The Predictive Analytics Suite “learns” patterns in both structured and

unstructured data; the more information presented, the better will

be its forecasts and predictions. Blue Yonder is now working with

businesses in many sectors:

Big DaTa iS Big BUSiNESS

NeuroBayes health care

logisticsmanufacturing

bankingtele

com

mun

icat

ions

online retail

automotive industry

part

icle

phy

sics

insurance

real estate

Making accurate forecasts of stock demand to minimize waste in

food retail

Using data from GPS sensors to allow just-in-time logistics,

maximizing efficiency

Helping insurers make more accurate assessments of risk

Allowing retail banks to manage the lifecycles of their customers

Predicting demand for a wide range of products and optimizing

prices in online retail

food

reta

il

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ThE aPPliCaTiONS Of BlUE YONDER

iNDUSTRial Big DaTa

In March 2013, a study by the Aberdeen Group reported that the

most successful manufacturers were building up their Big Data

resources and employing analytics to help improve their products

and processes. Real-time information from suppliers and the factory

floor can be exploited to respond proactively, rather than retroac-

tively, to solve problems as they emerge.

Using Blue Yonder’s Predictive Analytics Suite, manufacturers can

use their Big Data resources to correct defects, streamline produc-

tion and improve customer satisfaction. Data generated by sensors

RETail fOOD iNDUSTRY

The retail food industry is a complicated field. Customers are quick to

compare and are more environmentally savvy and price savvy than

before. In an environment where sales prices are stagnating, many

companies can increase their profit by optimizing their processes.

Nowhere is this more evident than in storage. Too much stock, espe-

cially in perishable goods, leads to stark price reductions and losses.

On the other hand, if too few products are on the shelves, the retailer

will lose income and customers.

The large grocery chains have thousands of branches and have to

keep tens of thousands of products, for millions of customers. Man-

ual order processes proved useless in this case. That is why retailers

use enterprise resource planning software (ERP), in order to organ-

ize their goods procurement. However, ERP software is only capable

of viewing the future and cannot process the very large amounts of

information generated by the store. In contrast, Forward Demand

from Blue Yonder uses all this information to be able to look into the

future and make accurate forecasts for replenishment requirements.

Waste can be reduced and the profit, as well as the customer satisfac-

tion, can be increased.

For some customers, Blue Yonder delivers over 600 million forecasts

per day. Forward Demand uses more than just basic information

like sales, storage levels and prices. The solution also takes seasons,

weather conditions, school vacations and breaks, opening hours,

sales promotions and pay-days into account in its analysis, in opti-

mizing procurement.

With predictive analytics, retailers are in a position to make perfect

forecasts and to calculate sales with empirical accuracy. The Blue

Yonder solution continually learns and this is reassuring, because its

predictions are always up to date and correct.

in a wide range of consumer products after they leave the factory can

also be used to detect defects and improve manufacturing quality.

Most modern cars are continuously connected to the Internet, allow-

ing car manufacturers to monitor the performance of their vehicles

and to perform predictive warranty analysis in real time. This infor-

mation can also be used to develop intelligent safety systems such

as automatic braking, making cars safer and more reliable. The use

of predictive analytics to make the most of Big Data is becoming in-

creasingly important for success in manufacturing.

shelf life

holidays

opening times

paydays

special events

price

holidays

stock level

promotional offers

shelf life

package size

customer histories

historical demand

weather forecasts

competitor prices

Blue Yonderaccurateforecasts

continual re-learning

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ThE aPPliCaTiONS Of BlUE YONDER

iNSURaNCE

There is no industry where forecasting the future is more important

than insurance. Accurately calculating the true risk that a customer

presents is essential, both in minimizing payouts and in offering fair

premiums.

Customers are increasingly demanding policies that are tailor-made

for them. This can seem an impossible task for companies offering

life insurance, health insurance, home insurance and car insurance

policies to millions of customers.

With its Predictive Analytics solution, Blue Yonder is helping insur-

ers make precise calculations of risk, making accurate forecasts of all

relevant issues that are updated in real-time, as more data arrives.

hEalTh CaRE

There is no such thing as an average patient, yet doctors generally

treat patients as if they were average. Improving patient outcomes

requires tailoring their treatment programs to their particular circum-

stances – this can be achieved by effectively using the large quanti-

ties of data generated by medical tests such as MRI scans, CT scans,

X-rays and blood tests.

RETail BaNkiNg

As it becomes easier for retail banking customers to switch between

competitors, understanding your customers has never been more

important in maintaining a competitive advantage. Customers

require a high-quality service, customized to their needs. Blue

Yonder’s Predictive Analytics solution can help.

The first stage is to understand your marketplace. Blue Yonder can

analyze inputs from your bank’s internal data, as well as external

sources such as social media, macro-economic data and regional

economic indices.

These precise risk estimates allow Blue Yonder’s customers to offer

fair premiums. Consider a newly qualified driver looking for a car in-

surance policy. By using predictive analytics the insurer can make an

accurate prediction of the risk he or she presents, taking into account

a range of data on the customer and from the insurance market. This

allows the insurer to offer competitive but realistic premiums, poten-

tially developing a lifelong relationship with the customer.

Blue Yonder also helps manage the lifecycle of a customer. By analy-

zing customer behavior data, Blue Yonder can predict the best time

to offer a customer a new policy. Perhaps they have just moved into

a new home, just had a child, or are coming up on retirement. Blue

Yonder will help you offer the right policy to the right customer, as

well as warning you when a customer is at risk of taking their busi-

ness elsewhere.

It is then possible to study each customer in detail, analyzing data

from their account as well as text mining to understand when

customers can be attracted to new products and what interest rates

they are willing to pay.

By taking this wide range of inputs into account, Blue Yonder can

help banks offer personalized services to their customers, forecasting

the right time to make an approach and the best means to do so. This

allows banks to maintain a high level of customer satisfaction and

maximize the impact of their products.

By analyzing medical Big Data on a large scale and then taking the

individual patient’s medical records into account, predictive analytics

reveals the best treatment of a particular individual, helping medical

practitioners save lives and improve outcomes for all.

What applies to the food industry also applies to the retail and con-

sumer goods industry. The structural changes from one-dimensional

retail to multi-channel retail, the changes in customer behavior to

“exclusive” and “the now”, as well as the availability of every piece

of information for every customer through every channel poses new

challenges for companies. This is where Blue Yonder comes into

play, because Big Data analytics is the new motor driving efficiency.

Through the transfer of international cutting-edge research to sim-

ple-to-use data-driven apps, materials managers and purchasers can

plan short-notice and also long-term, precisely. Storage levels and

write-offs can be reduced in this way by ten percent, and profit in-

creases by avoiding out-of-stock situations can be attained.

Customers want to be addressed individually and personally by

retail and consumer goods companies. Intelligent customer analysis

enables situation coupling direct at the point of sales as well as indi-

vidually driven marketing campaigns. Added to this is the fact that

it is imperative in online retail to adjust price dynamics in real-time.

In order to steer these automatically, large amounts of data need to

be evaluated and proper connections need to be found. Blue Yonder

offers the right solution for these areas of application.

RETail aND CONSUmER gOODS iNDUSTRY

12

aBOUT SPORTSChECk

SportScheck is one of Germany’s leading sports retailers. Founded in

1946 by Otto Scheck, an old military tailor in Munich, the company

has now grown to a company employing 1,500 people with 16 stores

throughout Germany offering 30,000 products and 500 brands. The

company was taken over by the Otto Group in two purchases in 1988

and 1991.

ThE amBiTiON

SportScheck’s online retail service is an increasingly important part of

its business, with its website clocking up 52 million individual visits

each year. Blue Yonder came in to improve the accuracy of sale pre-

dictions, to allow SportScheck to manage its stock levels efficiently

and anticipate demand for its individual products.

BlUE YONDER’S aPPROaCh

SportScheck have been working with Blue Yonder for a number of

years. Blue Yonder analyzed a multitude of different factors affecting

the online marketplace and effectively utilized the huge quantities of

data available. Blue Yonder was also able to analyze the behavior of

customers visiting the online retail site.

ThE RESUlT

SportScheck’s faith in Blue Yonder’s Predictive Analytics Suite was

vindicated, dramatically. Forecasts were improved by between 20%

and 40% over the traditional approach with the average absolute

deviation of predicted sales figures from actual sales slashed in two.

SportScheck gained a significant competitive advantage, as well as

allowing it to react quickly to developments in the rapidly changing

online space.

CaSE STUDY:

SPORTSChECk

“Blue Yonder bundles methods,

forming a unique solution in order to predict

sales figures precisely. This is crucial for success

in the competitive online business.” Günther Harant, Purchasing Manager, SportScheck

40%fORECaST QUaliTY imPROvED BY UP TO

iNDiviDUal viSiTS/YEaR52 m

ON ThE wEBSiTE

13

aBOUT Bgv/BaDiSChE vERSiChERUNgEN

BGV is an insurance group comprising several insurance companies and is head-

quartered in Karlsruhe, Germany. BGV offers property, liability, legal, injury and motor

vehicle insurance to private and commercial customers in the Baden area. The orga-

nization has around 700 employees and had a turnover of 260 million euros in 2012.

ThE amBiTiON

BGV/Badische Versicherungen approached Blue Yonder, looking for

a way to better exploit their large quantity of customer and insur-

ance data. Their aim was to produce more accurate calculations of

risk for their auto insurance customers and to offer fairer premiums

as well as to identify customers who are at risk of terminating their

policies.

ThE RESUlT

Blue Yonder used their unique Predictive Analytics solution to effi-

ciently and fully exploit BGV’s auto insurance data. The results were

premiums that were tailored to individual customers, improving

competitiveness and minimizing risk exposure. Blue Yonder also

successfully provided predictions of customers who were likely to

take their business elsewhere, identifying their concerns and allow-

ing BGV to improve customer retention through targeted marketing.

All this improved BGV management’s ability to plan strategically.

CaSE STUDY:

Bgv/BaDiSChE vERSiChERUNgEN

“Given a multitude of factors, Blue Yonder reliably reveals

relationships for clearly defined target groups ... the predictive analytics

software has identified other characteristics that are important for

pricing. Now, we can offer customized premiums.” Heinz Ohnmacht, Chair of the Executive Board, BGV/Badische Versicherungen

The future is bright for Blue Yonder with its cloud-based Predictive Analytics

solution. The world of the future will be one dominated by Big Data, and the

difference between success and failure in business and science alike will be in

knowing how best to make use of this valuable resource.

There are many opportunities for the application of Blue Yonder’s unique

offering. Industry and manufacturing can benefit greatly from the use of

predictive analytics. Large data sets generated by sensors on vehicles, in

products and on factory production lines can be used to identify defects in

manufacturing processes in real-time, allowing for on-the-fly error checking

and process optimization.

The large quantities of data generated by industrial processes can only be

fully exploited using predictive analytics that is capable of learning and

improving, as well as taking all the correlations between data inputs into

account, in order to provide the most precise and relevant forecasts. This will

make possible increasing automation of defect detection and improvements

in manufacturing quality and efficiency.

PREDiCTiNg ThE fUTURE wiTh BlUE YONDER

15

The roots: fundamental research

Blue Yonder started as a spin-off from the most abstract basic research. Even

if the problems that physicists solve in business projects seem far removed

from daily life, the results have a significant effect on the research done in

physics, for example in the medical research and now, thanks to NeuroBayes,

also in forecasts in business companies.

By emulating the function of the human brain and through self-learning

abilities, NeuroBayes software is able to identify patterns in data and to

evaluate all available data. This makes NeuroBayes perform better than run-

of-the mill methods.

Use in the business world

This type of instrument has found several uses in the business world. Blue

Yonder makes it possible for business organizations to get real value from

their data with the help of predictive analytics. With Predictive Analytics from

Blue Yonder, companies make the right decisions quickly and in a fully auto-

mated way.

Democratizing Big Data

Blue Yonder makes predictive analytics easy to use for users and plays a

big role in the democratization of Big Data. With Blue Yonder’s Predictive

Analytics software, huge amounts of data are analyzed, the probabilities

of different scenarios are demonstrated, decisions are supported and the

decision-making process is automated. In this way, Big Data is not only

accessible for data experts, but it becomes a practical tool for everyone and

becomes the deciding success factor in an organization.

Prize-worthy innovation

Blue Yonder won the prestigious Data Mining Cup three times with

NeuroBayes. Blue Yonder has also won the 2013 DLD FOCUS Digital Star

Award, the 2011/2012 CyberChampions Award and the 2012 CyberOne

Award.

1616

Blue Yonder UK Limited

6–9 The Square

Stockley Park

Uxbridge UB11 1FW

England

Phone +44 (0)203 008 717 0

Fax +44 (0)208 610 606 0

Blue Yonder GmbH & Co. KG

Karlsruher Straße 88

76139 Karlsruhe

Germany

Tel. +49 (0)721 383 117 0

Fax +49 (0)721 383 117 69

info@blue-yonder.com

www.blue-yonder.com

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