How to become a predictive enterpriseData-driven decision management as a key driver for future business success

Software for data analysis and accurate forecasting

Executive Summary

Predictive enterprise solutions are enabling best-of-class companies to outper-

form their peers by using data and algorithmic intelligence to achieve higher

profits, shorter time to market and constant innovation. This white paper will

show you how you can benefit from predictive analytics and automated deci-

sion-making and transform your company into a predictive enterprise. Predic-

tive analytics can inspire a revolution throughout your organization. It is based

on the data already available in your business – from your ERP systems, your

materials management systems and from many of your other IT systems both

current and dating back to the first systems installed. The data is all there – you

may as well use it to become a more efficient, more customer-centric enterprise

that knows how to achieve higher revenues and bigger profits.

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Contents

Paradigm shift: from product focus to customer focus 4

The predictive enterprise – a model for success 6 The cloud as enabler 8

From standard business analytics to predictive enterprise 10

Customization: the catalyst for a personalized experience 12

Predictive models – opportunities and risks 14

On the way to the predictive enterprise 1: the smart factory 16

On the way to the predictive enterprise 2: the ‘closed loop approach’ in retail 18

Outlook for the future: smart services 20

Conclusion and next steps 22

Paradigm SHift: from ProduCt foCuS to CuStomEr foCuSThe economic world has changed radically. The growing digitalization of products and

processes in all industries is leveraging essential changes in processes and systems, ways

of working and entire business models. The three key factors that have to be considered

today are:

In its latest publication, the German National Academy of Science and Engineering’s Smart

Service World working group states that in the near future “the business models of suppli-

ers, manufacturers and operators alike will be faced with a genuine revolution as a result of

being systematically digitised, analysed, augmented with Smart Products and Services and

networked with each other.”1

This extraordinary paradigm shift is taking place in economies all over the world, so indi-

vidual product and service suppliers will have to adapt their thinking and planning. Prod-

ucts and product innovations will no longer be at the center of the new business models –

as they have been for over 150 years –, customers will, and they will be expecting a superior

experience. This means that software and data is needed to optimize business processes

that could not previously be optimized.

This is where data-driven decision management and predictive analytics come into play.

The key is to offer the best customer experience at any time and in real time. Not only do

the best decisions need to be made as fast as possible, they also need to be automated

to eliminate human latency and bias, and free human resources for high-touch and high-

creativity tasks. In order to achieve this, Big Data has to be analyzed and prediction ac-

curacy has to be automated so that decisions and ultimately customer experience can be

optimized.

1. Pervasive data availability (Big data)

2. Cheap processing, operational and access resources (cloud, mobile), cf. amazon, google, apple

3. Software becoming a key differentiator in many business models

1 Smart Service World: Recommendations for the Strategic Initiative “Web-based Services for Businesses”, March 2014.

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tHE PrEdiCtivE EntErPriSE – a modEl for SuCCESS In a recent report, Cisco estimated that 99.4 percent of physical objects that may one

day be part of the Internet of Everything are still unconnected. With only about 10 bil-

lion out of 1.5 trillion things currently connected globally, there is vast potential to “con-

nect the unconnected”. Cisco predicts that $14.4 trillion of value will be “at stake” over

the next decade, driven by “connecting the unconnected” (people to people, people

to machines, machines to machines, etc.) via the Internet of Everything.2 That future

scenario aside, there is already a vast amount of unused data available – the potential

is enormous.

the key benefit of predictive analytics is that it enables organizations to profit from data collected over recent decades as a result of business process digitalization.

forecast outcomes

recognize patterns automate decision-making

Proactively optimize processes

develop smart services

Enabling the predictive enterprise

access and analyze historic and current information (structured and unstructured) in real time >>

Predictive analytics

Connect what’s happening inside and outside business

Apps Processes Mobile Mainframe Sensors Cloud ERP Systems Services Events Files Partners Social Media

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$14.4tr

2 Joseph Bradley, Joel Barbier, Doug Handler. Embracing the Internet of Everything To Capture Your Share of $14.4 Trillion: More Relevant, Valuable Connections Will Improve Innovation, Productivity, Efficiency & Cus-tomer Experience. Cisco Public Information, 2013.

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Why should you become a predictive enterprise?

there are five good reasons for becoming a predictive enterprise:

make the right decisions Data-driven decision management improves and reinforces intuition and

experience with real data. Decision makers gain new insights and confi-

dence for their decisions. Moreover, vast numbers of decisions are enabled

in real time.

Enable new business models By unearthing the value of collected proprietary data, enterprises can use

this data in new ways to enable new business models based directly on the

data, e.g. brokering, and sharing data or by leveraging data, for instance in

market places. If these market places do not exist, there is an opportunity

for creating them (e.g. Amazon selling computing resources at spot prices;

real-time bidding in digital marketing; startup Uber creating market places

for transportation).

Stay ahead of the competition By predicting the future, you are able to respond faster and more effectively

than your competitors. Instead of simply reacting to customers’ needs and

market developments, you can proactively shape your business, optimize

your supply chain and offer new and superior services.

manage risks better, avoid fraud The predictive enterprise is able to minimize non-compliance as well as

fraud. Using superior knowledge to act in time, you can adapt your risk man-

agement promptly, and so avoid damage to the company and reduce costs.

increase profitability With the help of accurate forecasts you are able to discover trends as well as

emerging opportunities ahead of time and adjust your business accordingly.

Furthermore, you can target offers and marketing campaigns more precisely

and meet individual customers’ needs at the right time.

Research from analysts at Gartner has forecast that by 2016,

70% of the world’s most profitable companies will manage their business

processes using real-time predictive analytics or “extreme collaboration”.

Gartner Business Process Management Summit 2013

1.

2.

3.

4.

5.

70%

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tHE Cloud aS EnaBlErSmart-data projects in the cloud are slowly but steadily conquering all kinds of compa-

nies. According to the “Predictive Analytics in the Cloud” research by strategists Decision

Management Solutions, predictive analytics in the cloud is rapidly becoming mainstream;

60% of survey respondents are already using predictive analytics in the cloud, while over

90% said it was likely they would have a predictive analytics solution widely deployed in

the next few years.3

Companies deploying predictive analytics face three

major challenges:

The need for predictive solutions

that require only little expertise. This

can be scaled by allowing experts to

use standardized frameworks and

platforms.

The need for large-scale data

processing applications supported

by massive amounts of

hardware with very unbalanced

load patterns.

The need for universally

available data.

1. 2.

3.

3 Decision Management Solutions: Opportunities, Trends and the Impact of Big Data 2013.

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the cloud addresses these three pain points by:

providing a standard platform with standardized frameworks and aPis for building applications

providing a framework for deploying hardware and software environments to handle massive loads in a scalable and economical way

providing scalable storage and easy access through network effects

Already using predictive analytics in the cloud

Planning to implement a predictive analytics solution

60%Predictive analytics in the cloud

90%

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from Standard BuSinESS analytiCS to PrEdiCtivE EntErPriSE

Prescriptive analyticsThe journey to becoming a predictive enterprise starts with transforming standard business analytics into

predictive analytics. Whereas conventional business intelligence focuses on historical data and describes

the past – What’s happened and why? –, predictive analytics is concerned with accurately predicting future

behavior and trends and answers the question: What will happen?

‘Prescriptive’ analytics goes one step further. It enables organizations to comprehensively automate their

decision-making and planning. Existing business processes can be made more efficient and new business

models can be implemented. Pattern recognition, forecasts, simulations, optimization and automated

decision-making are integrated into a broader and more dynamic array of processes. Prescriptive analytics

answers the question: What can we do? The approach is dynamic: The data-driven predictive enterprise

continuously optimizes itself using data-driven apps especially developed for business users.

What will happen?Why?Was has happened?

DATA

PATTERN

What can we do?

FORECASTS

In�uence on business performance

Data Mining and conventional Business Intelligence Predictive Analytics Prescriptive Analytics

Automated decisions

Support for decisions

Compared to conventional BI, predictive and prescriptive analytics more directly influence business performance.

KEy faCtorS

real-time executionAutomated decision systems that allow real-time execution

integrationEasy integration via standard APIs

real-time simulationsAccurate forecasts can be created based on the data collected and analyzed, and these exceed human capa-

bilities many times over – a commercial enterprise can generate billions of sales forecasts every day solely on

the basis of the data gleaned from their ERP and materials management systems. Before an operative deci-

sion is made, simulations can be carried out that accurately depict the impact of that decision. For example, a

simulation based on real-time, accurate data can help determine how an action such as prominently advertiz-

ing a specific article on the website would affect its sales.

Similarly, simulations can be used to find out if the production utilization ratio could be improved by fewer

machine stoppages or how selected test groups would view and accept differing financial packages. The

opportunities for substantial business benefits don’t stop at machine monitoring or sales. They are virtually

endless − limited only by your imagination.

democratization Eliminating the job of analysis and enabling non-analyst end users to understand,

review and trust the decisions made by the predictive system

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CuStomization: tHE CatalySt for a PErSonalizEd ExPEriEnCE Predictive analytics allows commercial enterprises to analyze customer data so that they can forecast

customer behavior and needs. It also puts them in a position to easily target several million custom-

ers, which further adds to the accuracy and quality of the analysis. Specialist software for predictive

analysis takes recommendations, prices and financial products and then adapts them to the current

and future situations of specific customers. Very quickly, this can lead to increased sales, and customer

retention improves as a result of the strong relationships that develop based on trust.

It isn’t just those enterprises that are focused on the end consumer that can benefit from predictive

analytics; the manufacturing industry is also ideally positioned to take advantage of this principle.

Machines that can generate their own data on throughput and operations create forecast analyses

that can be used to increase flexibility. The predictive factory can plan smaller, easier to manage batch

sizes that can be controlled more effectively and manufactured more efficiently.

Effective use of dataExperience has shown time and again that in almost every enterprise, Big Data can be the catalyst to

freeing hidden potential. Let’s take the example of an airline company. Based on the availability of

mass data, the purchasing department can determine which meals need to be supplied per airplane,

per day. Further analyses of the booking data support the airline as it plans its personnel utilization.

And while the airplane is still in the air, the ground staff can analyze live sensor data from jet turbines.

Rolls-Royce provides a good example of how this works and how effective it can be: Its airplane en-

gines continually send signals to the company headquarters in Derby, Great Britain, where problems

are identified and acted on immediately. Technicians are provided with information about what they

have to do when the plane has landed and all the necessary steps, including ordering replacement

parts, are prepared. This advanced technology means that the airline does not have to have long

‘forced breaks’ on the ground, which leads to fewer delays and a more positive customer experience.

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PrEdiCtivE modElS – oPPortunitiES and riSKSRunning a successful business involves making numerous timely decisions, but

gathering, presenting and analyzing the data on which these decisions are based

by hand is too time-consuming and means that the key decision point is missed.

Automated decision-making based on accurate predictions is a huge differentia-

tor; however, there are some risks that come with overly simplistic approaches, and

these have to be considered carefully:

more accurate forecasts, fewer risksThe predictive enterprise mines for data throughout the company, even delicate

areas like liquidity planning and financial planning. It looks at raw material prices,

sales, stoppage risks and many more cost and revenue factors that are hard to calcu-

late and even harder to analyze promptly. Continuous analysis of Big Data enables

the predictive enterprise to determine each customer’s payment risk and include it

in its financial planning. This will help ensure bad debt is reduced and cash flow is

maximized. External factors, like seasonal sales, are also taken into account when

determining capital requirements. Being a predictive enterprise puts you in an ex-

cellent position in terms of risk management and also helps you minimize the capi-

tal you have tied up unproductively.

Every percent of prediction improvement has an impact on the top or bottom line, so predictions should be monitored and improved constantly.

trust in predictions often crystallizes in a few ‘outlier’ predictions that can damage confidence in the whole automated decision-making process. don’t measure success on the basis of spotlights and anecdotal evidence, but on real data across the entire business!

Predictive models can be prone to overfitting and describing the past, but not being able to deal with the future if it differs from the past. this means models must be capable of learning and forgetting.

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ON THE WAY TO THE PREDICTIVE ENTERPRISE 1: tHE Smart faCtory

Sensor dataMore and more machinery and plant equipment is being fitted with integrated, interactive sensors. The

data that is permanently and continuously captured by these sensors is sent via the internet directly to

the systems in the company service departments, creating an invaluable knowledge base. For example,

many sensors in vehicle engines can send information at short intervals to the manufacturer regarding

fuel consumption, temperature, torque, etc. In virtual real time, the software can record if the engine

is deviating from usual behavior. If this is recognized in time, many engine problems can be solved

remotely and the manufacturer gains a positive reputation for its great service. Without it, the customer

has the inconvenience of having problems and resolving them personally, which will have a negative

effect on the relationship.

The Experton Group estimates that data volumes

in industry will grow annually by 20 to 30 percent.

Experton Group, 2012

+30%

i will be running only at 80% capacity over the

next 36 hours!

Warning! Change the ball bearings here!

i still need to be filled up!

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Generally, service employees only have to look into

of the errors reported by conventional alarm systems –

but which ones?

machine monitoringBetter monitoring of machines means that production processes can be adapted to optimally coordi-

nate with one another. By creating a ‘smart factory’ where all the machinery is automatically monitored,

manufacturers can ensure that the production operation is highly efficient and uses minimal personnel

and materials. Knowledge-based diagnostic technology measures and records each individual process

and makes use of known patterns. It differentiates between conventional monitoring system alarms

and problems that could cause a drop-off in the machine’s performance. The service employee then

only needs to intervene when necessary. This means personnel can monitor more machines, and any

item can be systematically tested to identify potential areas for improvement.

Self-driven productionCisco estimates that in the year 2017, there will be around 1.7 billion machine-to-machine connections,

and by 2015, about six billion devices will be equipped with an IP address.4 Industrial Big Data (Industry

4.0) automates production processes so that skilled workers don‘t need to stop as often to make indi-

vidual operative decisions because they monitor a system made up of intelligent components that – to

a large extent – regulate and optimize themselves; machines report their own condition, and work-

pieces carry RFID chips with information on their history as well as details of production steps. Modular

machine concepts make it possible for individual machines, plants and also parts to be individually

integrated into production. This makes production environments more flexible and manufacturing in

small batches easier, so individual customers’ wishes can be implemented at a reasonable cost.

Erro

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Start uP

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SolvE

loWEr

maintEnan

CE

ov

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HEating

loWEr

1/5

the cold chain has been broken!

i will need maintenance in 24 hours!

machine-to-machine connections

devices will be equipped

with an IP address

4 Cisco Visual Networking Index: Global Mobile Data Traffic Forecast for 2012 to 2017.

1.7Bn

6Bn

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ON THE WAY TO THE PREDICTIVE ENTERPRISE 2: tHE ‘CloSEd looP aPProaCH’ in rEtailthe king of disciplines: automated mass decisions

The predictive enterprise intelligently connects Big Data and decision-making. Ideally, data from the

core systems of the enterprise, for example from the ERP and CRM systems, from production software

and many other internal and external sources are taken into account and used as a basis for automat-

ing operative decisions. The more closely they are connected with the enterprise’s core business, the

greater the effect: Sales increase, costs fall and profits rise.

The multi-channel retailer OTTO handles 20 million articles every day, and these need to be forecast.

Decision-making processes involve immense data volumes, a multiplicity of influential factors and a

permanent need to act in real time and under extreme time pressures. This coupled with high cus-

tomer expectations mean that, for example, multi-channel retail companies like OTTO are continually

looking for new decision-making approaches. Using predictive analytics, OTTO can fully exploit its vast

amounts of data, not only selectively but along the entire product life cycle. Machine-learning predic-

tive analytics software is able to process millions of data records each day, turn them into smart data

and use them to improve the retailers’ forecast quality.

technology-driven product life cyclesThe product life cycle in the fashion and lifestyle department at OTTO can be divided into four phases,

which work together creating a closed spiral (closed loop): Trend recognition, planning, forecasting,

and sales optimization. From all this information, the predictive enterprise determines order propos-

als for the entire product range and for each individual branch. Because automated decision-making

is the basis of reliable sales forecasting, OTTO avoids both out-of-stock situations and high write-offs.

Planning• Setup of product range

structure• Collection idea

• Quantity estimate• Business control of

self-management

trend recognition• Early recognition

• New information sources• Which product does the

customer want in the future?

CloSEd looP

forecasting• Procurement

• Sales/returns forecast• Publication management

Sales optimization• Flexible pricing

• Recommendation engine• Returns management• Efficient stock control

Closed loop

Holistic view of different areas along the product life cycle

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trend recognitionPredictive analytics can be used even in the very early planning phase to inform retailers as early as

possible which articles customers will want in the future and what the foreseeable trends are. Predic-

tive analytics can provide meaningful support to the trend experts here. Today their decisions are

largely based on a wealth of personal experience with store checks, visits to trade fairs and production

markets, as well as historical data. In future, with the help of predictive analytics this information will

be more precisely analyzed by including additional information sources such as Google and social

media.

Sales planningPrecise sales forecasts are critical for success. In order to plan collections, accurate calculations of pre-

dicted sales and order quantities are required. Predictive analytics makes available unique analyses

that include historical data, customer data and unstructured data like search requests on the shop

website, as well as new information sources like social media or Google. With predictive analytics,

trends can be recognized sooner and product range planning can be more simply and more accu-

rately carried out.

Sales optimization through dynamic pricingIn retail, the optimal price for a product depends on many influencing factors that can vary on a daily

basis. The predictive enterprise always adjusts its prices according to these market factors. Whether

your aim is to “sell at any price” or for maximum profit, dynamic pricing provides a highly effective

solution for achieving your company’s goals.

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outlooK for tHE futurE: Smart SErviCESIt has become apparent that the predictive enterprise is much more than just a model for optimizing

existing business processes and services; it is a whole new way of thinking. Enterprises will have to

invent new business models based on smart services and smart networks. Instead of the current cor-

porate structures that are based on process and resource efficiency, there will be a shift to ecosystems

based on innovation, information and personalization, which develop and provide smart services.

Only if the information and knowledge derived from smart data is used as the ‘fourth factor of produc-

tion’ will enterprises be able to successfully implement new business models. Smart products are com-

bined with physical and digital services to create smart services that then can be marketed as flexible,

on-demand services.

“Individual suppliers of traditional products and services will no longer be at the center of the new

model but consumers in their respective roles as users, patients, employees, technicians, passengers,

entrepreneurs, etc. Consumers will expect the right combination of products and services to meet their

individual needs anytime, anywhere.”5 This will shape new business models, and enterprises will have

to continuously adapt and/or expand their product and service portfolio and use data-driven decision

management in order to face the business challenges of tomorrow.

5 Smart Service World: Recommendations for the Strategic Initiative “Web-based Services for Businesses”, March 2014.

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ConCluSion and nExt StEPSUsage of data evolves from descriptive to predictive to prescriptive. By using data

intelligently to automate decisions, companies can turn themselves into success-

ful predictive enterprises that outperform the competition on time to market,

profitability and innovation.

learn how other companies in your space are using data to optimize their business.

Create a data inventory to discover what data is available in your organization.

Create a decision inventory to identify which decisions are made on a repeated and regular basis.

develop predictive algorithms for supporting decisions.

operationalize these algorithms using predictive enterprise applications.

next steps in becoming a predictive enterprise

Blue yonder

Blue Yonder is the leading SaaS provider for predictive applications in the European market.

We help enterprises gain valuable knowledge for automated decision-making processes and

attain profitable growth from their data volumes. This is achieved by using the organizations’

growing data volumes from advanced sensor networks. Blue Yonder’s leading team of data

scientists produces accurate forecasts of the highest quality using innovative techniques

such as predictive modeling and machine learning.

The Blue Yonder project team consists of data scientists and software developers with spe-

cialized scientific backgrounds, having gained extensive knowledge in utilizing and process-

ing enormous data volumes (Big Data) at international research institutes such as CERN. We

develop solutions for challenging tasks in both business and industrial organizations, based

on our vast experience in enterprise software and in Software as a Service (SaaS).

We provide our predictive applications in the form of ‘Platform as a Service’ (PaaS) in the

cloud. The machine-learning approach ensures that changing conditions are permanently

recognized and included in the analyses, thus enabling us to continuously deliver precise

forecasts.

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BY_0

8201

4

Blue Yonder UK Limited

6-9 The Square

Stockley Park

Uxbridge UB11 1FW

United Kingdom

Phone +44 (0)203 008 717 0

Fax +44 (0)208 610 606 0

E-mail infouk@blue-yonder.com

www.blue-yonder.com

Blue Yonder GmbH

Ohiostraße 8

76149 Karlsruhe

Germany

Phone +49 (0)721 383 117 77

Fax +49 (0)721 383 117 69

E-mail info@blue-yonder.com

www.blue-yonder.com

Join other predictive enterprises and contact us!