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SAS® HIGH-PERFORMANCE ANALYTICS
Adding value along the entire supply chainKey success factor #1: Analytics
Predictive
inside
Manufacturing
SAS® IN MANUFACTURING
SAS® IN MANUFACTURING
Industrial production is undergoing a transformation to what has become known as
advanced manufacturing. Advanced manufacturing describes the digitization of production
and everything surrounding it. It focuses on the ongoing exchange of data between
machines, logistics systems, production equipment, and control systems.
Thanks to these developments, analytics is taking on a whole new meaning for the industrial
sector. Because you can’t have advanced manufacturing without analytics. Analytics
software is the only way to enhance data from production activities, materials, machinery,
and operations in such a way that communication can focus on the exchange of intelligent
information rather than raw data alone. Only with analytics can meaningless data acquire
significance that goes beyond the current production status to reveal hidden insights that
tell what the future will hold. It’s all about being predictive. Even complex interactions
involving multiple departments such as production and marketing or customer service can
be explored using analytics.
This is no pipe dream. It’s already taking shape in the form of predictive maintenance,
embedded analytics, and quality lifecycle assessment. Not only are these things techni cally
possible, they’re happening as we speak. More and more companies have recognized
the value of their existing troves of data and are using analytics to enhance planning and
management processes, paving the way for the smart factory of tomorrow.
We would like to demonstrate this vast potential on the following pages with several exam
ples of enterprises that are pioneering the use of analytics in the industrial sector. They use
SAS® Analytics for a diverse range of tasks but still have one thing in common: they all
profit from profound insights that allow them to manage their businesses with an eye to
the future.
I hope you will find valuable and relevant insights in our user stories below.
Gerhard Altmann
Senior Director Industry Unit Manufacturing EMEA /AP
Gerhard AltmannSenior Director Industry Unit Manufacturing EMEA /AP
Analytics — the driving force behind the next Industrial Revolution
SAS® IN MANUFACTURING
SAS® in Manufacturing
Demand- and Production planning
Preparation
SourcingWarehousing
Provision of material
Production Packaging
Machine maintenance
Quality control andFinished goods release
Warehousing and Shipping
Customer service andmaintenance
Demand-Driven Forecasting & Optimization
SAS Solutions
Supply Chain Intelligence Center & Inventory Optimization
SAS Solutions
Predictive Asset Main tenance & Visual Analytics
SAS Solutions
Quality Lifecycle Analysis & Quality Assurance
SAS Solutions
Demand-Driven Forecasting & Inventory Optimization
SAS Solutions
Service Parts Optimization & Warranty Analysis
SAS Solutions
Overview of
SAS Manufacturing Solutions
Overview of
SAS Manufacturing Solutions
6
ShellShell drills into big data analytics, extracts tens of millions of dollars
Analytics transform big data at Shell
Exploration and Production into sound
exploration decisions, highquality wells,
reduced costs and lower environmental
impact. The company uses SAS Predictive
Asset Maintenance software to extend
equipment lifespan and run times which
can account for tens of millions of dollars
in increased gas and oil production.
Touching all aspects of operations, analyt
ics helps Shell boost both effi ciency and
effectiveness — keeping the company on
top by bolstering the bottom line.
“SAS eliminates guesswork from our
business processes,” said Tom Moroney,
Manager of Technology Deployment and
Geosciences at Shell Exploration and
Production, Upstream Americas, Deep
water. “We analyze tremendous volumes
of realtime data to improve process and
asset effi ciency, well performance and
reliability. When our SAS alerts signal a
performance gap, we can quickly diagnose
it, interrogate the system and prevent or
mitigate critical upsets.”
Shell engineers are using these surveil
lance insights from analytics to improve
performance of the company’s newest
platform, the Perdido spar. Below 10,000
feet of water and another 9,000 feet
of mud, salt and rock, lies an ambitious
target, a swath of seabed the size of
Houston that holds enough oil and natural
gas to produce up to 130,000 barrels a
day.
SAS Predictive Asset Maintenance boosts
oil and gas exploration and production
SAS® PREDICTIVE ANALYTICS
© S
hell
7
HyundaiTechnology drives decisions at Hyundai
In its 30 years of operation, Hyundai Motor
Company (HMC) has risen to become the
topranked automaker among its domestic
competitors, as well as one of the world’s
largest automotive producers. HMC’s
business strategy revolves around a con
tinuous focus on customer satisfaction,
advanced technology, top quality, human
ism, and reliabilitywith the goal of becom
ing the top auto producer for the 21st
century. With more than 47,000 employ
ees and capital exceeding US$ 350 million,
Hyundai Motor Company currently has
the largest independent manufacturing
plant in Korea, located in Ulsan, South
Korea.
The company’s Enterprise Information
and Management System (EIMS) is the
fi rst executive information system built
with SAS software in Korea. Created in
three months for the current domestic
automotive market, the EIMS is not just a
corporatewide business management
system, but also supports and facilitates
executive decision making. For data ex
traction, Hyundai Motor Company chose
to use SAS/Warehouse Administrator
software, which provides a single point of
control for managing the company’s
data warehouse — a repository of decision
support information.
The system has three main components:
• A warning system allows executives to
view all levels of sales and production
volumes and to locate low levels of
performance by comparing these
fi gures with a previously defi ned warn
ing point.
• A goal-oriented system keeps execu-
tives informed on the progression
toward longterm goals.
• A decision support system provides
timely access to managementlevel
information.
Reports produced from the EIMS are deliv
ered directly to the company’s executives.
The EIMS warehouses information from a
wide range of departments in Hyundai
Motor Company, such as:
• Human Resources, which includes
organizational charts, personnel
records, and staff counts.
• Domestic Sales, including daily and
monthly sales, market share, and
market analysis (according to grade).
• Foreign Sales, consisting of foreign
exports, daily exports, local sales,
inventory, and competitive analysis.
• Production, including production per
factory and per model, target achieve
ment, and factory operation.
Hyundai has found that the best way to
protect its current capital investments is to
invest further into managing its most valu
able asset information. The EIMS gives
the company freedom to be proactive and
innovative instead of always reacting to
market movements, helping drive Hyundai
ahead of its competitors.
Freedom to be proactive and innovative instead of always reacting to market movements
SAS® ANALYTICS
SAS Predictive Asset Maintenance boosts
oil and gas exploration and production
8
A billion units roll off Nestlé production
lines every single day. This number illus
trates the sheer quantity of goods pro
duced by the world’s biggest food com
pany. To deliver on its promise of “Good
Food, Good Life,” Nestlé has brought
to market a whopping 10,000 products
aimed at improving consumers’ lives with
better and healthier foods and beverages.
To ensure the right amounts of those
products make it to the shelves and into
customers’ hands, Nestlé relies on fore
casting. After all, even the best marketing
promotions can backfi re if the shelves are
empty when the customers show up for
their favorite foods. It comes as no surprise
that Nestlé’s interest in closely managing
the supply chain and keeping inventories
within tight limits is proportionate with the
size of its operations. Its sheer size makes
planning on a global scale highly complex.
Product categories, sales regions and an
abundance of participating departments
combine to weave a tangled web. But it’s
also the nature of the food and beverage
industry that makes operational planning
a challenge. Seasonal infl uences, being
dependent on the weather to provide
a good harvest, swings in demand, other
retail trends, and the perishable nature
of many products make it diffi cult to plan
production and organize logistics. One
example is ice cream. This product needs
to be made before demand hits. But the
dairy products used as raw ingredients
must be available at affordable prices be
fore a production run can begin. Storing
frozen goods is also complicated and
expensive, as are the logistics involved. To
make matters worse, it’s diffi cult to predict
when and where the weather will result in
spiking demand for product.
Confl icting KPIs“Supply chain management is a welles
t ab lished, recognized stream and process
at Nestlé,” explains Marcel Baumgartner,
who leads global demand planning perfor
mance and statistical forecasting at Nestlé’s
corporate headquarters. “Our professionals
take care of transportation networks, run
effi cient warehouses and are the fi rst point
of contact with customers. One area
of focus is planning — or, more precisely,
demand and supply planning.”
According to Baumgartner, this process
tackles two important metrics: customer
service levels and inventory levels. One
can improve customer service levels —
defi ned as the percentage of complete
and ontime deliveries — by expanding
inventories. But that ties up capital, and
it’s often diffi cult to fi nd storage space.
The freshness of the product suffers as
well. Avoiding this way of working is the
principal objective of supply chain man
agement, says Baumgartner. “At Nestlé,
we don’t work like this. We are an ’and’
company. We have proven that we can
provide simultaneously high service and
optimize our inventory levels.”
The special considerations involved in
producing foodstuffs exacerbate the prob
lem. In this industry, products are pro
cessed in very large batches to keep unit
NestléHow to keep fresh products on the shelves
SAS® DEMAND DRIVEN FORECASTING & INVENTORY OPTIMIZATION
Perfect product availability at the
point of sale with SAS® Forecasting
9
prices low, ensure quality, and take ad
vantage of raw ingredient availability. This
maketostock production strategy con
trasts with the maketoorder principle
frequently seen in other sectors such as
the automobile industry. “To have the right
quantity of the right products at the right
place and time, we rely heavily on being
able to predict the orders our customers
will place as precisely as possible,” says
Baumgartner.
Other business metrics, such as budgets
and sales targets, are also important fac
tors in addition to more strategic objec
tives. The overarching goal, according to
Baumgartner, is to be able to “take proac
tive measures instead of simply reacting.”
To accomplish this, Nestlé focuses on
strong alignment processes, stronger col
laboration with customers and the use
of the proper forecasting methodology.
To attain the highest degree of precision
possible in its forecasting, the company
needed to use advanced forecasting
methods; therefore, Nestlé chose SAS.
There are two main options for generating
forecasts. The subjective method is mainly
dependent upon on the estimation and
appraisal of planners based on the expe
rience they draw upon. The statistical
method approaches the forecasting pro
blem with data.
Before using SAS, Nestlé was primarily
using SAP APO’s underlying forecasting
techniques, together with models from
the opensource statistical software R,
integrated into APO. Those forecasts were
then revised by the Nestlé demand plan
ners. SAS enhances this, and thus com
plements SAP APO perfectly.
Statistical forecasting tends to be more
reliable if sufficient historical data is avail
able. “But one thing has become clear
to us — you can’t predict the future with
statistics by simply looking at the past. It
doesn’t matter how complex your models
are.”
So it’s not the statistical methodology
that’s the problem for Baumgartner and
his team. The critical factor in this com
plex environment is being able to assess
the reliability of forecasts. Two elements
have attracted the most attention within
this context: dealing with volatility, and
SAS. “Predictability of demand for a certain
product is highly dependent on that
product’s demand volatility,” says Baum
gartner. “Especially for products that
display wide fluctuations in demand, the
choice and combination of methods
is very important. SAS Forecast Server
simplifies this task tremendously.”
Of particular importance for demand plan
ning are the socalled “mad bulls”, a term
Nestlé uses to characterize highly volatile
products with high volume. A mad bull
can be a product like Nescafé, which nor
mally sells quite regularly throughout the
year, but whose volumes are pushed
through trade promotions. A simple statis
tical calculation is no more useful in gen
erating a demand forecast than the expe
rience of a demand planner for these less
predictable items. The only way out is to
explain the volatility in the past by annotat
ing the history. Baumgartner and his team
rely on the forecast value added (FVA)
methodology as their indicator. The FVA
describes the degree to which a step in
the forecasting process increases precision
or, in other words, reduces the degree of
error.
More knowledge, less guessingAccording to Baumgartner, SAS Forecast
Server is the ideal tool for this scenario.
“Statisticians like me just love it,” he says.
The solution’s scalability allows a handful
of specialists to cover large geographical
regions. Manual input is kept to a mini
mum — selecting the appropriate statisti
cal models becomes a largely automated
process, which is seen as one of the
strongest features of SAS Forecast Server.
“At the same time, we’re now able to drill
down through customer hierarchies and
do things such as integrate the impact of
promotions and special offers into the
statistical models.”
The results paint a clear picture. In a
comparison between the conventional
forecas ting method and SAS Forecast
Server underlying procedures — for the
most part using default settings — the
results showed that Nestlé often matches
and improves its current performance for
the predictable part of the portfolio and
thus frees up valuable time for demand
planners to focus on mad bulls.
Last but not least, Nestlé emphasizes that
even a system as sophisticated as SAS
Forecast Server cannot replace professional
demand planners. “Particularly for mad
bulls, being connected in the business,
with high credibility, experience and knowl
edge is key.” With more time available to
tackle the complicated products, planners
are able to make more successful pro
duction decisions. And that means really
having enough Nestlé ice cream at the
beach when those hot summer days
finally arrive.
10
Meyer Werft in Papenburg, Germany, is
one of Europe’s most modern shipyards.
Thanks to fl exibility and innovative readi
ness, the company has been able to secure
one of the top positions in the highly com
petitive cruise ship market. This tradition
bound shipbuilding company, founded in
1795, relies on SAS solutions to coordinate
their complex planning and production
processes.
Construction of a ship from the initial plan
ning stage to fi nal launch seldom takes
less than 24 months; and depending upon
the size of the order, more than 10,000
employees from the shipyard and partner
companies are directly or indirectly involved
in the preparation process and their efforts
must be coordinated. In order for Meyer
Werft to keep a constant eye on all of
these complex interactions, the company
uses a selfgenerated software solution
called “InfoYard” which is based on SAS.
InfoYard is used to analyze operational
projects and capacities. It also functions
as an integrated information system
through which ongoing processes can
be observed. InfoYard thus creates trans
parency in an environment that could
not be monitored or managed holistically
without the support of technology.
With approximately 300 users, InfoYard is
currently running productively as a key
component of the ERP environment. The
solution assists numerous departments
in designing their planning workfl ow much
more effi ciently. Classic planning tasks,
such as the timely and optimal dispatching
of processes, recognition of interdepen
dencies, and goaloriented control of
pending tasks related to the production
stage, can be completed with the same
number of personnel, despite the increas
ing quantitative and qualitative require
ments.
The production processes mapped in
InfoYard are also extremely diverse, and
pertain to workfl ows in steelworking,
as well as to the placement of electrical
cables or paint work, while also taking
into account the design/engineering and
production aspects.
Cleverly thoughtout reporting functions
within an integrated information system
support users in the departments and
company management in project control
by means of stoplight functions and drill
down functionalities, for example. With
balanced scorecard methods and early
warning routines, erroneous trends can
also be detected at an early stage and
provide the end user with a wellfounded
basis for making decisions about project
work. These software tools help keep a
contract on course and actively controlled
through every phase of the project.
Meyer WerftMeyer Werft continues on course
On-time planning —
24 months in advance
SAS® PERFORMANCE MANAGEMENT
11
POSCOHow this giant is light on its feet
POSCO is the world’s largest steel manu
facturer, which has two large production
plants with around 19,000 employees
working to produce 28.5 million tons of
steel annually. POSCO has reported a net
profi t of more than US$3.6 billion on reve
nues of nearly $19 billion. On this enor
mous scale, a performance management
strategy — such as Six Sigma — can
have a tremendous impact on profi tability.
Six Sigma indicates the performance of a
process according to a given metric and
it leads to almost zero defects — actually,
3.4 defects in 1 million opportunities.
A process innovation (PI) program to
update 30yearold business practices
has been essential to improving effi ciency
and competitiveness at POSCO over
recent years. Both the fi rst and second
PI programs have been built with SAS
software. First, POSCO used SAS to
extract, transfer and transform its enter
prise resource planning and legacy data
into a SAS data warehouse, allowing data
to be compared on a likeforlike basis
and quality checked.
In the second PI program POSCO imple
mented SAS Analytics as a basic com
ponent for a Six Sigma Project Tracking
system. “Now, to fi nd out what’s going on
with a particular project, all we have to
do is enter the Six Sigma portal and select
the project title and CTQ name. Data is
gathered automatically by SAS, enabling
daily and monthly monitoring, also done
with SAS software,” says IllChul Shin,
Manager and “Master Black Belt” at
POSCO’s Six Sigma Academy.
The fi rst PI phase achieved a more than
50 percent reduction in lead times for
standard hot coil production (from 30 to
14 days), and a 60 percent reduction in
inventory (from 1 million to 400,000 tons).
As an example of a Six Sigma project,
Shin explains how POSCO addressed the
issue of unacceptable scrap losses on
hot coil. “Traditional statistical analysis
could not really help us. Only SAS and
its analytical power empowered us to
discover fundamentally new insights into
our physical processes. The end result
was that we could decrease the scrap
ratio from 15 percent to 1.5 percent, giving
us a $150,000 return on the investment
on this part of the process alone.”
Another project, this time in cold roll steel,
identifi ed the reasons for large variations
in profi tability by plant, item and specifi
cation. By using SAS to identify the
reasons for these variations and isolating
the factors critical to high profi tability,
POSCO was able to improve its strategy,
delivering an annual return on investment
for the project of $1.2 million.
SAS has directly contributed an ROI
of $ 14 million on Six Sigma projects
and an additional $ 1.5 million on other
projects — in less than two years
On-time planning —
24 months in advance
SAS® QUALITY LIFECYCLE ANALYSIS
12
Dow ChemicalNew solutions for a new world
As an organization of engineers and
scientists, Dow Chemical has always
valued data, but several years ago Dow
Chief Information Offi cer Dave Kepler
recognized that the company’s analytical
efforts were too internally focused.
“We started out collecting data from our
process systems or our research and
we still use basic analytical tools to solve
problems,” says Kepler. “But we needed
to apply advanced analytics to information
from both inside and outside the company
to detect complex patterns and trends
that would help us fi nd and capitalize on
new markets and opportunities.”
To support this approach Kepler built the
Business Services Group; one of its attri
butes is a centralized group of analytic
experts who are a resource for all of Dow.
Every project Dow has applied analytics
to has shown signifi cant improvement.
Those projects include:
• Enhanced sales forecasts. One
hundred percent of the projects done
using advanced analytics have signif
icantly reduced forecasting error.
• Aggressive energy consumption
reduction. The company has saved
$9 billion in energy costs since 1994.
• Early insight for business units. By
day 12 of every month, units know if
they will make targets and can adjust
strategy accordingly.
• Quick response to deteriorating
economic conditions. The Business
Services Group pushes critical
information to business units daily.
• Deep insight into the role exchange
rates play in margin. Dow developed
regional exchange rate risk models
to help make decisions about where
raw materials are purchased and
pricing for fi nished goods.
• Keen understanding of staffi ng levels.
A human resources supply/demand
model helps the company hire just the
right talent at the right time.
“We can go back and show billions of
dollars of savings,” Kepler says. “And it
helps with margin expansion. It’s all about
how we can be in the market with the
right product at the right time and get
focused on that, and then go back and
measure our success, model future
success and predict what we need to
do next.”
As for next steps? Kepler affi rms that
Dow needs analytics to stay ahead. “The
competitive advantage that companies
are going to have going forward is making
better decisions than other companies,”
says Kepler. “How you collect data and
how you make decisions off that data is
what’s going to differentiate you from your
competition. In the next 10 to 20 years,
businesses that know how to harvest
and use that information will be in the
forefront. And we want to be at the fore
front.”
Billion-dollar savings
in energy consumption
SAS® PREDICTIVE ANALYTICS
13
Euramax Coated ProductsEnvisioning the future with data visualization
Euramax Coated Products is a premium
coil coater, serving the European, Middle
East and Asian markets. Its three coil
coating lines manufacture precoated
aluminum and steel for applications in
architectural products, transportation and
corporate identity design. Euramax’s pre
coated metals cover all kinds of products,
from building facades to household appli
ances, working with some of the most
prominent brands in the world.
Euramax uses SAS Visual Analytics. The
company’s objectives in employing the
SAS solution were to gain more dynamic
reporting and data exploration capabilities,
to provide for more probing research and
to enhance mobility, including the ability
to carry data out into the fi eld and share it
with customers.
“We wanted to have our data available at
any time, to gain quicker insights and
make better decisions, anywhere,” Wijers
says, and to be able to present data in a
variety of easytograsp formats.
The most common problem with static
reporting, Wijers says, is that you can see
deviations in the end result but still don’t
know the causes. Requests to analysts
for detailed information take time and,
generally, the more detailed the results,
the more questions that are raised.
“Often an analyst’s gut feeling is right, but
he doesn’t have the means to easily verify
it,” he says. “SAS Visual Analytics reporting
tools allow users to quickly and easily
add fi lters or drill down to a more detailed
level of information.” But sometimes those
gut feelings are wrong — and here, as
well, SAS Visual Analytics comes in handy.
“Sticking to those gut feelings can hinder
employees in their search for improve
ment,” Wijers says. “While identifying
outliers, visual analytics allows you to see
correlations that weren’t expected, and
the focus can be put on the real causes.”
Wijers sees visual analytics as opening
up the opportunity to explore new areas
of effi ciency and innovation — to answer
questions that haven’t previously been
posed.
“It’s a common fact that when analysts
take a lot of time in offering fi ndings,
management’s motivation to request dif
ferent approaches to the analysis wanes,”
Wijers says. “But with SAS Visual Analytics,
once the data is loaded, analysts are
off and running, without the need for any
specialized support. With that level of
freedom and fl exibility of analysis, answers
can be found much faster, and with a
higher degree of quality.”
Focus on the real
causes
SAS® VISUAL ANALYTICS
14
Intelligent production processes
thanks to analytics
15
Machines now exchange more data among themselves than human beings communicate
to one another. The fourth industrial revolution is advancing full steam ahead straight into
an avalanche of data. As this development progresses, new questions are coming to light.
How, for instance, will we obtain meaningful insights from these massive amounts of
raw data? And what will it take to establish intelligent control of machinetomachine
communications? The majority of the industrial sector is aware of the significance these
issues hold according to a survey of a representative sample of German industrial firms
conducted by the German market research institute Forsa.
Nevertheless, most companies are still nowhere close to taking full advantage of the data
currently available to them. In the real world, analytics solutions that go beyond the
obvious to seek hidden correlations among production and operations data remain few
and far between. Anywhere reporting systems are being used to merely summarize
historical data that are buried in the past, what is actually needed are analytics systems
capable of anticipating future developments and delivering accurate forecasts.
Real-time process controlThere’s a good reason why we have so much catching up to do. Not too long ago, many
analyses were impossible from a technical standpoint. Or they took too long to complete.
But recent technological developments have reshuffled the deck in industry’s favor.
Now, data can be analyzed at volumes and speeds that were impossible as little as a year
ago. Big data — and that is clearly what we’re talking about with M2M communications —
has transformed from a thorny problem into a true competitive advantage for pioneering
enterprises because they now have access to more and better information than the
competition.
Analytics software from SAS, for instance, is able to analyze as many as 1 billion records
in a mere nine seconds. Analyses which could take anywhere from one to two days in the
past can now be completed in just a few minutes. New inmemory technology and rapidly
declining memory prices precipitated this giant leap in performance. Inmemory means
that all the required data is loaded into main memory and analyzed there directly. The
resulting speed increase gives us the ability to conduct analyses in real time, which is
particularly important in a production scenario. Modern analytics solutions are able to
continually monitor production processes in order to not only look at the past but also to
predict events that lie in the future. And that’s exactly what’s so special about analytics
for production. The goal is not just to report on individual metrics. Instead, all the different
sources of data are juxtaposed to detect correlations and derive insights for enhancing
production processes.
The more information that is gathered, stored, and analyzed, the more detailed the picture
of how the smallest elements within a production process from individual machine com
ponents through to QM processes correlate with one another. Suddenly, the origins of
16
production issues and which variables signal upcoming problems become clearly visible.
A classical application of machine data analysis would be an early warning system that
provides advance notice of impending machine downtime, declining product quality, or
production inefficiencies and tells operators what needs to be done and where, allowing
the process to get back on track — in other words, predictive asset maintenance.
Process optimization plays an important role as well, particularly with regard to workforce
management. Quality lifecycle assessments, in which product quality is assessed over
the course of the entire product lifecycle, also profit considerably from the use of analytics.
These assessments can for instance involve the use of service reports to identify early
indicators of problems so that corrective action can be taken as soon as possible while
minimizing costs.
New business modelsManufacturers who integrate predictive analytics into their machinery enhance the profit
ability of their customers by boosting uptime to as much as 100 % in ideal situations. Such
a high degree of availability makes customers more satisfied and loyal. Machine data analysis
can also serve as a springboard for developing new business models such as service
packages which can have a positive impact on top line revenues. Existing data can also
help improve processes to increase capacity utilization, for instance. And let’s not forget:
data that describe how customers behave makes it possible to draw conclusions about
customer preferences and optimize accordingly. In this way, valuable differentiators can be
achieved.
The industrial sector needs analyticsTo sum up: if you want set up machinetomachine communication, you need to ensure
that your data is relevant, meaningful, and comparable. Only analytics is able to prepare
machine data and data generated during production or operations in such a way that it is
actually worth communicating. Analytics ensures this data has significance. It can inform
managers of the status of ongoing production activities as well as foresee future events
whose likelihood lies concealed beneath the data. That’s the reason why the industrial
sector so desperately needs dependable and highperformance analytics solutions. But if
you want to transform the opportunities that advanced manufacturing has to offer into a
true competitive advantage, there’s no time to waste. You need to get started now. And
SAS Manufacturing Solutions have everything you need to hit the ground running.
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SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Copyright © 2014, SAS Institute Inc. All rights reserved. P1
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95
NestléHow to keep fresh products on the shelves
8Meyer WerftMeyer Werft continues on course
10POSCO How this giant is light on its feet
11
Analytics The driving force behind the next Industrial Revolution
5Shell Shell drills into big data analytics, extracts tens of millions of dollars
6HyundaiTechnology drives decisions at Hyundai
7
Dow Chemical New solutions for a new world
12Euramax Coated Products Envisioning the future with data visualization
13Advanced manufacturing Intelligent production processes thanks to analytics
14
