Manufacturing and the data conundrum

Manufacturing and the data conundrum
Too much? Too little? Or just right?
A report by The Economist Intelligence Unit
Commissioned by

Manufacturing and the data conundrum: Too much? Too little? Or just right?
© The Economist Intelligence Unit Limited 2014 1
Contents
I. Executive summary 2
II. Ready or not, here it comes 4
III. Quality first 7
Case study: Meritor: Towards data-driven production perfection 9
IV. Where theory meets reality 10
Case study: ABB/Sandvik: Reducing deviations, eliminating imperfections 12
V. From monitoring to alerting, predicting and solving 13
VI. The plentiful returns of data success 15
Case study: GE’s factory built to produce data 17
VII. Conclusion 18
Appendix: Survey results 20

I Executive summary
About the research
This Economist Intelligence Unit study, commissioned by
Wipro, examines how manufacturers now collect, analyse
and use the complex, real-time data generated in production
processes. By far the most important finding is the increased
understanding of how to use process data to improve
product quality, but manufacturers are also realising gains in
reliability, throughput and maintenance practices by tuning
into what their production processes are telling them.
This report, a follow-up to our 2013 omnibus report on
data usage, The data directive: How data is driving corporate
strategy—and what still lies ahead, is based in part on a
survey of 50 C-suite and senior factory executives from
North America (50%) and Europe (50%) from companies
that produce a broad range of industrial goods. These
include electronics (12%), machinery (12%), chemicals
and gases (12%), vehicle parts (10%), rubber or plastics
(10%) and more. Respondents are from intermediate to very
large organisations; 32% have global revenues in excess of
US$5bn, 32% have revenues of between US$1bn and US$5bn
and 36% have revenues of US$500m-$1bn. To complement
the survey, the EIU conducted in-depth interviews with
senior manufacturing executives and academics, as well as
related additional research.
The report was written by Steven Weiner and edited by
David Line. Our thanks are due to all survey participants and
interviewees for their time and insights.
Interviewees (listed alphabetically by organisation) included:
• Peter Zornio, chief strategic officer, Emerson Process
Management
• Stephan Biller, chief manufacturing scientist, GE Global
Research
• Joe ElBehairy, vice president, engineering, quality and
product strategy, Meritor
• Kent Potts, manager of industrialisation, Meritor
• Daniel W Apley, professor, industrial engineering and
management sciences, Northwestern University
• Shiyu Zhou, professor, Department of Industrial
Engineering, University of Wisconsin-Madison
Respondents by job title
(% respondents)
Chief operating officer
34%
Other “C-suite” role
14%
4%
16%
10%
Head of plant/
factory
6%
10%
Chief financial
officer
Chief technology
officer
2%
4%
Chief quality officer
Chief strategy
officer
Chief, supply chain
management
Chief safety officer
Respondent companies by revenue
(% respondents)
2 © The Economist Intelligence Unit Limited 2014
36%
12%
32%
20%
US$500m to US$1bn
US$1bn to US$5bn
US$20bn or more
US$5bn to
US$20bn

Key findings from the survey include:
l Manufacturers have significantly ramped up
their shop floor data collection.
Some 86% of survey respondents report major
increases in the amount of production and
quality-control data stored for analysis over
the past two years. But it hasn’t been easy—
only 14% of those surveyed report no problems
managing the data glut from real-time
production sensors and associated reporting
and analytical models.
l A minority of manufacturers has an
advanced data-management strategy.
Fewer than half of respondents (42%) have
what they consider to be a well-defined
data-management strategy. A further 44%
say they understand why shop floor data is
valuable and, consequently, are putting in
place resources to realise that value. There is
no doubting its importance, though: every
single manufacturer surveyed reports that
data collection is a priority concern for their
business.
l Manufacturers find it difficult to integrate
data from diverse sources—and to find the
skilled personnel to analyse it.
Difficulty integrating data from multiple
sources and formats is the most commonly
cited problem in managing greater volumes
of data, picked by 35% of respondents—no
surprise, given the age of most manufacturing
plants and that technology is transitory while
infrastructure is durable. Companies also find
that because of the speed of data-technology
advancement they often lack the internal
expertise necessary to maximize the benefits
of collected information (cited by 33%).
l While data collection from monitoring is
common, data analysis to predict issues or
solve problems is less so.
While almost all manufacturers find it normal
to monitor production processes—for
example, 90% or more say their companies
have mature data analysis capabilities for such
essentials as asset and facility management,
safety, process design and supply chain
management—less than half have in place
predictive data analytics, and less than
40% use data analytics to find solutions to
production problems.
l Data is delivering stellar quality and
production-efficiency gains…
Using insights gathered from production-data
analysis, two-thirds of companies report
annual savings of 10% or more in terms of the
cost of quality (that is, net losses incurred
due to defects) and production efficiencies,
and about one-third say their savings on both
measures have been in the range of 11% to
25%. This may explain why more than three-quarters
of respondents identify aggressive
data programmes as an important way to
boost efficiency and lower costs.
l …but collecting data doesn’t automatically
yield benefits.
Despite many manufacturers reporting
impressive savings from data analysis, 62%
are not sure they have been able to keep up
with the large volumes of data they collect,
and just 50% are sure they can generate
useful insights from it, as it comes from too
many sources and in a variety of formats and
speeds.

II Ready or not, here it comes
Manufacturers have used data to measure
production since at least 3000 BC, when the
oldest discovered cuneiform tablets were marked
with pictographic words and numbers. All it
took was a reed or stick to mark damp clay, and
the number of sheep, bags of grain or output
of spears was readable, but only to the literate
overseer.
Similarly, today’s industrial data, displayed on
computer screens, is understandable and useful
only to the trained overseer. But there is far more
of it, and it is available instantly, so that as issues
arise process adjustments can be made quickly.
In today’s ideal digitally networked production
environment, complex data can be used far more
easily than ever to improve product quality,
boost throughput, improve shop floor reliability,
enhance safety and predict maintenance
requirements, eliminating unscheduled
downtime.
That is the ideal, at any rate. In the past decade,
as more manufacturers have implemented a
broader array of digital controls—in the process
linking together production machinery that used
to operate independently—it has become an
appealing vision of what making things might
actually become everywhere.
“Today’s integrated operations go above and
beyond what has been the traditional realm of
process control,” says Peter Zornio, chief strategic
officer of Emerson Process Management, a unit of
St Louis, Illinois-based Emerson Electric Company.
“We think there are three big ideas at the heart of
it. The first is pervasive sensing. You can get more
and more data points than ever before.
“Second, integrated operations means multiple
disciplines can analyse and discuss data from the
plant together, not just one discipline at a time.
And third is the realm of big data and equally big
analytics.”
Stephan Biller, chief manufacturing scientist
for GE Global Research—a group responsible,
among other things, for finding ways to make
General Electric’s 400 factories as efficient as
possible—says the latest iteration of thinking
there is called the “brilliant factory.” The brilliant
factory idea works together with the industrial
internet and software development that GE calls
“Predictivity,” mirroring what theorists believe
can be a manufacturing world so all-knowing
that it routinely predicts production and product
problems and solves them, too.
“It’s the entire digital thread from engineering
and design, to manufacturing engineering, the
factory and our suppliers,” says Dr Biller of the GE
factory. “What’s new is envisioning the feedback
loop from the factory in real time, through
factory engineering and from the service shops.
The amount of data is quite astounding.”
In fact, at GE’s new battery production plant
in Schenectady, New York, 10,000 variables
of data are collected, in some cases every 250
milliseconds. “We now have an infrastructure in
the plant, data highways, that match what we
have in the public Internet,” says Dr Biller.
“Today’s
integrated
operations go
above and beyond
what has been the
traditional realm of
process control.”
Peter Zornio, chief strategic
officer, Emerson Process
Management

964
88 10
88 10
86 12
66 18
The allure of this vision is pervasive. In a survey
of manufacturers conducted by The Economist
Intelligence Unit for this paper, 86% say that
during the last two years they have significantly
increased the amount of production and quality-control
data stored for analysis. Nearly two-thirds
say they use sensor-generated data from
networked machines—an essential element of
the integrated factory—and 20% say they plan to
use data from networked production machinery
(Figure 1). Equally telling, two-thirds of those
surveyed say they also use sensor-generated
data from external sources, off their shop floor,
for comparison purposes—a move into the more
complex and analytically difficult world now
generally called “big data”.
But not everything is settled when it comes
to collection and use of digitised data. Most
factories are decades old and predate in
their design any consideration of this type
of technology. The most recently completed
complex greenfield oil refinery in the US began
operations in 1977, for example. Despite
the decades of post-World War II quality
improvement programs—such as the teachings
of statistical process control guru W Edwards
Deming, the scholarship of Joseph Juran,
Japanese kaizen process improvement teams,
Six Sigma programmes, the Toyota Way and Lean
Figure 1
Manifold data sources
What sources of data are used by your company to lower the cost of quality and improve
manufacturing efficiency? Select all that apply.
(% respondents)
Customer feedback system—Compliance/incidents
management data
Manufacturing execution system (MES) process historian
Enterprise data (ERP)
Supply chain management system/supplier data
After sales failure data
Supplier-provided test data
Sensor-generated data from external sources for
comparative purposes
Sensor-generated data from networked machines
Operator logs
Sensor-generated data from individual machines
RFID
42 8
62 20
52 16
90 6
78 18
74 24
34 14
82 10
Accounting/finance data
Demand forecasts
Use now Plan to use

Manufacturing—tens of thousands of factories
in North America and Europe are light years
removed from advanced, cutting-edge digital
processes.
Most of these plants installed control systems
along the way, many of them proprietary systems
that have been locally customised and continue
to operate—producing, perhaps, batch reports
on operations at the end of the day.
“Migrations from systems of this nature are not
for the faint of heart,” notes a senior executive
in the control systems industry. He tells the
story of one large factory, with annual revenue
of US$500m, where production is controlled
by orders written on coloured pieces of paper,
one colour for each day of the week. If every
workstation in the plant is using the same colour,
the process is in sync.
It is therefore no surprise, in this environment,
that only 14% of surveyed companies say they
have experienced no problems as they manage
increasing volumes of machine-generated
process and quality data. Companies wrestle
with efficiency and quality-improvement data
from so many sources that confusion and apples-to-
oranges comparisons are easily made. The
number-one source of data, used by 96% of
surveyed companies, is old-fashioned customer
feedback, followed by process historian systems
(90%), existing enterprise resource planning
systems (88%), accounting and financial data
(88%), pre-existing supply chain management
systems (86%) and after-sales failure data (82%;
Figure 1).
“There is an enormous amount of data, and
it’s a challenge to figure out how to integrate
it,” says Daniel W Apley, professor of industrial
engineering and management sciences at
Northwestern University in Evanston, Illinois.
“What you would like to use it for is to identify
root causes of quality problems and product
variation. People have been talking about this
for decades. But the truth is, there are still many
open research challenges and no real established
methodology that can be used to trace quality
problems back to the root causes when there are
thousands of upstream process variables that are
potential root causes. When there are thousands
of variables, you typically need data for hundreds
of thousands, or millions of parts in order to
find meaningful statistical associations between
problems and root causes.”
As GE’s Dr Biller says, “When you think about
all the tasks that people have to do—the
maintenance system, scheduling, material
handling, incoming material, the machines
themselves and their error codes, how much
material is in each of the buffers, does the
part pass or fail—and each plant has 10 to 15
individual systems. This is what makes the task
somewhat difficult.”

III Quality first
Respondents to the EIU survey conducted for this
report see product quality management as the
area in which greater volumes of data are most
likely to make the biggest difference. Nearly
three-quarters (72%) pick this in their top three
business areas likely to see gains from more
data, a much larger proportion than for any of
the other areas and 28 percentage points more
than the proportion picking process controls, the
number-two area of potential gains (Figure 2).
Shiyu Zhou, a professor in the Department of
Industrial Engineering at the University of
Wisconsin-Madison, says that discussions about
the need for better data analytics are “typically
reactive” to customer queries or complaints—
which often link back to quality issues. In fact,
he says, it has become easier than ever to hear
the voice of the customer because of data-driven
product designs that report performance
issues automatically to manufacturer service
departments. Examples, he says, are medical
equipment, such as magnetic resonance imagers
or CT scanners, or jet aircraft engines that are
linked to the Internet and communicate on their
own when service is needed. Emerging problems,
in turn, lead to an enhanced need to boost
analytic capacity linked directly to shop floor
production processes.
The machines themselves, in other words, feed
the need for process data, leading to installation
of more linked machines, and more actionable
data in the factory. In this view, products that
ask for service are like the razor blade, which by
steadily growing duller creates the need for more
razor blades, and a strategy for making them.
At Meritor, a maker of drivetrains, axles brakes
and other commercial vehicle components,
customers tend to focus on one metric—the
number of rejected parts per million (PPM)—to
evaluate suppliers. “When you take into account
high-level manufacturing processes—we do
casting, forgings, stampings, machining,
heat treating and assembly—and every truck
buyer wants to have the truck the way they
want it with specific transmission, axles, and
brakes—the variations are in the thousands,”
says Joe ElBehairy, Meritor’s vice president for
engineering, quality and product strategy.
What’s more, truck demand can swing wildly in
volume, which stresses manufacturing systems,
where long and stable production runs most
often reduce product variation. To respond,
Meritor has as much as quintupled the amount of
data it collects at its 28 manufacturing plants.
Meritor began to track defect rates not just
by part, but also by individual production
operations. It also decided to differentiate
between reject PPM of products shipped to
customers and supplier PPM, which takes into
account quality levels from component suppliers.
In 2013, Meritor’s reject rate was 139 PPM.
During the first quarter of 2014, with more plants
working to improve the traceability of production
issues, the rate fell to 67. One plant, producing
an entirely new type of air brake, achieved
perfection—zero PPM (see case study on page 9).

Figure 2
Where data can make the difference
In which of the following areas do you see greater volumes of data yielding the biggest gains?
Select the top three.
(% respondents)
Product quality management
Process controls
Operations management
Process design and improvements
Predictive maintenance/asset management
Supply chain management/sourcing
Safety & facility management
Targeted capital spending
72
44
42
36
30
30
20
12
10
Throughput improvement
“A key element is that we realise, as a company,
that quality is valuable to our customers,” says Mr
ElBehairy. “Some of the principles we applied are
not new or earth-shattering, but we’ve been able
to apply them to the complexity that we provide
in our products.”
With the proper analysis of complex production
data potentially yielding such dramatic gains in
quality and efficiency, it is perhaps no surprise
that the rush to collect it still outpaces planning
to use it. Indeed, just 42% of companies
responding to the EIU survey say they have
a well-defined data management strategy,
although a slightly larger proportion (44%) say
they understand the value of shop floor data
and are working to capture that value. This is
despite the fact that all realise the paramount
importance of data: every single company
surveyed places a priority on data collection.
GE’s Dr Biller emphasises that an important part
of any change in data strategy, and consequent
alterations to production processes, is careful
and considered planning. “It’s a step process,”
he says. “First you gather data and network it.
Then you give the people in the plant the ability
to operate the system using the data. You need
to go through the steps rather slowly so that
people in the plant understand what we’re trying
to do, and so that we can work with them as a
collaborator. Most of the time, the people in the
plant know far more about it than you do.”
Equally important to realising value from this
kind of initiative, says the senior executive
from the control systems industry, is absolute
commitment from senior management, especially
the CEO, to building an integrated data process.
“You can put in all the components to make it
work, the computers and software, but if you
don’t have leadership skills and trust, it can lead
to failure no matter what system you have,” he
says. “Having commitment from the CEO is an
absolute prerequisite.”
“First you gather
data and network
it. Then you give
the people in the
plant the ability to
operate the system
using the data. You
need to go through
the steps rather
slowly so that
people in the plant
understand what
we’re trying to do,
and so that we can
work with them as a
collaborator.”
Stephan Biller, chief
manufacturing scientist for
GE Global Research

Case study: Meritor: Towards data-driven
production perfection
Like most manufacturers, Meritor, of
Michigan, has been on a long and determined
drive to improve processes and products at
its 28 factories in a dozen countries. The
company makes drivetrain, braking and other
components for trucks, trailers, off-highway,
defence and speciality vehicles.
The latest iteration of company strategy,
dubbed M2016, made operational excellence
a renewed priority. “We adopted as our top
metric reject parts per million [PPM],” says
Joe ElBehairy, vice president for engineering,
quality and product strategy. In 2013,
companywide this figure was 139 reject PPM.
With a goal of lowering that to 75 PPM by 2016,
Meritor has turned, in part, to carefully heeded,
real-time shop floor data.
“Our data collection is an order of magnitude
larger than it was several years ago,” says
Mr ElBehairy. “Some of it is related to safety,
but a lot of our data gathering is related to
traceability.” If something goes wrong, Meritor
wants to know where and why it happened.
“And it’s not just collecting data, but real-time
acting on that data,” says Kent Potts,
industrialisation manager and leader of a
quality improvement push at Meritor’s factory
in York, South Carolina.
At York, three workstations assemble calipers
for Meritor’s EX+ air disc brake from start to
finish. More than 40 steps are required for the
basic brake, but that’s only the beginning of the
product’s complexity. Originally launched with
14 different specifications of weight, stopping
power, pads, packaging and the like, EX+
assembly ballooned to 169 specifications after
sales volume rose sharply following a contract
award two years ago. A major customer wanted
the brakes, but insisted that rejects had to be
10 PPM or less.
To comply, the York plant added sensors,
monitoring gear, a programmable controller
system and its own custom programming.
Employees were trained on an error-proofing
system that verifies that the correct parts and
processes are applied for each brake. Bar codes
are used to keep track of parts, and Meritor
devised a system called “fit to light”, in which a
computer keeps track of the assembly steps for
each brake and turns on lights over the correct
bin for the next component. Reach for the
wrong component, and a red light flashes.
Meritor used tools that communicate with the
programmable controllers and socket trays
so that the tools could be used for multiple
assembly operations and brake specifications.
The programmable controllers verify that
the correct socket and torque gun recipe is
used for each assembly process; each piece
receives the correct customised treatment.
“Additionally, process data are stored in our
manufacturing genealogy database for each air
disc brake that’s assembled. The data includes
the brake serial number, who assembled it, the
component parts installed and process data
such as fastener torques,” says Mr Potts.
The result of this application of real-time
networked data to improve shop floor processes
has been better than any manufacturer usually
expects. During the year from March 2013 to
March 2014, the York factory had a zero defect
rate. No product rejects. Error-free production
also permitted improvement of the on-time
delivery rate to 98%—the best of any Meritor
plant.
Techniques like these and tighter attention to
quality lowered the company’s overall reject PPM
in the first quarter of 2014 to 67, below the 2016
goal. Now, the goal is to sustain the progress.

IV Where theory meets reality
For every brilliant idea in building real-time,
system-wide data collection and analysis,
and for every example of skillfully fine-tuning
a complicated production process using the
industrial Internet and sensor data, there’s a
real-life story that brings you back to actual
shop floors.
The control systems industry executive describes
a steelmaker where, after sophisticated,
automated process controls were installed,
operators replaced intricate plant-wide
readouts—temperatures, process adjustments
to compensate for feedstock variations and so
forth—with data of greater personal interest. (In
the background, the integrated digital system
continued to monitor and collect vast amounts
of production information).
Figure 3
Internal siloes make it difficult to employ
data effectively
“There are just two numbers [on display],” he says.
“One is variable income being produced right now,
and the other is how much bonus money will be
made at the end of the month.” Precisely calibrated
automated readouts, displaying thousands of
variables every second, were turned off because
machine operators from each shift preferred to
compete manually with operators from other shifts
rather than with an automated system.
“One of the guys at an oil refinery told me, ‘We’re
staffed to run; we’re not staffed to change,’”
says Emerson’s Mr Zornio. “The capital-spending
priority, and the manpower priority, is to just
keep the place going. There is no manpower
or capital to put in place the next generation
of stuff that gets the plant to the next level of
improvement.”
Common problems
What problems have you experienced in managing greater volumes of machine-generated
process and quality data? Select all that apply.
(% respondents)
Difficulty in integrating data from multiple
sources/formats
Lack of personnel/expertise necessary to
analyse data from diverse sources
We find it difficult to formulate the right
questions to use the data appropriately
Analysis of data frequently does not produce
actionable information
35
33
21
21
9
We have experienced no problems 14
“One of the guys
at an oil refinery
told me, ‘We’re
staffed to run;
we’re not staffed to
change.’”
Peter Zornio, chief strategic
officer, Emerson Process
Management

Companies that responded to the EIU survey
raise a series of impediments to the enhanced
use of complex, machine-generated data to
improve their processes. Number one on the
list, cited by 35% of manufacturers, is difficulty
integrating data from multiple sources and
formats (Figure 3).
“The bottleneck is not in sensing; we
have incredible sensing technology,” says
Northwestern’s Professor Apley. “It gets back
to the thousands of variables and identifying
which is the root cause of the problem.” Older,
proprietary systems, including some enormously
popular ERP systems, produce only summary,
batch reports; newer ones may crank out data, in
different formats, four times each second.
“Most of these older factories are not
networked,” says Dr Biller of GE. “The data stays
within the production machinery. If you want
to improve a system’s performance, you have to
get the data out of the machine, then integrate
it into an IT system—some kind of intelligent
platform.”
But simply installing that platform isn’t the
whole answer, either. Thirty-three percent of
surveyed companies say an important issue is
finding highly trained people to use it. A related
problem—asking the right questions of your
systems to generate the right answers, was cited
as an issue by 21% of surveyed companies. Also
problematic: companies organised into feuding
siloes that don’t share essential information
(also cited by 21%).
Talent, says Northwestern’s Professor Apley, is
thin on the ground. “Relative to 20 years ago, it
is more difficult now to find young people who
are highly trained in analytics and data sciences
and who want to go into manufacturing,” he
says. “They are often more drawn to financial
companies, or companies like Google and
Facebook.” Even so, Northwestern is among
the universities that have recently launched
an engineering-oriented masters of science in
analytics program. The student body is roughly
one-third international and two-thirds domestic
students, and so far, all have received multiple
job offers. “There are just so many companies
looking for people who have the skills to analyse
large amounts of data,” Professor Apley says.
Mr Zornio has found siloing can be a significant
issue because when “every facility makes their
own decision” about which efficiency controls
to put in place, interplant uniformity becomes
impossible. Nonetheless, centrally controlled
manufacturers may make decisions about best
practices that individual facilities resist because
of inevitable local variability.
“In this big-data world, you may know that you
don’t have the people who can look at all the
data and figure out what needs to be done,” he
says.
But suppose you do have the people. The next
tripwire, says the senior executive from the
control systems industry, is “information
overload. There’s not enough intelligence in
software to sort out this overload.
“For example, let’s say there’s a machine that
sends out an alarm to the operator. It needs
grease or whatever. But what if three or four
machines do this? Then suddenly you have alarm
overload, and then you have to have alarm
management. It is easy to find 500 things to do
in a plant. But it’s damn tough to find the 497
things we are not going to do. That’s the real
challenge.”
“It is more difficult
now to find young
people who are
highly trained in
analytics and data
sciences and who
want to go into
manufacturing.
They are often more
drawn to financial
companies, or
companies like
Google and
Facebook.”
Daniel Apley, professor of
industrial engineering and
management sciences,
Northwestern University

Case study: ABB/Sandvik: Reducing deviations,
eliminating imperfections
ABB, based in Zurich, Switzerland, makes
power, automation and electrical products and
provides a range of industrial control services.
One of its recent successes came in helping
Sandvik Materials Technology of Sandviken,
Sweden (about 190 km north of Stockholm),
which makes specialty stainless steel, titanium
and alloys for equally specialised uses.
Sandvik faced the problem that as the uses
of steel grew more intricate, with greater
precision required from each delivery,
production equipment had to keep pace.
In 2013, as part of a long-term process
improvement effort, Sandvik’s attention turned
to an important component of the production
system—a bidirectional rolling mill, or Steckel
mill, used to make metal strips thinner and
thinner with each pass through the rollers.
Sandvik had no plan to replace the equipment.
Instead, it opted to improve how it used the mill
with a few new sensors feeding digital controls.
Based on a tightly defined model of perfection,
these would continually adjust rolling speeds,
pressures and the number of passes through the
mill to compensate for variation.
First, Sandvik installed additional sensors
to measure precisely the width of the rolled
metal and its temperature, which changes
during rolling as the metal interacts with the
machinery. In some factories, dozens or even
hundreds of sensors might be required, but
Sandvik made do with just nine. To control the
process, the company needed more data, but
not a flood of it.
Every rolling job begins with a detailed model
of what the exactly right outcome should be.
This means the system—provided by ABB—must
take multiple factors into account, including
the material being rolled; its thickness, width
and grade; the target thickness; the number
of passes through the mill that should be
required; and the adaptations to rolling
pressure, temperatures, rolling speed, torque
and flatness that must be made. Increasingly,
customers want thinner steel, but thinner
steel strips can easily be brittle and prone to
deformation and in-process separation.
“We run all kinds of special steels, the entire
range from stainless to high-alloy,” says
Patrick Högström, hot rolling mills production
manager at Sandvik. “The variety of steel
grades and sheet dimensions make production
very complex and knowledge intensive. The
model makes it possible to optimise rolling in
a completely different way from what a human
being is capable of. It becomes smoother,
and with noticeably less scrap, which means
increased yield.”
Thanks to the new sensors and related process
controls, Sandvik can now roll specialty metal
to thinner tolerances while maintaining
the metallic properties, such as strength
and formability, required for the final use.
Compared with its old process, the new controls
have reduced the degree of deviation from
perfection by 35%, and the average volume of
imperfections has dropped by 80%.

From monitoring to alerting,
predicting and solving V
Reporting normal operations
Alerting about problems
Asset maintenance/management
Even with the many complications between
shop floor data theory and practice, companies
surveyed by the EIU have found a number of
comfort zones where the benefits of real-time
machine-generated information are accessible.
More than 80% of companies report “mature”
data analysis capabilities when it comes to
everyday issues of safety, facilities management,
supply chain management, formulation of
capital spending plans, process design, the
use of process controls, asset maintenance
and generalised product quality management.
In other words, when production processes
are normal, their comfort level with digital
technology is at high levels.
But outside of monitoring normal operations,
confidence levels drop precipitously. For
example, when it comes to analysing responses
to alerts about problems and their causes, half
or more of surveyed companies lack mature
capabilities. Two-thirds of companies report
analytical weakness when it comes to dealing
with asset maintenance and throughput alerts,
and 76% lack mature capabilities to analyse
potential process design issues (Figure 4).
Figure 4
Reporting yes; alerting/predicting/solving—not yet
For which of the following functions and areas does your company have mature data analysis
capabilities?
(% respondents)
100%
80%
60%
40%
20%
0%
Process controls
Throughput
Safety and facilities management
Process design Operations management
Supply chain management
Capital spending
Predicting future problems
Prescribing solutions to problems

Although state-of-the-art digital systems
can predict problems and suggest solutions
in advance of actual need, more than half of
manufacturers aren’t confident that their
analytical skills are up to the task. Just 22% of
surveyed companies have predictive analytical
capabilities for production throughput, for
example; just 16% have mature analytical
capacity to generate potential solutions.
In terms of functions, the highest levels of
predictive capability are found in supply
chain management (44%) and safety and
facility management (48%). Mature analytical
capabilities to prescribe solutions are most
prevalent for asset maintenance (38%) and
product quality management (38%).
Of course, when evaluating the current state
of manufacturing’s digital readiness, two
additional issues must be considered.
First, not all factories actually need the most
advanced possible integrated, real-time data.
“If you operate a smaller factory, and you have
a supervisor who can see where everything
is, you don’t need all that much data to run it
efficiently,” says Dr Biller. “If I have a plant that
can already run at optimal efficiency, I don’t
want to implement sophisticated technology
because there is no return on investment from
it. Once you ‘lean out’ a plant and make every
process as simple as possible you don’t want to
automate it.”
Second, even the most sophisticated, integrated
digital system is of limited use if companies
don’t ask the right questions about their
processes or quality issues. Data collection and
analysis return rewards only when root causes of
issues are addressed, rather than just symptoms
of those issues. One practice used in Six Sigma
quality-improvement systems is to repeatedly
use the question “Why?” until the root cause
is identified. For example: A door hinge isn’t
working as it should. Why? Because it is slightly
too large for the fitting. Why? Is it because
the wrong materials have been used to make
it, leading to slight deformations of the part?
Are processes slightly maladjusted, leading to
hinges of the wrong size? Is the part designed
the way it should have been?
“Why” questions apply equally to the use of
shop-floor data analytics. “You must ask the
right question to arrive at the right answer,” the
old adage goes, and nothing about the modern
digital world has changed the truth of this.

VI The plentiful returns of data success
26
After all is said and done, it turns out that
coordinated digital control systems can and do
produce insights that are extremely valuable.
Two-thirds of companies say data from the shop
floor have realised savings or gains of more than
10% annually in both new production efficiencies
and the cost of quality. Some 34% of those
surveyed have generated annual savings of more
than 25% annually in the cost of quality; the
same proportion report 25% or better efficiency
improvements annually (Figure 5).
Although the results should be interpreted with
caution given the sample size—and the fact that
correlation does not prove causation—the survey
results nonetheless indicate that the most data-adept
companies are also the most profitable.
In the survey, manufacturing companies with
average earnings growth of 10% or more over the
past three years are more likely to have a well-defined
data management strategy than those
who experienced slower or no earnings growth
(59% vs 10%). They are more likely to find ways
to improve efficiency and lower the cost of quality
(45% vs 25%). They are also less likely to lack
staff with sufficient data-analysis capabilities to
meet their needs (21% vs 50%; Figure 6).
Although the potential for improvement
varies widely by facility, Dr Biller says that
Figure 5
Savings from data
To what extent have insights from shop floor/machine-generated data delivered gains in the
following areas?
(% respondents)
Cost of quality Production efficiency
40
35
30
25
20
15
10
5
0
34
No impact 1-10% 11-25% 26-50% >50%
8
30 32
36
32
0 0 2

Figure 6
70
60
50
40
30
20
10
“High growth” = average EBITDA growth of 10% or more for each of past three years
“Low growth” = average EBITDA growth of under 10% for each of past three years
optimising factory systems provides substantial
opportunities. “If you look at the supply chain
and driving out excess inventory, we talk about
savings of perhaps 6% to 20%; we think 10%
is actually a conservative number for those
implementations,” he says. “At GE, where we
carry US$15bn in inventory, a 10% saving can
be huge. How about optimising scheduling to
improve throughput? Ten percent is a pretty
conservative number for that, and that means
you can save in plant and equipment investment
because we don’t have to buy additional
machines. Machine optimisation can produce
gains of 20%. The benefits can be huge.”
Current gains also involve more than money or
ROI that justifies the necessary investments
in sensors, RFID and bar code equipment,
computers, software, training, process
realignment, improved production machinery
and continual product improvements. Evangelists
for widely integrated production systems believe
a new industrial revolution is in process. Fully
50% of surveyed companies agree with the
statement: “A rigorous and advanced data
analysis capability can be a differentiator for our
company.” This marks an important attitudinal
step for manufacturing companies into real-time
controls and rapid responses to production
variations.
“Ultimately, the goals of decades of work on
inspection and quality control boil down to
understanding variation,” says Professor Apley.
“Perfect manufacturing hypothetically produces
the same part, identically, with no variations.
What we see now is that huge amounts of data
are collected, but largely underutilised. But
data analytics is a new and rapidly expanding
area that has great potential for helping to
understand variation.”
“Think of the manufacturing revolution that’s
happening,” says Dr Biller. “Yes, we want to make
all of our factories more agile, but the great part
of this is that we’re building an ecosystem that
allows people all over the world to innovate.
We’re all working on how we can turn that into
traditional manufacturing, and then traditional
manufacturing into smart manufacturing.
“We have every science and engineering
discipline working for GE in research. Of course,
“At GE, where we
carry US$15bn
in inventory, a
10% saving can
be huge. How
about optimising
scheduling
to improve
throughput? Ten
percent is a pretty
conservative
number for that.”
Stephan Biller, chief
manufacturing scientist,
GE Global Research
Profiting from data
(% respondents)
High growth Low growth
Well-defined data-management
strategy
Routinely analyse data to
find ways to improve efficiency
and lower cost of quality
Lack key staff with skills
to analyse data
0
50
59
10
45
25 21

Case study: GE’s factory built to produce data
The General Electric factory in Schenectady,
New York, was built, on one level, to make
the company’s new-technology Durathon
battery. But for the GE Global Research arm, the
factory’s most important product is data.
GE has packed more than 10,000 sensors into
the US$170m plant. All of them are linked
into the most tightly integrated digital
control and information systems of any of the
company’s 400 global factories. The sensors
monitor performance indicators for every
manufacturing process, building information
such as energy use, temperatures and humidity,
and even extend to the rooftop weather
station. If something goes wrong, the sensors,
using wi-fi links to staff members wielding
hand-held computers, send alerts, and can,
if necessary, send text messages to plant
employees at home.
The plant, which began battery production in
July 2012, is the test bed and most pronounced
expression of GE’s research into ways to create
and use the emerging industrial Internet—
called by some the “Internet of things”—to
build what the company calls the “Brilliant
Factory”.
“This plant has one of the most advanced
implementations of our process suite from
GE Intelligent Platforms,” GE’s process
controls business, says Stephan Biller, chief
manufacturing scientist for GE Global Research
of nearby Niskayuna, New York. “Initially we
used the data to improve our own processes.
What input parameters do we have to change?
If a battery fails, was it the humidity of that
particular process, or the temperature, or
operators who we didn’t train well enough? All
of this was useful in improving the process.”
With precise manufacturing parameters set
for the Durathon—which stores and produces
electric power on demand using a sodium-nickel
chemical process—GE has begun to use
the geyser of data to improve efficiency and
throughput.
“The questions now are how do we improve
costs and get more out of that factory?” says Dr
Biller. “The key is to think of the entire system,
not just parts of it, and by doing this, you can
reach an optimal state. I don’t want to optimise
each individual machine by itself, but as part
of a whole system. This permits me to find the
bottlenecks. That’s not a trivial task, and it’s
systems thinking that gives you the gains.”
smaller manufacturers can’t do that. But
remember, 80% of our parts are designed and
made by suppliers. We want them and all the links
to be part of this digital thread.”

VII Conclusion
What do these findings mean for manufacturers
large and small? Several conclusions seem
apparent.
Firstly, as the science-fiction novelist William
Gibson noted, the future has already arrived—
it’s just not evenly distributed. This is as true
of manufacturing as any other field. At the
cutting edge are companies that operate with
complete transparency from end to end of their
design, supply, production, shipping and quality
control processes. They typically have a good
grip, through sensors, RFID tags, bar codes
and other devices, on how things are going in
their factories. They can electronically send
precise adjustments to production gear and
workstations. They can measure quickly whether
a part isn’t performing as it should. They can
schedule supplies and shipments accurately and
respond to problems effectively. Small variations
at one part of their data chain don’t produce
major surprises elsewhere because their systems,
integrated and networked, see the potential
impact and fix it. GE and others imagine a perfect
factory that will never stop producing, ever,
because every potential variable is monitored
and adjusted and every product is exactly what it
should be.
However, the reality is that very few of today’s
aggressive, heads-up manufacturers are close
to this vision yet. This is partly because the
process of integrating data systems and analysis
into manufacturing is far from simple. In this
arena, one size definitely does not fit all. In fact
there really is no standard approach other than
to match a company’s appetite for change. In
addition, the transition is rarely straightforward
because most factories are not new and many
may not be ideal environments for complex
data collection and analysis. Older machinery,
computer systems that don’t provide real-time
data or that are incompatible with others in
a production process, and time-honoured
production habits clash with the promise of
efficiency and quality improvements from the
digital world.
Secondly, the research shows that once
manufacturers have decided to implement
analytical models they need to think carefully
about how data needs to be stored, processed
and used. Volumes of data have become so large
that some companies aren’t certain where to
store it all. Many still don’t know how to use the
information, or even how to formulate production
questions that fit the capacity of sensors and
software to provide answers. Many lack the
resources, and probably the trained personnel,
to look at much more than monitoring of simply
networked shop-floor data. Companies that now
use multiple separate types of monitoring—with
each machine or portion of the production
process isolated digitally from the others—
won’t find it easy to create such networks. But
it is worth the try, and in this technological
environment, with relatively inexpensive wireless
data collection commonplace, the financial
commitment is manageable.

Thirdly, customers will increasingly see the
digitised processing, collection and analysis of
complex data by manufacturers as a guarantor
of quality—over and above traditional
accreditations. This will become another means
of differentiation among peers; companies that
lag in this area may find themselves compelled
by competition to ramp up their complex data
analysis capabilities.
Finally, the research has shown that the embrace
of shop-floor data even on a relatively small
scale can be beneficial for even the smallest
of producers. By monitoring production
machinery and processes, efficiencies can be
gained and product quality is likely to improve.
Therefore the question for manufacturing is not
whether advanced, integrated, systemic data
capabilities are beneficial or the pathway to
greater productivity and operations excellence—
certainly, they are, and in many cases, the
gains from adoption of the latest digital control
techniques can be substantial. The real issue is
how manufacturers can make this vision of the
future a reality today.

Appendix:
Survey results
Note: Percentages may not total 100 due to rounding or the ability of respondents to choose multiple
responses
What is your job title?
(% respondents)
Chief financial officer
Chief quality officer
Other “C-suite” role
Chief, supply chain management
Head of plant/factory
Chief strategy officer
Chief operating officer
Chief technology officer
Chief safety officer
34
16
14
10
10
6
4
4
2

In what country is your company headquartered?
(% respondents)
United States of America
United Kingdom
Germany
Netherlands
Canada
Japan
Denmark
34
14
8
8
8
6
6
Sweden
6
Italy
4
France
2
Switzerland
2
Finland
2
Which category best describes the types of goods made in your company’s factories?
(% respondents)
Electronic goods
Machinery
Chemicals/industrial gases and related products
Rubber and/or plastics
Automotive/vehicle parts
Aircraft and/or aircraft components
Primary metals
Medical devices and supplies
Building products
12
12
12
10
10
8
8
6
6
Petroleum refining and related activities
Glass, stone, clay and/or concrete
4
4
Fabricated metal products
Electrical gear
4
4

What are your company’s global annual revenues in US dollars?
(% respondents)
$500m to $1bn
$1bn to $5bn
$5bn to $20bn
$20bn or more
36
32
12
20
Which of the following statements best describes your company’s approach to data management as it applies to using
shop-floor data to improve the efficiency of its core production processes?
(% respondents)
We understand the value of our shop-floor data and are currently marshalling resources to take better advantage of them
We have a well-defined data management strategy
We collect data but it is very difficult to make use of the information from our varying IT systems
We do not collect enough data to produce significant manufacturing efficiency gains
We do not prioritise data collection
44
42
14
0
0
In your business, in which of the following areas do you see greater volumes of data yielding the biggest gains?
Select the top three.
(% respondents)
Process controls
Process design and improvements
Predictive maintenance/asset management
Throughput improvement
Targeted capital spending
72
44
42
30
30
20
12
10
36

For which of the following functions and areas does your company have mature data analysis capabilities?
Select all that apply.
(% respondents)
Reporting normal operations Alerting about problems Predicting future problems Prescribing solutions to problems
Asset maintenance/management
Process controls
Throughput
Process design
Capital spending
90 32 18 38
84 52 26 38
88 54 38 28
74 32 22 16
82 52 36 26
90 24 28 24
92 30 16 14
92 46 44 30
92 46 48 28
Regarding production efficiency and cost of quality, how would you describe your company’s experience with regard to
analysis of machine-generated data from the shop-floor? Select the best answer.
(% respondents)
We frequently, but not always, identify production problems and ways to lower the cost of quality from the data we collect
We now routinely find ways to improve manufacturing efficiency and lower the cost of quality from the data we collect
We infrequently identify production problems and ways to lower the cost of quality from the data we collect
We are yet to use data to improve manufacturing efficiency and lower the cost of quality
54
36
10
0

What sources of data are used by your company to lower the cost of quality and improve manufacturing efficiency?
(% respondents)
42 8 34 16
Over the past two years, have you significantly increased the amount of production and quality-control data you store for
analysis?
(% respondents)
Analysis of data frequently does not produce actionable information
Use currently Plan to use Do not plan to use Does not apply
Sensor-generated data from individual machines
Sensor-generated data from networked machines
Sensor-generated data from external sources for comparative purposes
Operator logs
Manufacturing execution system (MES) process historian
Supplier-provided test data
Enterprise data (ERP)
Accounting/finance data
Supply chain management system/supplier data
Demand forecasts
RFID
After sales failure data
Customer feedback system—Compliance/incidents management data
62 20 10 8
66 18 10 6
52 16 30 2
90 6 4
78 18 2 2
88 10 2
88 10 2
86 12 2
74 24 2
34 14 28 24
82 10 6 2
96 4
Yes
No
86
14
What problems have you experienced in managing greater volumes of machine-generated process and quality data?
Select all that apply.
(% respondents)
Difficulty in integrating data from multiple sources/formats
Lack of personnel/expertise necessary to analyse data from diverse sources
We find it difficult to formulate the right questions to use the data appropriately
Internal siloes make it difficult to employ data to effectively
We have experienced no problems
35
33
21
14
9
21

To what extent have insights from shop floor/machine-generated data delivered gains in the following areas?
(% respondents)
Up to 10% annual savings/gain 11-25% annual savings/gain 26-50% annual savings/gain More than 50% savings/gain
Cost of quality
Production efficiency
34 32 32 2
30 36 26 8
Do you agree with the following statements?
(% respondents)
Agree Neutral/no opinion Disagree
Aggressive data collection and analysis are an important way to improve manufacturing efficiency
Aggressive data collection and analysis are an important way to lower the cost of quality
Our analytical capabilities have not been able to keep up with the large volumes of data we now collect
We are unable to derive insights from data as it comes from too many sources
A rigorous and advanced data analysis capability can be a differentiator for our company
Our competitors do a better job than we do with data collection and analysis
The cost of setting-up and maintaining a strong analytics function is larger than what our company can afford
78 20 2
76 20 4
28 34 38
20 30 50
50 42 8
16 84
6 22 72

While every effort has been taken to verify the accuracy
of this information, The Economist Intelligence Unit
Ltd. cannot accept any responsibility or liability
for reliance by any person on this report or any of
the information, opinions or conclusions set out
in this report.
This report was commissioned by Wipro
About Wipro
Wipro Ltd (NYSE:WIT) is a leading information
technology, consulting and business process services
company that delivers solutions to enable its clients
to do business better. Wipro delivers winning business
outcomes through its deep industry experience
and a 360 degree view of “Business through
Technology”—helping clients create successful
and adaptive businesses. A company recognised
globally for its comprehensive portfolio of services, a
practitioner’s approach to delivering innovation, and
an organisation-wide commitment to sustainability,
Wipro has a workforce of over 140,000, serving
clients in 175+ cities across six continents. For more
information, please visit www.wipro.com
About Wipro Council for Industry Research (WCIR)
Wipro set up the Council for Industry Research,
comprising domain and technology experts from the
organisation, to address the needs of customers. It
specifically surveys innovative strategies that will
help customers gain competitive advantage in the
market. The Council, together with leading academic
institutions and industry bodies, studies market
trends that provide organisations a better insight
into IT and business strategies. For more information,
please visit www.wipro.com/insights/

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