EY-Performance-Products

This article is an extract from Performance, Volume 8, Issue 2, May 2016. The full journal is available at
ey.com/performance
Volume 8 │ Issue 2
Providing insight and analysis for business professionals
Leapfrogging innovation
Digital technologies in emerging markets
Your products are talking
But are you listening?
IT transformations
Going far beyond the IT function
When fast
paced becomes
commonplace,
will you be ready
for the sprint?
The internet of things (IoT)
is starting to revolutionize
how automotive, aerospace
and industrial equipment
manufacturers design,
manufacture and sustain their
products. As these products
become more intelligent
and connected, it will be a
competitive necessity for
manufacturers to listen
to them individually and
collectively — anticipating
product failures, improving their
designs and avoiding costly
repairs or damage to the brand.
Your products
are talking, are
you listening?
66 Volume 8 │ Issue 2

ey.com/performance
IT transformations
When fast
paced becomes
commonplace,
will you be ready
for the sprint?
Authors
Kevin Reale
Senior Manager, Advisory Services,
Performance Improvement,
Supply Chain Management,
Product Life Cycle Management, EY, US
Anton Bossenbroek
Manager, Advisory Services, Performance
Improvement, Advanced Analytics, EY, US
Jake Darlington
Staff, Advisory Services, Performance
Improvement, Advanced Analytics, EY, US
67

ey.com/performance
IT transformations
When fast
paced becomes
commonplace,
will you be ready
for the sprint?
Your products are talking, are you listening?
T
he sensors that monitor
systems inside digital products
are generating vast amounts
of diagnostic trouble codes1
and operational machine
data.2
This data, which is
communicated to manufacturers using
telematics,3
also provides information to
its operators on machine health and status.
Now, data scientists can use reliability-
based advanced analytics to improve the
detection, diagnoses and prediction of
in-service product failures — that could
reduce detection to correction (D2C)
cycle times by months, or even years.
Most manufacturers are still using
warranty-based submissions to detect
and analyze failure events, which
can result in the following challenges:
► Submissions can take 90 to 120 days
to process for analysis.
► Specific in-service cycles, when the
problem occurred, or operating
conditions at the time of the issue
are not represented.
► In-service failures that have the greatest
negative impact to a product’s reliability
are not represented. Some failures that
cause downtime may not generate a
warranty or service call.
► Incident or failure rate analysis may
not detect an emerging issue until
it is reported on a Pareto chart.
1. Diagnostic trouble codes (DTC) identify and communicate
where and what onboard problems exist on a machine
or vehicle.
2. Operational machine measures are the absolute value
provided by machine and vehicle sensors (e.g., temperature,
pressure and speed).
3. Telematics is the transmission of machine data to a remote
data consumer. It has grown increasingly prevalent as
telecommunication has become more reliable and
less expensive.
Rio Tinto has approx. 900heavy mobile equipment (HME) trucks.
The entire fleet provides approx. 4.9terabytes of data per day.
Each truck has approx.200sensors.
Source: Rio Tinto, Internet of things world forum, 2014, http://www.riotinto.com/documents/141014_Presentation_
Internet_of_Things_World_Forum_John_McGagh.pdf, accessed March 2016.

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69
The speed that
manufacturers
develop or improve
their ability to listen
to their intelligent
products, generate
insights and integrate
this information into
action will provide
tangible benefits that,
ultimately, increase
shareholder value.
Increasing amounts of products are
evolving from having sensors that simply
inform operators, for example, the engine
is overheating to communicating the health
and status of an entire fleet of machines.
In response, manufacturers need to be
ready to manage and use this data to drive
continuous improvements to existing and
new products. Armed with this intelligence,
engineering, manufacturing and after-
sales service stakeholders can improve
product quality and reliability, reduce costs,
strengthen customer relationships and loyalty,
and increase equipment and parts sales.
The speed that manufacturers develop
or improve their ability to listen to their
intelligent products, generate insights and
integrate this information into action will
provide tangible benefits that, ultimately,
increase shareholder value.
The digital product and
its digital twin
Machines with onboard controllers
connected to remote data repositories via
telematics are collectively called “digital
products” and comprise what is referred
to as the internet of things (IoT). Digital
products have enabled comprehensive
monitoring of a machine’s health and
operations, allowing companies and
customers to track a product’s operating
characteristics, history and usage.
In the last decade, the concept of the
“digital twin” has also emerged. As defined
by Dr. Michael Grieves,4
“The concept of
a virtual, digital equivalent to a physical
product is the digital twin.” The model
has three main elements:
Legal and statutory
Customer requirements
Management emphasis
Competition
Warranty and service costs
Public liability Development risks
Perceived risks
Market pressure
Safety
Figure 1: Perceived risks from the internet of things
4. Dr. M. Grieves, Digital twin: manufacturing excellence
through virtual factory replication, 2014, http://innovate.fit.
edu/plm/documents/doc_mgr/912/1411.0_Digital_Twin_
White_Paper_Dr_Grieves.pdf, accessed April 2016.

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1. Physical products in real space
2. Virtual products in virtual space
3. The connections of data and
information that tie the virtual
and real products together
The unified repository (UR) provides the link
between the physical in-service world of the
product and its digital twin, operating in a
virtual, data-driven environment. Figure 3
shows all the potential interconnections
between the UR, the product and the digital
twin via data from the various internal
enterprise systems, external sources and
that generated by the digital product itself.
Data sources
The primary content for the digital twin is
the data generated by the digital product
over its life cycle. Figure 2 provides a
relative comparison of some of the data
sources. This data will be in a variety
of formats:
► Transmitted data — digital products
transmit two major types of data:
► Health status — machines
communicate health status through
diagnostic trouble codes (DTCs).
A DTC is a stored location- and
time-stamped response to an
issue in a machine or vehicle.
It occurs when there is a violation
of control limit thresholds.
These codes can be specific to a
manufacturer or generic, as in the
case of the automotive industry’s
common onboard diagnostic
classification (OBD-II).
► Operational machine measures
— these provide a detailed,
time-ordered view of specific
machine conditions, such as
oil pressure, flow rates or
temperatures. When combined
with health status data, operational
measures can deliver critical
information to diagnose the cause
of a specific failure, or provide
additional insights to a warranty
or service submission. Transmitted
data will account for the majority
of data managed in the UR.
► Enterprise data — in addition to
broadcasting health and operating
data, a digital product generates a
vast amount of metadata during its
life cycle. Connecting all of these
data sources together defines the
product’s DNA for the “digital twin.”
X*
*X denotes the number of machines
Machines Warranty
claims
Machines
deﬁnition
DTC Machine
operational
measures
10X 250X
Thousands Millions Billions
600X 150,000X
Digital products
have enabled
comprehensive
monitoring of a
machine’s health and
operations, allowing
companies and
customers to track a
product’s operating
characteristics,
history and usage.
Figure 2: Relative sizes of data types

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Enterprise systems
External sources
Analytics
Digital twin
Manufacturing execution
systems (MESs)
• Production history
• Inspection results
• Test results
Prescriptive
Identify measures to improve
outcomes or correct issues
How can it be
avoided?
Unique to a serial number
• As-built product definition
• As-maintained product
definition
• Production history
• Service history
• Performance history
Data validation
and preparation
Date, time and location
based:
• Fault codes
• Operational measures
• Generic time and
location-based data
• Unique serial number
service history
Generic (per variant)
• As-design product
definition
• Product and process
quality data
• Lightweight geometry models
• Logical product definition
• Product and process
change effectivity
Unified
repository
Enterprise resource
planning (ERP)
• Item master
• Warranty claims
• Service bill of materials
(BOM)
Customer relationship
management (CRM)
• Call center
• Product survey
• Customer details
Quality management
system (QMS)
• Failure mode effects
analysis (FMEA)
• Corrective actions
• Test and verification
results
Product life cycle
management (PLM)
• System BOM
• Engineering BOM
• ECO and ECR
• Geometry
Dealer management
system (DMS)
• Service records
• Parts inventories
Third-party data
• Weather
• Geography
• Prices
Predictive
Identify measures to improve
outcomes or correct issues
What will happen?
Diagnostic
Examine causes of reduced
product performance or
failure
Why did it happen?
Descriptive
Capture product's condition,
environment and operation
What happened?
HindsightInsightForesight
Digital product
Figure 3: The unified repository and the digital twin

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► External data — these sources provide
additional attributes that can be used to
augment understanding of a machine’s
health status and operational measures.
Some examples of external data
sources include weather, temperature,
social media feeds, traffic patterns,
geographical terrain and sustainment
inventories. External data can help
aid in answering questions such as
whether moisture affects operation
or terrain impedes performance.
But the sheer volume of data generated
makes analysis a major challenge.
According to one North American
vehicle manufacturer, a vehicle
operating 6 to 10 hours can generate
up to a petabyte of data per day.
There are several ways to address
this issue. Some organizations store
telematics data in inadequate conventional
repositories — resulting in data being lost
because it will not fit in the available space.
Others try to reduce the data volume by
recording it in units of hours instead of
minutes, or even seconds. Often, despite
these efforts, databases still grow at
unmanageable rates and struggle to
provide the responsiveness required to
analyze fleet data. Many manufacturers
have, simply, yet to determine what to
do with their machine data.
Detection to correction
Because of the data warehousing challenge,
as well as inadequate analytical tools
and technical expertise, many digital
product manufacturers still use traditional
means, such as warranty submission
analysis, to detect and diagnose product
issues. This leads to significant D2C cycle
times. One North American automotive
manufacturer indicated that just one day
on a major product issue can equate to
US$1m in warranty and recall related
costs. As demonstrated in Figure 5, the
time associated to detect, analyze and
communicate a product issue, using IoT-
enabled processes, could be significant.
By leveraging the data generated by
digital products, a manufacturer can
reduce the overall D2C cycle time by
50%–90%. This means that a problem
with a machine is addressed within the
warranty submission process, effectively
bypassing the associated delays. Instead of
a warranty claim signaling that a problem
has occurred, reliability-based predictive
analytics provide the signal by which a
failure can be detected and prioritized. Product plaLife cycle stage
Product stage
Enterprise systems
By leveraging the data generated by digital
products, a manufacturer can reduce the overall
detection to correction cycle time by 50%–90%.

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Product planning
Feasibility and
requirements
Product and
process development
Production
Operations and
sustainment
As-design As-planned As-built As-maintained
PLM MES
ERP
CRM
QMS
DMS
Figure 4: Data systems used throughout a product’s life cycle

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Three key components enable a
digital product manufacturer to harness
its data and deliver insights:
► Data repositories and infrastructure —
by far the largest innovation in big data
storage has been Apache Hadoop,
an open source, Java-based software
framework designed to sit above a
server farm of commodity computers.
Hadoop enables quick processing of
large amounts of data by distributing
data storage and processing activity
across the network. While the software
is free, implementation requires
expertise and physical space for
servers. To quickly mine insights from
the entirety of a manufacturer’s digital
product data, a Hadoop implementation,
or some similar framework, is essential.
► Advanced analytics — turning the
data in the UR into actionable insights
requires advanced analytic techniques.
Figure 3 provides an overview of the
various techniques used to improve
reliability and quality, and for reducing
costs relating to nonconformance.
► Reliability engineering analysis —
manufacturers require the right tools
for converting the various data sources
into actionable insights. The primary
tool for assessing the potential
impact and criticality of failures is
product reliability analysis, i.e., the
probability that a device will perform
its required function, subject to stated
conditions, for a specific period of
time. It is quantified as “mean time
between failures” for a repairable
product and “mean time to failure”
for non-repairable products.
Product reliability analysis involves
determining the rate or speed at
which a product reaches the end of
its estimated useful life. This type of
analysis is referred to accelerated failure
Preparation
and storage
Analytics
generated
Issue
detected
Breakdown
occurs
DTC
generated
Digital data
transmitted
Digital data
analyzed
Stakeholder(s)
notiﬁed
Dealer and
technician
repairs
Warranty
and service
submission
170 days
Potential savings: US$ 1.8*
Traditional
methods
Detect and identify time: 120 to 200 days
Detect and identify time: hours to 30 days
IoT methods
*Savings calculation assumes daily production volume of 100, average claim cost of US$225 and failure rate of 50%, i.e., 100 x 50% x US$225 x 164 = US$1.845m.
Figure 5: Improvement in D2C cycle times

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time (AFT). In AFT models, the speed of
failure can be accelerated or decelerated
by accounting for time-dependent and
time-independent covariates:
► Time-independent — the
characteristics of a product (such
as build configuration) that result
in an AFT.
► Time-dependent — the operating
conditions (such as temperature
or hydraulics pressure) that result
in an AFT.
The advantage of AFT, compared with
other types of analysis, is that the
resulting conclusions have close ties to
engineering. However, because of the
Intelligent digital products can drive actionable
insights if the information is integrated across
the value chain of stakeholders responsible
for developing, manufacturing and sustaining
those products.

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large amounts of data, the challenge is
identifying the covariates that significantly
impact the failure rate. Data mining
permits identification of the covariates
that accelerate or decelerate the life of a
machine in an automatic fashion, allowing
unique insights from the extremely large
volume of data collected by machines,
through the IoT, across their lives.
By making monitoring of machines
more accessible, the IoT not only enables
manufacturers to detect which operating
conditions lead a product to reach the
end of its life sooner, it also enables more
parts within a product to be monitored.
This results in an increase in the amount
of data that must be digested in the D2C
processes. It therefore becomes essential
to offer D2C experts an insightful view that
permits prioritization of part failures.
RAAK algorithm
The RAAK algorithm offers a unique
approach to help identify and prioritize
failures across a large number. It aggregates
metrics from the AFT models, as well as
other data, presenting each failure as a
bubble in a chart — the size of the bubble
indicates how critical the failure is. The
metrics comprise impact, importance,
predictive likelihood and propagation
frequency of failures. Implementing the
RAAK algorithm opens the wealth of
information that is available from leveraging
the IoT to a much wider audience and far
more quickly than many other methods,
as no training in AFT is required.
The volume of the product being
monitored drives the frequency of how
often the data is updated. Because the
data is continuously flowing in from the in-
service digital products, it can be assessed
daily. Typically, a more frequent analysis is
done at product launch to identify early life
failures and emerging issues.
The RAAK algorithm visualization has
two primary benefits. First, it allows deep
in-service failure analysis. As a result of
the fact it can utilize the full fleet dataset,
it displays patterns in aggregate failures as
well as having the capability for in-depth,
single-machine analysis. However, if the
data is collected and analyzed in near-
real time, the algorithm provides insights
into emerging and developing failures.
This enables manufacturers to initiate
corrective action, such as part replacement,
before the issue becomes a costly problem.
The future is now
Intelligent digital products can drive
actionable insights if the information
is integrated across the value chain of
stakeholders responsible for developing,
manufacturing and sustaining those
products.
As more connected objects or “things”
are embedded in industrial, automotive
and aerospace products, the management
and use of machine diagnostic data
will be critical to improving the reliability
of existing and future products, while
reducing the cost of nonconformance.
Listening to your products and
interpreting what they are saying will
introduce technology, process and
organizational challenges that can seem
overwhelming. Here are some actions that
can help make this journey manageable:
► Establish a business-driven unified data
environment to enable the digital twin —
start by preparing a list of measurable
questions that the organization is trying
to answer: what systems have the
greatest negative impact on product
reliability within the first 100 hours
in service? What sequence of events
lead up to or follow a product failure?
Is there anything unique about the
product’s configuration, manufacturing
location, component supplier, build
date or operating conditions that could
have caused the failure? Manufacturers

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should avoid the costly and time-
consuming endeavor of integrating
millions or billions of records prior
to understanding the questions
they really need to answer.
► Reach functional alignment
on product reliability — some
manufacturers may use different data
sources to establish product reliability-
related metrics. For example, one
organization might use warranty data
to calculate failures per machine, while
another may use DTCs to determine
mean time between failures (MTBF)
or mean time to fail (MTTF). The
manufacturer uses these measures
and others to detect, select and
prioritize the failures that require
corrective actions. Both approaches
may be necessary, depending on the
type of failure and the relationships
established by the systems and quality
engineers. The key for manufacturers is
to establish product reliability metric(s)
across various functions, ensuring a
holistic approach to reducing D2C.
► Embrace systems engineering-based
product reliability analysis — leading
manufacturers have tools and processes
that systemically create and manage
the relationships between a machine’s
systems, components, software, service
parts and onboard diagnostics used
to measure and detect a system fault.
For example, several manufacturers
have been able to detect an issue, as
a result of DTCs, months before the
problem emerging as a detectable
warranty claim.
► Close the loop — the corrective action
insights developed from advanced
analytics need to drive the allocation of
resources and investments required to
mitigate a product issue — whether in
the design, manufacture or sustainment
of the product. The root cause and
corrective actions need to be fed
back into the system, design and
Intelligent
Products generating large
volumes of operational and
environmental data
Insight
Advanced analytics and data mining
using data generated by digital
products and processes
Integration
Synchronized information
across the value chain

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process FMEAs5
to prevent future
problems, and improve the design
and processes for next-generation
products, as well as manufacturing
improvements.
► Don’t go it alone — the process,
technology and organizational changes
required comprise a journey for which
you might be best served by having
someone to help show you the way.
Many leading manufacturers have had
early success in this area by engaging
various partners to assist them in the
development and deployment of parts
of or, sometimes, the entire strategy. 
5. Failure mode effects analysis (FMEAs).

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