IIoT - A data-driven future for manufacturing

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5 Steps to Optimize & Mitigate RiskFactors Driving Adoption
Despite the rapid growth of sensors and the buzz around the
Internet of Things, adoption in manufacturing has lagged behind
the hype. Based on a recent IDG Research survey of 100 executives
in the manufacturing industry, this white paper explores the
various challenges standing in the way of adoption, the security
implications of an Industrial Internet of Things (IIoT) strategy for
businesses, and what manufacturing organizations should do to
optimize their IIoT implementation while mitigating risk.
“For a while, companies have been
doing certain things to connect
machine-to-machine without
necessarily referring to it as the IIoT…
The demarcation is in the IIoT’s ability
to integrate data that previously
resided in silos and transform the way
we process that information in order
to inform decision making.”
Jay Monahan,
Global SAP shop floor lead for Dell Services
Learn more about Smart
Manufacturing and IIoT.
The IIoT has generated substantial buzz,
driven by three main trends: the explosion
of sensors in everyday items, the arrival of
inexpensive storage options, and better
chipsets for connectivity. Today, sensors
that record, transmit, and share information
are interwoven into the fabric of everyday
items, from the watches and clothes we
wear to the thermostats that regulate our
home temperatures. But, while the biggest
buzz is around consumer IoT, the Industrial
IoT in businesses such as manufacturing is
where the biggest strides are being made.
Even though manufacturers have used
statistical process control and data analysis
to help optimize their systems for years,
the term “IIoT” is relatively new. With IIoT,
new integrated sensor data, combined with
advancements in connectivity, security,
interoperability, and analytics, creates
immense potential for organizations.
Industrial Internet of Things:
Challenges Impeding Adoption Conclusion

Challenges Impeding Adoption 5 Steps to Optimize & Mitigate RiskFactors Driving Adoption
The consumer market was quick to adopt the IoT. But companies with a
business-to-business focus have been slower to invest.
According to a recent study by Dell and IDG Research Services on emerging
technologies, only 29% of enterprise respondents in manufacturing are investing
heavily in the IIoT, while a quarter say they’re doing little in this area.
And when asked at what stage of investment in the IIoT they expect to be
in 24 months, respondents predicted little progress beyond researching and
piloting solutions toward the goal of generating a network of connected devices
integrated with enterprise applications.
“Particularly in manufacturing, those
companies that collect, merge, and
analyze the various data sets recorded on
the factory floor—from unit production
and equipment operation data to process
and human operator data—will stand out
from their competition through better,
informed decision making,”
Prasoon Saxena,
Managing Executive,
Global Manufacturing Services at Dell.
Of manufacturers utilizing IIoT:
have improved in
business innovation.
53%
have increased their
competitive edge.
50%
have reduced total
cost of ownership.
50%
Conclusion

Factors Driving Adoption Challenges Impeding Adoption 5 Steps to Optimize & Mitigate Risk
Factors Driving
Adoption
“The tools available today are capable of
ad hoc recording, enabling end users to
move though processes faster. Inquiries
that traditionally take hours, days, or even
weeks can be performed on-demand.”
Jay Monahan
Equipment across the factory floor can
generate thousands of data types and
petabytes of data in only a few days.
The real value is not the data itself,
but the ability to improve the decision
making by providing end users the power
to view multiple business scenarios
instantaneously and react in real time.
In manufacturing, process variability stems from
numerous factors that can often be monitored via sensors.
That data is strongly correlated to yield, quality, and output.
When integrated, that sensor data provides insights that
show when processes are getting out of whack.
Through data analytics and visualization tools, organizations
can interpret those results and make informed decisions
about emerging quality issues and predictive maintenance.
This limits hardware failure and downtime, which ultimately
reduces cost and improves productivity.
Given myriad types of parametric, product, and equipment
data available, these benefits are the tip of the iceberg.
With additional data mining and advanced analytics, new
business value and efficiency gains emerge, fueling a
competitive advantage.
Download:
ARC real-time
analytics whitepaper
Conclusion

In addition to improved component uptime, increased
yield and throughput, predictive maintenance and
reduced component failure, data analytics and IIoT enable
organizations to gain insights into factory floor processes
that improve their decision making, business innovation
and position in the market.
Real results:
In fact, the top benefit linked to the IIoT is improved business
innovation, cited by 53% of respondent organizations in IDG’s
study, followed by reduced cost of ownership, cited by 50%.
However, to realize the efficiencies and innovations that
can be garnered through instrumenting the physical world,
successful companies indicate that careful planning and a
clearly defined roadmap is critical. These benefits, among
others, should drive investment in the IIoT beyond what
the survey respondents’ expect.
Improved business innovation
Reduced cost of ownership
Competitive edge
Improved process performance
Better decision making
Improved service response time
Improved personnel productivity
Faster time to market
Reduced machine/asset downtime
Improved asset utilization
Better working with/serving
our suppliers/customers
53%
50%
50%
49%
44%
41%
37%
37%
36%
33%
26%
Source: IDG Research Services, September 2015
Benefits of IoT
Conclusion

Real-world example:
Predicting equipment failure in a production environment.
IIoT technology is capable of detecting defective Test Interface Units (TIUs)
that wrongly categorize good units as bad. Prior to the IIoT, if a faulty TIU
categorized a unit as bad, the unit would be scrapped and replaced with a spare
part during regular preventative maintenance, even if it was operating properly.
In a recent pilot conducted in one of its manufacturing facilities, one Fortune
500 company found that data analytics, applied to factory equipment and
sensors, were able to predict up to 90% of potential TIU failures before being
triggered by the existing factory’s online process control system, helping to
save inventory that would have been categorized as faulty and reducing yield
losses by up to 25%.
Solution Blueprint:
Increasing Manufacturing
Performance with IoT
Conclusion

Challenges
Impeding
Adoption
While the business benefits of the IIoT are
attainable, various hurdles continue to slow
adoption of IIoT strategies and solutions.
For enterprises to invest in the IIoT, they will
need to overcome organizational resistance,
security concerns, the added demand that the
IIoT places on infrastructure, and the inherent
ambiguity that surrounds new technologies.
Since the IIoT is a relatively new term, its definition
lacks consistency across industries. As with many
emerging technologies, this ambiguity muddies
the value proposition and obscures a clear path
to profitability.
27%
say unclear financial
benefit is a barrier.
48%
say budget constraints
are a barrier.
Conclusion

Return on investment matters, and with budgetary
constraints inhibiting technology investment, an inability to
demonstrate immediate value has kept proponents from
making a strong business case for the IIoT.
Security, cited by 34% of IDG respondents, poses another
challenge. The scale of the IIoT creates an unprecedented
attack surface for hackers as the proliferation of sensors on
factory equipment expose the organization to risk that must
be mitigated.
Moreover, in order for companies to analyze information on
process variability holistically rather than in isolation, they
must integrate the data generated by these sensors. This
includes data pertaining to material, process recipes and
methods, and equipment differences. Thus, in addition to
shoring up the network and extended attack surface, the
various platforms and protocols of the sensors cataloguing
data must be managed.
Concern over the demand the IIoT would place on
existing IT infrastructure has further slowed adoption.
Today, imagination is the only limit to what can be monitored
and measured. However, the more data that IT collects, the
more it must store, process, and analyze and concerns over
insufficient resources and the need to purchase expensive
hardware has had an impact on IIoT investment.
Finally, organizations have struggled to develop a clear
roadmap for the IIoT. Industry use cases or best practices
are still emerging. Accordingly, many organizations are
unsure of where to begin, or even whether their
infrastructure and business processes are equipped to
handle an IIoT implementation.
Conclusion

“With all of the information we’re able to track
today, organizations often have the tendency to
want to start too big and end up getting lost in
a snowstorm of data points… You cannot launch
an IIoT initiative with the attitude that you have to
collect everything. Rather, start small with a pilot
program focused on a known bottleneck and
define key, manageable KPIs.”
Jay Monahan
Budgetary constraints
Security/compliance concerns
Unclear financial benefit/difficulty calculating ROI
Lack of interest from executives/stakeholders
Lack of in-house skills
Organization doesn’t recognize business value
Diversity of niche providers and solutions
Data Volume and complexity
No barriers
Lack of data protocol standards
48%
34%
27%
25%
23%
20%
17%
17%
15%
13%
Source: IDG Research Services, September 2015
Barriers to investment in IoT
Download:
Learn more about data swamping
and strategies to avoid it.
Conclusion

Challenges Impeding AdoptionFactors Driving Adoption 5 Steps to Optimize & Mitigate Risk
Steps to Optimize
Benefits and Mitigate Risks
Associated with IIoT5
Conclusion

5 Steps to Optimize & Mitigate RiskChallenges Impeding AdoptionFactors Driving Adoption
1
Join forces.
IIoT is the intersection of Operations Technology (OT)
and Information Technology (IT) to achieve business
outcomes, and collaboration between OT, IT and business
management is essential. No single department has
the knowledge and experience to address all essential
functions, yet differences in departmental culture,
operating methods and KPIs can derail integration and
data sharing. Build strong partnerships to overcome
differences and leverage organization-wide expertise.
2
Clarify business
outcomes and ROI.
There is hype and confusion about what is possible
with IIoT, and the risks associated with it, which can
result in inertia in the face of growing competitive
pressure. Getting help from a trusted partner to identify
IIoT opportunities and use cases, supported by concrete
return-on-investment analysis, can pave the way forward.
Look for a holistic, objective methodology, rather
than one-size fits all approach to determine the
right combination of IIoT devices, applications,
communications and data management tools for
your organization’s specific use cases.
Conclusion

3
Start small with
what you have.
Beginning with small IIoT projects that use the
equipment and data you already have allows you
to prove ROI and establish best practices before
full-scale implementation. With data growing
exponentially, it is tempting to want to analyze it
all. Don’t. Everything is more manageable when
you begin small, pilot a concise program with
well-defined KPIs, establish a new flow of data,
develop policy, and expand it once it’s refined.
That said, prepare a solution that can scale.
Realize new value from legacy assets
Good news: Even if your assets and systems
weren’t originally designed to connect to the
Internet and share data, they still can. By attaching
intelligent gateways to existing assets, you can collect
data from these previously untapped resources. So
getting the benefits of an IIoT solution doesn’t mean
starting from scratch.
Conclusion

4
Think security first.
Security can be an afterthought when it comes to
implementing IIoT, which puts critical organizational
operations and data at risk. Secure sensors, smart
devices and software are available, so be sure to
understand the in-built security posture of solutions
you are considering before buying.
The key to IIoT security is this: be cautious but
not paralyzed. Prior to embarking on an IIoT
deployment, be sure to modernize your
applications to ensure security requirements
are met and proper protocols are installed
and aligned. Review and strengthen your
current IT security, identity, privacy and
management practices to prepare
for potential additional risk exposure.
5
Architect for
analytics.
Data itself is not valuable; the value comes from the
insights you can glean from the data. The power of
data analytics stems from the ability to integrate and
correlate the vast amount of data that goes unused
throughout today’s plant and extract meaningful
insights from it. Integrate data currently stored in
silos and you will unlock insights buried in the data
you already have.
Learn more:
Getting past the
Big Data hype
Conclusion

ConclusionChallenges Impeding Adoption 5 Steps to Optimize & Mitigate RiskFactors Driving Adoption
Conclusion
Although current IIoT adoption has largely lagged
behind hype in the B2B space, pioneering manufacturers
are taking the necessary steps to capitalize on its
potential by investing in enabling technologies and
mitigating their risk factors. Many first movers have
already begun extending successful pilot programs more
broadly to augment productivity and efficiency gains.
The cost savings and added throughput recognized by
these organizations have in turn improved gross margins
and bottom-line results creating new opportunities for
reinvestment. In order for manufacturing companies
to remain competitive, they need to continue to
differentiate themselves through the efficiency
optimization afforded by the IIoT.
At Dell, we know manufacturing and supply chain operations firsthand.
Being a manufacturer ourselves, we understand your business challenges.
As a technology leader, we know how technology can turn those
challenges into competitive advantage. We speak your language to help
IT, Operations and Engineering collaborate to make better technology
decisions and get the most value from every dollar spent.
Dell takes a pragmatic approach to the IIoT, starting small with the
equipment and data you already have, we architect for analytics and
think security first. With the industry’s broadest portfolio of IIoT enabling
technologies and rich partner ecosystem, Dell reduces the complexity
of deploying IIoT solutions and enables you to make the promise of
IIoT a reality today.
What’s the next step?
Let’s talk.
Join the
conversation.

IIoT - A data-driven future for manufacturing

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