driving payback through industrial internet of things the internet of things or iot is a network of...
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DRIVING PAYBACK THROUGH INDUSTRIAL INTERNET OF THINGS
In this issue
Driving Payback Through Industrial Internet of Things 2
IIoT Platforms in an Industrial Environment 3
The Layers of an IIoT Platform 4
Identification of Key Use Cases 5
Applicability and Payback Period of IIoT Use Cases for Specific Verticals 7
Principles That Govern a Successful IIoT Implementation 12
Methodology 15
Research from Gartner: Survey Analysis: As More Companies Deploy IoT, They Increasingly Focus on Best Practices and Payback 16
About Altizon 24
Driving Payback Through Industrial Internet of Things
The Internet of Things or IoT is a network of physical
devices or ‘things’ that are interconnected and can
exchange information about their operation and about
the environment in which they function.
The application of IoT to industrial assets is termed
as The Industrial Internet of Things or IIoT. These
assets could be within a manufacturing facility,
such as manufacturing plants or remote assets
distributed across a geographical area. IIoT platforms
are specialized forms of generic IoT platforms that
offer enhanced functionality in asset connectivity,
data analytics and AI models that are more suited to
industrial assets.
The rise of IIoT has also given rise to several business
initiatives that are interrelated and significantly
overlap, as outlined below.
■ Smart Manufacturing: Connecting manufacturing
processes and assets and using analytics for
improved performance and quality
■ Digital Transformation: Digitizing and
transforming processes, specifically in related
manufacturing operations
■ Smart Factories: Building a production facility that
is connected and leverages smart manufacturing
and digital transformation
■ Industry 4.0: An umbrella term that is equivalent
to smart factory
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IIoT platforms act as a foundation layer for these
initiatives.
The rapid expansion of IoT and its wide application
in smart manufacturing can be attributed to
advancement in several complimentary areas.
■ Network: Improving data bandwidth along with
the rise of efficient and low power networking
and communication protocols such as LoRa and
ZigBee that are suited for connected devices
■ Sensors: Robust and affordable sensor
technology that has the ability to communicate
built in
■ Cloud Computing: On-demand availability of
compute and storage resources that can process,
store and analyze the tremendous amount of
information generated by IoT
■ Edge Computing: Technology that enables
processing of sensor data and related
information close to its source thus saving on
bandwidth and improving response time
■ Big Data Analytics: Software frameworks and
analysis techniques that can be used to make
sense of the tremendous amounts of data being
generated by IoT
■ Machine Learning: Advancements in AI that let
you build a ‘digital twin’ of an asset and predict
outcomes on how that asset will behave
The focus of this whitepaper is to help provide a clear
perspective to organizations on IIoT, offer insights on
realizing payback across various verticals and provide
a framework for a successful IIoT implementation.
IIoT Platforms in an Industrial Environment
An industrial environment is extremely complex with
several elements that can be broadly classified into:
■ Industrial Machinery and Equipment: These
perform the core functions and operations within
a factory
■ Sensors: These measure the performance of the
machinery and the environment or condition in
which they function
■ Automation, Control and Operations Systems:
These are software systems such as SCADA,
DCS, MES, Historians and Quality Management
Systems that are used for the day-to-day
operations of a plant
■ IT Systems: These are software systems used
for managing business processes – these include
PLM, ERP and Business Intelligence solutions
In this multi-process milieu, an IIoT platform acts
as a convergence layer, with IT Systems on one side
and Operations Systems, machines and sensors on
the other. Information from these systems is unified
to create a manufacturing operations data lake.
Analysis of this data allows for wide-ranging and
global decision making.
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Existing IT and Operations Systems have started
acquiring IIoT platform-like capabilities. Such
approaches, however, are topical at best and
cannot scale as the scope of the implementation
expands. It is, therefore, important to understand
what constitutes an IIoT platform so that the right
parameters for putting the system in place are
established.
The Layers of an IIoT Platform
An IIoT platform consists of the following layers or
sub-components:
The Edge
The Edge is the software component installed at the
edge of the network, close to the source of data. It
might run on dedicated IoT gateway hardware or on
servers within a plant network.
The Edge layer is responsible for collecting data from
machines and operations systems and sending it
reliably to the manufacturing data lake. IIoT data can
be extremely large so the Edge is also responsible for
processing the data locally for improved throughput
and scale. The Edge can perform advanced
analytics and AI, and has the ability to power local
applications.
The Manufacturing Data Lake
The Manufacturing Data Lake is the repository for all
machine and manufacturing operations data across
the enterprise. The Edge layer streams data in real-
time to this layer. It is necessary for the Data Lake
to be highly scalable to deal with the sheer volume
of data.
Advanced Analytics and Machine Learning
This layer provides the ability to analyze
manufacturing operations data. It is powered by
big-data technology designed to handle the volume,
velocity and variety of data across the enterprise.
This layer should have the ability to perform
standard and advanced analytics. It should also have
the capability to build machine learning models
of the data to create a digital twin of the asset or
operation being analyzed.
Figure 1. IIoT Platform in an Enterprise
Source: Altizon
5
Manufacturing Intelligence
This is a vertical-specific layer that provides standard
implementations of critical KPIs across Productivity,
Quality, Planning and Maintenance. It allows for
rapid implementation and fast payback for IIoT in an
enterprise.
Integrations
The integrations layer provides standard modules
to rapidly connect manufacturing operations
data to IT systems such as ERP, PLM and BI.
Open data accessibility is key to a successful IIoT
implementation.
Security and Deployment Flexibility
It is important for an IIoT platform to run on any
infrastructure — including all major public and
private cloud vendors. It should also have the ability
to be deployed fully on-premise. The platform should
be multi-tenant, allowing for rapid rollouts across
the enterprise. The platform should also be fully
compliant when it comes to global information
security standards.
Having established the layers of an IIoT platform, it
is important to identify those use cases that will help
drive payback.
Identification of Key Use Cases
Within manufacturing plants, the following IIoT use
cases have the most significant business impact:
Productivity: Significant system-level throughput
improvement with optimal conversion cost
■ Real-time and accurate measurement of Overall
Equipment Effectiveness (OEE) of critical
machines and bottlenecks
■ Identification of top reasons behind unplanned
downtime, followed by the identification of root
causes to minimize them
■ Instantaneous production monitoring, booking
and inventory updates in ERP/Planning Systems
■ Close monitoring and control to sustain actions
for improvement
Figure 2. Sub-Components of an IIoT Platform
Source: Altizon
6
Predictive Maintenance: Extended asset life,
reduced downtime and reduced cost of spare parts
and tooling
■ Real-time monitoring of critical machine
parameters, enabling a change from time-based
to condition-based maintenance followed by
predictive maintenance
■ Applying machine learning techniques on
historical operational data of an asset to build
a digital twin – this represents the normal
operating condition of the asset
■ Leveraging the twin to predict failure when the
performance of the asset deviates from normal
■ Multi-dimensional modeling where data from
traditional techniques such as vibration
monitoring, thermography, tribology and visual
inspection is correlated with machine operating
variables such as load, speed and product
quality, leading to the root cause of mechanical
and electrical failures
Quality: Significant reduction in direct material costs
and expenses owing to poor quality; improvement in
overall rolled throughput yield and cutback on rework
■ Real-time monitoring of machine process
parameters that impact part quality
characteristics
■ Correlation between input – process – output
leading to a more informed root cause and CAPA
analysis
■ In-process Poka-Yoke to eliminate product
defects
■ Machine learning-based models to predict
potential quality failures and prescription of
settings to avoid them
■ Digital audit compliance and baseline for FMEA
Genealogy and Traceability: Significant reduction
in liabilities linked to non-compliance with various
standards
■ Linking of critical process and operational data
to the product as made in the manufacturing
value chain from upstream to downstream
■ Early warning signals of potential process
capability deterioration
■ Complete operations traceability that enables
compliance in the event of product quality audit,
withdrawal or recall
■ Extension of the system to critical part suppliers
in the supply chain
Energy and Utilities: Significant reduction in energy
and utilities consumption leading to lower conversion
cost, reduction in system-level energy leakages,
compliance with ISO 50001 and lower carbon
footprint
■ Monitoring and analysis of critical utilities such
as electrical energy, compressed air, steam and
water
■ Establishment of specific consumption patterns
across product categories
■ Analysis and identification of opportunities for
reduction in consumption
■ Deployment of sensors at critical points in the
energy network to identify leakages in real-time
■ Prediction of sub-optimal consumption patterns
Planning: Leveraging operations data for improved
planning reliability and effectiveness, enhanced
fill rate, dynamic master data updates and better
customer satisfaction
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■ Real-time visibility into status of planned orders
and likely deviations from promised due dates
■ Notification on significant events that may need
re-planning
■ Monitoring and reporting of significant changes
in master data linked to bill of materials,
routings and lead times
■ Machine learning-based decision support system
for planning and scheduling optimization
■ Enabling a system to reach near-zero latency
state in supply chain planning and optimization,
problem identification, re-planning for feasibility
and optimality, and implementation of changes
Having identified these IIoT use-cases, their
applicability and potential to generate rapid payback
across various industry verticals can be examined.
Applicability and Payback Period of IIoT Use Cases for Specific Verticals
Altizon has been closely working with manufacturing
companies across various industrial verticals since
2013. In this period, repeatable patterns have
emerged on the kind of problems that IIoT can
solve. This section provides an analysis of problem
classification and payback periods for key use cases
across Automotive, Tire, Chemicals, Textiles and CPG.
Automotive Plants
In this world of connected cars and electric
powertrains, automotive plant operations are gearing
to go digital across their entire supply chain. The
adoption of industrial IoT has mainly been focused
around removing the bottlenecks in operations and
controlling conversion costs.
Productivity is the most prevalent use case followed
by predictive maintenance.
Productivity has been the use case with the earliest
payback period of 6 months on average followed by
Quality and Traceability.
Figure 3.0 Use Case Percentage in Automotive Plants
Source: Altizon
8
Figure 4. Automotive Use Cases With Associated Payback Period in Months
Source: Altizon
Figure 5. Use Case Percentage in Tire Plants
Source: Altizon
Tire Plants
Electric vehicles are presenting a unique opportunity to the tire industry in design and manufacturing.
Increasing product variety, volatile raw material prices and asset and energy-heavy production
processes are putting significant pressure on traditional tire manufacturers to innovate.
Altizon has deployed its platform at multiple tire manufacturing sites to introduce predictability in
operations, reduce energy and utilities cost, and improve productivity.
Energy and Utilities cost reduction in the Tire industry vertical has been the use case with the
earliest payback period of 10 months on average.
9
Figure 6. Tire Manufacturing Use Cases With Associated Payback Period in Months
Source: Altizon
Chemical Manufacturing Plants
Chemical manufacturing plants engage in continuous process or batch manufacturing. Most of these
plants have been connected and controlled for decades with data being generated in silos.
Primary use cases at chemical manufacturing plants revolve around improving overall rolled
throughput yield, optimizing golden batch parameters, reducing energy and utilities consumption,
and predictive maintenance. Quality is the most prominent use case followed by energy and utilities
cost reduction. Quality Improvement in chemical manufacturing plants has been the use case with
the earliest payback period of 6 months on average.
Figure 7. Use Case Percentage in Chemical Manufacturing Plants
Source: Altizon
10
Textile Manufacturing Plants
Textile manufacturing is characterized by increasing product variety, decreasing batch sizes and immense
pressure to reduce material and conversion cost. There is also an endeavor to reduce impact on the
environment and focus on employee health.
Primary use cases at textile manufacturing plants include unplanned downtime reduction and process
adherence. Productivity is the most prominent use case followed by quality and process adherence.
Textile plants often require capex investment in machine and infrastructure upgrades to be IIoT ready. Due to
this, average payback periods tend to be longer, with a minimum 15 months for all use cases.
Figure 8. Chemical Manufacturing Use Cases With Associated Payback Period in Months
Source: Altizon
Figure 9. Use Case Percentage in Textile Plants
Source: Altizon
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CPG
Predictability in CPG/Food/Beverage supply chain and operations is critical for the industry to improve sales
at the lowest possible cost. This industry usually has a growing product mix and small packaging sizes, with
customers demanding a digital trace to raw material sources and processing conditions. Industrial IoT use
cases in CPG span the supply side (raw materials processing, storage), core manufacturing and downstream
packaging operations.
Productivity optimization has been the most prominent use case in CPG (focused primarily on line speed and
changeover analysis) followed by Quality and Traceability.
Productivity has been the use case with the earliest payback period of 6 months on average followed by
product Genealogy and Traceability.
Figure 10. Textile Manufacturing Use Cases With Associated Payback Period in Months
Source: Altizon
Figure 11. Use Case Percentage in CPG Plants
Source: Altizon
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Principles That Govern a Successful IIoT Implementation
As organizations get started with their IIoT initiatives,
it is critical to keep best practices in mind to ensure
success and maximum payback. Here’s how:
■ Select one end-to-end qualified partner for the
complete digital transformation initiative.
■ Avoid over-strategizing. Define KPIs best suited
to the vertical in question and begin small. Fail
fast, learn faster.
■ Establish a strong Project Management Office
that is multi-disciplinary.
■ Use the first implementation to build a template
for every subsequent implementation. This
methodology will provide insights and highlight
issues in infrastructure, process and people,
which is its intended purpose.
■ Adhere to First Principles of Manufacturing
Excellence (Toyota Production Systems, Lean
Six Sigma, Industrial Engineering, Theory of
Constraints) to bring focus to the use case and
its benefits.
Figure 12. CPG Use Cases With Associated Payback Period in Months
Source: Altizon
■ Have local plant leaders and managers
take responsibility for behavioral change
management, training and implementation. The
IT and the Chief Digital Office should advise and
facilitate the plant’s IIoT journey.
■ Integrate with IT systems such as ERP, Planning,
QCM and CRM early on to ensure that operations
data forms an integral part of the decision
making process.
■ Start with the power of correlations before going
down the path of elusive root cause analysis.
■ Become an engineer again. In the long term,
the responsibility of change management must
shift from managers to engineers and should be
driven by data. Technical discipline matters.
Digital Transformation With DMAIC
Follow a DMAIC (Define – Measure – Analyze –
Improve – Control) data-driven approach to drive,
improve and stabilize business processes. DMAIC
is an integral part of Six Sigma and has proven to
be effective in IIoT-driven digital transformation
initiatives.
15
Having a combination of technology, domain
understanding, process improvement techniques and
rapid deployment will help ensure a successful IIoT
implementation with payback that is predictable and
meaningful.
Methodology
The information presented is based on Altizon’s IIoT
driven Digital Transformation and Smart Factory
implementations. The primary research was done
using a combination of data analysis and workshops
with key customer personnel involved in these
initiatives.
Disclaimer: The results mentioned in this study is an
average across implementations in each vertical.
Source: Altizon
Survey Analysis: As More Companies Deploy IoT, They Increasingly Focus on Best Practices and Payback
Gartner survey results reveal enterprises have
an increasingly mature approach to Internet of
Things initiatives. Application leaders should
leverage these insights to help their enterprises
improve the rate of success and relevance for
their IoT projects.
Key Findings
■ Twenty-three percent of enterprises surveyed
had deployed Internet of Things (IoT)
projects before 2018, and another 37%
had deployed by year-end 2018 (YE18),
highlighting the still-relatively early stages of
IoT adoption.
■ Eighty-six percent of enterprises have
specified a time frame for financial payback
for their IoT projects. The average IoT project
requires three years to achieve financial
payback.
■ A quarter of enterprises are using
enterprisewide best practices for their
IoT initiatives, including key performance
indicators (KPIs), to track their business
outcomes and establishing a center of
excellence (COE) with responsibility for all IoT
implementations.
Recommendations
Application leaders responsible for IoT
implementations should:
■ Avoid making mistakes that are common to
complicated new IoT deployments by first
conducting market and internal project reviews
to identify lessons learned and best practices.
Research from Gartner
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■ Align IoT implementations with a well-defined
strategic business initiative by creating clear,
measurable goals, such as project payback
timelines and targets.
■ Mandate the use of best practices like the use of
KPIs, a COE and a shared solution architecture
to help improve the success of their IoT projects.
Strategic Planning Assumptions
By 2024, 35% of enterprises that invest in IoT
projects will set a payback target of one year or less,
up from 10% today.
By 2024, 90% of all companies that invest in IoT
projects will have well-defined KPIs to help them
measure the success of their IoT investments.
Survey Objective
The purpose of this survey was to test our
working hypotheses regarding the maturity of IoT
deployments. The main elements factored into this
maturity analysis of enterprises, across sectors from
government to retail to pharmaceutical to utilities,
included:
■ The year these enterprises deployed their first
IoT project and thus the time to develop IoT-
centered experience and skills
■ The use of financial payback targets for their
IoT projects and the average number of years
needed to achieve a payback
■ Best practices for use of KPIs, COEs and
an enterprise solution architecture for IoT
deployments
Results presented are based on a Gartner study
to help companies that implement IoT to better
understand what kinds of business benefits IoT
delivers and how they need to organize IoT to best
deliver on those benefits. The primary research was
conducted online from 15 May through 27 June
2019 among 511 respondents from the U.S. For
more details, see the methodology section.
Data Insights
Highlights of this survey indicate that, while IoT
deployments remain complex, key lessons and
best practices are emerging from enterprises that
have been deploying IoT capabilities, both in their
products and their operations. These include:
■ Enterprises increasingly adopt IoT but have
a limited history with it: While IoT adoption
is proliferating, 23% of enterprises that have
deployed it report doing so before 2018, which
means that experience with the innovation is still
relatively immature.
■ IoT has migrated from being a technology
initiative to a business initiative: A majority
of IoT implementers (86%) report specifying
a time frame for financial payback of their IoT
investments, and on the average the time frame
is three years.
■ Best practices are emerging for IoT initiatives:
Although implementing IoT is still a relatively
new endeavor, a majority of implementers of
IoT (61%) report adopting best practices (e.g.,
leveraging KPIs to monitor IoT success).
Enterprises Increasingly Adopt IoT but Have a Limited History With It
Enterprises working on IoT initiatives that cross
functional areas to build end-to-end IoT-enabled
business solutions often require time and practice
to build maturity. Enterprises need this time
and opportunity to practice across business unit
operational or engineering teams, the IT organization
and corporate leadership groups. Our hypothesis
from speaking with IoT implementers has been that
many types of IoT projects have been implemented
over the past few years, providing enterprises with a
core portfolio of lessons learned. But to gain more
insight into how experienced IoT implementers are
overall, we asked survey respondents when their
organization had completed or planned to complete
at least one IoT project. Figure 1 shows current
status of IoT use and year of first deployment.
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Key Survey Result Finding and Analysis
■ Finding: Respondents indicated that 75% of the
organizations surveyed had deployed IoT projects
by the first half of 2019. However, only 23% of
enterprises had deployed IoT projects before
2018.
■ Analysis: While IoT has been a topic of
interest for over a decade, these survey results
emphasize that:
Proportionately few enterprises to date have extensive experience with IoT.
■ This survey highlights the caution that a
limited percentage of enterprises have had
IoT deployments for more than one year. Few
enterprises have had a chance to conduct
extensive numbers of IoT projects across the
enterprise and obtain lessons learned. These
lessons would run the gamut of business,
cultural and technical range. Lessons could be as
basic as establishing the risk level of IoT projects
and the implications this has for the internal rate
Figure 1. Current Status of IoT Use, Year of First Deployment
of return these projects have to meet. It can be
as complex developing a strategy to socialize
the trustworthiness of IoT-generated data and
convincing line workers in another geography,
culture and language to use this in their daily
processes.
IoT implementations require time. Objectives need
to be set (business and financial). Assets need to
be connected. Data needs to be integrated into
business processes. People need to be trained. Since
few enterprises have extensive experience adopting
IoT-based digital technologies, this will take time
for all enterprises. IT has the opportunity to help
the business units drive digital transformation and
become more competitive and differentiated using
IoT capabilities.
Recommendations
Application leaders responsible for IoT enabling their
applications should:
■ Avoid making mistakes that are common to
complicated new IoT deployments by first
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conducting market and internal project reviews
to identify lessons learned and best practices.
This may require an audit of projects from your
business units and IT and engagement with
comparable companies to obtain their lessons
learned.
■ Use these lessons and best practices to develop
a step-by-step roadmap that starts small and
accelerates as you accrue expertise. Verify that it
reflects your business unit’s digital literacy.
■ Capture lessons learned in a knowledge bank,
and combine that information with lessons
from other enterprises conducting IoT projects.
Communicate lessons learned and success
cases extensively in the enterprise. Work with
executive leadership, both IT and business
units, to leverage these insights into your IoT
adoption roadmap. Mine your past for lessons of
cross-enterprise collaboration, particularly from
business transformation initiatives and from your
ERP implementations.
■ Work with IT leadership and the business unit
leadership to establish policies and procedures
to not just forgive failure but to reward the
implementers and to obtain the lessons learned
from these IoT deployments. Encourage agile
approaches and a fast-fail/fast-learn mentality.
IoT Has Migrated From Being a Technology Initiative to a Business Initiative
Historically, IoT initiatives have been the purview
of technology departments, focused on proving the
technology. Yet since the majority of enterprises
are driven by business results, IT leadership needs
to align how it views IoT projects. We hypothesized
that IoT projects have to provide a clear payback for
their costs and that they have to drive new business
capabilities.
Our starting hypothesis was that enterprises and
decision makers are getting smarter about planning
a time frame for the ROI related to their IoT
implementations and that over time we should expect
the IoT ROI to occur earlier. Figure 2 shows the time
frame for expected payback of an IoT project.
Figure 2. IoT Projects Require a Well-Defined Financial Payback
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Key Survey Result Finding and Analysis
■ Finding: A significant proportion of
organizations (86%) have specified a time
frame to achieve financial payback for their IoT
projects.
■ Analysis: This indicates the IoT projects
are increasingly no longer just technology
demonstration projects or proofs of concepts
(POCs). They are part and parcel of a business
unit’s set of initiatives that align to its business
objectives. In a business unit, meeting or
exceeding a financial payback target is just a
starting point for an initiative.
Business stakeholders, not IT, tend to own IoT projects, and their expectations of ROI reflect both business objectives and the realities of the enterprise.
These projects require scarce skilled employees.
So these IoT projects have to also meet key
business objectives for cost optimization or
process improvement or revenue generation and
so forth. This highlights the central tension for IoT
projects in general. These projects leverage internet
technologies, which tend to be the purview of IT. But
they are most often sponsored, approved and paid
for by the business unit that determines how fast
the enterprise will use these digital capabilities in its
business process.
■ Finding: Our survey respondents indicated that
an IoT project requires three years on average to
achieve financial payback. Data not shown: 32%
of organizations estimated they would achieve
financial payback for their IoT projects in one to
less than two years. Ten percent of organizations
estimated they would achieve financial payback
in less than one year for their IoT projects. Only
8% of enterprises estimated they would have
project financial paybacks that exceeded five
years.
■ Analysis: These results emphasize that, although
IoT proliferation remains in the early stages of
adoption on a global basis, organizations are
assessing IoT projects and competing for funding
using standard business metrics such as project
payback. This business payback is nuanced by
the usual sets of internal financial targets and
their risk analysis of IoT projects.
Time to payback expectations may be set too high
for enterprises still in the early phases of adoption.
The risk factors that may require a shorter
payback include the lack of IoT experience by the
enterprise, lack of trust for the vendors involved,
and the potential for the culture to reject IoT project
implementation. Internally focused IoT projects,
where the focus centers on operational efficiency,
may have short paybacks due to clearer objectives
with more parameters under the enterprise’s control.
Externally focused IoT projects, such as smart
connected projects, may have longer paybacks as
business leaders understand they have to educate
their customers on how to engage the product and
its data.
This puts pressure on the IoT team to have solutions,
not just IoT technologies, to meet enterprise
requirements. This requires understanding their
enterprise’s business, organizational and technology
maturity.
Recommendations
Application leaders responsible for IoT enabling their
applications should:
■ Develop a clear understanding of the enterprise’s
business objectives and the strategic imperatives
the CEO is espousing. You must work closely with
business stakeholders to develop IoT initiatives
and projects to ensure the outcomes align with
the strategy.
■ Build a roadmap of your enterprise’s IoT
maturity using a business, organizational and
technology framework to help frame the types of
IoT projects and the types of paybacks likely to
be needed.
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■ Seek guidance from the CFO’s team to establish
a clear set of financial payback metrics based
on analysis of the reality of IoT maturity of your
enterprise and the risk factors working against
success. Initially favor smaller, leaner projects
with as short as possible a payback so the
business unit and IT leaders can more quickly
achieve and emphasize successful outcomes (or
fail fast and learn from mistakes).
Best Practices Are Emerging for IoT Initiatives
While few enterprises have deployed IoT at scale,
there have been a variety of concerted efforts to
develop technology expertise for IoT initiatives. We
hypothesized that most enterprises are not mature
Figure 3. Enterprises Adopting Key Business, Organization, Architecture Metrics (Percent)
enough and have variances in maturity from a
business, cultural and technology perspective to
adopt IoT on a wide-scale basis. Thus, we asked
IoT implementers to what degree they had adopted
IoT project KPIs, used an IoT COE or adopted a
shared IoT solution architecture. Figure 3 shows
organizations’ overall maturity of IoT adoption.
Key Survey Result Finding and Analysis
■ Finding: Sixty-one percent of the participating
organizations conducting IoT projects indicated
they had a mature approach to IoT adoption.
These involved using key parameters for business,
organizational and technology maturity: KPIs, a
COE and a solution architecture.
22
■ Analysis: Enterprise IT organizations are
leveraging best-practice approaches that have
worked for them in other IT initiatives such
as adopting cloud computing or deployment
of consolidated business applications (e.g.,
ERP). This is a fundamental requirement for
IoT as it requires extensive coordination and
engagement among IT, the operating and
business unit elements of the enterprise, and
senior leadership. Thus, KPIs enable IoT projects
to focus on business outcomes. A COE approach
facilitates sharing goals, lessons and resources,
and it provides a venue to resolve disagreements
among stakeholders. Finally, an IoT solution
architecture points to opportunities to optimize
technical deployments and to help avoid
reinventing the wheel throughout the enterprise.
A caution on this measure of maturity is that
we may see these best practices appear in only
parts of the enterprise or even just in IT.
Recommendations
Application leaders responsible for IoT enabling their
applications should:
■ Engage an existing IoT or digital COE, or help
establish a new COE for IoT to share goals,
lessons and resources. This will require teams
from senior leadership to help drive goals
and allocate teams and budgets, operations
groups that have responsibility for the assets or
products, and IT for its process and technical
expertise.
■ Incorporate the business objectives and related
KPIs that the business leadership or COE shared
into the IT strategy and roadmap to support IoT
initiatives. Decline to support projects that are
not aligned to corporate objectives and do not
have KPIs unless there is a specific strategic
reason to do so. To limit wasted effort by your
teams, stop any IT-based IoT initiatives that do
not clearly align back to the business objectives.
■ Establish an IoT solution architecture. Ensure it
is flexible enough to address two main challenges
from typical IoT deployments. First, it must be
able to address a distributed computing and
data topology from edge to the cloud. Second, it
must be able address IoT projects with different
cost or complexity levels (e.g., projects to
manage a series of homogeneous digital lighting
assets versus projects to manage data from
heterogeneous manufacturing assets).
Methodology
Results presented are based on a Gartner study
to help companies better understand what kinds
of benefits IoT delivers, how they need to organize
IoT to best deliver on those benefits, and how
to overcome the technical challenges of IoT. The
primary research was conducted online from 15 May
through 27 June 2019 among 511 respondents from
the U.S.
Companies were screened out for having annual
revenue less than $100 million. They were also
required to complete, or plan to complete,
deployment of at least one use case or project of IoT
by YE20.
Respondents were required to be at manager or
above and should have a primary involvement
and responsibility for making decisions in IoT
implementation.
The study was developed collaboratively by Gartner
analysts and the Primary Research team.
Disclaimer: Results of this study do not represent
the market as a whole but are a simple average
of results for the targeted country, industries and
company size segments covered in this survey.
Definitions
Internet of Things: The Internet of Things (IoT)
is a core building block for digital business and
digital platforms. IoT is the network of dedicated
physical objects that contains embedded technology
to communicate and sense or interact with its
internal states and/or the external environment. IoT
comprises an ecosystem of assets and products,
communication protocols, applications, and data
and analytics.
23
Additional research contribution and review:
Kanwarpreet Oberoi
Evidence
Results presented are based on a Gartner study
to help companies better understand what kinds
of benefits IoT delivers, how they need to organize
IoT to best deliver on those benefits, and how
to overcome the technical challenges of IoT. The
primary research was conducted online from 15 May
through 27 June 2019 among 511 respondents from
the United States.
The survey was developed collaboratively by a team
of Gartner analysts who follow the market, and it
was reviewed, tested and administered by Gartner’s
Research Data and Analytics team. The results of
this study are representative of the respondent base
and not necessarily the business/market as a whole.
Source: Gartner Research, G00428586, Alfonso Velosa, Benoit Lheureux, Martin Reynolds, 29 October 2019
Driving Payback Through Industrial Internet of Things is published by Altizon. Editorial content supplied by Altizon is independent of Gartner analysis. All Gartner research is used with Gartner’s permission, and was originally published as part of Gartner’s syndicated research service available to all entitled Gartner clients. © 2020 Gartner, Inc. and/or its affiliates. All rights reserved. The use of Gartner research in this publication does not indicate Gartner’s endorsement of Altizon’s products and/or strategies. Reproduction or distribution of this publication in any form without Gartner’s prior written permission is forbidden. The information contained herein has been obtained from sources believed to be reliable. Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. The opinions expressed herein are subject to change without notice. Although Gartner research may include a discussion of related legal issues, Gartner does not provide legal advice or services and its research should not be construed or used as such. Gartner is a public company, and its shareholders may include firms and funds that have financial interests in entities covered in Gartner research. Gartner’s Board of Directors may include senior managers of these firms or funds. Gartner research is produced independently by its research organization without input or influence from these firms, funds or their managers. For further information on the independence and integrity of Gartner research, see “Guiding Principles on Independence and Objectivity” on its website.
For more information visit:
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About Altizon
Altizon leverages the power of IoT to drive smart
manufacturing initiatives. Altizon’s Datonis Industrial
IoT Product Suite uses advanced analytics and
machine learning on operations data to generate
actionable insights.
Altizon is a leading Industrial IoT platform provider
recognized in the 2019 Gartner Magic Quadrant for
Industrial IoT Platforms.
Altizon is headquartered in Palo Alto (USA) with
offices in Boston (USA) and Pune (India).