is iiot living on the edge in industrial environments? · pdf fileby arc advisory group arc...

By ARC Advisory Group

ARC MARKET REPORT

NOVEMBER 2017

Is IIoT Living on the Edge in Industrial

Environments?

Introduction ................................................................................ 1

Acceptance of IIoT and Edge Concepts ........................................... 6

Reasons for Embracing the Edge .................................................... 9

Planning for the Edge ................................................................. 11

Summary and Recommendations ................................................. 15

VISION, EXPERIENCE, ANSWERS FOR INDUSTRY

ARC Market Report • November 2017

2 • Copyright 2017 © ARC Advisory Group • ARCweb.com

Which companies are leveraging the edge

for IIoT use cases? How are companies

planning investments and prioritizing

requirements? This is what ARC asked a

group of over 300 end users across the

globe to get a feel as to the current and

future state of the market.

Introduction

Given the hype about IIoT, the ARC Advisory Group asks if it is really

time to focus on the edge. The promise of edge computing in industrial

environments means getting the right device data in near real-time to

drive better decisions, and maybe even control industrial processes. For

this to work, it means that the edge device, its

embedded software, edge servers, the gateways

and cloud infrastructure must all be up and run-

ning correctly all the time.

Who is operating this way already? What triggers

upgrades in the plant? How long does it take to

get a secure and reliable distributed control and

analytics system? This is what ARC asked a group of over 300 end users

to get a feel as to the current and future state of the market. Our key take-

aways are below in the executive summary

Executive Summary

1. The concept and role of the edge among our audience is well under-

stood

a. Nearly three quarters of respondents agree that on-premise com-

puting systems deployed outside the data center can be defined as edge devices - irrespective of how they will be managed.

2. A mix of edge and cloud based technologies will be the foundation for the future automation infrastructure

a. ~ 60 percent of respondents plan to take a hybrid approach by bal-

ancing future investments in the edge as well as the cloud

3. Operational factors, on premise based real time analytics and APM will drive edge adoption

a. The top three drivers for deploying systems and connectivity at the edge are operational i.e. analyzing and controlling devices, im-

proving process speed/reducing latency issues, and reducing data security risks.


Copyright 2017 © ARC Advisory Group • ARCweb.com • 3

b. The majority of respondents expect to deploy real-time analytics

capabilities on premise and as close to the manufacturing process

as possible, either at the edge, or on the plant floor level

c. Operational concerns also largely drive user interest in applying

edge-based analytics, e.g. to improve asset performance and

maintenance and the need to improve and optimize production,

i.e. prevent unplanned downtime

4. Organizations are planning for the edge

a. The majority of respondents will be moving forward with imple-

menting an edge infrastructure, using a combination of internal

and outside resources to build and maintain it

b. Organizations overwhelmingly anticipate the need for training ex-

isting staff, or adding personnel proficient in edge analytics

technology

5. How the edge is expected be managed

a. Within their companies, respondents expect hybrid IT/Operations

teams to take responsibility for the edge, reflecting an overall trend

toward IT/OT convergence.

b. An almost absolute majority of respondents stated that they expect

simplified edge infrastructures that can be remotely managed

c. Remote management responsibility is expected to stay in house ac-

cording to more than half of respondents

The Edge and IIoT

Let’s first define the edge as a place where computing occurs, in between

the data center and the cloud. The growth of IIoT extends the edge to in-

dustrial devices, machines, controllers and sensors. Edge computing and

analytics are increasingly being located close to the machines and data

sources. As the digitization of industrial systems proceeds, so does analy-

sis, decision-making, and control being physically distributed among edge

devices, edge servers, the network, the cloud, and connected systems, as

appropriate. These functions will end up where it makes most sense for



Edge computing and analytics are increasingly

being located close to the machines and data

sources. As the digitization of industrial

systems proceeds, so does analysis, decision-

making, and control being physically

distributed among edge devices, edge

servers, the network, the cloud, and

connected systems, as appropriate.

them to be. This makes it essential that to-

day’s automation assets be designed to

leverage IIoT and the edge.

Edge computing and IIoT embody IT/OT

convergence in their role of bridging these

two areas of the architecture. This is particu-

larly obvious as edge devices evolve beyond

their traditional role of serving field data to

upper level networks and emerge as an integral part of the industrial in-

ternet architecture. Today, the IT organization owns more and more of the

architecture and standards associated with the industrial internet, includ-

ing both clouds and networks.

With edge computing and analytics, data is processed near the source, in

sensors, controllers, machines, gateways, etc. These systems may not send

all data back to the cloud, but the data can be used to inform local machine

behaviors as it is filtered and integrated. The edge systems may decide

what gets sent, where it gets sent and when it gets sent. Placing intelli-

gence at the edge helps address problems often encountered in industrial

settings, such as oil rigs, mines, chemical plants, and factories. These in-

clude low bandwidth, low latency, and the perceived need to keep mission

critical data on site to protect IP. Now, let’s see how end users feel regard-

ing the edge and the IIoT.

Respondent Demographics

Our survey generated significant interest among the industrial user com-

munity. We collected 327 responses, from a wide swath of discrete and

process industries. For our analysis, responses were grouped into the Pro-

cess and Discrete sectors, with 154 and 173 respondents, respectively.



Nearly half of survey respondents were located in North America, and

almost a third in Asia, where China and Japan were well represented. A

fifth of respondents hailed from the EMEA region, with representation

slanted toward Western Europe. The smaller number of respondents

were from Latin America, including many from Brazil.



Acceptance of IIoT and Edge Concepts

Survey respondents almost universally accept the fundamental aspects

that form the underpinnings of the Internet of Things, and can well appre-

ciate the benefits of its implementation in the production environment.

Nearly three quarters of respondents agree that on-premise computing

systems deployed outside the data center can be defined as edge devices -

irrespective of how they will be managed. Variance between process and

discrete users was slight, similarly between geographies.



There is an even stronger consensus around distributing computing be-

tween edge devices and the cloud to form the basis for the industrial

automation infrastructure. A full 93 percent of respondents agree with

this conception.

Respondents strongly support the use of robust and capable edge devices

to enable real-time decision making, with near-unanimous agreement with

the concept. Users have long recognized the value of being able to process

data and execute programs as close to the manufacturing process as possi-

ble, with the aim of maximizing process efficiency and reducing or

virtually eliminating the time between acquiring data and acting on it.



While edge computing presents a compelling opportunity for most, a ma-

jority of respondents still plan a hybrid approach when it comes to making

future investments in the cloud and edge, with nearly 60 percent taking a

balanced approach. About one quarter, will invest more heavily in edge

computing resources, and slightly less will skew towards the cloud. Clear-

ly, users want to harness the inherent benefits offered by both edge

devices and cloud computing resources.

The majority of respondents expect to deploy real-time analytics capabili-

ties on premise and as close to the manufacturing process as possible, by

and large. Thirty percent expect to perform data analytics at the edge, and

slightly fewer at the plant floor level, i.e. 58% of users interviewed would

not want to use the cloud as an intermediary (likely to ensure reliability

and reduce response times) nor have it reside in the data center. Only 18

percent of respondents would kick the analytical function up a level to the

data center, while about one-quarter would rely on cloud resources.



Reasons for Embracing the Edge

Asked about which specific operational and infrastructure issues were

driving their interest in deploying edge solutions, respondents were more

likely to cite operational concerns that centered on process efficiency and

asset reliability.

When ranking their top three needs for deploying systems and connectivi-

ty at the edge, users emphasized operational issues. Chief among them

were analyzing and controlling devices, improving process

speed/reducing latency issues, and reducing data security risks. This

overlays nicely with user’s perceptions of the benefits of edge computing

as enabling faster, better decisions at the production level, and translating

that into action on the spot that we saw earlier.



Rightly or not, users have concerns about having their data handled and

stored in the cloud. They generally accept the reliability and availability of

cloud resources, given the relatively low ranking of minimizing cloud fail-

ure risks. However, reducing data security risks by keeping data and

control at the edge level may indicate that some may have concerns about

relying on the cloud fully.

Operational concerns also largely drive user interest in applying edge-based

analytics. The primary reasons for consideration are to improve asset per-

formance and maintenance (considered by many to be the breakthrough

application for the IIoT), particularly among process respondents (especially

in North America and EMEA) and the drive to improve and optimize pro-

duction, i.e. to prevent unplanned downtime. Of significantly lesser

importance to respondents were reducing risk and waste, and complying



with regulations, perhaps because these are not as immediately tied to pro-

duction reliability and efficiency, or are addressed in other ways.

Planning for the Edge

Survey respondents were asked about their future plans to implement

edge computing and analytics, how the program will be administered, and

where responsibilities for tending and managing the edge infrastructure

will reside. While about a quarter will still be investigating the concept, a

fair number will be moving forward with implementing an edge infra-

structure, using a combination of internal and outside resources to build

and maintain it.

A majority of respondents expect to be in fact-finding mode over the next

year, or just beginning their practical applications of edge technology.

Slightly over a third will be investigating the feasibility of the edge for

their organization or selecting potential technology providers, and a third



expect to be conducting a pilot program during the next year. The re-

mainder will be implementing or already have their edge infrastructure in

place. Slightly over 10 percent will be actively collecting and using data

from the edge to improve their production operations; among respond-

ents, discrete users appear to be further along on their journey to the edge.

Within their companies, respondents expect that responsibility for the

edge will be shared between IT and Operations departments, reflecting an

overall trend toward IT/OT convergence. The transition from using pro-

prietary, often disparate systems on the production line to off the shelf,

more homogeneous, networked computers and systems, along with in-

creased connectivity rates have led to greater involvement by IT into the



traditional domain of operations. Conversely, the vast business process

knowledge OT teams have, will force a further blending of the functional

lines between these departments.

A clear majority of respondents envision sourcing new skills or personnel

in their organizations to leverage edge analytics solutions. Respondents

expect their companies to be training their personnel to be conversant in

edge analytics technology, or to add staff that already is. Having staff

members dedicated to the edge will likely free operations people and IT

staffers to perform their respective roles, without distracting them unduly

from their fields of expertise. This will result in more detail and attention

paid to IT and OT respectively without adding additional resources.

Respondents overwhelmingly want to keep the complexity of their edge

infrastructure manageable, and expect that management will be done re-

motely. This is logical, considering not only the inherent connectivity, but

also reflects an overall trend in industry toward remote, centralized man-

agement of production assets, often a collection of many plants. This

allows organizations to ensure greater availability and efficiency around

the clock, enables sharing of best practices across entire organizations, and

also reduces the need to have dedicated resources at individual plants.



More than half of respondents expect to keep their remote management of

the edge in-house, and more expect that their IT departments will take re-

sponsibility, particularly among process users. Discrete users assume

operations will assume a somewhat greater role in comparison. A sizea-

ble portion of respondents feel that outside contractors will be involved in

managing their edge infrastructures, perhaps because they are unable or

do not want to cultivate or add this expertise in their organizations, or di-

vert resources that could be applied elsewhere.



Summary and Recommendations

In Conclusion

Edge is a relatively well understood concept. Many companies already

understand the concept of edge as differing from data center and

cloud.

Respondents recognize that more complex computing is going to take

place at the edge. Better systems will be needed at the edge, but they

will also need connectivity to the data center and cloud.

Workloads like advanced analytics to better control devices, improve

processes, and reduce data security risk are desired.

Responsibility and ownership for these edge systems and applications

will be held by a new hybrid OT/IT person.

Remote management will be important due to limited resources and

skills sets. Also, important will be simplified edge infrastructure for

these reasons.

Recommendations

If you haven’t begun thinking about your edge strategy, start now.

Your peers already are.

As IT/OT converge, think about your own company. How do you

think you will merge the two cultures and start assigning responsibili-

ties?

Either way, look for simple, remotely manageable edge computing in-

frastructure to mitigate your resource constraint risks. Make sure it’s

continuously available, and can connect to both your data center and

cloud.

As manufacturers implement IIoT ecosystems that connect their machines,

equipment, and production systems to the digital enterprise, this survey

concludes that both process and discrete end users would like to see real-

time intelligence “at the edge.” In today’s connected factories and plants,

edge computing will provide the foundation for the next generation of



As manufacturers implement IIoT

ecosystems that connect their ma-

chines, equipment, and production

systems to the digital enterprise, this

survey concludes that both process and

discrete end users would like to see

real-time intelligence “at the edge.”

smart connected IIoT devices and the digital enterprise. These intelligent

edge devices can aggregate and analyze sensor and other data and stream

information to support predictive analytics platforms and even extend the

concept to the digital twin, another feature that ARC feels will result from

the trend toward edge computing.

Hybrid approaches utilizing edge computing and

the cloud will enable process and discrete end users

to provide actionable information to support real-

time business decisions and support asset monitor-

ing, data analytics, process alarming, and process

control, as well as machine learning and the emerg-

ing AI ability for machines to make sense of and act

on complex data patterns. Increasingly, the compu-

tational capabilities from both edge and cloud computing are migrating

into the gateways and edge devices for IIoT networks.

It comes as no surprise that many end users expect to perform data analyt-

ics at the edge, followed by the plant floor level. If industry is to move to

ecosystems of smart connected machines and production systems, the first

step is to create a digital environment that securely connects factories and

plants using intelligent devices that can access, capture, aggregate, and

analyze data at the production process and provide actionable information

to enable operations, maintenance, and plant and product engineering and

support groups to optimize how products are designed, manufactured,

and supported.

Factors Driving Connectivity at the Edge

Operational issues, such as analyzing and controlling devices, improving

process speed/reducing latency issues, and reducing data security risks,

will drive end users to deploy edge computing, as well as the need to im-

prove asset performance and maintenance to reduce unplanned or

unscheduled downtime, and the need to improve and optimize produc-

tion. However, for edge computing and devices for machines, equipment,

and production systems to continue to proliferate, cybersecurity concerns

must be addressed. While IIoT and edge devices afford a way to connect

factory ecosystems, products and equipment in the field, and even the

manufacturing supply chains; these devices and connections must be

made secure and reliable or manufacturers will slow down the deploy-

ment of edge and cloud technologies.



Operational issues, such as analyzing and

controlling devices, improving process

speed/reducing latency issues, and

reducing data security risks, will drive end

users to deploy edge computing, as well

as the need to improve asset performance

and maintenance to reduce unplanned or

unscheduled downtime, and the need to

improve and optimize production.

Smart manufacturing and edge computing with

information-enabled operations offers virtually

infinite potential to improve business perfor-

mance. Companies will be able to use data that

has long been stranded inside machines and pro-

cesses to quickly identify production

inefficiencies, compare product quality against

manufacturing conditions, and pinpoint potential

safety, production, or environmental issues. Re-

mote management of this edge infrastructure will immediately connect

operators with off-site experts to be able to avoid or more quickly trouble-

shoot and resolve downtime events.

Finally edge and cloud computing architectures will accelerate IT and OT

convergence. As a result, IT and OT professionals who previously only

oversaw their own individual systems are learning about the counterpart

technologies. IT professionals must have the skills to transfer their experi-

ence of enterprise network convergence and ubiquitous use of Internet

Protocol into manufacturing applications. OT professionals must possess

the skills to migrate from yesterday’s islands of automation to today’s

plant-wide, information-centric edge and cloud architectures to enable the

secure flow of information throughout the manufacturing enterprise and

beyond. These skills are critical for end users to source to fully leverage

their hybrid edge and cloud infrastructure.



Analysts: Craig Resnick and Allen Avery

Editor: Paul Miller

Industry Sector Breakdowns Used in Report

Process

Cement & Glass

Chemical

Electric Power Generation

Food & Beverage

Metals

Mining

Oil & Gas

Pharmaceutical & Biotech

Pulp & Paper

Refining

Textiles

Water & Wastewater

Discrete

Aerospace & Defense

Automotive

Electronics & Electrical

Fabricated Metals

Furniture & Wood Products

Machinery

Medical Products

Plastic & Rubber

Printing & Publishing

Semiconductors

Founded in 1986, ARC Advisory Group is the leading technology research and

advisory firm for industry and infrastructure. ARC stands apart due to our in-

depth coverage of both information technologies (IT) and operational technol-

ogies (OT) and associated business trends. Our analysts and consultants have

the industry knowledge and first-hand experience to help our clients find the

best answers to the complex business issues facing organizations today. We

provide technology supplier clients with strategic market research, and help

end user clients develop appropriate adoption strategies and evaluate and se-

lect the best technology solutions for their needs.

All information in this report is proprietary to and copyrighted by ARC. No part

of it may be reproduced without prior permission from ARC.

You can take advantage of ARC's extensive ongoing research plus the experi-

ence of our staff members through our Advisory Services. ARC’s Advisory

Services are specifically designed for executives responsible for developing

strategies and directions for their organizations. For membership information,

please call, write to, or visit our website:

ARC Advisory Group, Three Allied Drive, Dedham, MA 02026 USA

781-471-1000 • www.arcweb.com

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