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IIoT AND BIG DATA ANALYTICS: How Manufacturing System Architecture Is Being Transformed

lnsresearch.com

IIoT AND BIG DATA ANALYTICS:How Manufacturing System Architecture Is Being Transformed

lnsresearch.com

TABLE OF CONTENTS

Section 1: IIoT: State of the Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Section 2: Understanding Digital Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Section 3: Adoption of IIoT Connectivity and Big Data Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Section 4: Building the Business Case and Recommended Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

SECTION 1

IIoT: State of the Market

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Introduction

The Industrial Internet of Things (IIoT) is a general term denoting the

concept that standard Internet technologies are broadly applicable

and will transform all areas of the industrial sector. Because of its

expansive and transformative nature, the concept and term “IIoT” is

starting to be used almost everywhere: manufacturers, automation

vendors, enterprise application vendors, system integrators, man-

agement consultants, government sponsored consortia, and indus-

trial companies as well.

As an analyst firm, LNS Research is dedicated to helping all of these

market players simplify, improve understanding, and more quickly

capture the value of emerging technology. In early 2014 LNS began

researching and advising companies on the IIoT, culminating in the

2015 research report: “Smart Connected Operations: Capturing the

Business Value of the IoT.” In this work we gave the first, now broadly

accepted, definition of the IIoT Platform, consisting of four main

buckets of capabilities: Connectivity, Cloud, Big Data Analytics, and

Application Development. We also put forth concepts of how this

platform would enable new data and system architecture that would

flatten existing hierarchies, provide data from anywhere to anywhere

capabilities, and enable next-generation business applications.

Since that time there have been dramatic moves by new start-ups

and many incumbent vendors, including announcements of home-

grown capabilities as well as mergers and acquisitions. All of these

moves have confirmed the assertion that for the foreseeable future,

the IIoT Platform space will be an ecosystem play that brings togeth-

er both IT and OT vendors to enable new business models.

In this new research, we will explore new survey data showing

the increased market adoption of IIoT Platform capabilities and how

these new technologies are transforming architectures today; not

some unknown date in the future. We will also examine how the LNS

Research Digital Transformation Framework can help industrial com-

panies overcome IIoT challenges and get started now on the journey

towards using Big Data Analytics to achieve a competitive advantage

and better business results.

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Research Demographics

The data presented in this eBook represents over 300 completed

surveys and was collected from the middle of 2015 to the middle

of 2016. LNS Research deploys a social research model where our

online format English language surveys are open to the general

public. Companies participate in LNS Research surveys to gain

access to the LNS Research library, meaning survey participants

are research consumers as well. Each respondent is contacted with

multiple emails and phone calls and each response is reviewed by

an LNS Research analyst for accuracy.

The industry demographics of the survey largely match the

broader demographics of the industrial landscape, with discrete

being the largest segment, followed by process and batch indus-

tries. Our research also has a broad split across industries and

company sizes.

COLOR BY INDUSTRYCOLOR BY HQ LOCATION

Process Manufacturing

Discrete Manufacturing

Batch Manufacturing

North America

Europe

Asia/Pacific

Rest of World

2016 Metrics That Matter SurveyINDUSTRY

2016 Metrics That Matter SurveyREVENUE

2016 Metrics That Matter SurveyGEOGRAPHY

COLOR BY COMPANY REVENUE

Small: Less than $250 Million

Medium: $250 Million - $1 Billion

Large: More than $1 Billion

45% 49%41%

10%28%

15%

12%

37%

48%

15%

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IIoT Adoption Platform Technology Adoption

A major change in the market from 2015 to 2016 was in regard to one

of the biggest challenges limiting the adoption of IIoT technologies.

In 2015, nearly half (44%) of companies did not know or understand

the IIoT. In 2016 this number has reduced to 19%.

Call it hype or call it hard work by many of the thought leaders

in the space, but the majority of companies now understand what

the IIoT is. In our 2015 research we showed that the IIoT market

was largely still an early adopter market but would likely follow the

typical adoption curve for major new technology innovations. We

also stated three things would have to occur to move the market

toward mainstream adoption:

• Time would need to pass

• The market would have to better understand

the technology

• Early adopters would have to prove results for

easier business case validation

Two of the three have occurred and it is now time for the market

to prove the value and demonstrate the business case, which we

will hopefully begin later in this eBook.

Please indicate how the IoT is impacting your business today

Do not understand or know about IoT

We understand and our customer demands

are driving us

We are still investi-gating the impact

We understand/are aware and see value to our oper-

ators/customers or both

We understand but see no impact at this time

We understand and have already seen

dramatic impact

0% 5% 10% 15% 20% 25% 30% 35% 40% 45%

2016

2015

19%

33%

18%

13%

8%

8%

44%

21%

16%

9%

6%

4%

2015: 44% 2016: 19%

Do not understand IoT:

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Top IIoT Challenges

Unfortunately, building the business case and securing the funding

are the two top challenges facing IIoT technology adoption. Surpris-

ing to many, security concerns and technology scalability do not top

the list. This result is likely due to some companies just looking to

get pilot projects off the ground and not having tackled these tech-

nology issues yet. For other companies it is likely because they have

already done the research and are confident that IoT technology is

scalable to the industrial space.

In either case it is clear that building an effective business case is

the key to unlocking the potential of IIoT technology and Big Data

Analytics. In the next several sections we will show how building the

business case fits within the larger context of a Digital Transformation

Framework, how it must support these objectives, and how it needs

to be viewed as a journey rather than a singular, one-off decision.

Funding

Building a business case

Understanding what IIoT is and how it applies to your business

Security

Standards

Finding the right technology partner(s)

Gaining insight from Big Data

Developing new IIoT software applications

Company culture

Data gathering from legacy systems

Product design and development complexity

Hiring the right talent

Executive support

Scaling to 1,000s or 1,000,000s of devices

0% 5% 10% 15% 20% 25% 30% 35%

32%30%

26%25%

22%17%

16%14%14%

13%12%

8%8%

5%

What are the top challenges your company faces in deploying IIoT technology?(N=269, all respondents)

SECTION 2

Understanding Digital Transformation

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CEO/COO

Business Leaders

CDO, IT/OT Leaders

Functional Managers, SMEs

Business, IT/OT Practitioners

Digital Transformation Framework

Many industrial companies today are pursuing Digital Transformation

initiatives. What many organizations are missing is a systematic

approach to manage this transformation across all levels and functions

of the organization. The LNS Research Digital Transformation

Framework is designed to help industrial companies understand how

to connect all of these simultaneous and interconnected initiatives.

SOLUTION SELECTION

BUSINESS CASE DEVELOPMENT

OPERATIONAL ARCHITECTURE

OPERATIONAL EXCELLENCE

STRATEGIC OBJECTIVES

Eliminating Bias and Finding Long Term Partners

Evaluation

Team

Research

Pilot

RFPDISCOVERY

PLANNINGBUSINESS CASE

SELECTION

ProjectCharter

Defining Immediateand Long Term ROI

Managing IT-OT Convergence and Next-Gen IIoT Technology

Realigning People,Process, andTechnology

Reimagining BusinessProcess and Service Delivery

COSTS TOTAL YEAR 1 YEAR 2 YEAR 3 YEAR 4 YEAR 5

HARDWARE

SOFTWARE LICENSING

THIRD PARTY SOFTWARE

APPLICATION SOFTWARE

DOCUMENTATION & TRAINING

MAINTENANCE

INSTALLATION

INTEGRATION

LEGACY DATA LOADING

PROJECT MANAGEMENT

SUPPORT

TOTAL:

CONNECTIVITY

SMART CONNECTED ENTERPRISE

APPLICATIONDEVELOPMENT

CLOUD

BIG DATA ANALYTICS

IoT Enabled Business SystemsL4

Smart Connected Operations - IIoT Enabled Production, Quality, Inventory, MaintenanceL3

L2 L1 L0

IIoT EnabledNext-Gen Systems

L5 IoT Enabled Governance and Planning Systems

Smart Connected Assets -

IIoT Enabled Sensors, Instrumentation, Controls, Assets, and Materials

APMEHS

ENERGY QUALITY OPERATIONS

People – Process – TechnologyOperational Excellence Platform

OPERATIONAL EXCELLENCE SUPPORT

Fall short on any pillar and your OpEx platform becomes tippy

Fall short on two or more pillars and yourOpEx platform becomes totally unstable

DIGITAL TRANSFORMATION FRAMEWORK

The LNS Research Digital Transformation Framework offers a systematic approach

to undertaking simultaneous and interconnected IIoT initiatives

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Digital Transformation Framework (Cont.)

STRATEGIC OBJECTIVES: At the highest level industrial companies today need to be thinking

about how many of these new technologies, like the IIoT, can

disrupt and transform products, value chain business processes,

and connected service delivery. At the strategic level, companies

should be doing 5, 10, and even 20-year planning, and often

these transformative visions are built around the competitive

differentiators of the firm, the changing nature of service delivery,

and existing models like Industrie 4.0, Smart Manufacturing, or

Smart Connected Assets.

OPERATIONAL EXCELLENCE: People, processes, and technology are the underpinnings of

Operational Excellence initiatives, and these are typically owned

by the business leaders in the organization. Leading companies

today have developed maturity models to help set goals and

growth plans for people, process, and technology capabilities

along with metrics programs to evaluate performance across

all areas of operations. Most companies have had Operational

Excellence initiatives in some form or fashion for 10 years or more.

Often these initiatives also incorporate the multiple management

systems and continuous improvement capabilities of the firm, like

Lean or Six Sigma. Moving forward, manufacturing companies

need to continue to evolve Operational Excellence initiatives to

not only be the continuous improvement engine of the company

but also the driving force for innovation. Often this means moving

to more of more of a lean, start-up mentality of “fail often and

fail fast,” with pilot projects that have the potential of delivering

much more than the typical 1%-2% benefits promised by most

continuous improvement initiatives.

OPERATIONAL ARCHITECTURE: Traditionally Enterprise Architecture has been owned by the IT

organization and has been typically focused on establishing robust

processes for evolving the enterprise application landscape and

supporting IT stack. Separately, automation, corporate engineering,

and/or advanced manufacturing (often now referred to as OT)

owned the technology architecture for plant level technology.

With the emergence of IIoT, LNS Research recommends industrial

companies adopt an Operational Architecture approach that

applies the formalized rigor and process of Enterprise Architecture

to the entire IT-OT stack. For this to be effectively accomplished,

industrial companies need to create supporting and collaborative

groups that incorporate both IT and OT, and as the role of Chief

Digital Officer emerges, the success of this new collaboration as a

key part of his or her charter.

BUSINESS CASE DEVELOPMENT: Often industrial companies begin business case development and

solution selection without also thinking about the connection

to broader Strategic Objectives, Operational Excellence, and

Operational Architecture. Typically these business case development

initiatives are successful when driven by deep subject matter experts

that understand both the process and technology.

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Digital Transformation Framework (Cont.)

Identifying these experts can be a challenge, but often they are

located in advanced manufacturing, hybrid IT/OT roles, are a

leader within specific business functions, or are a technical fellow

supporting the organization. Although these other areas of Digital

Transformation do not need to be complete before a business case

is started, they are interconnected. As such, it is important industrial

companies do not view technology investments as a one-off business

case but rather as a business case journey that aligns with Operational

Architecture goals, depends on increasing Operational Excellence

maturity, and supports long-term Strategic Objectives. It is also

important to note that a strong business case will also incorporate

risk-based principles into the decision making and explicitly look at

‘no decision’ as an active choice.

SOLUTION SELECTION: Often industrial companies view Digital Transformation upside

down, starting with solution selection, which then drives all

other portions of the framework, rather than vice versa. Again,

with solution selection, it is important to put the activities within

the context of the broader initiatives. Solution selection is never

successful in a vacuum and when it is done in such a fashion, change

management becomes an insurmountable challenge and adoption

wanes. For success, build an effective solution selection process

that is quantitative to eliminate bias and a team that incorporates all

relevant portions of the organization, including IT, OT, and cross-

functional business leaders.

Solution selection should always be viewed within the broader context of

Digital Transformation initiatives; never in a vacuum

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A New Model for Operational Architecture

In moving to a new model of Operational Architecture, industrial

organizations need to move to an expanded scope of Enterprise

Architecture. This expanded scope should account for managing

“things” with edge analytics and applications across the value chain of

suppliers, internal operations, customers, and products. It should also

span an application and analytics environment that includes cloud/

on-premise and time series/structured/unstructured data types. Upon

careful inspection this expanded model should also incorporate the

main components of the IIoT Platform: Connectivity, Cloud, Big Data

Analytics, and Application Development.

This expanded scope is also too broad to address the right level of

detail for making meaningful architectural decisions across the en-

terprise. LNS Research recommends a three-level approach, where

at Level 1 the entire scope is encompassed.

LEVEL 1OPERATIONAL ARCHITECTURE

Big Data Analytics, Collaboration, and Mash-Up Apps

Connectivity and Data Model

ANALYTICS & APPSANALYTICS & APPSANALYTICS & APPS

SUPPLIERS OPERATIONS CUSTOMERS & PRODUCTS

EDGE ANALYTICS AND APPLICATIONS

EDGE ANALYTICS AND APPLICATIONS

EDGE ANALYTICS AND APPLICATIONS

EDGE ANALYTICS AND APPLICATIONS

EDGE ANALYTICS AND APPLICATIONS

EDGE ANALYTICS AND APPLICATIONS

EDGE ANALYTICS AND APPLICATIONS

EDGE ANALYTICS AND APPLICATIONS

EDGE ANALYTICS AND APPLICATIONS

Applying an expanded scope to Operational

Architecture that includes the IIoT

Platform allows for the management of “things” across the value chain

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A New Model for Operational Architecture (Cont.)

At the next level of detail, a particular element of the high level archi-

tecture should be driven into. For example, an organization’s Level 2

Operational Architecture for structured data analytics and apps would

largely map to the traditional scope of enterprise applications.

When building this architecture, LNS Research recommends not

focusing on the traditional applications, such as ERP, PLM, MES, SCM,

and CRM, but instead on the functional areas and map these to the

corporate systems/management systems/value chain systems used

across execution/planning/analytics. Then the different applications

can be mapped to this model, not vice versa.

LEVEL 2OPERATIONAL ARCHITECTURE

Structured Data Applications and AnalyticsTRADITIONAL ENTERPRISE ARCHITECTURE

CORPORATE SYSTEMS - Defined by Sites and Organizational Structure

HR, Procurement, Finance and Accounting, IT ManagementANALYTICS

HR, Procurement, Finance and Accounting, IT ManagementPLANNING

HR, Procurement, Finance and Accounting, IT ManagementEXECUTION

Quality, Environment, Health, Safety, Energy, Sustainability, Risk, AssetsANALYTICS

Quality, Environment, Health, Safety, Energy, Sustainability, Risk, AssetsPLANNING

Quality, Environment, Health, Safety, Energy, Sustainability, Risk, AssetsEXECUTION

MANAGEMENT SYSTEMS - Defined by Sites and Organizational Structure

ANALYTICS

PLANNING

EXECUTION

Marketing Sales

Sales

Sales

Engineering

Engineering

Engineering

Manufacturing

Manufacturing

Manufacturing

Warehousing

Warehousing

Warehousing

Distribution

Distribution

Distribution

Retail

Retail

Retail

Service

Service

Service

Suppliers AssetManagement

AssetManagement

AssetManagement

Suppliers

Suppliers

Marketing

Marketing

VALUE CHAIN SYSTEMS - Defined by Sites and Organizational Structure

Big Data Analytics, Collaboration, and Mash-Up Apps

Connectivity and Data Model

ANALYTICS & APPSANALYTICS & APPSANALYTICS & APPS

SUPPLIERS OPERATIONS CUSTOMERS & PRODUCTS

EDGE ANALYTICS AND APPLICATIONS

EDGE ANALYTICS AND APPLICATIONS

EDGE ANALYTICS AND APPLICATIONS

EDGE ANALYTICS AND APPLICATIONS

EDGE ANALYTICS AND APPLICATIONS

EDGE ANALYTICS AND APPLICATIONS

EDGE ANALYTICS AND APPLICATIONS

EDGE ANALYTICS AND APPLICATIONS

EDGE ANALYTICS AND APPLICATIONS

CELL 4CELL 3

CELL 1 CELL 2

Controller

Drive

I/O

HMI

Plant Data Center /

Application Server

SMART CONNECTED DEVICES AND ASSETS

CONNECTIVI

TY

DATA

FUNCTIONS

SETU

P AN

D CO

NFIG

URATION

SECURITY

HARDWARE SETUP& INSTALLATION

Plant Data Center / Application Server

Core Switches

DistributionSwitch

Instrumen-tation

External DMZ/FirewallExternal DMZ/Firewall

PLANT DEMILITARIZED ZONE

ENTERPRISE

Plant Data Center /

Application Server

Gateway to Plant or

IoT Network

Control

Mobile Device

ENTERPRISE

PLANT

Firewall

Firewall

Firewall

INTERNET

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A New Model for Operational Architecture (Cont.)

Another important area of Operational Architecture that has not been

traditionally managed in conjunction with enterprise applications is

the Level 2 Operational Architecture for the edge analytics and con-

nectivity across devices and assets in operations.

At this level, industrial companies need to manage both the net-

working and automation infrastructure that supports security and

the flow of data with context through the traditional control system

hierarchy, as well as next generation IIoT

protocols and gateways.

This is an area where the most innova-

tion and transformation is occurring. Many

industrial companies fear that the move-

ment towards IIoT technologies involves

the movement to exclusive cloud and

gateway use. However, for the vast majori-

ty of companies it will be a controlled and

hybrid model for the foreseeable future,

where information still flows in the tra-

ditional approach but also flows through

these new flattened hierarchies. What this means is industrial com-

panies will need a data and connectivity model that harmonizes

across device, gateway, on-premise, and cloud. It also means that the

plant floor, now as much as ever, needs a cost-effective, redundant,

and fault-tolerant connectivity, compute, and storage environment

that supports the move to IIoT.

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A New Model for Operational Architecture (Cont.)

At the most detailed view, or Level 3 Operational Architecture, indi-

vidual and specific elements of Level 2 will be integrated. Examples of

this could include the specific pieces of functionality that are included

within Manufacturing Operations Management (MOM) or the spe-

cific edge analytics, applications, security, device management, and

communication protocols that are used for smart connected devices.

MANUFACTURING OPERATIONS MANAGEMENTFuture: Integration and Collaboration Platforms

SMART CONNECTED DEVICESDATA

FUNCTIONS

SETU

P AN

D CO

NFIG

UR

ATION

SECURITY

Standards, Proprietary

CONFIGURATION:Platform Services, Modules/Apps

MODULES/APPS:Execution, Tracking

MODULES/APPS:Asset Tracking, MRO, RCM

MODULES/APPS:OEE, Quality

MODULES/APPS:Scheduling, Dispatching

MODULES/APPS:Time & Attendance, Training

MODULES/APPS:Purchasing, Warehouse

MODULES/APPS:EMI / OI, Reporting

ApplicationIntegration

Security& Access

Unified Asset& Production Model

Unified Operations Database & Historian

Global Deployment& Licensing

Integrated DevelopmentEnvironment

Collaboration& Workflow

Visualization & Mobility

COMMON APPLICATION FUNCTIONALITY PROVIDED BY MOM PLATFORMS:

Enterprise Applications

Industrial Automation

ESB, Standards

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The Difference Between “a Lot of Data” and “Big Data” in Manufacturing

LNS Research recommends taking a relatively generic IT view of what

Big Data is and then applying the definition to the industrial space.

One definition that has received broad acceptance is the three V’s of

Big Data:

• Volume

• Velocity

• Variety

In the industrial space we have typically had to deal with large

volumes and velocity of data. According to Boeing, the 787 produces

approximately half a terabyte of data per flight. What we have not had

to deal with is variety. All of this data has been relatively well struc-

tured process data stored as time series or transactional data stored as

structured data in enterprise applications.

With the advent of the IIoT, data might include images, video, un-

structured text, spectral (such as vibration), or other forms, such as

thermographic or sound. As all of these data types come together,

industrial companies will truly have to deal with Big Data in Manufac-

turing, which will bring together a whole new set of analytics opportu-

nities and challenges.

BIG DATA

Connectivity and Data Model

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The Difference Between “a Lot of Data” and “Big Data” in Manufacturing (Cont.)

As with defining Big Data, LNS Research also recommends taking

a relatively generic IT definition of analytics and applying it to the

industrial space. In the industrial space, even before Big Data,

companies were doing the full spectrum of analytics, including

descriptive, diagnostic, predictive, and prescriptive. Traditionally

these analytics have been focused on analyzing structured and

time series data to address the key drivers in industry: quality, pro-

duction, assets, delivery, innovation, and more.

Examples include:

• Descriptive: Metrics and Scorecards for Overall Equipment

Effectiveness (OEE), On Time Delivery (OTD), Scrap, Mean

Time to Failure (MTTF)

• Diagnostic: Reliability engineering, quality engineering,

root cause analysis

• Predictive and Prescriptive: Modeling and simulation,

statistical process control, advanced process control

However, as new solutions have emerged to manage Big Data,

new analytics have also emerged that are mainly targeted toward

predictive and prescriptive analytics. To add confusion, even though

these tools were developed to analyze Big Data, they can be used

with any data set: small, large, or big.

A common example of these new Big Data Analytics includes

Machine Learning, among many others. These new analytics are all

generally data focused, where the traditional tools are model and

process specific, which adds to the challenges in bridging the gap

between data scientists using Big Data Analytics and engineers using

traditional model based analytics.

Next generation Prescriptive Analytics are really about moving

beyond choosing what to do next, and optimizing operations and en-

abling innovation. In the next section we will examine the adoption

of these tools and how industrial companies can hasten the value

captured from using them.

BIG DATA ANALYTICS FRAMEWORK

DESCRIPTIVE DIAGNOSTIC PREDICTIVE PRESCRIPTIVE

What happened

What willhappen

What actionto take

Why it happened

SECTION 3

Adoption of IIoT Connectivity and Big Data Analytics

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What Are the Top IIoT Use Cases of Today and Tomorrow?

When considering adoption of IIoT and Big Data Analytics, it is infor-

mative to start with the use cases.

When it comes to the IIoT use cases being pursued today, there is

no single use case coveted by the majority of companies; instead, it

is spread quite evenly across the traditional drivers in the industrial

sector: energy, reliability, quality, production, etc. When comparing

the use cases of today vs. one year from today there are some inter-

esting insights. First, remote monitoring is top across both. Second,

energy efficiency is viewed as low hanging fruit and something more

likely to be pursued today than in a year. Finally, business transforma-

tion initiatives are viewed as a longer term use case and more likely

to be pursued in a year from now rather than today.

Remote monitoring

Energy efficiency

Asset reliability

Quality improvement

Production visibility

Internet enabled products

Business model transformation, e.g. selling capacity

Asset and material tracking

Traceability and serialization

Customer access to information

Improving safety

Supplier visibility

Improving environmental performance

0% 5% 10% 15% 20% 25% 30% 35% 0% 5% 10% 15% 20% 25% 30%

29% 26%25% 23%

24% 22%

23% 21%23% 21%

22% 20%19% 19%

19% 18%18%17%

15% 15%12% 12%

6% 8%

5% 5%

What are the top IIoT use cases your company is pursuing today?(N=252, all respondents)

What are the top IIoT use cases your company will start pursuing in the next year?(N=249, all respondents)

Remote monitoring

Asset reliability

Business model transformation, e.g. selling capacity

Asset and material tracking

Quality monitoring

Customer access to information

Production visibility

Energy efficiency

Internet enabled products

Traceability and serialization

Supplier visibility

Improving safety

Improving environmental performance

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IIoT Data Sources and Types Used Today

Although many industrial companies are looking for IIoT technol-

ogy to enable some previously unconsidered use case, as the data

shows, most companies are pursuing already existing and previously

unsolved problems instead. In many cases these are quality, manu-

facturing, and reliability issues that have plagued organizations for

years. Examples of these specific use cases include:

• Dead on arrival quality issues that slip through the finished

product and functional testing

• Engineering and manufacturing tolerances that are too tight

or loose, allowing failures to the field or keeping good

products from customers

• Unscheduled downtime occurring as an unknown failure due

to systemic issues and relationships that are not immediately

obvious, like component suppliers, manufacturing processes,

environmental conditions, and customer use scenarios

It is these earlier use cases that will allow for later transformation,

like transitioning from selling assets to capacity and providing value

added connected services.

Fortunately, because of this use case progression, industrial com-

panies can start slowly with the new data sources. As is shown in the

below graph, most companies can do quite well just by collecting

Manufacturing Execution System (MES) and Programmable Logic

Controller (PLC) data to start. Then as maturity increases, they can

add in data from smart devices.

MES, quality system, or other high level software

Individual controllers (PLC)

Complex equipment with embedded control

Individual smart devices

Other

Individual “dumb” devices (sensors, switches, analogue readings)

0% 10% 20% 30% 40% 50% 60% 70%

What information from the plant are you combining for Big Data Analytics? (N=30)

67%

47%

13%

13%

13%

7%

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Data Architecture Today

Surprisingly, even though most companies have not yet moved to

start broadly collecting data from non-traditional data sources,

already a third of companies are moving to a non-traditional, non-hi-

erarchical approach to data flow.

Although companies are not ripping and replacing existing control

to information system hierarchies, many companies are beginning to

deploy gateway to cloud architectures, at least in a limited capacity,

to begin delivering IIoT data to higher level enterprise applications

and analytics packages.

It remains to be seen if this information flowing through gateways

and to the cloud will be enabled on the “thing” side of gateways

with direct connections to existing automation equipment, or via a

secondary system of sensors and connectivity. There are pros and

cons to both and each will likely persist for the immediate future as

industrial companies experiment.

For just one example of the confusion, much of the data coming

from new sensor-to-gateway-to-cloud solutions is measuring data

points that are already collected within the control system but cur-

rently lack the context of the control system. But on the flip side,

these new sensor-based solutions are exclusively focused on deliver-

ing value from the new data coming from these sensors, deploy more

quickly and easily than with existing automation solutions, and often

provide a positive short-term ROI.

Today, how are you architecting the flow of IIoT data? (N=167, companies with IIoT initiatives)

Through traditional control andinformation system architecture

Primarily through traditional control and information system architecture with some use

of edge analytics, gateways, and cloud

Even split between traditional control and information system architecture and use of

edge analytics, IIoT gateways, and cloud

Primarily use of edge analytics, IIoT gateways, and cloud with some use of traditional control

and information system architecture

Other

Exclusive use of edge analytics, IIoT gateways, and cloud

0% 5% 10% 15% 20% 25% 30% 35%

34%

28%

17%

10%

8%

4%

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Data Connectivity and Data Ownership

As new data sources and systems come online, emerging questions

of data ownership and data sharing are becoming critical.

One of the most important questions is “who owns the new

machine data?” The maker of the asset/device, or the user? Interest-

ingly, we see the market split on this point today, and there may not

necessarily be a verdict or even a single answer any time soon. There

are, however, a few points becoming clear.

1. When customers do not own the data, they prefer not to pay

for the raw data coming back. Rather, they want to pay for the

value services being delivered back to them that may only have

been possible through data sharing.

2. Use of the machine matters. In scenarios where the use

of the machine has no competitive differentiator, like in com-

pressed air for example, data sharing and selling compressed

air instead of compressors is not an issue. When the use of the

machine does create competitive advantage, like with CNC ma-

chines or oil field service equipment, asset users are much more

protective of data.

Who owns the data coming from the machines you deliver to customers?(N=66, machine builders only)

The customer owns the data, we are data custodians

We own the data and do not share raw data with customers

We own the data but provide raw data access to customers

0% 10% 20% 30% 40% 50% 60%

50%

29%

24%

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Using Big Data Analytics

One of the most surprising results from the survey came from the

question asked regarding analytics expertise. The most common

response, representing 40% of the market, was the belief that com-

panies already had all the needed analytics expertise.

Given the data shown above regarding the IIoT use cases, new

data sources, and changing system architecture, it is unlikely this

many industrial companies actually have the right degree of analyt-

ics expertise.

We have strong analytics teams that will not require much expansion

We use or will use large scale consulting companies with specialist industry knowledge

Don’t know - This is a potential stumbling block

Don’t know - We’ll worry about this later

We plan to hire specialists in industrial analytics

We will use expert consultants fromour analytics software vendor(s)

0% 5% 10% 15% 20% 25% 30% 35% 40% 45%

From where does your company get or plan to get its analytics expertise?

40%

23%

17%

17%

13%

10%

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Using Big Data Analytics (Cont.)

What is more likely, as shown by this companion question, is that

most industrial companies are just doing descriptive analytics on

structured data sets rather than predictive and prescriptive analyt-

ics with Big Data.

To help address this apparent lack of understanding, industrial

companies need to invest in both the appropriate technology, but

more importantly, process and training as well. Just as Six Sigma

and Lean were built into the fabric of continuous improvement

initiatives and packaged for subject matter experts to use the ap-

propriate financial modeling, process optimization, and variability

reduction analytics without being a statistician, Big Data tools like

Hadoop and Machine Learning need to be packaged and made ac-

cessible to industrial subject matter experts, not just data scientists.

Algorithms used in analytics system

Trend analysis

Data visualization

Statistical distribution analysis

Statistical process control (SPC)

Optimization

Regression analysis

Predictive modeling

Material performance

Correlation analysis

Simulation

Condition based monitoring

Machine learning

Data mining algorithms

Sentiment analysis

0% 10% 20% 30% 40% 50% 60% 70%

59%

44%

41%

41%

33%

30%

26%

22%

22%

19%

11%

19%

11%

7%

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L5

L0

L1

L2L3

SMART CONNECTED ENTERPRISE

L4

IIoT Enabled Next-Gen Systems

MATERIALSAND

SUPPLIERS

PRODUCTSAND

CUSTOMERS

Enabling Smart Connected Assets and Smart Connected Operations

In LNS Research’s aforementioned eBook, “Smart Connected Op-

erations: Capturing the Business Value of the IoT,” we first hypothe-

sized that IIoT Platform technologies would precipitate the flattening

of the traditional hierarchical model. With this new research, we are

seeing the first quantitative evidence of this transformation occur-

ring, and as such there will be several key changes including:

• A transformation of control system hierarchy to move from one

of distributed controllers and centralized control to truly distrib-

uted control with the enablement of Smart Connected Assets.

• A transformation of MES to become an orchestra-

tion and optimization platform for Smart Con-

nected Operations, not simply an integration

and analytics middleware layer for execution

and compliance.

• A transformation of enterprise applications to more closely

map to operations instead of accounting models, and have

the ability to work flexibly with operational data and not just

structured transactional data.

• The enablement of mash-up applications and analytics that

can enable Big Data from anywhere to anywhere and support

true end-to-end value chain processes.

SECTION 4

Building the Business Case and Recommended Actions

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IIoT Challenges

There is not an industrial company in business today that would not

like easier access to operational data and the decision support tools

to better address quality, production, and reliability issues.

This is why for many it is counterintuitive that the top two chal-

lenges in IIoT adoption are not technical, but rather funding and

business case development

The reason is a classic catch 22: before Big Data Analytics are

implemented, companies cannot accurately predict their benefits.

Likewise, without a comprehensive understanding of Big Data ben-

efits, companies are reluctant to invest the time and resources on

their implementation.

Funding

Building a business case

Understanding what IIoT is and how it applies to your business

Security

Standards

Finding the right technology partner(s)

Gaining insight from Big Data

Developing new IIoT software applications

Company culture

Data gathering from legacy systems

Product design and development complexity

Hiring the right talent

Executive support

Scaling to 1,000s or 1,000,000s of devices

0% 5% 10% 15% 20% 25% 30% 35%

32%30%

26%25%

22%17%

16%14%14%

13%12%

8%8%

5%

What are the top challenges your company faces in deploying IIoT technology?(N=269, all respondents)

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A Business Case Journey that Aligns to Strategic Objectives and Maturity

To address this catch-22 companies should think of the Big Data

Analytics investment as a journey that is based on Operational Ex-

cellence maturity, scope, and metrics rather than a one-off ROI cal-

culation. Operational Excellence maturity is the driving factor for

increasing the scope and value of the business case. LNS Research

recommends using a 5-Level approach to quantifying maturity,

where at the lowest, Ad Hoc Level companies are unable to meet

the current and future demands of customers and at the highest,

Market Leader level companies are able to define and transform

markets, disrupting incumbents.

The following matrix will allow companies to evaluate their

current position based on their capabilities.

Globally integrated and Harmonized. Fully embracing

emerging capabilities

Predictive, role-based,real-time metrics connected

to corporate goals

Predictive, role-based,real-time metrics connected

to corporate goals

AD HO INNOVATION LEADERC CON. PRO. AGI.

STRATEGY& EXECUTION

LEADERSHIP& CULTURE

ORGANIZATIONALCAPABILITIES

BUSINESS PROCESSEXCELLENCE

TECHNOLOGYCAPABILITIES

PERFORMANCEMANAGEMENT & KPIs

Disconnected and disparate

Disconnected and disparate

Non-role based, manual KPIs, disconnected

from corporate goals

Disconnected from corporate objectives

Fully integrated with corporate objectives

Operational Excellence is a department rather than shared responsibility

Operational Excellence fully integrated into corporate structure

Operational Excellence is integral part of leadership

and culture.

Operational Excellence distinct from corporate structure. Not in goals or incentives

INNOVATION LEADERDrives standards and expectations

AGILEEvolved people, process, and technology across the enterprise

HARMONIZEDFlexibly unified at the organizational level

CONTROLLEDRepeatable within organizational, process, and/or technology boundaries

L1L1

L2L2

L3L3

L4L4

L5L5

AD HOCUnstandardized with significant variation

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A Business Case Journey that Aligns to Strategic Objectives and Maturity (Cont.)

When the business case is viewed as a journey, most industrial

companies should begin on the cost side of the equation and within

specific functional areas of operations. With limited maturity, there

is not a shared vision of how productivity gains will drive actual

financial benefits. By starting with a single area like quality, manu-

facturing efficiency, asset reliability, or energy usage, the need for

collaboration is minimized and cost reductions clearly go to the

bottom line, eliminating uncertainty of real results.

As maturity increases and initial cost reduction benefits are re-

alized, the scope of the business case can increase and the types

of metrics measured can move to being value based. As more and

more maturity is realized, industrial companies can more accurately

predict the economic benefits that will be realized from productivi-

ty gains and, ultimately, the achievement of strategic objectives like

business model transformation or the entry to new markets.

5$$$$$

VALUE CENTERCOST CENTER

Siloed

BUSINESS CASE AND OBJECTIVE SCOPE

OPER

ATIO

NAL

EXCE

LLEN

CE M

ATUR

ITY

METRICS

BUSINESS CASEJOURNEY

DEPARTMENT EXECUTIVECROSS-FUNCTION

Operational

Financial

Value**

Big Data**Big Data Analytics, Diagnostic, Predictive, Prescriptive

**e.g. Revenue and Earnings

1

2

3

4

$$$$$

$$$$$

$$$$$

$$$$$

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Recommended Actions

IMPLEMENT A BUSINESS CASE JOURNEY FOR BIG DATA ANALYTICS

Map your organization’s business case journey for Big Data

Analytics to the current and anticipated maturity of your

Operational Excellence capabilities. At lower level of maturity

you should focus on a narrow scope and cost focused benefits.

At higher levels of maturity, we recommend you focus on a

broader scope and value based metrics with direct connection

to strategic objectives (but not necessarily short-term

financial gains). Map the journey to align with an Operational

Architecture that accounts for Big Data Analytics across

operations. To improve Operational Excellence maturity,

ensure you invest in systems and training to make Big Data

Analytics accessible to existing subject matter experts, not just

data scientists.

CHOOSE AN INITIAL USE CASE FOR BIG DATA ANALYTICS THAT

ALIGNS TO YOUR COMPANY’S PAIN POINTS AND/OR COMPETITIVE

DIFFERENTIATION Often these initial cases are for quality,

manufacturing efficiency, or reliability. Quality is a great

starting point because improved quality can drive both short-

term ROI through reduction in scrap and rework, but also

long-term benefits for a differentiated customer experience

and improved product design based on quality information

coming from connected products.

IIoT Platform technologies are currently driving the most transformative

period in the industrial sector over the past 40 years. As industrial

executives attempt to establish high level strategic objectives, it is

critical that a formalized and structured approach is taken to Digital

Transformation that establishes an expanded view Operational

Architecture and captures the value of Big Data Analytics.

ESTABLISH A DIGITAL TRANSFORMATION FRAMEWORK

Establish a Digital Transformation leader and new group

responsible for a framework that connects and enables for

change all levels and functions of the organization. Incorporate

feedback loops at each stage of the journey and ensure that

high level strategic objectives are aligned with Operational

Excellence initiatives, system architectures, business cases,

and solution selection.

ESTABLISH AN OPERATIONAL ARCHITECTURE

Without a formal Operational Architecture, your organization

will not be able manage changing architectures based on new

IIoT Platform technologies and capture the potential value of Big

Data. Ensure that your organization’s Operational Architecture

includes a robust and flexible data and physical infrastructure

model that can:

o Tie together structured, semi-structured, and

unstructured data

o Manage IT and OT convergence

o Support traditional descriptive and diagnostic analytics like

dashboards, trend analysis, regression analysis, and more

o Support next generation predictive and prescriptive

analytics like machine learning

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lnsresearch.com

Presented by:

Connect:

IIoT AND BIG DATA ANALYTICS:How Manufacturing System Architecture Is Being Transformed

Author:

Matthew Littlefield,

President and Principal Analyst

matthew.littlefield@lnsresearch.com

© 2016 LNS Research.

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