industrial iot opportunities, challenges and developments · data from that location, learning...

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Industrial IoT opportunities, challenges and developments

Elson Sutanto, Principal Analyst Wednesday, 12th June 2019


Who are Juniper Research?

• Formed in 2001, Juniper Research focuses on the identification and appraisal of high growth

opportunities across the telecoms and media sectors.

• Juniper is the leading provider of research, analysis and consultancy services to the mobile and digital

technologies sectors.

• Our knowledge and understanding of these markets is unrivalled in the industry, having built up an

extensive database of market indicators and trends over the past 18 years. Our business is split into 6

streams:

• Our services are used by many of the leading operators, vendors, and investors to support a variety of

strategic and tactical planning purposes. Juniper has a global client base, with almost half based in

North America, 25% in Western Europe and 20% in the Far East & China.

• Juniper works with leading and upcoming companies across the digital market. Our clients include

operators, vendors, financial institutions, billing providers, Fortune 500 companies, platform providers, etc.

➢IoT & M2M

➢Telco Service Providers

➢Innovation & Disruption

➢Fintech & Payments

➢Content & Commerce

➢Smart Devices


Clients we have supplied in-depth strategic insights to

➢ Technology, manufacturing, telecoms and financial services companies --> progress on digital transformation journeys.

➢ Retail: affected by digital disruption, but there are still legacy retailers have not advanced their digital transformation paths

➢ Priorities: In some industries, such as goods production there are challenges in falling sales and tight margins; focus on short term results than transformation.

➢ Insurance and financial services: legacy players are facing intense competitive pressures from start ups and large technology companies.

➢ Long standing incumbents: desire digital transformation, but are challenged by: 1) Regulation, 2) Security of IoT and 3) addressing of complex internal processes 4) Managing connectivity between large numbers of sensors/devices

Overview: Industries at different stages of IoT transformation


Transformative digital technologies to take on in Industrial 4.0

➢Understanding: Companies aim to take on transformative technologies in industrial

applications, but seek understanding of the impact of those technologies virtually

before implementing them in the real world will continue to rise.

➢Simulation software for production and processes helps with understanding level of

investment in technology to take on; how these could impact operations.

➢Incremental change: Many industrial organization's operations are spread out,

fragmented. Their processes are manual relying on old equipment works, that may not

align with new technologies

➢Strong Design culture: Companies that have both 1) design strengths to seize

opportunities and 2) mindset to unite business, operations, IT early on can utilise Agile

software and DevOps to experiment, validate ideas and scale up solutions

➢IoT Partners: Working with Vendor/IT partners helps pilot technologies and develop

real time capabilities, scalability and integrates numerous data sources into actions,

contextualised insights

➢Industry 4.0 → Industrial technology: Robotics, Computers, Equipment connected to IoT, enhanced by machine learning algorithms.

➢Advances in sensors and connectivity modules allow equipment between sites to be measured, monitored, tracked, coordinatedfrom a central, remote location.

➢Deriving intelligent insight to improve efficiency and productivity of operations.

➢Rise of cloud computing, falling costs of data storage, increased data storage, advanced sensors feeding data into machine learning algorithms to help automate specific processes within an organization

General trends and drivers of Industry 4.0 / Industrial IoT


Key regions and markets driving Industrial IoT

Germany: Industry 4.0 strategic initiative from the Germany is advancing Germany’s digital manufacturing adoption

Japan: Strong manufacturing expertise and widespread use of technology in many industriesChina: Increased use of IT powered processes, and automation, in factories

• North America: Strong industrialised region (high level of automation - Automotive & Transportation, Machine & Plant Manufacturing, and Energy & Utilities, among others

Industrial sector (IoT deployed in enterprises and across verticals) will account for 58% of total IoT connections in 2019 at 15.1 billion

Capture accurate data from a variety of sources and

devices (eg sensors)

Deploying

Industrial

IoT

Increased

visibility of

operations in

real-time

Data is Key in Industrial IoT!

Enable

Intelligent data

driven

decisions

Understanding flow of Data visibly

Monitoring of data

and information

Integration and

maximisation of

operational efficiency

Analytics and

Evaluation of DataDevice Connectivity

(to capture data)

Flow of data in Industrial IoT system

➢ Remote access to

systems and devices

➢ Fragmented systems

leads to fragmented

information

➢ Reactive approach to

problems

➢ Alerts/information

given in real time

➢ Pro-active

monitoring of

systems, devices,

etc.

➢ Software used to

deliver data

reports

➢ Machine Learning

of different, varied

types of data

➢ Significant Analysis

of complex info

➢ Digital Twins used

to simulate physical

counterparts

➢ Insights derived to

support actions

➢ Robotic Automation

Process (automated

actions and alerts)

➢ Supply Chain

Forecasting

➢ Full visibility into all

operations across

company

ROI is greatest

➢ An ever expanding amount of IoT data requires a new approach to gather, analyse and understand data (eg pain points) to derive intelligent decisions with Cognitive IoT, AI and machine learning

➢ When combined with edge computing, cloud event processing can support both 1) low-latency responses to time-critical events and 2) complex multi-event analysis.

➢ Events can be analysed in context: with other events, conditions such as time of day, weather, etc, but depend on defining all event and contextual relationships.

➢ Machine learning can watch and analyse events and derive effective, recommended action steps to take

➢ Moving AI to an edge location, enables AI retrieve and process data from that location, learning whether the control responses initiated locally derive optimal results. These can be shared with other edge locations via neural network updates to constantly optimize the way AI responds to events.

Unlocking IoT value with EDGE Processing, IoT and AI

Sensors (Capturing

Data)

Cloud Storage

EDGE Processing (at source of

generation) Data Generation

Elements of Industrial IoT system

➢ Visual communication of crucial information about products in complicated operational environments

➢ Forecasts of realistic, likely scenarios of emerging errors/inefficiencies in connected physical object

➢ Analysis of data and systems’ monitoring to head off problems before they occur, preventing downtime, developing new opportunities and plan for the future

➢ Trend of distributed computing (‘’analyse at source’’) and Peer-To-Peer architecture being used

➢ Rise of Cloud Computing, and more power driven, data intensive algorithms run in Cloud

Digital Twin Intelligent Insight

Use of AI, Machine

Learning Algorithms,

to make sense of data

➢ IoT at EDGE addresses mission critical items that can’t be conducted in the Cloud

➢ Need real time decision and analysis at source & can be expensive to move data to cloud for processing

➢ Applications and use cases for computing should at the EDGE of network where data is produced (reduce latency)


Industrial IoT Challenges


Challenges within Industrial IoT?

•Downtime: On the factory floor, operators need to think and act quickly to keep machines running efficiently and avoid costly downtime.

•Disparate Systems: Access to plant floor information is critical, but too many disparate systems make real-time factory insights impossible with investments already made in existing systems

•Monitoring of machines: Need to connect disparate systems of information and deliver 3D/modelling instructions in context

•Real-time processes: Monitor machines and smart tools on the factory floor to ensure 1) execution of work in real-time and 2) streaming of data in real-time

•Training of workforce: Need for a flexible, efficient workforce without having to train multiple IT and OT systems

•Optimization: Optimization of factories with role-based industrial IoT apps to increase productivity, improve quality, and reduce training times

•Implementing predictive maintenance: challenging; hard to extract valuable insights from data.


Challenges within Industrial IoT: Data

➢Analysis of Data: Due to the complex and interlinked nature of

industrial processes, companies must have a solid understanding of

what they want from technology

➢Data analysis support: To make the right decisions with data there is

a need to use structured thought-processes

➢Data flow understanding: Need to analyze view whole system end-to-

end as data flows through an enterprise

➢Data: first part of business processes: see how each bit of data

relates to other data generated by other parts of firm

➢Collection of data: The sensors and data capturing devices must use

reliable connectivity via LPWANS, cellular LPWA, Ethernet, or wired

internet connection for stationary IoT applications

➢Legacy Technology: Many companies run legacy technologies, which

means changing an organization to adopt a new connectivity standard

can be very costly and time consuming to implement.


Industrial IoT leaders + Start Ups address pain points in Industrial IoT

•The IIoT space has been led by traditional industrial tech companies.. GE,

IBM, and Cisco, who see industrial IoT as a core part of their businesses

•Start-ups: Bring digitization and IoT infrastructure to asset-heavy

industries…such as manufacturing, logistics, mining, oil, utilities, and

agriculture are receiving a greater share of the deals into IoT ecosystem.

•Startups are developing sensors, cloud platforms, networking infrastructure,

machine learning software, addressing hardware, AI-powered analytics.

•Large corporates: in M&A - General Electric, acquired IoT platforms Bit

Stew Systems + Wise.io (to build AI capability) Nurego (to support IoT

networks linking big industrial machines) and 2 large 3D printing firms

• IBM acquired Oniqua Holdings, in June 2018, a global innovator in

Maintenance Repair and Operations (MRO) Inventory Optimisation solutions

and services in → mining, oil & gas, transportation, utilities, manufacturing;

asset-intensive industries.


AI developments


AI and ML Analytics developments and acquisitions in IoT

Tech companies are competing for high value AI driven firms and technologies

➢Semiconductor companies are seeking to boost their AI and data processing

capabilities Examples:

▪ ARM acquired analytics firm Treasure Data to deploy a “device-to-data” IoT

platform to derive “intelligence from IoT, enterprise and third-party data (Aug

2018)

▪ NXP launches Edge intelligence environment (eIQ) - a machine learning toolkit

(June 2018) in identification of anomalies in vision (image) and voice

▪ Qualcomm acquires Dutch AI company, Cyfer BV, (Aug 2016)

▪ Intel acquires Israeli automotive computer vision firm, Mobileye, in 2017


AI Acquisitions in IoT - 2018

• Tech companies are competing for high value AI driven firms and technologies; Microsoft leads the way

The difference between AI, DL, ML?

➢ Suited for specific and focused applications➢ Uses machines that process tasks that reflect human

intelligence (eg understanding language, objects, sounds) ➢ Predicts when machines need maintenance➢ Common sense hard to replicate in AI systems

➢ Trains algorithms to learn how to process data➢ Requires significant vol of data to feed into algorithm ➢ Algorithm adjusts itself, improves processing➢ Reaches a particular level of accuracy

➢ Is a subfield of machine learning inspired by the function of the brain: ‘’artificial neural networks’’

➢ Is based on learning data representations➢ Uses decision tree learning, logic programming, clustering,

reinforcement learning➢ Learning can be supervised/semi-supervised/unsupervised

➢AI and ML enhance smart machines to use data to sense, understand, learn and take actions

➢In manufacturing, AI and machine learning are already being applied to vision systems, CAD software programs and predictive maintenance initiatives

➢AI and ML aid with demand forecasting, optimizing manufacturing processes, and automating material procurement

Practical outcomes from using AI and/or ML for Manufacturing


Major Industrial IoT partnerships so far in 2019…


Tackling Fragmentation in Industrial IoT

• Efficiency of Production and Revenue generation hindered by complex,

proprietary systems → create data silos and slow productivity

• Open source components using open industrial standards and open data

models are needed

• Important to unlock, standardize data models to enable analytics and

machine learning scenarios; use data once locked in proprietary systems


Major IoT Partnerships to establish Open Source…

March 2019: Microsoft + BMW Group launch the Open

Manufacturing Platform, → Open technology framework,

open community to speed adoption of industrial IoT

April 2019: ABB and Ericsson strengthen commitment to accelerate

Industrial ecosystem, where flexible wireless automation to enable enhanced connected services, industrial IoT and AI technologies

April 2019: Siemens' MindSphere embeds SAS streaming

analytics to meet demand for IoT analytics with Artificial

Intelligence and Machine Learning capabilities, open

source streaming analytics → to enable near-real-time

embedded AI for IoT devices at the edge.

➢Fragmentation, security and cost in IoT

➢Solid partner ecosystem is needed

➢Vodafone IoT global platform and connectivity + Arm’s IoT

software and services to provide enterprises with programmable,

connected system on chip designs that eliminate the need for

traditional SIM cards = new IoT Products


Open Industry 4.0 Alliance + Open Manufacturing Platform Initiative

•Open Industry 4.0 Alliance,→ build an open ecosystem for digital

transformation of industrial manufacturing plants via a standardized

and open ecosystem for operating highly automated factories and plant

•Open Manufacturing Platform (OMP) by Microsoft and BMW “new

initiative’’ to drive Industrial IoT developments in Industry 4.0

•Push for open systems, de-siloing and collaboration and extend the

learnings of the IT industry out to the industrial sector, where

technology is still often proprietary and non-interoperable.

➢AIM 1: 1) overcome proprietary challenges 2) boost to the digital

transformation of the European industry.

➢AIM 2: Ensure 80 per cent of the machines in a smart factory to speak

the same language

The End

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Juniper Research IoT Portfolio 2019

➢ Digital Twins for IoT➢ Edge Computing for IoT➢ Internet of things for Platform Providers➢ Internet of Things for Retail➢ Internet of Things for Security Providers➢ Internet of Things: Consumer, Industrial, Public➢ Low Power M2M➢ M2M and Embedded Strategies➢ Smart Cities

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