Gain New Insights and Control in Commercial and Industrial ... · 6 Gain New Insights and Control...

Gain New Insights and Control in Commercial and Industrial OperationsA Blueprint to improving business outcomes with IoT

HPE POINTNEXT BLUEPRINTS: HOW TO GUIDES FROM SERVICES EXPERTS OF HEWLETT PACKARD ENTERPRISE

05C HALLENGESThe Internet of Things introduces both organizational and technical challenges for businesses.

TABLE OF CONTENT S

03EXECUTIVE SUMMARYThe Internet of Things (IoT) is driving digital transformation and fundamentally reshaping business operations.

11GUIDING PRINCIPLE 2Implement a distributed data architecture to ingest and manage disparate data at scale.

20HPE POINTNEXT RECOMMENDED IOT B EST PRACTICESWe believe the challenges of today call for a holistic approach to IoT.

23CONCLU SION

06GAIN NEW IOT INSIGHT S AND CONTROLGain new insights and control by leveraging the Internet of Things.

14GUIDING PRINCIPLE 3Use IoT platforms to collect and analyze data, and use machine learning or AI tools to derive contextual information.

08GUIDING PRINCIPLE 1Modernize enterprise networks, management platforms and security systems.

16GUIDING PRINCIPLE 4Act on real-time and global insights.

Value proposition

Digital Business Model

Connecting business

operation and infrastructure for speed of execution

Key partners

Key activities

Key resources

Customer segments

Customer relationships

Channels

New Value proposition

Natively embedding technology innovations

Financial/operation focus

Ecosystem/supply chain focus

Cost structure Revenue streams

Customer/edge focus

Businesses Must Embrace Digital Transformation to Reap IoT Benefits

Digital ideation Business focus

Technology focus

3 Gain New Insights and Control in Commercial and Industrial Operations | A Blueprint to improving business outcomes with IoT

Executive overviewThe Internet of Things (IoT) is driving digital transformation and fundamentally reshaping business operations. Enterprises are becoming increasingly instrumented, interconnected, and automated. Growing numbers of connected devices (autonomous endpoints, intelligent machines, mobile devices) are generating an ever-increasing variety, volume, and velocity of data.

By transforming this wealth of data into meaningful and actionable insights, enterprises in every industry can accelerate the pace of business, increase productivity, and streamline operations. Corporations can improve decision-making, create value by exploiting new business models, streamline business processes, and boost financial results by connecting the entire value chain. Enterprises can enable improved service capabilities and better products. Manufacturers and utilities can automate factory floors and industrial control systems, optimize supply chains and distribution networks, and improve worker safety and productivity. And governments can improve public services, optimize transportation systems, and reduce spending and waste. By creating new digital business models enterprises can connect business operations and infrastructure, accelerating execution, unleashing business value.

CloudCore

Edge

Data aggregators

Sensors and actuators

Things

Edge compute and analytics

IoT has Implications across Edge, Core, and Cloud


In order to thrive in a digital landscape all organizations must undergo a level of transformation across their business, operating, and infrastructure models. IoT has the potential to disrupt these models. Its unique challenges must be addressed.

Some organizations struggle to achieve business outcomes and demonstrate ROI from their IoT investments. Some of these challenges can be blamed on technology maturity, but many IoT initiatives stall due to technology complexity, lack of focus on business alignment, management of change, and defining ROI.

From a technology perspective, enterprises must take a fresh look at data architectures and network designs to take full advantage of intelligent systems and make the most of their IoT investments. HPE Pointnext, the services organization of Hewlett Packard Enterprise (HPE) believes the best way to achieve massive scalability is by implementing a distributed IoT architecture that pushes data processing and control functions to the edge of the network, and employs machine learning and advanced analytics to optimize workflows, improve operational efficiencies and transform business outcomes.

HPE Pointnext has developed a comprehensive blueprint to help enterprises overcome IoT scalability and performance challenges, transform raw data into clear and actionable insights, and boost business results. Based on real-world experience, this four-step guide helps you leverage the Internet of Things to gain unmatched visibility and control over your business.

https://www.hpe.com/info/internetofthings


The Internet of Things introduces both organizational and technical challenges for businesses. Until now, most businesses have treated information technology (IT) and operational technology (OT) as independent technology silos designed, operated, and maintained by different organizations. IoT converges the IT and OT worlds. Architects and system planners may need to acquire new skills to take on new technologies and overcome IoT scalability and performance challenges. And domain experts will need to work closely together to achieve desired results.

Previously separate teams must align goals and collaborate to ensure successful IoT outcomes. By improving communications and adopting common operating models, businesses can eliminate redundancies and inefficiencies, and boost business results.

The IoT introduces unprecedented performance and scalability challenges for architects and system planners. Vast numbers of connected devices will generate massive volumes of diverse data that must be collected, processed, analyzed, and acted upon quickly, reliably, and securely. An individual enterprise might gather and act upon billions of data objects per day from millions of distinct endpoints.

Common IoT adoption challenges include:

• Exploiting the full potential of IoT to fundamentally transform the business. To take full advantage of the Internet of Things enterprises must turn business insights into concrete actions to streamline business processes and improve decision making. By employing machine learning and artificial intelligence, recursively feeding information back into the system, and making full use of automation, businesses can revolutionize operations and gain a leg up on the competition.

• Securely connecting and managing millions of IoT devices. Legacy enterprise networks and management systems designed to support conventional IT applications and services can’t meet the increased scalability, agility, and security requirements of the Internet of Things. Tomorrow’s networks will need to connect millions of diverse endpoints, accommodate dynamic traffic flows, and protect enterprise applications, infrastructure, and data against increasingly sophisticated threats.

• Managing the sheer volume, variety, velocity, and veracity of IoT data. Conventional data acquisition and management approaches can’t satisfy massive IoT scalability requirements and performance demands. Enormous numbers of connected devices—sensors, meters, cameras, smart endpoints—will generate vast volumes of structured and unstructured data that must be efficiently collected and processed. Attention must be paid to the trustworthiness of the data collected. Businesses must rethink how they gather, process, and distribute data across the extended enterprise.

Challenges

See

Connect to and ingest the data

securely

Analyze

Explore and determine cause

and effect

Predict

Automate decisions with

analytics

Ignite

Act upon new insights to drive

new offerings and growth

Turning Raw Data into Actionable Insights that Drive Business Results

Helping enterprises seamlessly capture data’s value, end-to-end


• Transforming raw IoT data into meaningful and actionable insights. Conventional data reporting and visualization tools, designed to support traditional enterprise IT environments aren’t well suited for IoT data analysis. Enterprises must introduce new analytics platforms that can handle extreme IoT data speeds, volumes and variations, and provide the context and insight needed for fast and reliable decision making.

HPE Pointnext has developed a detailed blueprint based upon experiences helping many businesses gain new insights and control by leveraging the Internet of Things. The blueprint helps enterprises see, analyze, and integrate IoT data at scale, leveraging predictive analytics and automation to ignite business outcomes.

Gain New IoT Insights and Control with a Blueprint from HPE Pointnext

https://www.hpe.com/info/pointnext

1Modernize enterprise networks, management platforms, and security systems to meet IoT scalability, agility, and performance demands

2Implement a distributed data architecture to ingest and manage disparate data at scale

3Use IoT platforms to analyze data, derive contextual information, and gain global insights

4Act on real-time and global insights

Four Guiding Principles to Gain New IoT Insights and Control


The blueprint helps organizations address IoT scalability and performance challenges, turn raw data into clear and actionable insights, and accelerate business results. The four-step blueprint is based on extensive experience HPE Pointnext has garnered helping customers successfully plan and execute large-scale IoT deployments. It includes four guiding principles to help businesses fundamentally transform operations and make the most of their IoT investments.


Next-generation enterprise networks must satisfy the increased scalability, programmability, and agility demands of the Internet of Things. HPE Pointnext believes businesses must re-architect enterprise networks, introduce new IT management solutions, and adopt new security systems and practices to ensure successful IoT outcomes.

Resilient and adaptable enterprise networks are fundamental for ensuring high performance and availability for mission-critical applications and elastic services. Intent-based networking can be employed to address dynamic IoT traffic flows and ensure adequate service quality for diverse applications. With intent-based networking, infrastructure is automatically re-provisioned based on business rules, real-time conditions, machine learning, and network health analytics.

BUSINESS CHALLENGE: Securely connecting and managing millions of IoT devices.

Guiding Principle 1: Modernize enterprise networks, management platforms, and security systems to meet IoT scalability, agility, and performance demands

• 63 million IoT devices will be attempting to connect to the enterprise network each second by 20201

• 84% Internet of Things adopters say they have experienced at least one Internet of Things security breach2

- Source: 1 Gartner 2017, Strategic Roadmap for IoT Network Technology, 30 January 2017, ID: G00316319 2 HPE Internal Study


HPE Pointnext recommends enterprises introduce new orchestration and management solutions to automatically onboard new devices and simplify troubleshooting and lifecycle management. Many IoT endpoints will be deployed in unattended remote sites or in field locations with limited or no IT staff. Standardized onboarding mechanisms and remote management tools allow central administrators to easily commission, track, monitor, and manage vast numbers of autonomous devices scattered across the enterprise.

Businesses must also introduce strong security systems and practices to protect infrastructure against attacks and data loss. An end-to-end security framework that supports dynamic access controls, and intelligently identifies and responds to evolving threats, can help mitigate risks and ensure compliance with corporate policies and government regulations.

HPE Pointnext recommends businesses take a defense-in-depth approach to security, applying a wide range of security mechanisms to authenticate, authorize, and track endpoints, applications, and users across the distributed enterprise. By leveraging machine-learning, behavioral analytics, and contextual-awareness, enterprises can adapt defenses in real-time to combat evolving threats.

Intent-Based Networking Defined

Intent-based networking is a fundamentally new approach to building fully programmable and adaptable networks that deliver greater agility and resiliency. Leveraging AI and machine learning, these software-defined networks automatically adjust to address real-time application requirements and network conditions. By intelligently connecting IoT devices and provisioning network services in real-time, they deliver the network automation critical to achieving massive IoT scalability.

End-to-End Security Framework Defined

An end-to-end security framework takes a layered approach to security to eliminate vulnerabilities, minimize attack surfaces and mitigate risks. A comprehensive end-to-end security framework authenticates, authorizes, and safeguards devices, users, applications, infrastructure, and services throughout the entire IoT system. It leverages dynamic, policy-based security controls to combat evolving threats.

Ensuring Secure, Agile, and Massively Scalable Connectivity

Modernize & dynamically optimized

legacy infrastructure

Enable adaptive, elastic & highly available workload mobility

Deploy Intent-based

networking

Incorporate data to integrate innovative & cognitive insights

Assess network health via machine

learning and AI

Ensure compliance through standardization

Deploy flexible & analytics-based security solutions

Risk Migration

Operational Excellence

API-based innovation

Deploy unified, automated, intelligent, predictive, and autonomous management


Checklist:

1. Implement a policy-driven, extensible, and adaptable network aligned to business goals

2. Introduce gateway technology to bridge the IT and OT worlds

3. Deploy unified management and device orchestration systems

4. Institute an end-to-end security framework that leverages machine learning, applied behavioral analytics, location-based access control, and context awareness

Expected results:

• Scalable and agile network connects millions of dynamic, autonomous endpoints

• Strong security controls mitigate risks and ensure compliance

• Programmable infrastructure and intent-based networking unleashes automation, streamlines onboarding, and accelerates time-to-value for new IoT projects and applications

• Centralized management eliminates need for remote site expertise enabling massively scalable and dynamic IoT implementations


Intelligent systems must efficiently gather and process unparalleled volumes of diverse data from a wide variety of sources—extremely quickly and reliably. HPE Pointnext recommends businesses introduce new unified data architectures and data models to accelerate digital transformation and maximize IoT business results. Businesses should also leverage new artificial intelligence and machine learning techniques as appropriate.

BUSINESS CHALLENGE: Managing the sheer volume, variety, and velocity of IoT data.

Guiding Principle 2: Implement a distributed data architecture to ingest and manage disparate data at scale

Human- and machine-generated data is experiencing an overall 10x faster growth rate than traditional business data, and machine data is increasing even more rapidly at 50x the growth rate.- Source: insideBIGDATA, February 2017

https://insidebigdata.com/2017/02/16/the-exponential-growth-of-data/


To optimize IoT system performance and economics, it is important to know where data is collected and analyzed, and how it is distributed across the extended enterprise. Some data may be used to make real-time decisions (i.e., to trigger an industrial control action) and is best acted upon locally. Other data may be used for long-term analysis (i.e., to study a component’s life expectancy) and is best managed in the core.

HPE Pointnext has helped enterprises achieve massive scalability by implementing edge platforms that provide a distributed IoT architecture that pushes data collection and processing functions to the edge of the network. The latest IoT edge platforms optimize IoT system performance by offloading data gathering and processing tasks from centralized applications, and avoiding WAN transmission delays. (Data can be processed and acted upon locally without interacting with applications in the enterprise data center or cloud). In some applications a fraction of a second can be the difference between success and failure, or even life and death. Edge platforms also extend the resiliency and security of IoT implementations by distributing intelligence across the network and eliminating single points of failure.

Look for edge platforms that offer consumption-based licensing models to minimize upfront investments and tightly align ongoing expenses with usage. Best-of-breed edge platforms support high-density compute and all-flash storage for fast performance. They are also built to withstand shock, vibration, and extreme temperatures, for operation in harsh industrial environments or outdoor settings.

By making educated decisions about which data to gather, and by collecting, aggregating and processing the right data at the right place, you can overcome system scalability and performance challenges and lay a solid foundation for advanced analytics and control functions.

Checklist:

1. Determine the right data of interest for relevant use cases and ingest it. Understand the business goals and desired outcomes.

2. Introduce IoT edge platforms to collect and process data at the perimeter.

3. Aggregate, process, protect, and manage the data in the right place, at the right time.

4. Share and integrate data from edge to core/cloud. Cleanse and parse data, and perform preliminary analysis at the edge to unlock local, tactical insights and contain data sprawl.

5. Make relevant data available for business scenarios. Using artificial intelligence, machine learning, and advanced analytics select or build a model, train the model, validate the model and retool as required.

Optimizing Data Processing and Management Functions

Devices Edge infrastructure Core infrastructure

Core

con

nect

ivity

Edge

con

nect

ivity

Sensors and actuators

Device Management

Data Acquisition

Stream processing

Real-time dashboard/reporting

Alerts and events

Machine Learning

Data Lake

Programmable line cards

End user devices

Operational technology


Expected results:

• Distributed data architecture that satisfies rigorous IoT performance and scalability requirements

• Versatile IoT implementation that supports diverse endpoints and data types

• Extensible foundation for executing advanced analytics and control functions at the right place in the network


HPE Pointnext believes businesses must introduce new data analysis and reporting tools that can handle the volume, velocity, and variety of data accompanying the Internet of Things. The latest IoT platforms help data scientists and business analysts turn raw data into actionable insights to improve decision-making, optimize workflows and business processes, and boost financial results.

The HPE Connect IoT program fosters collaboration between customers, partners, and HPE to drive innovation, awareness, and growth through IoT and digital transformation. Part of the HPE Partner Ready program, HPE Connect IoT includes an ecosystem of digital transformation solutions including IoT platforms.

Contemporary IoT platforms support advanced descriptive, predictive, and prescriptive analytics for both real-time data streams and historical datasets. Many platforms support reusable templates to accelerate time-to-value and foster collaboration, as well as visual analytics that make it easy to identify data trends, patterns, and relationships. Some IoT platforms provide 3D modeling tools and augmented reality capabilities for running advanced simulations (digital twin analysis) to improve product design, production, and support functions.

BUSINESS CHALLENGE: Transforming raw IoT data into meaningful and actionable insights

Guiding Principle 3: Use IoT platforms to collect and analyze data, and use machine learning or AI tools to derive contextual information and gain global insights

40% of IoT created data will be stored, processed, analyzed and acted upon at the edge by 2019.- Source: The IoT Cloud: Infrastructure Options for Accelerating the Shift to Digital Business Services. IDC, 2016

PE Pointnext believes businesses must introduce new data analysis and reporting tools that can handle the volume, velocity and variety of data accompanying the Internet of Things. The latest IoT platforms help data scientists and business analysts turn raw data into actionable insights to improve decision-making, optimize workflows and business processes, and boost financial results. The HPE Connect IoT program fosters collaboration between customers, partners and HPE to drive innovation, awareness and growth through IoT and digital transformation. Part of the HPE Partner Ready program, HPE Connect IoT includes an ecosystem of digital transformation solutions including IoT platforms. Contemporary IoT platforms support advanced descriptive, predictive and prescriptive analytics for both real-time data streams and historical datasets. Many platforms support reusable templates to accelerate time-to-value and foster collaboration, as well as visual analytics that make it easy to identify data trends, patterns and relationships. Some IoT platforms provide 3D modeling tools and augmented reality capabilities for running advanced simulations (digital twin analysis) to improve product design, production and support functions.

Raw Data

Data Analytics

Artificial Intelligence

Machine Learning

Transforming Raw Data into Meaningful Business Information

Meaningful outcomesBusiness Process


By introducing contemporary IoT platforms, implementing right-place analytics, and leveraging descriptive, predictive, and prescriptive data analytics you can streamline operations in the near-term, while setting the stage for long-term business transformation.

Checklist:

1. Deploy modern IoT platforms that supports advanced analytics.

2. Implement right place analytics to optimize system performance and scalability.

3. Transform raw data from a variety of sources into meaningful information.

4. Use advanced simulation, augmented reality, and modelling tools to accelerate innovation and reinvent business functions.

Descriptive, Predictive, and Prescriptive Analytics Defined

• Descriptive analytics uses data aggregation and mining to examine what happened in the past; i.e., which types of customers purchased which types of products.

• Predictive analytics uses statistical models and forecasts to speculate what could happen in the future; i.e., a system component is likely to require maintenance or replacement after a certain period of use.

• Prescriptive analytics uses optimization and simulation algorithms to determine how to achieve a desired outcome; i.e., intelligently adjust inventory levels and product prices to align supply with demand and improve profitability.


Expected results:

• IoT data enhances business planning, eliminating inefficiencies, and improving decision making

• Business teams gain visibility into operational trends and patterns

• Manufacturing and industrial control processes become more efficient, improving product quality, and customer satisfaction, while reducing costs

• Infrastructure and knowledge in place for long-term business transformation

To take full advantage of the Internet of Things enterprises must turn business insights into meaningful actions. HPE Pointnext believes companies can fundamentally transform operations, unleash innovation, and accelerate the pace of business by judiciously applying machine learning, artificial intelligence, and automation.

Guiding Principle 4: Act on real-time and global insights

BUSINESS CHALLENGE: Exploiting the full potential of IoT to fundamentally transform the business

“We’re building a refinery of the future that combs through data and reveals how the entire plant is interconnected. It becomes like a living, breathing organic plant that knows how it should operate; if any part falls out of line, it flags for intervention.”- Linda Salinas, Plant Manager, Texmark Chemicals (HPE Customer)

https://www.hpe.com/services/actionableintelligence


You can streamline workflows and eliminate inefficiencies by injecting predictive and prescriptive analytics back into business flows and industrial control processes to automatically adapt and fine-tune operations.1 Real-time and near-real-time data can be used to trigger an action on a factory floor (automatically adjust a manufacturing process in response to sensor data) or to optimize supply chains (automatically reroute goods or materials based on real-time conditions).

Historical data can be used to improve production or system quality (proactively replace or service a component before it fails), fix a problem (automatically troubleshoot and resolve an issue based on diagnostic information and alert data) or optimize inventory levels (proactively adjust stock based on long-term trends and global data).

Recursive feedback, applied analytics, and adaptive controls can help you achieve disruptive change. By initiating evidence-based actions you can streamline operations, increase productivity, and improve business outcomes.

Checklist:

1. Employ machine learning and artificial intelligence to apply insights

2. Use real-time and historical data to take fact-based actions

3. Proactively adjust business processes and workflows to eliminate inefficiencies and streamline operations

4. Automatically take corrective actions to improve product quality and longevity

1 This process is sometimes referred to as a recursive feedback loop.

Using Business Insights to Effect Change and Transform Operations

Technical requirements

Material requirem

ents

Quality inspection & assurance

Traceability

Impr

ovem

ent o

f pro

duct

and

ser

vice

spe

cifica

tions

Quality specifications

Process performance

Final testing

Usa

ge s

cena

rios

As-designed

Prod

uct i

mpr

ovem

ents

Failure data

Voice of customer

Change orders

Field failures


Expected results:

• Fundamentally transformed operations through applied analytics and recursive feedback

• Improved productivity, economics, and business agility

• Greater product quality, customer satisfaction, and competitive differentiation

• Maximum return on IoT investments


HPE Helps Texmark Chemicals Improve Plant Productivity, Safety, and Economics

Texmark Chemicals turned to HPE to build a refinery of the future featuring advanced IoT capabilities. Designed to meet the stringent demands of the oil and gas and manufacturing industries, HPE’s Industrial IoT architecture supports edge analytics for real-time, actionable insights, and is bundled with services from HPE Pointnext and HPE partners to accelerate time-to-value. The solution includes an Aruba secure wireless mesh network that provides location-based services for plant safety and security, and HPE Edgeline Converged IoT platforms that power distributed control systems software and provide the foundation for advanced analytics. The results: increased up-time, better productivity, safer work environment, and 50% reduction in planned maintenance costs.

Management of Change

Design & Build Operate

Continuous adaptive innovation

5

On-premises as-a-Service

Edge as-a-Service

Pay-as-you-grow

4

Customer specifications

Digital foundation proven deployments repository

+

Align to business goals

1

Align to business strategy

2

Vision your future state

3

Design data/analytics needs


We believe the challenges of today call for a holistic approach to IoT as such solutions often mandate a bridge between the IT and non-IT, or operational technology worlds. Successful IoT implementations require significant levels of integration across a wide range of technologies, processes, and partners. We believe our heritage in technology and as the industry’s best partnering company puts us in the best position to be the preferred IoT ecosystem integrator for our customers.

HPE Pointnext Recommended IoT Best Practices


Based on experiences implementing projects with customers, HPE Pointnext recommends organizations consider applying these best practices:

• Focus on an alignment to business goals. Utilize a mature, proven end-to-end methodology designed to help customers transform to support the adoption of IoT solutions.

– Align to business strategy. Generate business value quickly and avoid “eternal prototyping” by pulling together a cross-functional team from IT, OT, and business disciplines. Develop a practical decision framework that addresses business, functional, technical, and implementation perspectives.

– Envision your future state. Transform vision into structured concepts with well-defined scopes, explicit stakeholder input and recommended actions for proceeding. Follow an agile methodology and constantly validate against business objectives and operational realities. Provide real-time “what if” scenarios to enable selection of the best approach for unique needs.

– Discover analytics needs. Identify the insight that is required to meet objectives and design the appropriate data model.

• Design, build and operate for a multi partner ecosystem. Translate conceptual information and requirements into an aligned architecture that results in a solution that is ready for implementation. Choose a partner who can engage across requirement analysis, integration of multi-vendor environments.

• Proactively manage change. A smooth transition is critical for driving adoption and value. Keep the system users in the loop throughout the transition to avoid disruption. Ensure all workers are properly educated on the benefits and uses of the new applications and services. Work across the enterprise to build the awareness, understanding, and confidence required to embrace change. Provide advanced notice of application upgrades, policy changes, and service changes. Invest in the training and support resources needed to ensure a successful transition.


The Flowserve Corporation, a multinational supplier of industrial and environmental machinery, turned to HPE to accelerate its digital transformation. The manufacturer leverages HPE Edgeline IoT systems along with IoT technologies from PTC and National Instruments to transform operations and slash expenses. Flowserve captures sensor data from heavy machinery, performs analytics, and applies machine learning to carry out predictive maintenance, avoid unplanned downtime, and improve product quality. The company also uses augmented reality to help support engineers visualize and repair problems by superimposing instruction manuals over machine images. Flowserve projects a $500 million increase in business over the next five years through digital transformation.

HPE Helps Flowserve Corporation Improve Uptime and Product Quality


Enterprises are looking to the Internet of Things to drive efficiencies, improve business insights, and transform operations. As part of a broader digital transformation of the business, IT organizations must revamp enterprise networks and data architectures to ensure successful IoT outcomes. Key principles include pushing data processing and control functions to the edge of the network and employing machine learning and applied analytics with recursive feedback. By following these guidelines enterprises can overcome IoT scalability and performance challenges, and turn raw data into clear and actionable insights that unleash innovation, improves customer experiences, and boost business results.

Many businesses lack the resources and expertise to carry out large-scale IoT initiatives on their own. HPE Pointnext can help you with every step of your IoT journey with comprehensive advisory and integration services. Backed by field-proven tools and methodologies, our experienced professionals can help you accelerate your digital transformation, reduce risk and uncertainty, and make the most of your IoT investments.

Conclusion

© Copyright 2018 Hewlett Packard Enterprise Development LP. The information contained herein is subject to change without notice. The only warranties for HPE products and services are set forth in the express warranty statements accompanying such products and services. Nothing herein should be construed as constituting an additional warranty. HPE shall not be liable for technical or editorial errors or omissions contained herein.

a00052579ENW, September 2018, Rev. 1

Additional resources

Visit us at HPE Pointnext

Learn about the HPE IoT Transformation Workshop

Understand the importance of Management of Change

Leverage HPE Artificial Intelligence Transformation Workshop

Get more details about our HPE Digital Prescriptive Maintenance Solution

Read about HPE GreenLake

Learn about the Texmark Refinery of the Future

Visit HPE Connect IoT

Read about Industrial IoT Solutions

https://www.hpe.com/us/en/services.html

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https://www.hpe.com/us/en/solutions/refinery-of-the-future.html

https://www.hpe.com/us/en/solutions/iot-partners.html

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Gain New Insights and Control in Commercial and Industrial ... · 6 Gain New Insights and Control...

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