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- Herausgeber: Packt Publishing
- Kategorie: Fachliteratur
- Sprache: Englisch
- Veröffentlichungsjahr: 2017
The Industrial Internet or the IIoT has gained a lot of traction. Many leading companies are driving this revolution by connecting smart edge devices to cloud-based analysis platforms and solving their business challenges in new ways. To ensure a smooth integration of such machines and devices, sound architecture strategies based on accepted principles, best practices, and lessons learned must be applied.
This book begins by providing a bird's eye view of what the IIoT is and how the industrial revolution has evolved into embracing this technology. It then describes architectural approaches for success, gathering business requirements, and mapping requirements into functional solutions. In a later chapter, many other potential use cases are introduced including those in manufacturing and specific examples in predictive maintenance, asset tracking and handling, and environmental impact and abatement. The book concludes by exploring evolving technologies that will impact IIoT architecture in the future and discusses possible societal implications of the Industrial Internet and perceptions regarding these projects.
By the end of this book, you will be better equipped to embrace the benefits of the burgeoning IIoT.
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Architecting the Industrial Internet
Copyright © 2017 Packt Publishing
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First published: September 2017
Production reference: 1200917
ISBN 978-1-78728-275-9
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Credits
Authors
Shyam Nath
Robert Stackowiak
Carla Romano
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Doug Ortiz
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About the Authors
Shyam Nath is the director of technology integrations for Industrial IoT at GE Digital. His area of focus is building-go-to market solutions. His technical expertise lies in big data and analytics architecture and solutions with focus on IoT. He joined GE in Sep 2013. He has worked in IBM, Deloitte, Oracle, and Halliburton, prior to that. He is the Founder/President of the BIWA Group, a global community of professional in Big Data, analytics and IoT. He is author of the IoT Architecture chapter in Internet of Things and Data Analytics Handbook, published by Wiley. He has often been listed as one of the top social media influencers for Industrial IoT. He is very active on Twitter (@ShyamVaran).
Robert Stackowiak is a technology business strategist at the Microsoft Technology Center in Chicago where he gathers business and technical requirements during client briefings and defines Internet of Things and analytics architecture solutions, including those that reside in the Microsoft Azure cloud. He joined Microsoft in 2016 after a 20-year stint at Oracle where he was Executive Director of Big Data in North America. Bob (his nickname) has spoken at industry conferences around the world and co-authored many books on analytics and data management including Big Data and the Internet of Things: Enterprise Architecture for A New Age, published by Apress, five editions of Oracle Essentials, published by O'Reilly Media, Oracle Big Data Handbook, published by Oracle Press, Achieving Extreme Performance with Oracle Exadata, published by Oracle Press, and Oracle Data Warehousing and Business Intelligence Solutions, published by Wiley. You can follow him on Twitter at @rstackow.
Carla Romano is director of development for big data and data warehousing at Oracle, focusing on industry solutions, including the Industry Data Model suite of products for airlines and transportation, telecommunications, retail, and utilities industries. She has an extensive background in Business Intelligence and data management, and is a frequent presenter at Oracle Openworld and the BIWA-SIG conferences. She is also a member of the IIC testbeds committee. She is currently developing utilities for Oracle Big Data Cloud Service. She previously worked at Lockheed Engineering Sciences and Unisys under contracts from NASA.
About the Reviewers
William Bathurst is a development manager at M2Mi with over 25 years of industry experience. He is currently working with the Industrial Internet Consortium to work out advanced tracking solutions for the Airline Industries.
Doug Ortiz is a senior big data architect at ByteCubed who has been architecting, developing, and integrating enterprise solutions throughout his career. Organizations that leverage his skillset have been able to rediscover and reuse their underutilized data via existing and emerging technologies such as Amazon Web Services, Microsoft Azure, Google Cloud, Microsoft BI Stack, Hadoop, Spark, NoSQL Databases, and SharePoint, along with related toolsets and technologies. He is also the founder of Illustris and LLC, and you can reach him at [email protected].
Doug has experience in integrating multiple platforms and products and holds certifications in big data, data sciences, R, and Python. He helps organizations gain a deeper understanding and value of their current investments in data and existing resources, turning them into useful sources of information, and he has improved, salvaged, and architected projects by utilizing unique and innovative techniques.
Doug regularly reviews books on topics such as Amazon Web Services, data science, machine learning, R, and cloud technologies. His hobbies include yoga and scuba diving.
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Table of Contents
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the color images of this book
Errata
Piracy
Questions
The Industrial Internet Revolution
How today's Industrial Internet came about
Earlier generations of the Industrial Revolution
Why is it time for the Industrial Internet?
Challenges to IIoT
The architect's roles and skills
Architectural approaches for success
Reference architectures for the Industrial Internet
The multi-tier IIoT architecture
A security framework for the Industrial Internet
A connectivity framework for the Industrial Internet
The industrial data analytics framework
Cloud and user experience considerations
Business strategy framework for the Industrial Internet
Summary
Architectural Approaches for Success
Architectural framework
Architectural viewpoints
Business viewpoint
Security considerations for the business viewpoint
Usage viewpoint
Security considerations for the usage viewpoint
Functional viewpoint
Control domain
Operations domain
Information domain
Application domain
Business domain
Cross-cutting functions and system characteristics
Computational deployment patterns
Security considerations for the functional viewpoint
Implementation viewpoint
Security considerations for the implementation viewpoint
Data and analytics
Data management
Analytics and advanced data processing
Integrability, interoperability, and composability
Connectivity
Intelligent and resilient control
Dynamic composition and automated interoperability
Using PoCs to evaluate design
Scope definition
Business case considerations
Solution definition
Building the PoC
Prototype scale
Evaluate/modify
Production scale
Architecture
Components
Continuing engineering
Summary
Gathering Business Requirements
Initial business discovery
Getting ready for business discovery
Gathering CSFs
Gathering KPIs
From data sources to KPI delivery
Prioritizing the building of solutions
Building the business case
Components of backend infrastructure cost models
Smart device and networking costs
Estimating implementation costs
Documenting future benefits
Financial justification of our supply chain project
Selling the project
Summary
Mapping Requirements to a Functional Viewpoint
The control domain
Basic edge device capabilities
Smarter edge device configurations
Selecting sensors and edge devices
The supply chain optimization control domain
The operations domain
The information domain
Solving information domain functional requirements
A supply chain optimization information domain
The application domain
Assessing business analysts and user skills
The supply chain optimization application domain
The business domain
DevOps and the agile movement
Agile approaches
Using microservices and containers to speed DevOps
Summary
Assessing Industrial Internet Applications
Architecture patterns for the Industrial Internet
Build versus buy decisions
Asset Performance Management (APM)
Assessing the analytics applications
Descriptive analytics
Diagnostic analytics
Predictive analytics
Prescriptive analytics
Fit gap analysis
Brilliant Manufacturing
Field Service Management (FSM) application
Summary
Defining the Data and Analytics Architecture
Data and analytics requirements and capabilities
Data reduction and analytics
Publish and subscribe
Query
Storage persistence and retrieval
Integration
Description and presence
Data framework
Rights management
Creating business value
Analytics functionality
Mapping analytics architecture to reference architecture
Advanced analytics
The Lambda architecture and IIoT
Analytics, machine learning, and analyst tools
A process for advanced analytics creation
Machine learning tools
Other analyst tools
Early Industrial Internet applications and historians
The speed layer and field gateways
The batch layer
Data lakes and Hadoop
Graph database
Data warehouses, data marts, and relational databases
Supply chain optimization in the batch layer
Summary
Defining a Deployment Architecture
Current state of deployment architectures for IT systems
Hosted systems and the cloud
Hosted services
Single-tenant hosting
Multi-tenancy
Cloud computing
Public cloud
Private cloud
Hybrid cloud
Billing
Enterprise Resource Planning (ERP)
Considerations for SaaS cloud versus on-premises
Customer Relationship Management (CRM)
Human Resource Management Systems
Data warehousing and big data
Data warehouse and decision support
Management considerations for data warehouse
Big data
Hadoop file systems
Data lakes
Management considerations for data lakes
Big data analytics and data science
Converged infrastructure and engineered systems
Deployment considerations
IIoT constraints
Incremental upgrades
On-premises versus cloud
Consumption models
Analytics capacity considerations
Analytics considerations
Key constraints in analytics architecture design
Design for the edge tier
Networking considerations
Connectivity transport layer
Network layer consideration
Topology
Edge connectivity
Management and support infrastructure
Summary
Securing the Industrial Internet
Examples of cybersecurity attacks
IIoT security core building blocks
NIST cybersecurity frameworks
IIoT security guidelines
Securing devices and the edge to the cloud gateway
Device considerations
Device to gateway connections
Securing the backend
Data lake security
Securing other NoSQL databases
Data warehouse security
Risk assessments and best security practices
Planning for security in the supply chain example
Summary
Governance and Assuring Compliance
Assessing governance, risk, and compliance
Data governance
Assessing risk and trustworthiness
International compliance certifications
International consortia and emerging standards
Government and public institution compliance
Non-U.S. government standards and certifications
U.S. government standards
Industry compliance certifications
Which guidelines apply
GRC in the supply chain optimization example
Summary
Industrial Internet Use Cases in Various Industries
Use cases versus case studies
Use cases within industry vertical
Use cases in agribusiness
Use cases in alternative energy and environmental control
Use cases in construction
Use cases in logistics and transportation
Use cases in manufacturing and CPGs
Use cases in oil and gas
Use cases in pharmaceuticals, medical equipment, and healthcare
Use cases in utility companies
Manufacturing IIoT architectures and examples
A manufacturing test bed
Factory operation visibility and intelligence
Omnichannel initiatives
Predictive maintenance
Airline industry background
Airline proactive and preventive maintenance
Preventive maintenance as a business
Asset tracking and handling
Baggage and cargo handling
Expanded baggage-handling services
Tracking tools in manufacturing and construction
Chemical industry automated tracking and replenishment
Environmental impact and abatement
Summary
A Vision of the Future
Maturing IIoT frameworks and applications
Evolving edge devices
The evolution of networking
Cognitive and mixed reality HMIs and deep learning
The impact on robotics and mobile devices
Improved security through blockchain technology
Quantum computing
The Industrial Internet's impact on society
Summary
Sources
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Preface
It seems that every day, one can pick up a technology journal or view an online technology article about the Industrial Internet of Things (IIoT). The articles usually provide insights into specific solutions to business problems or how a specific technology component is evolving to provide a function necessary in deploying an Industrial Internet solution.
If you are undertaking one of your first IIoT projects, this book will provide you with the background needed. The authors have attempted to provide both timely and timeless guidance. The IIoT ecosystem is rapidly evolving, and we'll describe some of those changes in various locations in this book. Yet, we also see that justifications for these projects and use cases are falling into repeatable patterns. The overall architecture is generally well-understood and is also largely repeated, even while individual technology components are growing more capable and sophisticated.
The Industrial Internet Consortium (IIC) provides useful documentation in defining key aspects of the IIoT architecture that the architect must consider. We'll reference the IIC documentation frequently in this book. However, we also felt a desire to provide guidance as to how the architecture is applied in projects as these solutions are defined.
An area of intense interest, as this book was published, is securing the IIoT and the governance of Industrial Internet solutions. In a portion of the book that covers these topics, we’ll provide what we believe is practical guidance and point to the many worldwide, regional, and industry standards that can impact your designs.
Solutions, component capabilities, and certifications around standards are very fluid and will probably have changed between the time we wrote this book and the time you read this. In areas where rapid change is occurring, the content should provide you with a launching point for you to do your own further discovery. We have a lengthy list of sources in an appendix in this book.
The authors of this book work at some of the leading providers of IIoT frameworks and solutions; we have called upon that experience in writing this book, but have sought to do so in a manner that should be largely vendor agnostic.
Our goal is to help you fully realize the complexity and promises of these projects, but also help you gain the experience needed to architect successful solutions. You are probably at an early stage in your journey that will consist of many stages. We hope that you will find the book a useful place to start or add knowledge where you currently have gaps.
What this book covers
Chapter 1, The Industrial Internet Revolution, describes how we reached today's IIoT solutions and the role of the architect.
Chapter 2, Architectural Approaches for Success, talks about architecture viewpoints, the implementation viewpoint, data and analytics, and using proof of concepts to evaluate design.
Chapter 3, Gathering Business Requirements, covers topics such as preparing for business discovery, gathering critical success factors, business benefits and key performance indicators, gaining an understanding of skills, evaluating data sources, value from early mockups and proof of concepts, prioritizing stages, building the business case, and selling the project.
Chapter 4, Mapping Requirements to a Functional Viewpoint, describes the control, operations, information, application, and business domains, and DevOps and the agile development movement.
Chapter 5, Assessing Industrial Internet Applications, covers architecture patterns, build versus buy considerations, asset performance management, analytics, the Brilliant Factory, and a field services application.
Chapter 6, Defining the Data and Analytics Architecture, describes typical requirements and capabilities, the Lambda architecture, analytics, machine learning and analyst tools, early Industrial Internet applications and historians, and the speed and batch layers in the architecture.
Chapter 7, Defining a Deployment Architecture, covers past and current architecture, on-premises and cloud deployment, designing for the edge, networking considerations, device management, management and support infrastructure, and consumption models.
Chapter 8, Securing the Industrial Internet, describes examples of cybersecurity attacks, core building blocks, NIST cybersecurity frameworks, security guidelines, securing devices and communications to the cloud and backend, risk assessment, and best practices.
Chapter 9, Governance and Assuring Compliance, covers assessing governance, risk and compliance, international compliance, consortia and emerging standards, government and public institutions, industry compliance, and determining the guidelines that apply.
Chapter 10, Industrial Internet Use Cases in Various Industries, describes summarized use cases in various industries and then provides more in-depth looks at manufacturing, predictive maintenance, asset tracking and handling, and environmental impact and abatement.
Chapter 11, A Vision of the Future, covers the possible impacts of maturing frameworks and applications, evolution in edge devices, networking, human machine interfaces and industrial robotics, and the applicability of blockchain and quantum computing in the future.
Appendix, Sources, provides a list of sources the authors used throughout this book that might prove useful in your own research.
In several chapters of this book, we apply what is learned to a supply chain optimization example that can be relevant in many industries.
What you need for this book
The book assumes that the reader possesses a basic IT and technology architecture background. There are no coding examples as that is beyond the scope of this book.
Who this book is for
This book is intended to be used by architects as they gather requirements, justify projects, and consider the components that they will include in the architecture of their Industrial Internet projects. Others in roles involved in defining these projects should also find this book to be of value.
Conventions
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
New terms and important words are shown in bold.
Reader feedback
Feedback from our readers is always welcome. Let us know what you think about this book-what you liked or disliked. Reader feedback is important for us as it helps us develop titles that you will really get the most out of.
To send us general feedback, simply e-mail [email protected], and mention the book's title in the subject of your message.
If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, see our author guide at www.packtpub.com/authors.
Customer support
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Downloading the color images of this book
We also provide you with a PDF file that has color images of the screenshots/diagrams used in this book. The color images will help you better understand the changes in the output. You can download this file from https://www.packtpub.com/sites/default/files/downloads/ArchitectingtheIndustrialInternet_ColorImages.pdf.
Errata
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To view the previously submitted errata, go to https://www.packtpub.com/books/content/support and enter the name of the book in the search field. The required information will appear under the Errata section.
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Questions
If you have a problem with any aspect of this book, you can contact us at [email protected], and we will do our best to address the problem.
The Industrial Internet Revolution
Today, we often hear the terms Internet of Things (IoT) and Industrial Internet used to describe an area of emerging technological focus, an opportunity for many start-up companies and technology giants, and a skill set much in demand. We believe that incorporating sensors and intelligent edge devices into an information architecture is the latest stage in an evolution that has been progressing for some time and will continue to evolve in the future. So, we thought it is quite timely to write this architect's guide in creating Industrial Internet solutions. We also hope it will prove to be somewhat timeless and useful for many years to come.
The term IoT covers a wide variety of business and consumer devices and applications and business solutions where data gathered from those devices is analyzed. We have chosen to focus this book on the Industrial Internet of Things (IIoT), the industrial side of the IoT. We will describe use cases and reference architectures that include those for industrial manufacturing; manufacturers of consumer packaged goods; and other sectors such as healthcare devices, transportation, aviation and energy generation, transmission, distribution, and controls. Some of today's initiatives focus on manufacturing quality, preventive maintenance, and improved service efficiency. We will also explore transportation use cases and reference architectures including those that solve aviation, automotive, rail, and supply chain problems. Additionally, we will explore solutions in the oil and gas industry and in the intelligent buildings and cities area.
IIoT can be defined as a system of connected things, machines, computers, and people, enabling intelligent industrial operations using advanced data analytics for transformational business and societal outcomes. In this chapter, we will begin by describing how we arrived at the Industrial Internet generation to provide you with some context for all that follows. Since there are many types and definitions of architects today, we'll describe their areas of focus and roles next. We'll then briefly describe the remaining chapters of the book to help you understand how their architecture role might align to what we will cover in each chapter.
Though we wrote this book to serve as an architect's guide, we realize it will attract a more diverse audience. If you are a manager focusing on the implications of the Industrial Internet today, you should find many portions of the book to be of interest. Similarly, developers who want to understand why and how these projects are initiated and the reference architectures behind them should find much content to be of interest. We hope to close the gap between professionals who handle information technology systems and those who manage operations and the associated technology.
The authors work at some of the leading companies that provide products that address various requirements when deploying these projects. That said, one of our goals in writing this book was to create a non-vendor-specific guide that should be useful regardless of what technology footprint you use. We will share with you the practical knowledge we gained in helping our own and other companies and organizations adopt the architecture patterns and solutions that we describe. This chapter will serve as an introduction to some of the fundamental concepts we discuss in the book and should provide you with some background if you are new to the Industrial Internet. The key topics are as follows:
How the Industrial Internet evolved from the Industrial Revolution
Why organizations are investing in IIoT solutions
Some of the challenges to prepare for in the deployment of IIoT solutions
Roles and responsibilities of the various architects and the roles tied to professional development paths
How today's Industrial Internet came about
Many organizations, including the World Economic Forum, describe the IIoT as being the fourth generation of the Industrial Revolution. The four generations have shared a common business goal such as running businesses more efficiently and producing goods and services more cheaply for stakeholders and consumers at large. They owe their existence to new capabilities created by inventions and advances in technology. In each generation, old manual jobs disappear, but new jobs and job types are created that operate at higher efficiency levels.
Today, pessimists point to the fact that many jobs will disappear during the age of the Industrial Internet. Optimists believe that because many new job types will be created, new jobs (albeit with different skills) will also be created. Time will tell if individuals whose jobs are displaced will be able to move into these new jobs, but many now feel that another revolutionary change is occurring. The future of work in the age of Industrial Internet is becoming a critical topic and connects it to the societal aspects of these innovative solutions. The term Internet of People (IoP) is sometimes used to remind us that people consume the benefits from the information that is extracted from data generated by people and/or the machines.
Earlier generations of the Industrial Revolution
Most agree that the first generation of the Industrial Revolution began in the middle of the 18th century. Let's go back in history to see how it led to the evolution of the IIoT today. The 18th and 19th centuries, which experienced the Industrial Revolution saw a transition from the manually intensive manufacturing processes to the mechanization of the manufacturing. This laid the foundation of the modern heavy industries. At the time, most people lived on farms and worked in agriculture. Factories were commonly located close to rivers and streams where they could be powered by water wheels, and there was usually much handwork involved. With the invention of the steam engine, factories could be located elsewhere. The power that was supplied to machinery by steam engines became more predictable, and more processes could be aided by machinery.
Great Britain experienced many technological innovations ranging from the first engine in 1712 by Thomas Newcomen to the steam engine in 1765 by James Watt to the first public railway line in 1825. The Industrial Revolution transformed manufacturing from the home and cottage industry level to a vastly more scalable level. With the introduction of railroads as a transportation alternative to river traffic and horse-driven carriages, faster travel between distant locations was enabled and provided a new means to deliver supplies to factories and products from them. This theme of decoupling the production facilities from the consumers can be seen in today's computing world where remote data centers can be decoupled from the information technology users. Over time, this Industrial Revolution led to a transition from human labor to the use of machines, spread over whole of Europe and to North America, leading to the industrialization of the world. Gradually, this led to consumerism as goods became available, accessible, and affordable.
The increased widespread availability of electricity through power grids and the invention of the assembly line in the first decades of the 1900s introduced the second generation of the Industrial Revolution. Once again, power became more predictable and the amount of space required for power generation in factories was reduced. Production became more optimized through assembly lines, and workers assumed new specialized roles. Motorized vehicles also appeared for the delivery of supplies and transporting finished products, thus enabling more variation in factory locations. We began an age of mass production as well as mass merchandising, which resulted in the creation of many additional, new kinds of job.
In the third generation, business computing was introduced and efficiencies were greatly improved. Mainframe computers became widely available with subsequent pricing adjustments, making them more affordable and hence more widely adopted in the 1960s. Still cheaper minicomputers and then personal computers followed. The Internet was in common usage for networking within companies and across the world by the 1990s.
The Internet evolved from a way for the military to connect and communicate and appeared in universities and then mainstream companies. The mid 1990s saw the transition from the military's Advanced Research Projects Agency Network (ARPANet) to the consumer Internet. In this wave, computers and servers connected across the world and then provided an information super highway for the people. This revolutionized how people interacted with each other and with the businesses leading to the growth of e-commerce and social media. New leaders emerged in this era, starting with Information Technology (IT) system providers, and companies such as Amazon, which started with online sale of books and went onto become a general-purpose e-commerce platform. Likewise, on the human interaction front, emails became mainstream and more interactive and rich multi-media evolved on the web. This led to the rise of Myspace, Facebook, Twitter, and similar social media platforms to essentially connect the people across the world. We refer to this as the consumer Internet.
Computing also became more accessible through improved software development tools and through business applications and tools that provided increasingly more intuitive user interfaces. Some refer to this as the beginning of the information age as line of business users could access and manipulate their data to measure and optimize their business activities.
While the consumer Internet focused on connectivity between businesses, consumers, the IT systems, and the computing devices such as servers, PC's, laptops, and emerging mobile devices, it largely ignored the machines from the Industrial Revolution. This led to a great divide between the machines for industrial operations and the traditional IT systems and set the stage for the fourth wave that we call the Industrial Internet Revolution.
The Industrial Internet can be defined as the connecting of industrial-grade machines and devices to networked computing devices with the goal of collecting the diverse data originating both inside the machine and the surrounding environment and processing or analyzing this data for meaningful outcomes. Such data originates in various forms and is often referred to as big data. The systematic organization and analysis of this data is referred to as big data analytics for industrial outcomes.
The Industrial Internet and IIoT are the industrial flavor of the IoT. While IoT refers to any physical object or thing connected to a network and the Internet, IIoT focuses on scenarios where the connected objects are primarily industrial in nature (such as manufacturing assembly lines, power generation equipment, or mass transportation vehicles). Thus, Industrial Internet is often used interchangeably with IIoT. There are three Ps important to an industry:
Products (machines and assets)
Processes (assembly lines and supply chain)
People (human stakeholders)
The following illustration captures the interaction of these three Ps:
Industrial machines and assets have a long life, especially when compared to many consumer devices. The following table serves as a reference to highlight the difference of scale in usable life comparing various industrial assets to a smartphone:
Industrial asset/product
Average life in years
1
Airplane
25
2
Automobile/car
10
2
Coal-fired power plant
40
3
Heating, ventilation, and air conditioning (HVAC) systems
20
4
MRI scanner
12
5
Oil rig
35
6
Smartphone
3
7
Water heater
9
Due to the long life and cost of ownership of industrial machines, it is important to provide ways to protect the investment in these machines over time. Thus, the optimization of field maintenance services is an integral part of the Industrial Internet. Service execution and service delivery platforms and applications are within the realm of the Industrial Internet architects, and this book will provide coverage to it.
The long life of industrial assets leads to two terms often used in the context of Industrial Internet solutions: greenfield and brownfield applications. A greenfield project refers to a scenario where a company decides to build a new infrastructure since it offers the maximum design flexibility and efficiency to meet a project's needs (an existing infrastructure limits the ability to change by its present design). From the Industrial Internet architecture perspective, the new infrastructure can add sensors to collect relevant data.
Brownfield projects leverage infrastructure that is already in use. The costs of starting up are usually greatly reduced with this approach, but it can be more difficult to modernize the infrastructure and incorporate the addition of sensors. Construction and commissioning times can be minimized using this approach. For Industrial Internet projects, brownfield systems can be retrofitted by adding external sensors to collect data. For example, external acoustic sensors might be added to the body of air compressors in a factory to do the harmonic analysis and determine air leaks in a brownfield project. Air leaks can cause wasted electricity in manufacturing plants where compressors are used to drive several pneumatic tools.
Some of the concepts we associate with the Industrial Internet today began to mature in the last few years. For example, in a world before widely available smart sensors, oil and gas exploration companies brought computers to the exploration sites, processed the data locally in relational databases, and transmitted the processed data and their conclusions back to their headquarters. Some referred to this as early edge computing on the remote computers. The following diagram reflects this type of deployment:
Data warehouses and data marts became common in most businesses. Batch-fed by Online Transaction Processing (OLTP) systems, they became the place to store historical data used to report on current trends and compare current data with past data through business intelligence tools. Of course, this footprint remains common today.
Predictive algorithms were also developed, tested, and deployed with increasing rapidity in certain industries and gained wider adoption over time. Some early use cases included understanding financial market investment strategies and insurance risk, and the prediction of the likely quality of expensive manufacturing processes to better optimize the production.
Each generation became shorter. Moving from the first generation of the Industrial Revolution to the next was a matter of centuries, but the subsequent generations took half the time of the previous change. This implies that future generations may come at a faster pace, and while we are embracing the Industrial Internet, we need to be prepared for the possible next generations as well.
Why is it time for the Industrial Internet?
In 2010, the IoT and Industrial Internet became familiar terminology. The World Economic Forum and others declared this to be the next generation of the Industrial Revolution. As in previous generations, several technological advancements came together to enable a new class of solutions and applications, changing business models and capabilities.
Sensors began to be mass produced at ever decreasing costs. As price points, size, weight, and power requirements for sensors decreased, engineers began to create device designs that included them in anticipation of being able to gather useful data on device status as soon as it became feasible. Since smart sensors can also be programmed and updated, they can evolve and become more "intelligent" over time. For example, inclusion of such smart sensors in automobiles led to rapid advancements in the development of autonomous vehicles.
The sensors themselves most often transmit semi-structured data in a streaming fashion. Coincidentally, analyzing mass quantities of semi-structured data became possible a decade earlier through development of NoSQL data engines (and Hadoop specifically) to solve the problems of Internet search optimizations and recommendations. Next generation platforms holding exabytes of data are deployed today by companies in the search engine business.
The development of new and innovative software solutions became more viable for start-ups and smaller organizations as cloud-based platforms became available (mostly eliminating an expensive upfront investment in infrastructure). The cloud also enabled faster time to deployment and elastic scalability that was difficult in classic data centers.
The cost of networking and bandwidth reduced over this time to provide ubiquitous connectivity for the IIoT. Some of the connectivity options and technologies used include Radio-Frequency Identification (RFID), Wi-Fi, Bluetooth Low Energy (BLE), and 2G/3G/4G with 5G on the horizon.
The growing popularity of open source software data management offerings and development tools also helped minimize early costs. Today, as the Industrial Internet has matured, we see many integrated solution footprints and applications that rely on underlying open source components.
The following diagram represents a common architectural pattern often seen in Industrial Internet implementations and is called a Lambda architecture:
The illustration shows streaming data feeds from smart devices. The streaming analytics engine analyzes this feed in real time and will sometimes have machine learning algorithms deployed to process the data. The data lake pictured is most often a Hadoop cluster and is designed to load and store massive amounts of data of all types. As in the previous generation, traditional data warehouses and data marts are batch fed. Business intelligence tools are shown pointed at the data mart, data lake, and streaming analytics engine in our illustration.
We'll describe these components in much more detail in subsequent chapters as we lay out the data and analytics architecture. Obviously, there is also a lot more detail in the information technology platform architecture, which we'll cover as well.
New manufacturing technologies are also now employed in Industrial Internet solutions. Robotics in manufacturing became common in industries where the cost of labor was high, such as in the automotive industry, around the turn of this century. The robotics that were deployed improved the consistency and quality of the products produced and helped to contain costs. The addition of intelligent or smart sensors to newer generations of these devices enabled more functional and flexible capabilities. The wider applicability and growing usage of robotics also led to decreases in their pricing, helping drive further adoption.
Many manufacturers and companies that design products are now experimenting with 3D printing. 3D printers enable the manufacturing of products and components anywhere; such a printer is deployed and accessible via a network. Such technologies are often referred to as additive manufacturing. The ability to print spare parts on demand for industrial machines can have a profound positive impact on the supply chain ecosystem, as the cost of such additive manufacturing continues to decrease.
Artificial intelligence (AI) and machine learning are also enabling more intelligent devices. As devices become self-learning, they can react to changing situations in real time. We'll discuss these topics and other emerging technologies when we explore what is likely to occur in the near and more distant future in the last chapter of this book.
These new capabilities are causing companies to rethink the value of their data and the kinds of businesses they are competing in. Many are facing new and non-traditional competition from other industries and are evaluating digital transformation strategies that sometimes include new strategies for monetizing their data assets. Some are becoming data aggregators, selling data to other companies and subscribers that find it useful.
The following diagram summarizes the four generations of the Industrial Revolution we described:
Challenges to IIoT
As always seems to happen when a new generation begins, there are some holdover problems from the old generation as well as problems introduced by the new architecture. One carryover from the previous generation is the need for projects to be driven by line of business requirements, not by IT. As it was earlier, projects will usually stall when IT-initiated proof of concepts do not really solve problems that the business needs and wants to address.
In Industrial Internet projects, architects and IT must also sometimes work with engineering designers who are specifying the types and locations of sensors in devices to assure that data needed for the proposed solution can be gathered. Similarly, these teams need to work together regarding networking requirements given the amount of data that might be transmitted. Continuous data gathering from equipment operated in industrial settings is key to enabling maintenance and field services-related solutions.
The mixture of semi-structured and unstructured data and the variety of data management solutions needed introduce complexity and the need for new skill sets that an organization might not possess and face difficulty in finding. Further adding to the complexity is the rate at which data is transferred over networks arriving in the data management engines and the data volumes that must be managed in them.
Of course, device and data security must be maintained throughout the ecosystem. Software and firmware updates that are pushed to intelligent sensors and devices must be secure and successful, or denied. Data transmitted to cloud-based solutions must meet or exceed industry-relevant certifications and country data sovereignty and privacy laws.
External threats can exploit vulnerabilities in under-protected Industrial Internet systems and thereby cause harm to the organization owning the assets and the associated business processes. Such concerns led to an increased focus on solving these security risks and adopting the emerging standards.
The architect's roles and skills
If your company or organization is like many, it defined many roles and job titles for its architects. Most often, the roles we will describe in this section reside in the IT organization. That said, linking these projects to business needs and requirements is critical as we previously noted. We'll describe the process to do that later in this book.
Many look to The Open Group Architecture Framework (TOGAF) as a place to begin to define the skills an architect must possess. TOGAF describes characteristics needed to define a business architecture, application architecture, data architecture, and technical architecture. As cloud-based computing has gained popularity, some of the architecture considerations and emphasis have changed a bit. Today, the following roles are the typically defined ones for each architecture type:
Business architecture
: This architecture includes the business strategy and goals, business processes, organization, and governance that are primarily driven by the lines of business and provides documentation for the business justification for projects
Application architecture
: This architecture maps the relationships between identified-needed business processes and the application footprints, the interactions among applications, and how the applications are to be deployed (such as defining cloud-based SaaS strategies)
Data architecture
: This architecture defines the appropriate logical and physical data structures aligned to business needs and the most appropriate data management platforms (choosing among relational databases, NoSQL databases, Hadoop, graph databases, and other options)
Technology architecture
: This architecture defines software, server, storage, and networking solutions (including cloud-based PaaS and IaaS strategies) in response to technical requirements
The TOGAF definitions became the basis for defining the role of the Enterprise Architect (EA) in many organizations and a certification process. An EA could become certified by demonstrating skills in each of these architecture areas. In truth, many of today's EAs have strong IT technology backgrounds because of their heritage but are weaker in other areas.
Because of the unbalanced skills often present in architects, many organizations designate specialists for each architecture area. So, they will have business architects, application architects, data architects, and technology, infrastructure, or cloud architects. An organization will sometimes also have a chief architect who serves as the lead strategist and participates in strategic planning across the different specialties.
The growing realization of the importance of secure data and data centers in always delivering a trusted and timely picture of true business state has caused many organizations to create the role of Chief Security Officer (CSO). Security architects or cloud architects with strong security backgrounds are sometimes part of the team. They bring skills in defining authorization, authentication, and encryption architectures, and a knowledge of secure networking designs and options. They also have knowledge of industry and country mandates, as well as security certification standards that must be adhered to.
In the crowded C-suite alphabet soup, a relatively new entrant is the CDO or Chief Digital Officer. CDO has also been used for Chief Data Officer. However, in the context of the Industrial Internet, the Chief Digital Officer often plays a pivotal role. A CDO is the leader who helps a private company or a public organization drive digital transformation initiatives to achieve well-defined outcomes.
Digital transformation can be defined as the change associated with the conversion from traditional and often analog business technologies to digital ones using one or more of the modern computing paradigms involving data, analytics, mobility, social media, or cloud computing. A simple example of the digital transformation of business in the public sector setting is the use of automated toll machines communicating with automobile transponders to process tolls on highways, thus eliminating coin-operated or human-operated tool booths. The transponder is a good example of a thing.
CDOs are appearing in more and more companies. Examples include leaders of IIoT projects and initiatives at General Electric (William Ruh) and ABB (Guido Jouret). CDOs will sometimes have the title of Vice President - Digital. Regardless of the exact title, the person in the CDO role is often closer to the business operations than the traditional Chief Information Officer (CIO). Such an individual can have a natural promotion progression to President of an operational division or CEO.
CDOs usually have a strong architecture background. In fact, a career path we have seen is evolution from one of the architect roles defined by TOGAF to chief architect and then CTO and finally CDO or Vice President of Digital. Thus, IIoT is introducing new career paths for architects.
The architects and similarly skilled individuals responsible for the Industrial Internet are increasingly becoming part of the CDO organization as opposed to the CIO organization. Such digital organizations are often tasked to help break the barriers between Operations Technology (OT) and IT. This convergence of IT and OT is key to the full realization of the value of the Industrial Internet. This idea of the convergence of IT and OT systems into IIoT systems in visually represented in the following illustration:
This implies that the organization needs to hire and develop skills based on these new demands. In some cases, companies are developing Digital Leadership Programs to groom professionals from the lines of businesses who are skilled in OT and pairing them with more traditional enterprise IT skills to accelerate the delivery of the Industrial Internet, inside and outside their organizations.
The traditional Systems Integrator (SI) and professional services companies are creating digital and IoT practices. They are creating reference architectures and building proof of concepts to showcase applications for Industrial Internet. As these organizations increase the number of Industrial Internet architects to implement these IIoT solutions, we will likely see the emergence of new kinds of training and certifications.
Architectural approaches for success
In this section, we will look at the need for an Industrial Internet-centric architectural approach to be successful in delivering the business outcomes. Just as civil engineers and building architects use blueprints to incorporate best practices in their work in a reusable way, reference architectures for IT systems have been extensively defined and used to prevent the reinvention of the wheel again and again.
Here, we will focus on reference architectures for IoT and more specifically on emerging reference architectures for the Industrial Internet and IIoT projects. To fully understand such reference architecture, a familiarity with system design principles, enterprise architecture, security frameworks, and networking architecture will be highly useful.
Reference architectures for the Industrial Internet
Reference architectures for the Industrial Internet can be very useful in facilitating the communication between the architects and the stakeholders in industrial manufacturing domains, including plant managers, field engineering managers, service professionals, business managers, and others. The solutions tend to address very specific business problems such as determining fuel efficiency and when engine maintenance is required. IT-centric architecture frameworks are less useful for understanding how the convergence of OT and IT will provide a means to achieve the business outcomes expected from the Industrial Internet solutions. However, there is a need for the reusability of this underlying IT architecture to scale the lessons that are being learned broadly.
Architects refer to the reference architecture and use it as a template as they capture the requirements. They design the specific implementation of the architecture and can convey a consistent understanding to internal and external stakeholders. Thus, interoperability, security, and other requirements are addressed upfront and do not become an afterthought.
