Introduction and Research Demographics
Introduction
Computing infrastructure is an ever-changing landscape of technology advancements. Current changes impact the way companies deploy Smart Manufacturing systems to leverage advancements. This LNS Research report reflects that shift, and builds on our previous research and developments with MESA International.
The rise of Edge and Fog computing capabilities coupled with traditional industrial control system (ICS) architectures provides increasing levels of flexibility. Furthermore, time synchronized applications and analytics augment or in some cases minimize the need for larger Big Data operations in the Cloud, regardless of cloud premise.
LNS Research believes that current Industrial Internet of Things (IIoT) technologies support highly distributed intelligence. This includes distribution of hosting and computing such that companies can implement a truly distributed control and information system. The result is real-time benefit and loss avoidance that improves production efficiencies and mitigates risk while segregating and federating production data and analytics to their appropriate constituencies.
Establishing an Operational Architecture with the flexibility in strategy to utilize distributed computing and virtualized servers creates a production environment to leverage people, process and technology in a scalable manner, without the need to lock into a specific vendor or solution construct. However, as many operations will rely on legacy systems to fit into an overall architecture, virtualized hosts, data management, and open API tools will be necessary to guarantee a future-proof production environment.
Research Demographics
This research ebook is based on the 2018 edition of the Analytics That Matter survey, ebook, and research project . The focus of that survey was across manufacturing executives responsible for operational technology (OT), but it was open to all. To the extent possible, the analysts eliminated vendor responses which resulted in a good cross-section of reliable respondents. At the time of publication (June 2018) the total number of responses for the analysis was approximately 350.
The essential starting points for Industrial Transformation are somewhat dependent on the specific industry. That holds true for the application of Edge computing; the definition, location, and indeed use cases for Edge depend entirely on the unique nature of each organization.
DISCIPLINE / ROLE
Defining the Industrial Edge
Consolidation and the centralized nature of cloud computing has proven cost-effective and flexible over recent years, but the rise of IIoT and mobile computing has put a strain on networking bandwidth. Ultimately, not all smart devices need to utilize cloud computing to operate. In some cases, architects can — and should — avoid the back and forth. Edge computing could prove more efficient in some areas where cloud computing operates.
Edge computing enables processing of data closer to where it's created (i.e., motors, pumps, generators, or other sensors), reducing the need to transfer that data back and forth between the Cloud.
Think of Edge computing in manufacturing as a network of micro data centers capable of hosting, storage, computing and analysis on a localized basis while pushing aggregate data to a centralized plant or enterprise data center, or even the Cloud (private or public, on-premise or off) for further analysis, deeper learning, or to feed an artificial intelligence (AI) engine hosted elsewhere.
There is no distinct hardware definition of industrial edge computing today; it’s in the eye of the beholder as to how much compute power or data response may be required in a given application or across a specific production process. Dedicated servers with virtualization can host apps with significant footprints, store related production data, communicate to the Cloud, and perform onboard analytics in the footprint of an appliance, a server, or a PC in a PLC rack. For the purpose of this discussion, we will define the functionality just described versus defining the specific hardware footprint it may occupy.
Above all else, organizations must agree internally on standards of functionality required for various processes and then on the appropriate hardware and vendor(s) to fulfill the need. When referring to Edge, it will almost always be on-premise or at-asset to avoid over-generalizing the IT infrastructure at the plant level.
Production Metrics and IIoT Architecture Impact
Industry 4.0 and Smart Manufacturing: Impact of Operational Architecture
Apps and analytics provide visibility and potential prescriptive responses for immediate impact on key performance metrics (KPIs). The Operational Architecture a company deploys will affect the degree of impact on those metrics.
METRICS WILL REMAIN
Industry 4.0 and Smart Manufacturing: Impact of Operational Architecture (Cont.)
Manufacturing organizations benefit as they leverage the IIoT for the Operational Architecture of the future. Technology and tools that are continuously evolving will bring about the widely discussed Smart Manufacturing models. There are dozens of associated technologies and uses, but this discussion focuses primarily on manufacturing and process operations and the link to the Digital Twin, operational improvements and the technology shifts affecting the metrics that matter.
INDUSTRIAL INTERNET OF THINGS PLATFORM
INDUSTRIAL INTERNET OF THINGS
PLATFORM
APPLICATION ENABLEMENT
• Integrated Development Environment: JAVA, HTML5
• IIoT Data Model and Execution Engine
• Workflow and Business Logic Modeler
• Collaboration, Social
• Mobile
EDGE AND CLOUD
• Private/Public/Hybrid
• IaaS - Compute, Storage, Network
• PaaS - Run Time, Queue, Traditional SQL DB/DW, Advanced NoSQL DB, Data Historian, In-Memory Database, Hadoop/Data Lake
• Search
• SaaS - Traditional Enterprise Applications, Next-Gen IoT Enabled Applications
• 3rd Party App Store
• Industrial Compute / Industrial Data Centers
• IIoT Gateways
• Industrial Cyber-SecurityAuthentication, Access Control, Configuration Management, Antivirus/Spyware, Cryptography, Logging, Data Tagging, Compliance
• Engineering Content Integration / Digital Twin
• Location Services
• Industrial Cyber-Security - Authentication, Access Control, Configuration Management, Cryptography, Logging, Compliance
ADVANCED INDUSTRIAL ANALYTICS
• Statistical Programming: R, SAS, SPSS
• Search, Text Mining, Data Exploration, Native Language Processing
• Collaboration / Visualization / Reporting
CONNECTIVITY
• Network InfrastructureWired, Wi-Fi, Cellular, Device Management, Device/Asset Inventory and Visibility
• Communications Standards / Protocols / Data
Acquisition - OPC-UA, MQTT, AMQP, DDS, APIs
• Statistics Based Models
• 1st Principles Based Models
• AI/ML Based Models
• Complex Event Processing / Edge Analytics
• Industrial Cyber-Security - Authentication, Access Control, Intrusion Detection/Prevention, Firewalls, Application Whitelisting, Antivirus/ Spyware, Cryptography, Logging, Data Tagging, Compliance, Anomaly Detection, Asset Inventory, Secure Media, Risk Management, etc.
© LNS Research. All Rights Reserved.
SECTION 3
Dynamics of Edge Adoption
Digitization of Manufacturing: The Shift from Traditional Tech to Modern Equivalents
Edge can sit at the control level or above it, providing real-time responses and structured cost benefits.
EVOLUTION OF TRADITIONAL ARCHITECTURES
TRADITIONAL ISA-95 APPROACH
MAINFRAME
Finance and analytics apps, supply chain
DATACENTER
Production scheduling, records, inventory
L2/MES SERVERS
SCADA, runtime history, operator workflows
CONTROL
Control, sensing, peer communications
• Less IT infrastructure
• Deeper learning
EMERGING OPERATIONAL ARCHITECTURE APPROACH
CLOUD, ERP
Finance and analytics apps, supply chain, AI and ML
ON PREMISE CLOUD
• Less IT infrastructure
• Flexible hosting
• Real-time compute, host, store
• Network / control bandwidth
Production scheduling, records, inventory
EDGE COMPUTE
MES, SCADA, runtime history, operator workflows, app hosting
CONTROL
Control, sensing, peer communications
EDGE COMPUTE AND ANALYTICS:
As close to the source as required by response timing
Reference Model for Analytics and Apps Requires Multiple Compute Layers
The new LNS Research take on Operational Architecture based on the IIoT platform views analytics in the same context as all other applications. It also supports the concept of Cloud to Edge without implying any difference between them. The definition of Edge leans towards a hardware-centric view of the enterprise – any system that is below a plant data center (or corporate one if no plant data center exists) is considered part of the Edge. That’s not a hard and fast rule, but excellent guidance to further the discussion about Operational Architecture with distributed applications.
INDUSTRIAL ANALYTICS + APPS
GUIDE TO OPERATIONAL ARCHITECTURE
Common Data, Apps, and Analytics
BIG DATA MODEL
DIGITAL TRANSFORMATION
FRAMEWORK by LNS Research describes a systematic approach to simultaneous and interconnected digital initiatives, in order to manage transformation across all levels and functions of the organization.
Click to learn more about the Digital Transformation Framework
COMPUTE + STORAGE
INDUSTRIAL OPERATIONS
FIRST PRINCIPLES STATISTIC SBASED
ARTIFICIAL INTELLIGENCE ( A I ) / MACHINE LEARNING (ML )
Edge is in the Eye of the Beholder
Depending on organizational viewpoint, Edge infrastructure can be complementary to or inclusive of level 1 or 2 control and information layers of the production process. An organization can define industrial Edge as an extension of Cloud activities or as an extension of Control activities based on requirements of speed, data structure, volume, and velocity.
Organizational agreement on location (e.g., an unmanned pumping station), capabilities, use cases and desired outcomes is critical before conducting any pilots or scalable implementations.
ROLE AFFECTS PERCEPTION OF EDGE
OPERATIONS
Edge Adoption Lags Cloud; Edge Services Not Well Defined
Edge is still in early stage adoption, but one thing is clear: Edge devices are subject to large-scale investments from cloud suppliers to offload bandwidth and latency issues caused by an explosion of Internet of Things (IoT) data in both industrial and commercial applications.
In the near future, Edge will likely increase in adoption where there are questions to the applicability of cloud for the specific
Corporate Analytics: Location
use case. While Cloud level interfaces and apps will migrate to the Edge, and industrial application hosting and analytics will become common at the Edge, using virtual servers and simplified operational technology (OT) friendly hardware and software. Benefits in network simplification, security and bandwidth accompany the IT simplification.
Industrial Analytics: Location
Edge in Operational Architecture
Traditional Models Challenged by Highly Distributed Intelligence and IIoT
Across operations segments, utilization of distributed data processing will be provided in three primary areas: Cloud, Edge, and controller. As processing power and reliability of in-plant devices improve, more cloud operations will move closer to the asset to eliminate complexity and latency.
This compression of compute power will ultimately result in more control-based data processing (PLC-based analysis or PC in the
PLC) and highly distributed compute power in edge devices hosting applications and analytics tied closely to the PLC/DCS/device and its attributed data. Where common data types can be structured or semi-structured, edge devices can act as virtual hosts for apps and analytics that span machines or lines providing visibility, historians, tracking and analytic functions.
Big Dat a A naly t ic s, C ollabor at ion, and Mash- U p A pps
C onn e c t ivit y and Dat a Model
Adding Compute Capabilities to Common Reference Architecture
Operational views of the architecture have focused on network architecture and security while users continue to deal with legacy control and network diversity issues. Connectivity between OT and IT and the inherent security measures are necessary however these architectures fail to address the distribution of compute and hosting activities necessary for effective production management and analytics.
LEVEL 2
While cloud platforms have become popular in their adoption for advanced industrial analytics, the latency and costs of pushing data through cloud services may not be the best answer for realtime impact on production issues. Adding Edge computing to the Operational Architecture allows us to simplify data flows, security and network traffic while maintaining the integrity of the converged plantwide ethernet (CPwE) standards.
Connectivity: Industrial Networking
Edge Is Powering Up
Manufacturing is only one of many IoT universes where Cloud and Edge coexist. However, the growing number of IoT devices and applications requiring immediacy of response dictates that more cloud-like functions will move back to the Edge where on-sight management and response can be managed similarly to a hosted public environment.
Eventually, the Cloud will be overwhelmed by smart transportation, cities, infrastructure, agriculture, and healthcare providers and the dilemma of too much cloud data will sacrifice time-critical applications. Further, the sophistication of algorithms and the data thirst of machine learning will require ever more compute power in the Cloud.
Every major cloud provider recognizes that the growth of IoT devices will require more localized cloud services with less latency and deterministic closed-loop response. Therefore they are investing billions of dollars into Edge compute infrastructure. Similarly, PLC vendors are providing more robust in-rack solutions for compute and minor app hosting to provide high response without the dependencies on outside providers. The ultimate answer will be a compute power shift to smaller, at-the-asset devices that OT staff can manage with minimal support post-startup.
Response, reasoning, and reaction in real time will be in the fluid edge domain, shared by PLC-based compute and stand-alone edge hosts. This, in turn, will save cloud services for non-time critical data aggregation and analytic functions.
We don’t differentiate between applications and analytics running at the Edge from those running in the Cloud. An Operational Architecture is primarily software-based, and applications and analytics can run anywhere in the enterprise architecture that makes sense. This approach means you can build the Operational Architecture without concern for hardware limitations. For example,
a company could decide to provide sufficient processing power in a PLC to run local analytics. That might be cost-effective and fit with the analytics goals, but it doesn’t preclude the company from running analytics elsewhere, as long as there is a logical connection to the architecture without being tied to hardware.
Ultimately, decisions about PLC versus Edge versus Cloud-based hosting and analytics depend on data volume, structures, and velocity accompanied by the response latency and the degree of difficulty in data normalization between disparate data producers.
For example, a PC in a PLC may host a historian or light analytic apps but may not be suitable to aggregate other sensor or machine data outside its specific vendor's legacy network without extensive programming across PLC platforms. An on-premise, supplemental, edge device may be:
• Simple to configure as a virtual host;
• Capable of hosting multiple (and larger) apps and analytics across more machines;
• Easier to execute necessary data normalizing and protocol conversion techniques;
• Better at unburdening in-rack processing for more reliable speeds and memory utilization;
• Easier to configure for high-reliability situations;
• Easier to upgrade and update for evergreen operations; and
• Better to eliminate cloud latency issues while still utilizing cloud for more extensive data operations.
The Evolving Role of Edge Computing
Distributed control and compute will merge as cloud applications become more portable and edge devices become more powerful, including additional power at the controller.
Real-time analytics, artificial intelligence (AI) and app hosting at the Edge will form virtualized intelligent agents, supplementing traditional PLC or DCS control. In some cases, companies will equip devices themselves with chips that contain AI or with analytic chips embedded.
This movement to the Edge, regardless of platform, solves many issues relating to IT infrastructure and cloud-based hosting or aggregation: network availability, latency, bandwidth, and security specifically.
EDGE SOLVES INFRASTRUCTURE ISSUES FOR OPERATIONAL ARCHITECTURE
Edge Gains Traction
Edge computing will become more necessary as analytics become more mainstream and Cloud becomes more crowded. As we move into more advanced fields such as Edge analytics and Big Data analytics in the Cloud, data abstraction and cleansing will become ever more important. Managing local analytics at the deep Edge (e.g., on a motor controller) and directly feeding the control system changes the dynamics of data. We often talk about the “four V's” of data –velocity, volume, variety, and veracity. In the deep Edge example, we
want to be able to store fast and voluminous data locally for a short time while we conduct local analytics. Longer term decision making will take place at a higher level in the data stack (perhaps in MOM or in the Cloud) and require reduced velocity and volume through consolidation. Similarly, the longer-term feedback loops will not require much speed or volume, but they must deliver the necessary feedback to the system as designed.
Use Cases Should Drive Data Architecture
Data Architecture Considerations
When building the data and analytics architecture, companies should consider:
• DATA LOCATION: Edge (device/asset), on premise (plant data center, enterprise data center), Cloud
• DATA SPEED: decision time and bandwidth constraints; streaming/protocols; historical; pointers or replicated
• DATA TYPE: structured, semi-structured, unstructured
• ANALYTICS MODEL: statistical, first principles, artificial intelligence (AI)/machine learning (ML)
What are the top IIoT use cases your company is pursuing today?
(N=252, all respondents)
Remote monitoring
Energy efficiency
Asset reliability
Quality improvement
Production visibility
Internet enabled products
Business model transformation, e.g. selling capacity
Asset and material tracking
Traceability and serialization
Customer access to information
Improving safety
Supplier visibility
Use Case Scenarios: Edge Application Hosting and Analytics
Thin client hosting of operator intelligence, historians, light analytics.
Sub-second decision making, large volumes of streaming data with bandwidth constraints, including:
• Advanced process control
• Model predictive control
• Quality inspections
• Device failure
EDGE APPLICATION HOSTING AND ANALYTICS
SMART CONNECTED DEVICES AND MACHINES
Connectivity: Industrial Networking
Use Case Scenarios: Edge On-Premise Analytics
Seconds or longer decision making, sufficient bandwidth, supporting existing on-premise applications, data type limitations, or data sharing limitations, including: • Production performance dashboards • Reliability-centered maintenance
EDGE ON-PREMISE ANALYTICS
Enterprise Manufacturing Intelligence (EMI)
ENTERPRISE MANUFACTURING INTELLIGENCE (EMI)
Use Case Scenarios: Edge to Cloud Analytics
Seconds or longer decision making, sufficient bandwidth, supporting cloud applications, Big Data requirements, and data sharing requirements, including:
• Asset performance benchmarking
• Fleet/plant network visibility and benchmarking
• OEM
and services
Recommendations
Aligning People, Processes and Technology
When mapping out an Edge strategy, manufacturers should avoid making it just a discussion about technology; indeed it’s about properly aligning people, processes and technology to create the right environment to drive outcomes.
TECHNOLOGY:
• Companies will benefit with easier hosting of more complex apps provided by emerging software-as-aservice and rising dependence on virtualization.
• The potential for machine-as-a-service and evergreen upgrades will grow dramatically using edge computing on-machine.
• Production problem resolution will accelerate with the rise of scalable analytics and IA on small footprint edge computing devices. It will require less network bandwidth and sidestep many Cloud security risks when they use Edge on-premise.
• Edge in the PLC rack is a viable option today as are Edge compute and IIoT gateway systems; PC-based solutions are limited but that may change.
• Don’t discount the potential for network devices to manage the industrial control system (ICS) software suite, or potentially real-time control kernels.
PROCESS
Don’t underestimate the potential for Edge to offload cloud-based algorithms and AI to on-premise edge appliances. This scenario may signal the need for changes in process control and operator workflows to accommodate immediacy of response.
PEOPLE
Skills required to manage Edge/Cloud and analytics transitions are in high demand. Simplicity at the OT level is key to enabling the workforce. Industrial companies will need to up their production optimization game without becoming dependent on service providers or IT resources to manage hosting.
PEOPLE
PROCESS
Regardless of where the company is in its Industrial Transformation, it should think and act strategically to ensure greatest success, and maximum benefit:
1. DO WHATEVER IS REQUIRED TO ENSURE THE OPERATIONAL ARCHITECTURE strictly supports the organization’s Strategic Objectives.
2. ESTABLISH CONSENSUS ABOUT DATA MANAGEMENT, the analytics strategy, and the artificial intelligence implications of Industrial Transformation initiatives:
• Examine functional, vertical and horizontal processes for improvement
• Drive organizational alignment on decision factors that will dictate on-premise or off; Edge or Cloud; device, appliance or server-based compute and host
• Don’t forget about data location, decision time, bandwidth constraints, data type, and analytics type
3. INDUSTRIAL USE CASES SHOULD DRIVE THE DATA ARCHITECTURE . Architecture should be Edge and Cloud, not, Edge versus Cloud (consider analytics and virtual hosting).
4. START WITH SMALL PROJECTS (as pilots) to drive initiatives, with the right platform to scale quickly.
Author: Matthew Littlefield Principal Analyst matthew.littlefield@lns-global.com
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