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Breaking down an Industrial IoT reference architecture
- 1. © 2022, Amazon Web Services, Inc. or its affiliates. All rights reserved. Breaking down an Industrial IoT reference architecture Neel Sendas Principal Technical Account Manager – Amazon Web Services
- 2. © 2022, Amazon Web Services, Inc. or its affiliates. All rights reserved. Digital transformation is increasing across asset- heavy industries Discrete & Process Manufacturing Agriculture Power & Utilities, Renewables Energy (Oil & Gas) Healthcare & Life Sciences Automotive Consumer Packaged Goods (CPG)
- 3. © 2022, Amazon Web Services, Inc. or its affiliates. All rights reserved. We do track a lot of stuff on paper and some digitally but often forget to write things down We wish for a real-time view of what’s happening on the shop floor for all sites We may have more capacity... but no real-time view of inventory and supply chain to decide Most days we’re fairly productive… but some days operations are chaotic Our machines go down sometimes, but we lack data for root cause We have older machines that we need to keep and also make smart Common Business Challenges
- 4. © 2022, Amazon Web Services, Inc. or its affiliates. All rights reserved. Corresponding Technical Challenges Data Access Integrate data from new and legacy equipment, using different protocols Scale Manage assets, device fleets and data across sites Data Management Organize large amounts unstructured, disparate machine data Real time decision making Operate at the edge with minimal tolerance for latency Security & Compliance Keep operational assets and data secure
- 5. © 2022, Amazon Web Services, Inc. or its affiliates. All rights reserved. Create a unified OT/IT data backbone
- 6. © 2022, Amazon Web Services, Inc. or its affiliates. All rights reserved. Industrial Edge Framework Industrial Environment Historian PLC/DCS/SCADA Industrial Data Lake Cameras Asse t Secondary Sensors Production Engineer Data Scientist BI Engineer Digital Twin Deploy ML models to the Edge Reliability Engineer Plant Manager Data Engineer OT Personas IT Personas MES, LIMS, ERP, CRM, Enterprise Data Asset Modeling (Global View) Operational Datastore Industrial AI Services Advanced Analytics Process Engineer Edge Low-level Reference Architecture
- 7. © 2022, Amazon Web Services, Inc. or its affiliates. All rights reserved. Industrial Edge Framework Industrial Environment Historian PLC/DCS/SCADA Industrial Data Lake Cameras Asse t Secondary Sensors Production Engineer Data Scientist BI Engineer Digital Twin Deploy ML models to the Edge Reliability Engineer Plant Manager Data Engineer OT Personas IT Personas MES, LIMS, ERP, CRM, Enterprise Data Asset Modeling (Global View) Operational Datastore Industrial AI Services Advanced Analytics Process Engineer Edge AWS IoT SiteWise Edge AWS Panorama Amazon Lookout for Vison AWS IoT TwinMaker AWS IoT SiteWise AWS Glue (ETL) Amazon S3 AWS IoT Events AWS IoT Analytic s AWS Outposts Amazon Lookout for Equipment Amazon Monitron Amazon Lookout for Vison Amazon Forecast Amazon Lookout for Metrics Amazon Redshift Amazon QuickSight Low-level Reference Architecture
- 8. © 2022, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS IoT Greengrass AWS IoT SiteWise Edge Industrial Environment Historian OPC-UA MQTT AWS IoT Core Amazon QuickSight Amazon Athena AWS IoT SiteWise AWS Glue (ETL job) PLC/DCS/ SCADA Industrial Data Lake AWS Glue (Data Catalog) Amazon S3 (raw) Amazon Redshift Cameras Asset AWS Panorama Protocol Convertor AWS Storage Gateway AWS IoT Events Notification Amazon S3 Amazon S3 (Gold) Amazon Kinesis MES, LIMS, ERP, CRM, 3rd Party, Enterprise Data AWS Snowball AWS DMS Secondary Sensors AWS IoT TwinMaker Production Engineer Data Scientist BI Engineer Flexible Data Access (API, SQL) Amazon API Gateway Amazon RDS Modbus TCP EtherNet/I P Connector Amazon Lookout for Equipment Amazon Monitron Amazon Lookout for Vison Amazon Forecast Amazon Lookout for Metrics Industrial AI Services Amazon SageMaker AWS IoT SiteWise Monitor Amazon Managed Grafana Ops. Dashboards & Digital Twin Deploy ML models to the Edge Process / Reliability Engineer Plant Manager Data Engineer OT Personas IT Personas AI/ML Edge Lambda Function AWS IoT Analytics Connectors Low-level Reference Architecture - Breakdown
- 9. © 2022, Amazon Web Services, Inc. or its affiliates. All rights reserved. Repeatable Reference Architecture Across Industries Discrete & Process Manufacturing Agriculture Power & Utilities, Renewables Energy (Oil & Gas) Healthcare & Life Sciences Automotive AWS Well Architected Built-in Scalable, secure, and extensible Reference Architecture Repeatable & reusable for rapid deployment at scale Cost-effective (serverless, transient, pay-per-use) VALUE STATEMENT TO HEAD OF OPERATIONS Rapidly connect your industrial facilities to the cloud and unlock the insights in your data to optimize operations, improve productivity and your customer experience. FUTURE PROOF | FLEXIBLE | EXTENSIBLE https://aws.amazon.com/solutions/industrial/industrial-data-platform/
- 10. © 2022, Amazon Web Services, Inc. or its affiliates. All rights reserved. Execute a modern data strategy: evolving ISA95 Monolithic Pyramid • Standalone applications • Data silos • Poor upstream/downstream communication • Disparate proprietary protocols Smart Factory Near Future Applications Edge & ML Control & Field PLM ERP Big Data ML 3rd Party Security NLP Asset Mgmt. MES IoT Enabler of Industry 4.0 Edge/IoT Data Platform • IT - OT border is gone • Any to any communication • Data transparency • Edge / Cloud hybrid model • Cloud computing revolutionized IT • Flexible shop floor connectivity • Descriptive protocols • System integration Converging IT and OT Today/Tomorrow PLM MES ERP Big Data Machine Learning 3rd Party Security IT OT ERP PLC, DCS Sensors & actuators MES SCADA/HMI Management level Field and control level Traditional
- 11. © 2022, Amazon Web Services, Inc. or its affiliates. All rights reserved. EDGE CLOUD DATA MANAGEMENT • Seamless Integration with Enterprise and non-AWS Systems Catalog of repeatable patterns for a variety of use cases • External consumption OT/IT Data Service • Governance • Enriched process data with enterprise data • ML Pipeline integrated with available AI services • Searchable asset hierarchy • Process and Machine Modeling • Automated tag ingestion • Edge: connectivity blueprint, management, apps, inference @ scale • Shop floor manual data entry • Prescriptive insights (eg predictive maintenance, forecasting) • Data Science and analyst workbench • Near real-time KPI dashboards BUSINESS APPLICATIONS DATA INGESTION IT/OT DATA SOURCES Non-AWS APPS & TOOLS DATA INSIGHTS • Machine / equipment data across vendors • Operations data across plants and Supply Chain • External IT Systems data across vendors END-END SECURITY | DEV OPs | ML OPs •Maximum Insights • Raw material impact to production plan • SKU margin optimization • Utilization and product flow bottle necks • Democratized access to data •Minimum Tech Debt • Native integration with key systems • Automated deployment pipeline • Common tools across OT/IT • Central control, distributed execution • Repeatable for rapid deployment • Catalog of use case patterns •Future proof • Open platform – leverage best in class • Modular – start small, scale fast • Flexible - brownfield/greenfield Think Big -> Start Small -> Scale Fast (North Star Vision)
- 12. © 2022, Amazon Web Services, Inc. or its affiliates. All rights reserved. EDGE CLOUD SECURITY MANAGER DATA MANAGEMENT DEV OPS ML OPS BUSINESS APPLICATIONS DATA INGESTION IT/OT DATA SOURCES NON-AWS APPS & TOOLS INTEGRATION DATA INSIGHTS Energy Optimization Production & Asset Optimization Preventive Maintenance Automated Material Movement Ops. KPI Dashboard AI / ML Insights BI Reporting Data Normalization Data Warehousing Data Processing Pipeline Data Contextualization Hot Data store Partner Apps 3rd Party BI Tools Analytics Tools ERP Data@ABC … Manual Data Entry Apps Digital Twin Self-service Robotics Insights Forecasting Automated Quality & Defect Inspection Flexible Access Data Integration Big Data Processing ML Pipeline Data Catalog Warm/Cold Data store Data Quality Framework Data Governance Workflow Orchestration Discovery & Search Specialized Data store Connectivity Blueprint Ops KPI Dashboards Gateway Mgmt. Edge Apps (AWS & Partner) Protocol Conv. Integration AI/ML Insights Operational Data (Machine, Equipment, PLC) Historians Enterprise/IT/OT Data (MES, ERP, MRO, QMS, CSV…) SCADA Computer Vision Robotics Data Ingestion Connectors Hot Data store Why a holistic approach (Think Big -> Start Small -> Scale Fast) •Maximum Insights • Unified data backbone • Right tool for the job/persona • Data sharing across stakeholders •Minimum Tech Debt • Process agnostic blueprints • Avoid patch work of point solutions • CICD pipelines up/down stack • Leverage managed services to minimize undifferentiated heavy lifting • Cost effective • End-end security at rest and in transit •Future proof • 2-way door open architecture • Decoupled “data bus” (microservices) for modular design • Seamless integration with vendor solutions • Extensible and flexible for continuous improvement
- 13. IDP Enabled Use Cases • Simulation, modeling of plant floor operations • Reskilling/Upskilling, Worker training Predictive Maintenance Digital Twin & AR/VR Operations Planning • Real time tracking of high value inventory, WIP stock • Real-time insights to customers, ETA on goods • Track assets, equipment and parts out on maintenance • Shipment tracking Track and Trace Defect Tracking/Warranty Claims Predictive Quality Manufacturing Operations • Cycle Time Management and monitoring, automated dashboards • Identify micro-stoppages • Track output at each cell, automated dashboards of production KPIs • Live dash-boarding of plant floor productivity, operations • Baselining Equipment Measurements of ambient conditions – sound, vibrations, heat levels • Single pane of glass view in plant floor • Plant Floor operations, global operations view • Monitoring of sensors on the factory equipment • Categorization of faults can be analyzed across multiple assets, even multiple operators, to spot trends • Digital Twin of the Equipment/Machinery • Simulation Models Plant Control Tower • Single pane of glass on factory operations • Remote monitoring of Equipment/Machines – IoT sensors, vibration, acoustics, video/camera feeds • Remote monitoring of finished products, product usage trends, failure prediction, heartbeat Safety/Accident Prevention • Detect unsafe conditions for safety of workers • Computer vision, camera placement on plant floor • Safety wearables – belts, straps, monitor bending, unsafe worker movement • Detect defects early in production run, reduce scrap, defects at end of production cycle • Track inventory parts for each batch; detect micro- stoppages due to faulty parts • Trace warranty claims to supplier parts • Reduce warranty claims • Monitor product output, throughput • Computer Vision – product quality monitoring • Product quality variance - Historical analysis based on product performance data • Plan work-orders, scheduling, coordinate production planning, optimized workloads • Predictive modeling, Predict demand peaks • AI/ML for work order optimization, reduce change time • Computer vision, video/camera feeds • AI/ML based anomaly detection – Sensor, audio/video, Acoustic data AI/ML on Plant Floor Highly visible, impactful, and repeatable across Plants and Enterprise level Sustainability • Save energy (electricity, water) • Reduce carbon footprint
- 14. © 2022, Amazon Web Services, Inc. or its affiliates. All rights reserved. Learn about AWS training and best practices • Resources created by the experts at AWS to help you build and validate cloud skills 30+ free digital courses cover topics related to cloud security, including Deep Dive into AWS IoT Security Primer, AWS IoT Authentication and Authorization, Introduction to Amazon GuardDuty, and Deep Dive on Container Security Validate expertise with the AWS Certification Classroom offerings, such as Security Engineering on AWS, feature AWS expert instructors and hands-on activities Well-Architected Framework, IoT Lens, and IoT security best practices guide, OT whitepaper, Security Golden Rules
- 15. Engage with us Other AWS sessions © 2022, Amazon Web Services, Inc. or its affiliates. All rights reserved. Security at the Edge How AWS IoT SiteWise provides Industrial customers with a secure, cost-effective, and reliable field-to-cloud solution How to manage your industrial IoT's digital transformation safely & securely Sept 13th 11.30 am – 12.00 pm Sept 16th 12.00 pm – 12.30 pm Sept 13th 1.00 pm – 1.30 pm
- 16. © 2022, Amazon Web Services, Inc. or its affiliates. All rights reserved. Thank you! Neel Sendas nsendas@amazon.com
Editor's Notes
- Hello Everyone – Welcome to the session on Breaking down an Industrial IoT reference architecture. My name is Neel Sendas – I am a Principal Technical Account Manager at AWS. In my role I work an a customer advocate and operational excellence champion for the customer. I manager the operations of one of the worlds largest manufacturing companies in the world. I am also part of our AWS IoT field community and work closely with your IoT product manager to provide service feedback to our product managers.
- Like most legacy industrials, equipment and machines are few decades old with much of the production data produced trapped in these aging machines and customers want to make those machines smart. The common anecdotes we hear from our customers are: We are fairly productive most days but some days our operations are chaotic due to machines going down sometime. And when that happens, it takes us long to get to the root cause due to our data lives in silos. However, we do track most of the essential stuff on paper and some digitally as well. We may have more capacity but we have no near real-time visibility of what’s happening on the shop-floor such as monitoring OEE (Overall Equipment Effectiveness) in near real-time at plant, line, and all the way down to the machine level or a near real-time view of inventory and supply chain.
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- A key characteristic of Industry 4.0 systems is decentralized decision-making (Alan et al., 2015; Mittal et al., 2017), which means that the common hierarchical layout of shop floor IT needs to change. The idea is that every entity of the system becomes more autonomous with the ability to communicate directly with any other part of the system.
- If we sum up all our customer learnings thus far, these are the common use cases that IDP enables. There is a lot of talk about these but let me touch on the key ones. Most customers would like to start at predictive equipment maintenance that reduces if not eliminates unscheduled downtimes of key equipment and machines and hence able to bring productivity KPI up such as overall equipment effectiveness (OEE) that most manufacturers struggle to get to 85% being north star. One of the key findings from predictive maintenance engagements is that customers actually don’t have their data in the right place to see a predictive maintenance outcome and once they are successful, they also want to scale the solution immediately across plants and hence, the unified data backbone is critical as you work backwards. Sustainability is at the top of the mind for every Customer across the board. Simple outcomes such as saving energy (electricity and water) are simple to implement in weeks by increasing actionable visibility to the data. Complex use cases are machine learning based that looks at years of historical data and apply advanced techniques to reduce carbon footprint by providing prescriptive recommendations. Automated quality / defect management with near real-time visibility and root cause analysis to improve and eventually predict quality issues is the goal of every manufacturer. Within process manufacturing space, monitoring key process parameters that can affect product quality with actionable alerts/notifications is one of the common yet simple use cases that can save millions of $ by reducing scrap and warranty claims. Another common customer ask is to be able to monitor operations across all of their plants / sites in near real-time using a single pane of glass view with standardized virtual assets across plants while physical assets can continue to be the way they are setup. This helps them to compare and contrast the plants that are doing well vs that are not. In reality, no 2 plants run the same way, so this is one such outcome that is becoming a reality for lot of these customers due to unlimited cloud scale and managed services. Advanced customers want to build a Digital Twin about their physical assets and processes virtually to help with remote monitoring as an example so that a physical inspection is not required by an OT person. Advanced customers are looking to perform what-if analysis using physics based simulation before rolling out a new set of setpoints for an equipment or process in production.