Achieving a 360 degree view of manufacturing
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Continuously improving factory operations is of critical importance to manufacturers. Consider the facts: the total cost of poor quality amounts to a staggering 20% of sales (American Society of Quality) and unplanned downtime costs plants approximately $50 billion per year (Deloitte). The most pressing questions are: which process variables effect quality and yield and which process variables predict equipment failure? Getting to those answers is providing forward thinking manufacturers a leg up over competitors. The speakers address the data management challenges facing today's manufacturers, including proprietary systems and silo'ed data sources, as well as an inability to make sensor-based data usable. Integrating enterprise data from ERP, MES, maintenance systems and other sources with real time operations data from sensors, PLCs, SCADA systems and historians represents a major first step. But how to get started? What is the value of a data lake? How are AI/ML being applied to enable real time action? Join us for this educational session, which includes a rare view from one of our SWAT team experts into our roadmap for an open source industrial IoT data management platform. Key Takeaways: • How to choose an initial project from which to quickly demonstrate high value returns • Understand the value of multivariate data sources, as opposed to a single sensor on a piece of equipment • Understand advances in big data management and streaming analytics that are paving the way to next-generation factory performance MICHAEL GER, General Manager, Manufacturing and Automotive, Hortonworks and RYAN TEMPLETON, Senior Solutions Engineer, Hortonworks
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Achieving a 360 degree view of manufacturing
- 1. 1 © Hortonworks Inc. 2011–2018. All rights reserved Achieving a 360 Degree View of Manufacturing via Open Source Data Management Michael Ger General Manager, Manufacturing & Automotive, Hortonworks Ryan Templeton Solutions Engineer, Hortonworks June 20, 2018
- 2. 2 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Title Goes HereThe Industry 4.0 Imperative Total cost of poor quality amounts to a staggering 20% of sales American Society of Quality Warranty costs to companies amount to approximately 2-3% of revenues Warranty Week Deloitte Unplanned downtime costs manufacturers approximately $50 billion per year Poor maintenance strategies can reduce plant capacity by 5 to 20% Deloitte McKinsey Through 2016, just 16% of manufacturers have adopted Industrial IOT strategies
- 3. 3 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Title Goes HereBig Data For Manufacturing – So What’s New? Data Variety OT/IT convergence (Sensors, PLCs, Historians, MES, ERP, Quality) and New Data Sources (Image, Video, Audio, Social, RFID, Logs, etc.) Expanded Analytics Scope From individual process focus to end-to- end process optimization Data Volume Industrial Internet data to grow 2X faster than any other data source Computational Power New massively parallelized computing support ML and AI, critical to next generation manufacturing
- 4. 4 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Title Goes Here Highest value data Always on, always connected devices generate a constant stream of data related to the operations of industrial businesses These datasets contain: • What events occurred • Why and event occurred, or not • Quantification of an event’s impact These datasets go by many names: • “SCADA Data” • “Control System Data” • “Historian Data” • “Machine Data” • “Measurement Logs” • “Telemetry” How are my … People? Processes? Equipment? Lots of misnomers
- 5. 5 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Title Goes HereChallenges in accessing machine level data Control Systems Data is transmitted via proprietary vendor specific protocols Direct Integration with control systems requires protocol translation/parsing for each platform family Nifi’s is a toolbox of connectors Ingest text files and interrogate REST APIs using built in connectors Connect to industry standard protocols like OPC UA with custom processors Build your own Existing ICS Components PLC, RTU & DCS Open Source Tools Process Historians & OPC Servers Data is typically available via programmatic access such as OPC, API or SQL There is almost always an option to create text files Instrumentation Commonly only output is electrical signals Integration with sensors requires specialized hardware serial bus, or wireless are increasingly available
- 6. 6 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Title Goes HereThe Data Management Challenge for Manufacturers Data is Siloed • Datasets are dispersed and difficult to access • Insights are based sourced from the silos are narrow & incomplete • Historical data (years) is not easily accessible and often takes longer than expected to extract • Data required to solve real problems comes from several different sources (lab systems, product scheduling) and requires significant manual effort to pull the data together Considerable effort to leverage • Lack of connectivity to proper data analytics tools • Python • MATLAB • SAS • R • Inability to find data and a reliance on “tribal knowledge” and previous engineer’s spreadsheets to find tags and queries Data Analytics Missing Lack of Resources Inability to Leverage BOTH OT and IT Data Sources • Time dependence limits how data sets can be processed • Point solutions are almost always closed source, locking users into closed ecosystems • Do not scale with exponential data growth
- 7. 7 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Title Goes HereTime Series data is emerging as the fastest growing type of data
- 8. 8 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Title Goes HereThe Opportunity for a Connected Data Platform Real-Time Data from Connected Manufacturing and edge analytics drive the need to manage data in motion – Hortonworks Data Flow (HDF) is a comprehensive solution to manage data in motion Big Data Analytics require the ability to store and process vast amounts of big data at rest – Hortonworks Data Platform (HDP) is a comprehensive solution to manage data at rest Modern Connected Manufacturing applications require a Connected Data Platform - managing both data-in-motion and data-at-rest Real-Time Data Big Data & Analytics MODERN DATA APPS DATA IN MOTION DATA AT REST
- 9. 9 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Title Goes HereStepwise approach to these challenge Remote Field or Manufacturing Site RDBMS & EDW Files / Other Unstructured Data Video IoT Gateways WITSML SCADA, DCS, PLC, RTU, Historians Location 1 Data Consumers Data Marts Analytics, Statics & Science Visualization & Dashboards
- 10. 10 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Title Goes HereData lakes only address data access Field or Manufacturing Site RDBMS & EDW Files / Other Unstructured Data Video IoT Gateways WITSML SCADA, DCS, PLC, RTU, Historians Location 1 Data Consumers Data Marts Analytics, Statics & Science Visualization & Dashboards
- 11. 11 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Title Goes HereScalable data collection is required Field or Manufacturing Site RDBMS & EDW Files / Other Unstructured Data Video IoT Gateways WITSML SCADA, DCS, PLC, RTU, Historians Location 1 Data Consumers Data Marts Analytics, Statics & Science Visualization & Dashboards
- 12. 12 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Title Goes HereWinners Will Embrace the Connected Manufacturing Data Lifecycle Example: Equipment Predictive Maintenance (Plant Uptime) REAL-TIME DATA SOURCES ENTERPRISE TRANSACTION SOURCES ERP, MES, QMS, Maintenance, ETC. MANUFACTURING DATA LAKE 6 REAL-TIME MAINTENANCE ACTION ACT 1 I N G E S T 1 INGEST 2 STORE 3 PROCESS 4 •Data Discovery •Business Intelligence ANALYZE MONITOR 5 Deploy Connected Manufacturing Sensors PLCs Historians SCADAs 4 LEARN •Develop Models •Machine Learning Model Inputs • Historic sensor values • Historic production • Historic defects & repair orders • Historic maintenance records • Design parameters, etc.
- 13. 13 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Title Goes HereSolution Component Detail ENTERPRISE TRANSACTION SOURCES MANUFACTURING DATA LAKE 6 1 I N G E S T 4 4 Deploy INGEST MONITOR STORE AND PROCESS ANALYZE LEARN 1 5 REAL-TIME DATA SOURCES Connected Manufacturing Sensors PLCs Historians SCADAs ERP, MES, QMS, Maintenance, ETC. REAL-TIME MAINTENANCE ACTION ACT Model Inputs • Historic sensor values • Historic production • Historic defects & repair orders • Historic maintenance records • Design parameters, etc.
- 14. 14 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Title Goes HereThe Road to Big Data Transformation – Maturity Phases Data Consolidation into Data Lakes Data for Analytics Process 360 FOCUS BENEFIT EXAMPLE EXPLORATION Machine Learning End-To-End Optimization Predictive Maintenance, Yield Optimization FOCUS BENEFIT EXAMPLE OPTIMIZATION Streaming Analytics Real-Time Actions Real-Time Maintenance Actions FOCUS BENEFIT EXAMPLE TRANSFORMATION Individual Process, Equipment, Location Operational Dashboards FOCUS BENEFIT EXAMPLE AWARENESS Point Optimization
- 15. 15 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Title Goes HereManufacturing Operations – Sample Use Cases Process 360 • Single-point access to critical information for analytics Process & Quality Monitoring • Reduce non-conformance, downtime, scrap and late shipment costs Equipment Predictive Maintenance • Reduce equipment downtime and maintenance costs Quality Event Forensic Analysis • Reduce scope of service campaigns and recalls Quality & Yield Optimization • Optimize process variables to improve yields and quality C O M P L E X I T Y Use Case Benefits
- 16. 16 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Title Goes HereOptimizing Pharmaceutical Yields using Big Data Insights Manufacturing Major Biopharmaceuticals Manufacturer Core Use Cases • Manufacturing Yield and Quality Optimization After 15 billion calculations and 5.5 million batch-to-batch comparisons, discovered characteristics closely correlated to yield Solution: Manufacturing Data Lake with Yield Optimization Analytics Month 1: Data loaded into Hadoop (sensors, historians, ERP) Month 2: Analytics to cluster every batch of vaccine ever made Month 3: Development of models, testing against all historical data Problem: Higher than desired discard rates (lower yields) on certain vaccines Root cause analysis hampered by huge data volumes and spreadsheet based analytics Researchers could only look at a batch or two at a time
- 17. 17 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Title Goes HereExample (Pharmaceutical Manufacturer) Machine Learning
- 18. 18 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Title Goes HereFrom Refinery to Enterprise Level Analytics Problem: Refinery-level analytics sub-optimizes performance Analytics performed at each refinery in Excel spreadsheets Missed opportunities for optimization based on larger data sets Solution: Centralize data in Manufacturing Data Lake for analytics Ingest data from each refinery using with HDF into centralized Data Lake Initial data set was over 1 million data tags, grew to 6 million Data Types: Time series, raw materials, quality results, SAP work order data, etc. Benefits: Enterprise level-analytics to optimize performance ROI Analysis $106 million in cost savings per year 20X ROI annually Oil Refining Multinational Oil & Gas Company Core Use Cases • Blend Monitoring • Corrosion Prediction • Analyzer Reliability Analytics • Heat Exchanger Performance Analytics • Inferential Models Analytics
- 19. 19 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Title Goes HereData Lake for Connected Vehicle and Quality Analytics Problem: Data silos impeded Connected Vehicle, quality management and other cross-functional strategic initiatives Lacked infrastructure for storing long-term vehicle connected vehicle driving data Data silos impeded ability to achieve a 360 degree view of vehicle quality data for analytics Solution: Implemented Enterprise Data Lake for cross-functional analytics Creating an Enterprise Data Lake for a wide range of data including dealership repair orders, warranty claims, manufacturing shop floor and inspection data, electric vehicle usage and performance, vehicle status and usage patterns Creating a unified Quality Data Lake including design quality, production quality and final assembly quality data Benefits: Cross-functional analytics leverages All enterprise data Data lake supports cross-functional data analytics for all types of data Platform planned to store 500TB of data in 2017 Wide-ranging data analytics use cases planned for future Automotive Major Automotive OEM Core Use Cases • Connected Vehicle & Value-Added Services • Quality Event Detection & Traceability
- 20. 20 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Title Goes HereVehicle Lifecycle Quality Analytics Enterprise Quality Data Lake Provides Foundation for Rapid Time-to-Resolution Voice of Vehicle Telematics Voice Of Customer • DTC’s • Performance Parameters Social + Web (Forums, Surveys, etc.) Call Center Service & Warranty • Likes • Dislikes • Sentiment • Complaints • Claims What Happened? 1 Problem Voice Of Supplier Production and Operations Voice Of Factory Production and Operations • Man • Method • Material • Machine • Man • Method • Material • Machine Why Did It Happen? 2 Installed Base Voice Of Field • VINs • Configurations • Locations Contain The Problem Voice Of Design3 • Designs • Parts/BOMs • Features • Specifications Product Development
- 21. 21 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Title Goes HereWhy Open Source for Industry 4.0 Big Data Initiatives? Data Volume Innovation Lower Risk No Vendor “Lock-In” Open Source Community Economics
- 22. 22 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Thank You
Editor's Notes
- The American Society of Quality (http://ASQ.org) estimates that the total cost of poor quality (COPQ) amounts to a staggering 20 percent of sales According to Deloitte, poor maintenance strategies can reduce a plant’s overall productive capacity by 5 to 20 percent and unplanned downtime is costing industrial manufacturers an estimated $50 billion each year. On average, companies spend 2-3% of their annual revenue on warranty costs. And perhaps one of the biggest issues: consumers are informed and have choices: – 91% of unhappy customers will n ever purchase from that company again.
- Or lower this down….
- Quote manufacture size limits
- Market Intelligence indicates that Time series is a rapidly growing area of interest for IT departments as show by this chart from DB-Engines.com This is confirmed by our own experience in the field working with customers Capturing and managing time series data sets is always in the top few uses for our customers in many market segments
- From a market perspective, it’s important to understand and appreciate the intersection of the big data & analytics market and the Internet of Things market. Modern customer-centric data applications are fueled by both data-in-motion and data-at-rest. The result is actionable intelligence derived from ALL available data that aligns the business with its customers and drives next generation business models. [NEXT]
- Broadly our role at Hortonworks putting good data and high quality tools into the hands of the right people Like so many things this too is easier said than done and many organizations have navigated the “organizational tangle” with point to point connection of data sources to specific data consumers Every new consumer or data is a new end to end project Unfortunately this is very resource intensive to maintain and doesn’t support “self service” “or one stop shopping” information delivery models
- Step 1 for many organizations was to begin building central repositories or “Data Lakes” This approach simplified architectures from the unmanageable many to many to a many to one scenario Data democratization occurred because it became simple manage different consumption preferences from a single place However, data lakes did not make “getting the data” any easier for the ICS data sources Because: Large variety of data sources Simple Embedded systems such as instrumentation Software installed on commodity hardware Integrated hardware software suites Further there are cn wide spread standards in Communications media or protocols Data types Information models The data sources live at the tattered and ragged edge of most organizations network It is common to find devices that are remote, old, have limited power, and even more limited limited bandwidth Finally due to the potential for malicous mis use of ICS devices they must operate on secured networks to ensure Safety of the people and property Security of sensitive information ( process knowledge )
- Enter HDF and its robust data collection capabilities to aid our customers in acquisition of data from ICS devices and delivery of the acquired data to the access engines best suited for associated data consumer Nifi provides Access to a large variety of sources and sinks through its Build in Large library of processors Easy assembly of customer processors when customer protocols are encounters When devices are near that tattered and ragged edge of a network Nifi components such as Minify can enable Store and forward, compression, batch delivery capabilities to help overcome limitations imposed by the devices and networks A comprehensive suite of security tools are available to help meet the demands of ICS Security Requirements we data must be extracted from secured networks such as Kerberos, SSL, 20+ encryption cyphers for data, and standardized protocols amount the HDF components
- Challenges and Opportunities Experienced higher-than-usual discard rates on certain vaccines Investigation of causes hampered by huge data volumes and spreadsheet based analytics Data sources include process-historian systems on the shop floor that tag and track each batch. Maintenance systems detail plant equipment service dates and calibration settings. Building-management systems capture air pressure, temperature, and other readings in multiple locations at each plant, sampling by the minute. Aligning all this data from disparate systems and spotting abnormalities took months using the spreadsheet-based approach, and storage and memory limits meant researchers could only look at a batch or two at a time. Process In the first month, the team loaded the data onto a partition of the cloud-based platform, and used MapReduce, Hive, and advanced dynamic time-warping techniques to aggregate and align the data sets around common metadata dimensions such as batch IDs, plant equipment IDs, and time stamps. In the second month, analysts used R-based analytics to chart and cluster every batch of the vaccine ever made on a heat map. Spotting notable patterns, the team then used R to produce investigative histograms and scatter plots, and it drilled down with Hive to explore hypotheses about the factors tied to low-yield production runs. Using an Agile development approach, the team set up daily data-exploration goals, but it could change course by that afternoon if it failed to find solid data backing up a particular hypothesis. In the third month, the team developed models, testing against the trove of historical data to prove and disprove leading theories about yield factors. Benefits/Results Hadoop enables the pharmaceutical company to crunch huge amounts of data resulting in the ability to develop and bring vaccines to market faster and at lower cost. The team was able to come up with conclusive answers about production yield variance within just three months Through 15 billion calculations and more than 5.5 million batch-to-batch comparisons, Merck discovered that certain characteristics in the fermentation phase of vaccine production were closely tied to yield in a final purification step. Merck intends to optimize the production of other vaccines now in development. They're all potentially lifesaving products, according to Merck, and it's clear that the new data analysis approach marks a huge advance in ensuring efficient manufacturing and a more plentiful supply. NOTES Yield optimization is a high value Big Data use case relevant to all forms of complex process manufacturing, from Semiconductor to Storage, Biotech, and Merck is a great example. It is common for Biotech manufacturers like Merck to monitor more than 200 variables in the complex fermentation process to produce vaccines, and Merck was experiencing far higher discard rates on some of their vaccines and they needed to determine the root cause of their costly yield variances.
- Solution: Manufacturing Data Lake and Yield Optimization Analytics at Merck Month 1: data loaded into Hadoop and aggregated and aligned the data sets around common metadata dimensions Month 2: analytics to chart and cluster every batch of the vaccine ever made on a heat map, spotting notable patterns and investigating further Month 3: team developed models, testing against trove of historical data to prove/disprove theories regarding yield factors After 15 billion calculations and 5.5 million batch-to-batch comparisons, discovered characteristics closely correlated to yield
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