Improve Your Manufacturing With Insights From IoT Analytics

Transcription

1 Improve Your Manufacturing With Insights From IoT Analytics Accelerated Time to Value With a Prebuilt, Future-Proof Solution Dr. Zack Pu Offering Manager, Industrial IoT Hitachi Vantara Dr. Wei Yuan Senior Researcher and Team Leader, Global Center for Social Innovation North America, Hitachi America, Ltd.

2 Introductions Dr. Pu is the Offering Manager for Industrial IoT from Hitachi Vantara. He leads definition and roadmap of manufacturing solutions offered by Hitachi. He has extensive experience helping manufacturers globally implement IoT initiatives on the manufacturing shop floor with a focus on business values. He has over 10 years combined academic and industrial experience in advanced manufacturing, specialized in machining, materials science and the industrial internet of things. Before Hitachi, he worked at leading cutting tool companies including Sandvik and Kennametal, where he led the development of the first IoT solution for machining optimization. He holds one patent and published about 20 highly recognized research papers in the area of advanced manufacturing. Education Ph.D., Mechanical Engineering, University of Kentucky, USA Dr. Wei Yuan is a Senior Researcher and Team Leader at Hitachi America, Ltd. - R&D Division, where he leads the Advanced Materials and Smart Manufacturing team to support Hitachi Group companies in North America. Prior to this, Wei received his MS in Materials Science and Engineering and Ph.D. in Metallurgical Engineering from the University of Missouri-Rolla in 2008 and 2012, respectively. Wei received the United States Council for Automotive Research (USCAR) 2013 Research Partner award, the first Hitachi Inspiration of the Year Global Award (2nd prize) in 2014 and Hitachi Global Center of Social Innovation Global Award in Wei is the author of over 40 technical publications, including one book. He also surveys as an active reviewer for more than 10 international Journals. Wei is a member of The Minerals, Metals & Materials Society (TMS).

3 Table of Content Dr. Pu! Current challenges of IoT adoption in manufacturing! Why is it important to find an IoT partner with both OT and IT backgrounds?! What are the different levels of maturity regarding IoT in manufacturing?! How a prebuilt solution helps you speed up implementation and get quick ROI Dr. Yuan! How Hitachi is implementing IoT in its own factories! A case study for predictive quality Hitachi America, Ltd All Rights Reserved

4 Agenda Challenges for IoT Adoption in Manufacturing Hitachi and Lumada IoT Solutions Manufacturing Solutions by Hitachi OEE Manager Manufacturing Solutions by Hitachi Prediction Manager Q&A

5 Challenges for IoT Adoption in Manufacturing Disconnected Environment 90% of machines in factories are not connected Data Sits in Silos! Highly heterogeneous environment, a large variety of machine types/oems and proprietary protocols (OT Data Silos)! Data from different departments across planning, production, to quality inspection sits in its own databases (IT Data Silos) Most IoT Vendors Lack Domain Knowledge Hitachi is uniquely positioned for digital transformation that creates value 1910 Difficult To Calculate ROI!! 88% of respondents do not fully understand underlying business models Too much focus on technologies Cross pollination of knowledge between Hitachi Vantara and various Hitachi BUs is the key to success

6 Hitachi s IoT Ecosystem Hitachi Vantara Lumada Platform Services and Solutions Customer Challenges Co-creation Partners Technology Vendors Systems Integrators Hitachi Business Units Industrial Power & Energy Water Automotive Healthcare Urban Hitachi R&D Global Center for Social Innovation (CSI)

7 Hitachi s Lumada Portfolio Customer Solutions (Hitachi BUs, ISVs, SIs) Lumada Co-creation Outcome Workshops Solution Architecture Data Science Proof of Value Lumada Applications Maintenance Insights Manufacturing Insights Video Insights (more) Lumada Platform Services Edge Management Data Caching & Filtering Device Management Data Analytics Data Operations Digital Twin Modeling Video Data Management Machine Learning Orchestration Replication and Synchronization.. Stream Processing Data Streaming Reporting & Visualization Data Integration Embedded Analytics Data Pipeline Optimization Vantara Edge-to- Cloud Infrastructure Edge Appliances Converged & Hyperconverged Block, File and Object Storage

8 Manufacturing IoT Journey Mature Integrated Demand / Capacity Planning Digital Journey Early Predictive Quality - Early Detection - Root Cause Analysis Asset Utilization Optimization Anomaly Detection Manufacturing Solutions - OEE Manager - Downtime - Quality - Cycle Time - OEE Cycle Time Monitoring Manufacturing Solutions Transformation Manager Manufacturing Solutions Prediction Manager Real-Time Alerting Product Quality Improvement Predictive Maintenance - Remaining Useful Life - Performance Degradation Energy Optimization E2E Supply Chain Optimization Mass-Customized Products Function Specific Scope End to End

9 Manufacturing Solutions by Hitachi OEE Manager 9

10 Manufacturing Solutions OEE Manager What Problems Are We Solving? Solution Features Disconnected Environment Large variety of equipment types and OEMs that speak proprietary protocols Large Percentage of Unplanned Downtime Frequent interruptions to production, low machine utilization Poor On-Time Delivery Cycle time constantly exceeds plan causing production delays Product Defects and Rework Quality issues are detected far down the process Edge Capabilities Built-in support for common industrial protocols and edge computing Asset Utilization Optimization Real time visibility into asset production status; notify managers of any interruptions Cycle-Time Monitoring Built-in analytics to calculate cycle time and send alerts when deviation is detected Anomaly Detection Early detection of potential product quality or asset health issues using sensor data

11 Manufacturing Solutions OEE Manager! Built-in templates to satisfy most common needs from OT managers OEE Monitoring Cycle Time Tracking Pareto Analysis for Downtime or Quality Anomaly Detection! Templates: analytics and UI to translate raw data into actionable insights that OT managers can act upon! Pre-defined and customizable OT user personas! Significantly reduced time-to-value! Composable architecture that works with your existing investment! Flexible deployment: public cloud or on-prem 57% 85.1%

12 Manufacturing Solutions OEE Manager Cycle Time Template Downtime Tracking Template 61%

13 Manufacturing Solutions OEE Manager Sensor KPI Template Anomaly Detection Template Anomaly Index 61% 61%

14 Manufacturing Solutions OEE Manager OT Role-Based Solution! Head of Operations Focused on business goals for global operations, achieving overall production targets and making investment decisions! Plant Manager Seek to optimize production by reducing downtime, improve quality, control costs, maintain safety of labor and ensure compliance to regulatory norms! Line Supervisor Focused on daily operations of production lines, maintaining smooth operations and continuous improvement opportunities! Machine Operator Focused on daily operations of machines and respond to alerts

15 Manufacturing Solutions OEE Manager Live Demo

16 Manufacturing Solutions by Hitachi Prediction Manager 16

17 Manufacturing IoT Journey Mature Integrated Demand / Capacity Planning Digital Journey Early Predictive Quality - Early Detection - Root Cause Analysis Asset Utilization Optimization Anomaly Detection Manufacturing Solutions - OEE Manager - Downtime - Quality - Cycle Time - OEE Cycle Time Monitoring Manufacturing Solutions Transformation Manager Manufacturing Solutions Prediction Manager Real-Time Alerting Product Quality Improvement Predictive Maintenance - Remaining Useful Life - Performance Degradation Energy Optimization E2E Supply Chain Optimization Mass-Customized Products Function Specific Scope End to End

18 Use case: Automotive Component Factory Machining Center workpiece cutter Machining (metal cutting)

19 Challenges Faced! Unexpected downtime due to sudden quality problems! Difficult to implement 100% quality (geometry) check! Chance of defective part outflow to assembly line! Lack of digitized production quality data! Time-consuming defect root cause analysis Hitachi America, Ltd All Rights Reserved

20 Solution Needs Predictive Quality! Unexpected downtime due to sudden quality problems! Difficult to implement 100% quality (geometry) check! Chance of defective part outflow to assembly line! Lack of digitized production quality data! Time-consuming defect root cause analysis Process Quality Visualization Process Anomaly Detection Process Change Detection Hitachi America, Ltd All Rights Reserved

21 Predictive Quality IoT Solution Development Machining equip. In-Process Data Acquisition Controller DAQ module Storage Data (motor, CNC, sensor) Acquire setting data & key feedback data related to actual defects Process Quality Visualization Extract specified features & recognize pattern to detect known process anomaly Process Anomaly Detection DAQ module Processor Feature value B Acquired data Feature value A Process Change Detection Status indicator (Distance) Reference data Reference space Supervised learning Storage Staff PC Visualize process quality for rapid root cause analysis Web API Dashboard DAQ module Processor Extract whole features & recognize pattern to detect unknown process change Feature value B Acquired data Feature value A Status indicator (Distance) Reference data Data cluster Unsupervised learning Hitachi Hitachi Vantara America, Corporation Ltd All Rights Reserved

22 Predictive Quality Factory Implementation Storage Motor data CNC data Sensor data Processor Feature value A Unsupervised learning Alert Feature value B Process Anomaly Detection Operator Staff PC Process Change Detection Feature value B In-Process Data Acquisition Machining equip. DAQ module Alert Process Quality Visualization Production engineer System Installation Feature value A Supervised learning Hitachi Ltd All Rights Reserved Hitachi VantaraAmerica, Corporation

23 Predictive Quality Summary Rapid root cause analysis can be conducted by In-Process Data Acquisition and Process Quality Visualization 100% quality check is realized via Process Anomaly Detection Unexpected change can be captured by Process Change Detection Overall machine downtime reduced by ~ 15% Hitachi America, Ltd All Rights Reserved

24 Thank You Thank You

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