Slide 1 Aggregate, Contextualize and Visualize Your Operations Metrics with Wonderware Intelligence (WW OPS-16) Michael Schwarz Product Marketing Manager-MES, EMI Christian-Marc Pouyez Product Manager – Intelligence & CEM © 2012 Invensys. All Rights Reserved. The names, logos, and taglines identifying the products and services of Invensys are proprietary marks of Invensys or its subsidiaries. All third party trademarks and service marks are the proprietary marks of their respective owners. Agenda EMI problem statement & definition Metrics & KPIs for Business improvement Wonderware Intelligence EMI for everyone, Use your own device, Mobile EMI Demo • Pre-defined content, value in minutes • Mobile • Integrated demo, expand with workflow Business cases: Intelligence in action Roadmap Slide 3 Many Industries Use Analytics (Intelligence) to Manage Key Business Challenges Many Industries Use Business Intelligence to Manage Key Business Challenges • Sales & Marketing • Financial Performance More Data and “big Data” enabled more Knowledge & Benefits • Customer Data –what they buy, where they buy, what products… • On-line Sales and web visit analyses – what products, what features New technology drives new value • New storage solutions - faster analysis of large data • Analytics for the Masses – casual users • Mobile Analytics – more use Slide 4 Plants already have “big Data” – but a desire for information Slide 5 Manufacturing Performance Gaps In a 2006 AMR Research analysis of more than 200 manufacturers on manufacturing performance gaps, the following issues were rated as extremely important: • Multisite visibility of production key performance indicators (75% of respondents) • Enterprise visibility into production site financial performance (76%) • Information integration between plant-floor applications and ERP (81%) Slide 6 What does that Mean for the Shop Floor? The value of Intelligence approaches has been proven for improving sales, marketing, and financial performance • This is driving the market for solutions that can take Analytics to the masses • Industrial organizations have Big Data about their internal operations and a willing set of workers The time is now to empower operations with a new level of Industrial Intelligence and analytics and unleash their ability to drive performance to higher levels Slide 7 Enterprise Manufacturing Intelligence (EMI) EMI vs BI EMI bridges the transactional world with the real-time world to provide both a high level view as well as detailed, drill down views of your processes. BI—Business Intelligence—offers similar functionality, but its timeframe is not real/near time and its focus can be marketing, sales, vs operational Slide 8 What drives the desire for more Intelligence, even for the Enterprise Reduced costs & improved profits • Leads to operational excellence Integration of Operations Management • Link information from the shop floor to the business Collaboration • Supports & enforces standards & best practices Slide 9 Wonderware Intelligence Slide 10 Wonderware EMI Strategy Configuration & Administration Production/Process Information Model Wonderware & Non-Wonderware Data sources Slide 11 Share content & Collaborate Incremental approach Self-service access to information & content authoring Corporate Management req’s: • Compare metrics by site • Approved metrics definition • Granularity: month, site Plant Management req’s: • Track metrics by CI project • Granularity: week, line Engineering req’s: • Root-cause analysis by equipment, product, quality, downtime, etc. • Granularity: minutes, equipment Shift Supervisor req’s: • Track metrics by shift/team • Granularity: shift, line Operator req’s: • Track metrics variables during shift • Granularity: minutes, equipment Slide 12 Role-Based Information ∑ Put in context data from various sources and compute metrics Slide 13 Flexible, near-real time dashboards Ad-hoc analysis, drill-down, drill-through Consolidated reports, comparison across sites How does Wonderware Intelligence work Dashboards Analytics Reports Easily analyze, report and visualize: Quality by Product or Equipment Energy consumption by Equipment Energy Consumption by Product type Material cons. deviation by Product Information Wonderware Intelligence Software Energy consumption Product X Energy Data Corporate Energy Management Application MES Data Batch Data Equipment used Manufacturing Execution System System Platform Slide 14 EMI Data Material flow Process Data Historians, Alarm Logs Aggregation of Flow over production time Quality check CMMS Data LIMS Data Peer Systems LIMS, CMMS Mobile IntelaTrac Node ERP System Architecture View Dashboards Create Dashboards & Analytics 3rd Party Clients Wonderware Intelligence Standard Clients Microsoft Office Reporting Services SharePoint Portal Published to Intelligence Server Wonderware Intelligence Analytics Clients Launched from IDE Click once deployment Wonderware Intelligence Server Wonderware Intelligence Data Services Runtime Services Adapter Services ArchestrA IDE Dashboard Server Data Store Published Dashboards Information Model Manager SQL Server Databases Slide 15 Oracle Databases CSV Files via OLE DB WW Historian OSIsoft PI ServerTM Application Galaxy Server Repository Runtime Advantages of Intelligence Data Store • Metrics and KPIs often need data elements from several sources (MES, Historian, Lab, etc.) Data Store provides a unified schema with all required information • Contextualizing/Aggregating data sources can be very resource intensive Data Store isolates transactional systems from the additional load of endusers running reports and analysis • End-users often have to wait minutes for reports to generate Data Store is optimized for retrieval and provides quick response times Historian •Profile temperatures •Production counts •Energy Slide 16 MES Intelligence •Type of product running •Line selected •Material selected •Throughput •Downtimes •Efficiencies by site •Efficiencies by line •Yields vs environmental factors (heat, temp, etc.) Pre-Defined Content Intelligence Model and pre-defined dashboards for: • • • • • MES InBatch Alarms Corporate Energy Management ROMeo Process: 1. 2. 3. 4. Install Intelligence Import desired Intelligence Model package Edit data sources, and start time. Deploy Open Pre-defined dashboards and view data! Up and running within minutes!!! Slide 17 Mobile Wonderware Intelligence Slide 18 Mobile Devices are Everywhere Today… Slide 19 Mobile Trends Smartphones are Taking Over the Mobile Phone Market [Source: Ehud Gelblum, Morgan Stanley Research] Slide 20 Mobile “Bring Your Own” Phenomenon Many companies are finding that the widespread adoption of increasingly sophisticated end-user computing and communication devices is forcing them to accept and support devices of the user's choosing. Slide 21 Demo Slide 22 Business Cases: Intelligence in action Intelligence is applied in several industrial sectors: Slide 23 • Food & Beverage • Metals • Mining • Pharmaceuticals • Oil & Gas • Energy Management • Rail • Electronics Food & Beverage Intelligence Data Store Process data Wonderware Historian • Wonderware • MES • • LIMS Production Calendar Slide 24 Dimensions: Reports: - Work Orders • Inventory Usage - Jobs • Genealogy Report - ShiftBenefits • Batch Log Report Work orders Equipment Faster Operational Decisions with end-users empowered with • Shift Report & Jobs Product Self-Service access to information • Downtime Report Downtime - Inventory Lots Scrap Reduction by knowledge of most important issues - Downtime Reasons Dashboards Better Quality by early awareness of issues and visibility • OEE by Shift, through Quality genealogyMeasures: Equipment, Product ResultsCustomer Service - Production Better by quickcounts identification of complain • Quality by Shift, Lab Data issues and proactive notification of issues Equipment, Product - Process Data • Process Data - Downtime Summaries Shifts Mining Wonderware Historian or OSISoft PI • • • Stock Piles LIMS • Alarms Downtime Specifications Slide 25 Intelligence Data Store Dimensions: Process data - Stock Piles - Ore type Dashboards/Analysis - ShiftBenefits • Tonnage movement - Equipment • Conveyer Speeds Stock Piles Faster Operational-Decisions with end-users empowered with Alarm Definitions by Equipment, Shift Self-Service access- toDowntime information Reasons Stock Pile, Ore, … Downtime • Alarm Summary Optimization of ore movements Measures: • Downtime by Optimization Alarms of stock pile selection/movement according to - Tonnage moved Equipment, Shift, quality - Conveyers Speed Ore Type,… Assay Resultsin downtime with better understanding of root Reduction - Assay Data • Quality by Stock causes - Process Data Pile details - Downtime - Alarm Counts Specifications Energy Management Intelligence Data Store Wonderware Historian Process data Reports: Dimensions: • Billing Benefits - Meter Definitions • Energy Manager Rate Structure • Faster Operational Decisions with end-users empowered with • Consumption Building/Assets Self-Service access to information Energy Dashboards • Consumption Energy cost reduction by wiser energy usage in peak periods Measures: • performers Intensity by Wonderware • Definition of best practices by identification of best - Consumption CEM Weather, Building, - Costwith respect to weather conditions • Definition of benchmarks Asset - Weather conditions • Consumption/Cost - Intensity by Rate, Building, Weather Asset Weather Data Slide 26 Intelligence Release Plan (subject to change) • Concurrent Licensing (pending agreement with Tableau Software) • Multi-language support in Tableau Clients Q1 2013 Slide 27 • Flexible & Managed ETL, using SSIS • Visual design time experience • Faster, incremental back-filling of data • Pass-through access to data via Overview Client • More content • Unified EMI Experience (Design & Visualization) • Metrics & Calculation Engine • Integration with System Platform, Next Gen Visualization • More content Q2 2013 2014 What we intend to deliver in Intelligence 2.0 (subject to change) Accelerate time to success by delivering a library of defined Information Transformation Scenarios for the Industrial Space Extraction of data from a wider variety of sources, including stored procedures Definition of measures based on aggregated and/or detailed data Definition of the execution order for measures and dimensions Incremental, reverse(?) back-fill of measures and dimensions Control of update rules for slowly changing dimension Simpler configuration of transformations from historian data sources New visual configuration environment ETL Monitoring and diagnostics tools More Pre-Defined content on Wonderware sources Dimensional Model easily recognized by client tools Time-Slicing of Event data Slide 28 Complementary Solutions Right tool for the right role and need Wonderware Historian Client • Trending of Historian data Slide 29 Wonderware Intelligence • Trending of Historian data • Dashboards • Analytics Wonderware Information Server • Trending of Historian data For more information on Intelligence • Product Website: http://global.wonderware.com/EN/Pages/WonderwareIntelligence.aspx • Local Distributor • Product Manager: christian-marc.pouyez@invensys.com • Product Marketing Manager: michael.schwarz@invensys.com Slide 30