Aggregate, Synthesize and Visualize Your Operations Metrics with Wonderware Intelligence

Transcription

1 Slide 1

2 WW INFO-03 Aggregate, Synthesize and Visualize Your Operations Metrics with Wonderware Intelligence Christian-Marc Pouyez / #SoftwareRevolution Product Manager Intelligence & CEM /InvensysVideos /Wonderware /company/wonderware 2013 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.

3 Greg Central Engineering Director Global Food & Beverage company Slide 3

4 Greg s Task: Implement global IT system for energy use reduction Overall project objectives: Baseline current energy usage Compute energy metrics Provide adequate visualization & analysis tools for end-users Implement energy reduction targets & track progress General Requirements: Compare energy metrics by product, production line & time Compare progress at each site/plant Keep 5 years of historical data Slide 4

5 End-user information requirements 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 5

6 Data Sources Process variables Environmental variables Equipment states Wonderware Historian Manufacturing database Production runs Product information Shift information Wonderware CEM Meter Definition Energy consumption Energy rates Energy Targets Slide 6 Energy Metrics by Product Equipment Shift

7 Repeat and Rollup to headquarters for 100 sites! HQ Slide 7

8 Solution: Wonderware Intelligence Server Product Wonderware Historian Equipment Metrics Energy Usage Production Runtime Manufacturing database Energy Targets Wonderware Intelligence Server Wonderware CEM Slide 8 Intensity Batch Time/Shifts

9 Visualization and Analysis Example workflow Reports Interactive Dashboards/Reports Metric Trend Investigate Analytics Drill-down to details Alarms Slide 9

10 Corporate Management Engineering Plant Management Shift Operator Slide 10

11 Wonderware EMI Strategy Enterprise Manufacturing Intelligence User-friendly Configuration & Administration Production/Process Information Platform Share content & Collaborate Wonderware & Non-Wonderware Data sources Incremental approach Slide 11 Self-service access to information & Self-reliance content authoring

12 Aggregates Wonderware self-service information clients Intelligence Dashboards: Flexible access to metrics in context Intelligence Analytics: Slice and dice metrics with context SmartGlance: Metrics on any device, anywhere, anytime Details OverView: Navigate through data with context Historian Clients: Detailed time-series data

13 Intelligence Clients Powered by Tableau Software Analytics Client enables end users to: Quickly answer questions Build their own visualizations Publish and share results and findings Be self-reliant in information needs Have fun working with data! Dashboard Server enables to: Interact with visualizations in any browser Modify visualizations within the browser View the same viz on mobile devices Cache data for increased performance Brings life to production meetings! Slide 13

14 Interactive monitoring Drill down to root cause Few, Stephen, Effective Dashboard Design, 11/17/2005. ( Slide 14

15 Demo Slide 15

16 Why not just Tableau software??? One/two sources per chart Alarms MES Wonderware Historian LIMS Production Calendar Slide 16

17 Why not just Tableau software??? Common problems: Complex DB schemas Several sources Transformation of data Inconsistent relationships Solution: Intelligence Server User-friendly data model All sources in context in a single database Time slicing of data Slide 17

18 Time slicing is an Industry need What is it and why is it important? InBatch Shifts Batch No Start Time Product Quality 06:13:49 07:41:25 Scrap 07:41:25 09:01:31 Scrap Shift Id Team Id Alarms EndTime Tag Start Time EndTime 06:00:00 14:00:00 Alarm Start Time Alarm End Time Alarm Ack Time Water.Temperature 07:21:29 08:11:25 07:31:43.Load Tagname 07:25:15 08:24:31 07:26:02 Timestamp Value TK12.Temperature 07:23: TK12.Volume TK12.Pressure TK12.Temperature TK12.Volume TK12.Pressure Slide 18 07:24: :25: :23: :24: :25: No common key except Time!!!

19 Time slicing Intelligence slices by time and relates context InBatch Batch No Start Time EndTime Product Quality 06:13:49 07:41:25 Scrap 07:41:25 09:01:31 Scrap Shifts 06:13:49 07:41:25 09:01:31 Weekday, 06:00:00 14:00:00 Water.Temperature Alarms 08:11:25 07:21:29.Load 07:25:15 Start Time 06:00:00 06:13:49 07:21:29 07:25:15 07:41:25 08:11:25 08:24:31 09:01:31 EndTime Slide 19 06:13:49 07:21:29 07:25:15 07:41:25 08:11:25 08:24:31 09:01:31 14:00:00 08:24:31 Batch No Product Shift Team Water Alarm Temperature Temperature Temperature Alarm

20 Time slicing Intelligence aggregates data based on context Start Time EndTime Batch No Product 06:00:00 06:13:49 07:21:29 07:25:15 07:41:25 08:11:25 08:24:31 09:01:31 06:13:49 07:21:29 07:25:15 07:41:25 08:11:25 08:24:31 09:01:31 14:00:00 Start Time 06:00:00 06:13:49 07:21:29 07:25:15 07:41:25 08:11:25 08:24:31 09:01:31 Team Water Alarm Alarm Temperature Temperature Temperature EndTime Slide 20 Shift 06:13:49 07:21:29 07:25:15 07:41:25 08:11:25 08:24:31 09:01:31 14:00:00 Batch No Product TK12. Temp. TK12. Volume TK12. Press. Shift Team Water Alarm Temperature Temperature Temperature Alarm

21 Time slicing Intelligence aggregates data based on context Start Time EndTime 06:00:00 06:13:49 07:21:29 07:25:15 07:41:25 08:11:25 08:24:31 09:01:31 06:13:49 07:21:29 07:25:15 07:41:25 08:11:25 08:24:31 09:01:31 14:00:00 Slide 21 Batch No Product Shift Team Water Alarm Alarm Temperature Temperature Temperature TK12. Temp. TK12. Volume TK12. Press

22 Time slicing Intelligence data store easy to query What is the average temperature when running product by Team without a Water temperature alarm? Remember the initial EndTime Batch No data source structure? Start Time 06:00:00 06:13:49 07:21:29 07:25:15 07:41:25 08:11:25 08:24:31 09:01:31 06:13:49 07:21:29 07:25:15 07:41:25 08:11:25 08:24:31 09:01:31 14:00:00 Try answering the same question with this structure Slide 22 Product Shift Team Water Alarm Alarm Temperature Temperature Temperature TK12. Temp. TK12. Volume TK12. Press

23 Metrics in Context Process History T, P, L, F, S, Work Orders, Jobs, Materials Downtime, Shifts, Production Test results, Specifications, Quality, Samples, Inventory, Lots Slide 23

24 Metrics in Context Materials Equipment Metrics Consumption Production Intelligence Service Downtime Quality Specifications Work Order Slide 24 Time/Shifts

25 Metrics in Context Materials Lots Equipment Energy Type Metrics Inventory Energy Used Consumption Consumption Lost Production Demand Quality Downtime Target Quality Rate Specifications Location Work Order Slide 25 Time/Shifts Source

26 Metrics in Context Intelligence Clients SmartGlance Intelligence Service Data Store Information Server Slide 26

27 Quick start with Pre-defined content From installation to value in minutes!!! Pre-defined content for Invensys/Wonderware sources: MES Performance InBatch History Alarms database (wwalmdb) Corporate Energy Management ROMeo Installation process: Import Intelligence Model in IDE Set source location and credentials. Set Backfill date. Deploy Model View dashboards in Analytics Client! Slide 27

28 Intelligence in 2013

29 Concurrent Licensing Concurrent Licensing Add-on for Intelligence 2012 Manual acquire/release of license, or via scripting CANNOT mix named users and concurrent users Sold in increments of 5 concurrent users Slide 29

30 Tableau 8 for Intelligence 2012 Download from wdn.wonderware.com Web & Mobile authoring: edit views in a browser or tablet More flexible dashboards: overlay, visual grouping, data sets New charts: Treemaps, Bar charts with Treemaps, Bubble charts Scoped filters: apply filters to one, selected, or all views Forecasting: estimate future values based on historical data Data Engine API: create extracts with scripting Java Script API: control published views within another application Faster Response times and increased scalability Multi-language support (en,fr,de,es,pt,zh,ja,ko) Slide 30

31 Intelligence 2014 Target: Spring 2014 Improve backfilling by reversing it: Start from newest records and finish by oldest Refresh of current data has priority Support initial loading of large dimensions (millions of rows) Easier and better control of deployment Provide access to detailed data through Reporting Model MES Operations pre-defined content Slide 31

32 Technical Tools Wonderware Developer Network is your best reference point: wdn.wonderware.com Webcasts and Videos Pre-configured Virtual image Classroom Training Slide 32

33 Questions / Comments? Thank you! christian-marc.pouyez@invensys.com Slide 33

34 Slide 34