2014 software global client conference -...
TRANSCRIPT
2014 Software Global Client Conference
Drive Operational
Improvements With
Self-Service Analysis:
Wonderware
Intelligence
WW INFO-02
Christian-Marc Pouyez
Product Manager – Intelligence & CEM
Schneider Electric - Wonderware – 2014-05-06
Why do we need Intelligence?
Schneider Electric - Wonderware – 2014-05-06
It’s all about metrics!
Overall Equipment Effectiveness
Total Effective Equipment Performance
Energy Intensity
Performance
Quality
Availability
Loading
Good Production
Total Production
Unplanned downtime
Standard Production
Production Time
Energy Consumption
Daily Contribution
Manufacturing Cycle Time
Yield
Changeover Time
Schedule Attainment
Standard Costs
Schneider Electric - Wonderware – 2014-05-06
Metric decomposition
OEE
TEEP
Energy Intensity
Performance
Quality
Availability
Loading
Good Production
Total Production
Unplanned downtime
Standard Production
Scheduled Time
Energy Consumption
Calendar Time
MES
Performance
Wonderware
Historian
Wonderware
CEM TEEP: Total Effective Equipment Performance
OEE: Overall Equipment Effectiveness
Schneider Electric - Wonderware – 2014-05-06
Metric context
OEE
Product
Shift
Batch/Process Order
Team
Production Line
MES
database
Wonderware
Historian
Schneider Electric - Wonderware – 2014-05-06
A few other requirements…
• Metrics must be computed in near real-time
• Metrics must be recalculated for adjustments (often very late after the fact):
• Shift duration
• Production Count adjustments
• Downtime reason codes
• Upload of offline energy data
• Using customized efficiency calculation
• Reports must be quick to generate
• Minimize the impact on the source (transactional) systems
Schneider Electric - Wonderware – 2014-05-06
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
Schneider Electric - Wonderware – 2014-05-06
Major challenge!
Metrics
•Near real-time
•Equation
•Versioning
•Recalculation
Schneider Electric - Wonderware – Date
How to solve this challenge?
●Create spreadsheets manually
●Use IT to create custom reports and dashboards
●Use standard Business Intelligence tools
●Use Wonderware Intelligence
Schneider Electric - Wonderware – Date
Comparison of options Spreadsheets IT custom
content
Standard BI WW Intelligence
Near real-time No, often daily Can be Typically daily Yes
Relate time-series
data to production Manual process
Requires significant
work
Requires custom
coding/scripting Native
Self-reliant users Widespread knowledge Requires IT Depends on tool Most user-friendly
IT involvement Minimal Very high; required For Info. Model For Info. Model
Single version of
the truth Several versions Well controlled by IT Controlled by model Controlled by model
Impact on source
systems High; each spreadsheet
impacts source Typically each report
Minimal if using data
store Minimal, only data store
Recalculations Manual Needs to be planned Depends on tool and
model Native
Schneider Electric - Wonderware – 6-May-2014
Solution: Wonderware Intelligence Server
MES
database
Wonderware
CEM
Wonderware
Historian Metrics
Energy Usage
Production
Runtime
Energy Targets
Intensity
Equipment Product
Time/Shifts Batch
Wonderware
Intelligence
Server
Schneider Electric - Wonderware – 6-May-2014
Metrics in Context
Metrics
Consumption
Production
Downtime
Quality
Specifications
…
Equipment Materials
Time/Shifts Work Order
Intelligence
Service
Native support for
namespace:
• Wonderware Historian
• OSIsoft PI Server
Native support for:
• Time slicing
• Inferred relationships
Native support for:
• Multiple sources
• Incremental refresh
• Recalculations upon
source or context
changes
Native support for:
• Multiple sources
• Incremental refresh
Schneider Electric - Wonderware – 6-May-2014
Metrics in Context
Equipment Materials
Time/Shifts Work Order
Energy Type Lots
Source Location
Metrics
Consumption
Production
Downtime
Quality
Specifications
…
Inventory
Used
Lost
Quality
…
Energy
Consumption
Demand
Target
Rate
…
Schneider Electric - Wonderware – 6-May-2014
Metrics in Context
Intelligence
Service Data Store
Intelligence Clients
SmartGlance
Information Server
Schneider Electric - Wonderware – 6-May-2014
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!
Schneider Electric 17 - Invensys – Wonderware Intelligence – February 2014
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!
Schneider Electric - Wonderware – 6-May-2014
Wonderware Intelligence vs. Business Intelligence
Wonderware Intelligence Business Intelligence tools Business Value
Relates time-series data to
production transactions
Typically requires complex
scripting for time-series data
Historian data has no direct
relationship to production
transactions
Natively supports Wonderware
Historian and OSIsoft PI Server
namespaces
No support for namespaces Easier configuration and
maintenance of solution
Real-time computation of
metrics
Typically historical data
computed daily
Real-time metrics enable
immediate correction of plans
Model-driven configuration Configuration driven by data
transformations
Easier configuration and
maintenance of solution
Pre-defined models for
Wonderware data sources
(MES, Alarms, InBatch, CEM,...)
Not available Pre-defined content hides
complexity of data source
structures.
Schneider Electric - Wonderware – 2014-05-06
DEMO
Schneider Electric - Wonderware – Date
Intelligence Status & Roadmap
Schneider Electric - Wonderware – Date
Next release: Intelligence 2014 R2
● Immediate value after deployment
●Major performance improvements for big data
●Latest version of Tableau Software, with direct access to underlying
data sources (MES, Alarms, etc.)
●Technology Update:
●System Platform 2014 R2
● Latest available Windows and SQL Server support
Beta period in Q1 2015. Your participation would be greatly appreciated!
22 Confidential Property of Schneider Electric
Related Support, Services, Training & Expo Demos
> Intelligence
Support
Training
> Intelligence Classroom Training
> EMI Delivery group, lead by Alok Pathak
Services
Expo Demos
> Analytics & Energy Management booth
23 ©2014 Schneider Electric. All Rights Reserved.
All trademarks are owned by Schneider Electric Industries SAS or its affiliated companies or their respective owners.