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GEN-02 Operational InformationTransformation: From Historian toOperational Information System

© 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.

Tim SowellStan DeVries

Key Takeaways

1. Operational decision needs are changing to much earlier, morespecific context, and much more trustworthy information

2. To support this change, information processing is changing toinclude more transformation

3. Operations decisions use the new information to make many moredecisions, much earlier, including ahead of real-time

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What’s Taking Place Today?

• The traditional historian and data capture systems are transformingto federated operational information systems, (PIMS or PlantInformation Systems) which enable a unified information modelacross MES time series and plants, that enable consistency instructure and context and exception based notifications.

• This enables both operational information and knowledge fordecisions in the NOW, as well as the operational analysis andperformance teams required analysis.

• This session will outline the PIMS and Operational Information oftoday, and how they are different from the traditional historianapproach.

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• The traditional historian and data capture systems are transformingto federated operational information systems, (PIMS or PlantInformation Systems) which enable a unified information modelacross MES time series and plants, that enable consistency instructure and context and exception based notifications.

• This enables both operational information and knowledge fordecisions in the NOW, as well as the operational analysis andperformance teams required analysis.

• This session will outline the PIMS and Operational Information oftoday, and how they are different from the traditional historianapproach.

Changing the Experience

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Critical Project Trends We are Seeing!

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The Challenge of Alignment across aDynamic Value Chain

Evolution from Manned to Transition

Existing control roomsExisting operational KiosksRoaming/ Dynamic teams with Wireless

Central Co ordination• End to End View• Information TsunamiOperator/ ControllersProduct ManagementPlanners

Access to Expertise• Anywhere• Any deviceExpertise in ProcessExpertise in EquipmentWithin CompanyExternal to Company

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How to Achieve Trusted Common InformationReal-time collaborations, SharingEnable Timely Correct Operational Decisions / Actions

Flexible Team Coordination (OperationalCenters) Enterprise Control• Game-changer: use people differently for

efficiency or agility

• Operational Team works in coordinatedfashion over multiple sites

• A community of Experts provide theknowledge/ experience real-time

• Move from individual Operational Control torunning a flexible team across a network ofoperational interfaces to execute; usually anOperational center, virtual expert teams,roaming, working as one

• Built in work practices and operational taskmanagement so tasks can be passed betweenteam members smoothly

Business,Operationsand PlantFloorpersonnelworkingtogether innew ways

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• Game-changer: use people differently forefficiency or agility

• Operational Team works in coordinatedfashion over multiple sites

• A community of Experts provide theknowledge/ experience real-time

• Move from individual Operational Control torunning a flexible team across a network ofoperational interfaces to execute; usually anOperational center, virtual expert teams,roaming, working as one

• Built in work practices and operational taskmanagement so tasks can be passed betweenteam members smoothly

Business,Operationsand PlantFloorpersonnelworkingtogether innew ways

Concept: Virtual “Situation Room”Activities are Transformational across state,Collaboration is Natural, Awareness is native

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Rio Tinto Example:EOS – The Operator in Control

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The Evolving Operational Landscape

Agility thruEmpowerment

Trend

KnowledgeWorkers

Changing RequirementOperational Control• Role-based to action-based• Transformable Day in the Life• Natural collaboration across roles

Operational Decision Support• Situational awareness• Preemptive practices• To be vs. as is

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WorkforceTransition

Rotating Roles

Digital Natives

Time to Experience• Access to experience• Self training• Understanding the future

Task/ Work Mgmt• Work/Action Capture• Work/ Action Planning• Work/ Action Consistent with inbuilt SOPs

Operational Experience:Changing Face of User Base

• Move to “To Be State”• Awareness• Condition vs. Alarm• Access Experience

“System is Aware”

“Pre-Emptive/ Dynamic”• Auto Escalation• Auto Investigation to

determine condition• Auto awareness of condition

based on History pattern• Built in Procedure• Built in How to

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“Access to Experience”

• Auto Escalation• Auto Investigation to

determine condition• Auto awareness of condition

based on History pattern• Built in Procedure• Built in How to

• How to access experience• How to locate correct people• How to engage naturally• How to empower and synch state• How a use must be dynamic to

become an agent for task• Virtual teams

“Re-wiring” Operational Information…

• Boyd “OODA Loop” – a way to organize how information is designedand used to act faster and earlier than events

• Design like a fighter aircraft engineer, operate like a fighter pilot

• “Re-wire” operational information

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Design forperformance,establish“harmony”and “groundtruth”

Orient for“normal” and“abnormal,”economicperformance

Faster andearlier thansituationsevolve

Balancetargets andreality

2 Journeys in Operational Information

A historian tag’s journey

• Flow measurement (exiting aprocessing area)

• Used for:• Calculating efficiency

• Recognizing event frames

• Material and energy balances

• Calculating production

An operations team’s day

• Manage a familiar situationwhere the range of qualityhas moved

• Respond to a request toshare a production ratechange with another site

• Investigate a materialimbalance

• Find out why the night shiftout-produces the day shift

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A historian tag’s journey

• Flow measurement (exiting aprocessing area)

• Used for:• Calculating efficiency

• Recognizing event frames

• Material and energy balances

• Calculating production

An operations team’s day

• Manage a familiar situationwhere the range of qualityhas moved

• Respond to a request toshare a production ratechange with another site

• Investigate a materialimbalance

• Find out why the night shiftout-produces the day shift

Historian tag journeys:not-so-good and good…Not so good:• The tag data quality is poor

because the system doesn’tovercome outages

• Data structures don’t matchthe sources when thesources are reconfigured

• “Information” is in 1 milliontags instead of severalthousand structures

Good:• The tag data quality is

trusted because the systemovercomes outages

• Data structures are “living”and are trusted; theoperational information dataoften share the samestructures as the sources

• “Information” is in severalthousand, human-understandable structures

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Not so good:• The tag data quality is poor

because the system doesn’tovercome outages

• Data structures don’t matchthe sources when thesources are reconfigured

• “Information” is in 1 milliontags instead of severalthousand structures

Good:• The tag data quality is

trusted because the systemovercomes outages

• Data structures are “living”and are trusted; theoperational information dataoften share the samestructures as the sources

• “Information” is in severalthousand, human-understandable structures

Operations team’s day:not-so-good and good…Not so good:• Only one person has the

operational informationneeded by many

• That person spends morethan 50% finding andcleaning up information

• Information is processedonly as “projects” or aftersignificant events

Good:• The right person has the right

information in the right contextat the right time, which includesahead of real-time

• Knowledge workers spend lessthan 10% of their time on dataquality management

• Teams are proactive,understand “new normal” and“new abnormal”

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Not so good:• Only one person has the

operational informationneeded by many

• That person spends morethan 50% finding andcleaning up information

• Information is processedonly as “projects” or aftersignificant events

Good:• The right person has the right

information in the right contextat the right time, which includesahead of real-time

• Knowledge workers spend lessthan 10% of their time on dataquality management

• Teams are proactive,understand “new normal” and“new abnormal”

“PIMS” Definitions…

• Gartner: Application for theacquisition, display,archiving and reporting ofinformation from a widevariety of control, plant andbusiness systems.

• A critical component in anenterprise’s applicationarchitecture for creating acommon repository of plantinformation that can beeffectively leveraged inenterprise and supply chainmanagement applications.

• Invensys: Architecturepattern for standardizeddecision support information,which is integrated andderived from a wide varietyof control, plant andinformation systems.

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• Gartner: Application for theacquisition, display,archiving and reporting ofinformation from a widevariety of control, plant andbusiness systems.

• A critical component in anenterprise’s applicationarchitecture for creating acommon repository of plantinformation that can beeffectively leveraged inenterprise and supply chainmanagement applications.

• Invensys: Architecturepattern for standardizeddecision support information,which is integrated andderived from a wide varietyof control, plant andinformation systems.

Why Customers ChooseHistorian or “PIMS”?Historian• Organization – budget only

for basic storage andminimum client function

• Centralized – budget for“enterprise” historian

• De-centralized – budgetonly for “local” historian

PIMS• Strong influence from

Operations – need higher-value information for plantarchitectures

• Strong influence fromcorporate IT – need moretrustworthy information forenterprise architectures

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Historian• Organization – budget only

for basic storage andminimum client function

• Centralized – budget for“enterprise” historian

• De-centralized – budgetonly for “local” historian

PIMS• Strong influence from

Operations – need higher-value information for plantarchitectures

• Strong influence fromcorporate IT – need moretrustworthy information forenterprise architectures

Historian vs. PIMSHistorian PIMS

Manage the data quality(disconnects, out of range,wrong type)

Restructure the integrated datainto historian tags

Restructure the data into tags oradd structure in objects (eithertag-centric or model-centric)

Derive information from historizeddata mainly with algebra andsimple statistics (averages, totals,counts)

Derive information fromhistorized data mainly withalgebra and simple statistics(averages, totals, counts)

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Derive information from historizeddata mainly with algebra andsimple statistics (averages, totals,counts)

Derive information fromhistorized data mainly withalgebra and simple statistics(averages, totals, counts)Derive information from onlinemodel-based applications mainlywith calculus and advancedstatistics – applications are oftenfocused on customer industriesor equipment

Traditional Historian Approach vs. Unified Information Approach

Traditional Enterprise Historian Approach• Collect data without structure or cleansing• Put data into historian as early as possible• Add cleansing and context after the

historian• Cleanse each data element in more than

one place• Context is reference information (“dead”)

for data only

Model-Centric Information Approach• Collect data with structure• Structure and cleanse as close to the

sources as possible• Cleanse each data once• Context is used for user roles, graphics,

workflow, calculations (“living”)• Context is everywhere – at each plant,

both tiers

Tier 2

ONE Active Model

Centralized InformationNear real-time modelingNear Real-time analysis

“Changing the Game”:The Invensys PIMS Approach

Data Access

Increase People & Asset Effectiveness

PumpPump

• ODBC• API• Web Service

Model Driven• Notifications• Awareness

Living Assets Model• Living Assets• Provides structure• Provides validation• Provides situational

awareness• Triggers events/

Workflows• Inbuilt calculations• Escalation

Information Model

Slide 21

Unified Real-time Asset/ Process Model built on Managed StandardsRenormalizing of data into effective information/ intelligence

One Namespace, UnifiedManaged Configuration

Data CaptureData Validation




Storage: Tier 2 Historian(Near Real-time Analysis)

PumpPump

• Living Assets• Provides structure• Provides validation• Provides situational

awareness• Triggers events/

Workflows• Inbuilt calculations• Escalation

Each Site:• Local AOS• Local Historian

Storage:Tier 1

Historian

Storage:Tier 1

Historian

Storage:Tier 1

Historian

Storage:Tier 1

Historian

Comparison

Invensys Historian-based

Re-structure the data andmanage the quality

Push into the Historian(50 thousand objects)

Derive information Derive information

Workflows Alerts

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Integrate the data andkeep the source datastructure

Integrate the data anddiscard the source datastructure

Structure the data andmanage the quality once

Push into the historian(1 million tags)

Re-structure the data andmanage the quality

Push into the Historian(50 thousand objects)

Changing from Reactive to Proactive

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The Changing Landscape to EnableDecisions

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PIMS Example 1Performance

PIMS summary: As a user, I want tocompare KPI’s across multiple assets &products, across multiple event frames,so that I can reduce the variation inperformance (Centerlining)

Small plant historian:self-service “chart”recorder

Slide 25

Small plant historian:self-service “chart”recorder

PIMS Example 2Performance

PIMS summary: As a user, I want tocompare KPIs across multiple assets &products, across multiple event frames,so that I can reduce the variation inperformance (Centerlining)

Small plant events: self-service “event” recorder

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Small plant events: self-service “event” recorder

PIMS Example 3Performance and Prescriptive

ARPMSimSci


Plant event frames: self-service integration ofproduction frames (e.g.batches, shift, productchange…) withexception event framesin context with processdata

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ARPMPlant event frames: self-service integration ofproduction frames (e.g.batches, shift, productchange…) withexception event framesin context with processdata


ARPMSimSci


Multiple plant eventframes: self-servicemulti-site “event”recorder to view plantexceptions across eventframes in context withprocess data

Slide 28

ARPMMultiple plant eventframes: self-servicemulti-site “event”recorder to view plantexceptions across eventframes in context withprocess data


ARPMSimSci


Complex events:composite KPIs,complex events andevent timeframes frommy plant data

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ARPMComplex events:composite KPIs,complex events andevent timeframes frommy plant data


ARPMSimSci


Multiple plant PIMS:calculate & compareKPIs, Events and framesfor multiple plants,assets & productsacross multiple eventframes

Slide 30

ARPMMultiple plant PIMS:calculate & compareKPIs, Events and framesfor multiple plants,assets & productsacross multiple eventframes

PIMS Example 7Performance, Predictive and Prescriptive

ARPMSimSci


Predictive PIMS:predictivetrends/scenarios

Slide 31

ARPMPredictive PIMS:predictivetrends/scenarios

Operational Excellence Journey

Where have we been?• Departmental approach• Function specific metrics• Use of spreadsheets/

manual processes• Scheduled/timed process• Tools based• Historical focus

Where are we now?• Spreading across depts.• Processes fully defined but

not streamlined orinstitutionalized

• Strategic & operationalmetrics defined but notaligned

• Technology in place tosupport data integration anddynamic reporting but thereare gaps

• Tools and process based• Near real time

Where are we going?• Clear financial and

operational metrics mappedto strategy and goals

• Business processesstreamlined and optimized

• Cause and effect fullyunderstood

• Dashboards in full use• Trends analysis• Exception handling• Collaboration and

accountability acrossfunctions

• Near real time

Where do we want toreach?• Empowerment of operators• Culture of performance and

accountability• Collaborative and dynamic

decision making• Performance information

pinpoints everyone’scontribution to goalattainment

• Clear transparency• Wide use of analytics• Sense & response

Consistency, Repeatability, Predictability

Where mostorganizations are

today

Slide 32

Stage

1

Reacting toBusiness

Stage

2

ImprovingBusiness

Stage

3

DrivingBusiness

Stage

4

Driving theMarket

Where have we been?• Departmental approach• Function specific metrics• Use of spreadsheets/

manual processes• Scheduled/timed process• Tools based• Historical focus

Where are we now?• Spreading across depts.• Processes fully defined but

not streamlined orinstitutionalized

• Strategic & operationalmetrics defined but notaligned

• Technology in place tosupport data integration anddynamic reporting but thereare gaps

• Tools and process based• Near real time

Where are we going?• Clear financial and

operational metrics mappedto strategy and goals

• Business processesstreamlined and optimized

• Cause and effect fullyunderstood

• Dashboards in full use• Trends analysis• Exception handling• Collaboration and

accountability acrossfunctions

• Near real time

Where do we want toreach?• Empowerment of operators• Culture of performance and

accountability• Collaborative and dynamic

decision making• Performance information

pinpoints everyone’scontribution to goalattainment

• Clear transparency• Wide use of analytics• Sense & response

Recommendations

• Review the changes in roles and tasks first, then design for therequired functions and information

• Consider “prescriptive” and “predictive” interactions (i.e. more thanperformance)

• Design for trustworthy information (end-to-end)

• Design for higher availability – distributed, multi-tier, redundancy,disaster recovery

Slide 33

• Review the changes in roles and tasks first, then design for therequired functions and information

• Consider “prescriptive” and “predictive” interactions (i.e. more thanperformance)

• Design for trustworthy information (end-to-end)

• Design for higher availability – distributed, multi-tier, redundancy,disaster recovery

Key Takeaways




Slide 34




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