September 10, 2008

Mobilizing Transparency

Gregg Le BlancChief Michael Doppelganger

Transpara Corporation

Agenda

• Using KPI’s effectively• About Transpara & Visual KPI• How Visual KPI is used• Demo / Screens• Visual KPI 4.0 and beyond

The downside of data wealth

Information poverty• What does the data mean?• Is the data the same?• Does more data help?

Conflicting actions• Does it mean the same thing

to everyone?• Which system or person is

right?

Transpara• Founded 2005• 100+ installed systems

Visual KPI• Visualization for Business Intelligence and Mfg Intelligence• Thin layer used to “Composite” together data from many systems• Can roll out complete new site in a single afternoon• KPIs, Scorecards, Dashboards are quickly assembled using Excel• Distribution to any browser, including WM 5/6, iPhone, Blackberry

About Transpara & Visual KPI

Simply having KPI’s is not enough• Studies have shown KPI’s that are well

integrated into your business are a tool for change.

• However, sometimes KPI’s:– Increase stress on employees– Create unintentional parallel workflows– Can dissolve collaboration between groups

NEED TO CREATE A ECOSYSTEM WHERE CULTURE, INFORMATION, AND ACTION COMBINE!

KPI’s aren’t just for executives anymore

• Mechanic• I&C Technician• Control Room Operator• Performance Engineer• Regulatory Manager• Test Engineer• PPO Engineer• Quality Assurance• Mfg Sciences Engineer

• Executive• Field Supervisor• Plant Engineer• Operation Manager• Control Manager• Maintenance Manager• Watch Supervisor• Process Engineering• Process Development

Executive

Characteristics of good KPI’s

• MESA Metrics that Matter study– Companies that found:• A 10% improvement in a single key area• A 1% improvement across at least 6 out of 11 areas

– They have this in common:• Metrics linked to operations• Fully automated data collection• Rapid recalculation• Timely action taken based on metrics

The process of KPI creation*

People sit in a room and decide what’s important

Someone wrangles data together

Someone creates visualizations

Someone tells thecompany how important these KPI’s are

* Not to scale

Assumptions about KPI’s

• You have all the information you need– Systems talk to each other– The important metrics you derive can be answered

• You have a culture ready to accept KPI’s– “Is ‘Management’ spying on me?”– “I don’t know how that KPI was made.”

• KPI’s lead to (unique) action– When my favorite KPI dips below the low limit I…– When someone takes action, there is no duplication

KPI evolution from Central to Local

People sit in a room and decide what’s important

Someone wrangles data together

Someone creates visualizations

Sources:•SAP PM•SAP BW•OSIsoft PI•MS SQL•Oracle

Mobile Consumer

Desktop and Mobile User

Actionable Decisions

Actionable Decisions

`

User and KPI Creator

Local Sources:•Site Web services•Site databases

Centralized Intelligence Localized Intelligence

Visual KPI

KPI’s at real customers

• Large installation– Using around 125,000 KPI’s to track a site– Currently deployed in 3 sites, expanding further– At least 5 different user roles use Visual KPI daily

• Targeted installation– Research In Motion (RIM)– Tracks their Blackberry production rate on their

Blackberrys (Blackberries?)

Now KPI’s can come from everywhere

Solving the transparency problem

• Make use of what you have– Leverage existing technology investments– Leverage mobile technology already in the hands of

employees

• Align strategies– Map corporate strategy (will of the few) to

collective strategy (will of the masses)– Link strategy to execution

Leverage existing data sources

• Re-purpose existing data• Assembled, not programmed • Extend value of data already created

• Example: MW Delivered to Customer X– Target value from EMS / DMS– Actual real-time data from PI– High and Low quality limits from SQL Server– Max and Min of line capability from MRO

Visual KPI 3.x architecture

PI System Real-time Data Meta Data Equations

Any RDB Existing Data External KPIs

LOB App Link to financials Planned values SAP, MRO, etc.

Visual KPI Server Composite KPI Engine All meta data in SQL 2005 or 2008 Windows Server 2003 or 2008 XML and Web Services-based Extensible, Programmable

`

Visual KPI Excel Editor Excel 2003 or Excel 2007

with VSTO Configuration only, no run-

time association or storage

Publish Scorecards via Web Services

Data Sources Any Mobile or Desktop Client

Web S

erv

ices

Web S

erv

icesXML over HTTP

VISUALIZING KPI’S

Anatomy of a KPI

Min Max

Actual

Low Low High High

Low High

Target

Status = GOOD

Anatomy of a KPI

Min Max

Actual

Low Low High High

Low High

Target

Status = HIGH

Anatomy of a KPI

Min Max

Actual

Low Low High High

Low High

Target

Status = HIGH HIGH

Typical KPI Configuration

• KPI Attributes can include:– Actual Value (the only required attribute!)• Sourced from PI, AF 2.0, RDB or an application

– Dynamic Attributes (time-varying)• Sourced from PI, AF 2.0, RDB or an application

– Static Attributes (non time-varying meta-data)– Auxiliary Data• Responsible Party• Notification Definition• Associated Displays and Links

Anatomy of a single Scorecard

• A collection of KPIs related to each other in some significant way at run-time

• Collection criteria can be a combination of Dynamic and Static KPI Attributes

• Some examples:– All KPIs for Equipment Type 1300, With

Priority 1 Alarms in the Western Region– All KPIs for Asset 67 with Status <> Good

Metadata and you

Derived by Visual KPIDerived by Visual KPI

Derived from live dataDerived from live data

Visual KPI metadata

Min MaxLow Low High HighLow HighTarget

Status = GOOD

Plus 20 user definable attributes of metadata goodness:Create what you like – Area, Unit, Asset, Type, Material, Product… Plus 20 user definable attributes of metadata goodness:Create what you like – Area, Unit, Asset, Type, Material, Product…

Tip:•Create a standard set of 20 categories for KPI’s•Create a set of consistent values for the categories for uniform scorecarding everywhere!

Tip:•Create a standard set of 20 categories for KPI’s•Create a set of consistent values for the categories for uniform scorecarding everywhere!

How attributes work as metadata

KPI 1KPI 2

KPI 3

KPI 4KPI 5

KPI 6

KPI 7KPI 8

KPI 9

KPI 10KPI 11

KPI 12

KPI 13KPI 14

KPI 15

KPI 16KPI 17

KPI 18

Scorecards

Views

KPI’s

Each KPI can have different attributes

KPI 1KPI 2

KPI 3

KPI 4KPI 5

KPI 6

KPI 7KPI 8

KPI 9

KPI 10KPI 11

KPI 12

KPI 13KPI 14

KPI 15

KPI 16KPI 17

KPI 18

Scorecards

Views

Scorecard organizationSELECT KPI’S WHERE EQUIPMENT = TURBINE AND

PLANT = ST. PAUL AND FUEL = WIND

KPI 1KPI 2

KPI 3

KPI 4KPI 5

KPI 6

KPI 7KPI 8

KPI 9

KPI 10KPI 11

KPI 12

KPI 13KPI 14

KPI 15

KPI 16KPI 17

KPI 18

Scorecard 1 Scorecard 4Scorecard 8 Scorecard 14

Scorecard 15

KPI’s

Scorecards

Views

Scorecard organizationSELECT KPI’S WHERE EQUIPMENT = TURBINE AND

PLANT = DENVER AND FUEL = HOPE

KPI 1KPI 2

KPI 3

KPI 4KPI 5

KPI 6

KPI 7KPI 8

KPI 9

KPI 10KPI 11

KPI 12

KPI 13KPI 14

KPI 15

KPI 16KPI 17

KPI 18

Scorecard 1 Scorecard 4Scorecard 8 Scorecard 14

Scorecard 15

KPI’s

Scorecards

Views

View organizationSELECT SCORECARDS WHERE ASSETS = TURBINE

KPI 1KPI 2

KPI 3

KPI 4KPI 5

KPI 6

KPI 7KPI 8

KPI 9

KPI 10KPI 11

KPI 12

KPI 13KPI 14

KPI 15

KPI 16KPI 17

KPI 18

Scorecard 1 Scorecard 4Scorecard 8 Scorecard 14

Scorecard 15

View 13View 14

View 16View 17

KPI’s

Scorecards

Views

Dealing with Many KPIs is Hard

• Most companies have hundreds or even thousands of KPIs

• Beyond a few dozen KPIs, the Scorecard Format suffers. Enter the KPI Map

• KPI Map is good for up to hundreds of KPIs

• Example:– All KPIs from the NE region– All Wind Farm Assets

Even more KPIs – Use Rollups!

• The downside of typical rollup strategies:– Rollups typically use “worst-case”– Overstates low-level problems

• Transpara’s True Roll-Up (TRU):– Designed to accurately reflect the state of the entire

hierarchy regardless of the number of KPIs involved.

INTRODUCING TRANSPARA’S TRUE ROLL-UPTM

What is True Roll-UpTM?

• Not “worst-case” but the entire state map of all KPIs

• TRU Chart as Percentage Bar or a Percentage Pie chart.

• Drill-downs automatically adjustfor total number of KPIs in hierarchy

• The TRU Chart at any level in the hierarchy shows the percentage in any state for all KPIs below that level

Example Screens

Configuration

VISUAL KPI DEMO

ThunderstormRamp EventDemo

Gives field personnel “one version of the truth”

Increases compliance with unified view of assets

Speeds response to critical events

On-demand data

100’s of Data Sources

• Leverage data from existing systems• Use any desktop, tablet or laptop • Access from any mobile device

Reported Financial Benefits

• Western Power– Projected: over $35 million USD in benefits in first 3

years• National Grid– $100,000’s saved after initial roll out– Cost avoidance – saves up to $100,000/incident– Cost savings – leverages existing

• Mobile devices, networks• In-place systems• Reduced overtime

– ROI in less than 6 months– Wide acceptance: more expected savings

Visual KPI enhancementsVisual KPI enhancements• Scalability:

– Response times and reliability– More robust connection to PI– Friendly PI data management

• SharePoint 2007:– Visual KPI Web Parts– Interoperable with RtWebParts from OSIsoft

• Visual KPI SDK:– Mashups– Integration with desktop apps– Auto-creation of scorecards based on databases

• Time-based KPI’s– Embedding PI into Visual KPI– Allows time-based selection:

• Show me all the KPIs whose status has been High or HighHigh for at least 1 hour• Show me all the KPIs who have entered a non-normal state in the last 30 minutes

• Visual AF:– Walks in-place AF hierarchy– Allows users to easily create scorecards based on AF

Visual KPI 4.0 and beyondVisual KPI 4.0 and beyond

• Scalability improvements- 100K KPIs• Export Trend & scorecard data to Excel• Auto column expansion• Pagination, Sort by column, Second y-axis• Multi-select Actuals from Scorecard to Trend• Table scorecards• KPI Type, Color Schemes• Write-backs to PI

Visual KPI Summary• Creates Corporate Transparency by repurposing and

delivering hard-to-access data to mobile and desktop devices

• Uses existing security infrastructure; leverages existing technology investment

• Encourages new use and improved analysis of existing data – do more with less

• Meets user demand by providing actionable information sized to fit display restrictions of device

• Deployment and configuration is simple and can be accomplished in a few hours – AEP, Genentech, Allegheny and National Grid projects were

less than a single day.

• Contact information:– Michael@Transpara.com (both e-mail and IM)

– (925) 218-6983– Gregg@Transpara.com

• Try the demo on your own device:– http://demo.transpara.com