Turning Data Into Dollars

John W. Rusher, Eli Lilly & Co.John W. Rusher, Eli Lilly & Co.Robert H. McCafferty, Curvaceous SoftwareRobert H. McCafferty, Curvaceous Software

Pharma – IT SummitPharma – IT SummitMarch 18th, 2004March 18th, 2004

Pharma IT SummitPharma IT Summit

The Benefits of Integrating and The Benefits of Integrating and Exploiting DataExploiting Data

John W. RusherJohn W. Rusher

What is the purpose of What is the purpose of collecting and analyzing data?collecting and analyzing data?

To detect, interpret, and predict qualitative To detect, interpret, and predict qualitative and quantitative patterns in data, leading and quantitative patterns in data, leading to information and knowledge.to information and knowledge.

Now, what’s the real objective?Now, what’s the real objective?

Reap the benefits of our infrastructureReap the benefits of our infrastructure Improve quality, safety and/or efficiency Improve quality, safety and/or efficiency Process optimizationProcess optimization

• Minimize costsMinimize costs• Maximize throughputMaximize throughput• Minimize riskMinimize risk

Rapid recovery from abnormal situationsRapid recovery from abnormal situations Ultimately, to increase revenue and/or Ultimately, to increase revenue and/or

decrease expensesdecrease expenses

Examples of Real-World Examples of Real-World BenefitsBenefits

Reducing production cycle times by using on-, in-, Reducing production cycle times by using on-, in-, and/or at-line measurements and controls. and/or at-line measurements and controls.

Preventing rejects, scrap, and re-processing. Preventing rejects, scrap, and re-processing. Improving batch disposition process.Improving batch disposition process. Decreasing time to resolve deviations.Decreasing time to resolve deviations. Control system optimization. Control system optimization. Development of process knowledge to improve Development of process knowledge to improve

efficiency and manage variability.efficiency and manage variability. Using small-scale equipment (to eliminate certain scale-up Using small-scale equipment (to eliminate certain scale-up

issues) and dedicated manufacturing facilities. issues) and dedicated manufacturing facilities. Improving energy and material use and increasing capacity.Improving energy and material use and increasing capacity.

I collect a bunch of data, isn’t I collect a bunch of data, isn’t that enough?that enough?

Data Does Not Equal Knowledge!Data Does Not Equal Knowledge! Data and technology are sometimes confused Data and technology are sometimes confused

with knowledge. with knowledge. The computer, database management The computer, database management

software, data warehouses, data marts are software, data warehouses, data marts are equated with information and knowledge. equated with information and knowledge.

These are data access vehicles, they are These are data access vehicles, they are information and not knowledge.information and not knowledge.

So What is the Difference Among So What is the Difference Among Data, Information and Data, Information and

KnowledgeKnowledge

I. Spiegler / Information & Management 40 (2003) 533–539

So, for The Techies Out There:So, for The Techies Out There:“The Transformation Algorithm”“The Transformation Algorithm”

““If data becomes information when they If data becomes information when they are organized to add value, then are organized to add value, then information becomes knowledge when it is information becomes knowledge when it is analyzed to add insight, abstraction, and analyzed to add insight, abstraction, and better understanding.” – I. Spieglerbetter understanding.” – I. Spiegler

Data

Temperature = 39 DegC

Flowrate = 45 lpm3 deviations/lot

Potency = 95%

Information

O = F(I1, I2, …In)

KnowledgeThruput = 400 Bkgs/mth

Org

aniz

e

Ana

lyze

And, for you “Non-Techies” out And, for you “Non-Techies” out there:there:

The Restaurant SimileThe Restaurant Simile ‘‘…‘‘…data are the symbols on the menu, data are the symbols on the menu,

information is the understanding of the information is the understanding of the restaurant’s offerings, knowledge is the restaurant’s offerings, knowledge is the dinner. You don’t go to the restaurant to dinner. You don’t go to the restaurant to lick the ink or eat the menu’’ (by Lewis lick the ink or eat the menu’’ (by Lewis Perelman).Perelman).

OK, so it sounds great, but it OK, so it sounds great, but it can take lots of effort…can take lots of effort…

Oceans of DataOceans of Data Disparate SystemsDisparate Systems Various ways to accessVarious ways to access

Data May be Spread Across Various Data May be Spread Across Various ProcessesProcesses

Many Tools and Techniques for Data Many Tools and Techniques for Data AnalysisAnalysis

Competing Business PrioritiesCompeting Business Priorities

Oceans of data, Islands of Oceans of data, Islands of knowledgeknowledge

In support of manufacturing pharmaceuticals, In support of manufacturing pharmaceuticals, large volumes of data are collected to:large volumes of data are collected to: Ensure compliance with cGMP, safety, purity, and Ensure compliance with cGMP, safety, purity, and

quality standardsquality standards Track lot history and genealogyTrack lot history and genealogy Improve product quality, process reliability, and Improve product quality, process reliability, and

overall production performanceoverall production performance Demonstrate to regulatory agencies that Demonstrate to regulatory agencies that

manufacturing systems are in control and reliablemanufacturing systems are in control and reliable

Worth the EffortWorth the Effort

We invest resources to generate and archive data, We invest resources to generate and archive data, but often fail to maximize the value of these data but often fail to maximize the value of these data because it is stored in various and unrelated because it is stored in various and unrelated databasesdatabases

$

Data Aggregation

Data Integration

Data Acquisition

Process DataManufacturing ExecutionLab Data

Process Automation

Change Control

In-Process Analytics

Deviations

Maintenance History

PFDs and Control Logic

DHRs

Access & Analysis

-5

0

5

10

15

20

25

30

35

5-H

T 1

D 1

NP

08/

09/

991

1/0

1/99

01/

10/

000

3/2

7/00

05/

22/

000

7/3

1/00

10/

17/

001

1/0

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11/

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1/2

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Control Charts

Regulatory ReportsMetrics

Tables, Figures, Listings for Reg.

Documents

Technical Reports

Ad-hoc queries

Why Integrate Disparate Data Why Integrate Disparate Data Systems?Systems?

Accessing and organizing data for:Accessing and organizing data for: Lot dispositionLot disposition Production controlProduction control Root cause investigationRoot cause investigation Process optimization/learningProcess optimization/learning

Without Integrated data we spend an inordinate amount of Without Integrated data we spend an inordinate amount of time extracting, collating and reformatting data prior to use. time extracting, collating and reformatting data prior to use.

What types of data are needed What types of data are needed to integrate for effective to integrate for effective

analysis?analysis? Process DataProcess Data

Critical Process Parameters (CPPs)Critical Process Parameters (CPPs) Criteria for Forward Processing (CFPs)Criteria for Forward Processing (CFPs) Release SpecificationsRelease Specifications

Analytical results and controlsAnalytical results and controls DeviationsDeviations Changes Changes MaterialsMaterials Equipment & MaintenanceEquipment & Maintenance

Analysis Of Data Across the Analysis Of Data Across the Supply Chain:Supply Chain:

The Lot Genealogy IssueThe Lot Genealogy Issue

S10 S09 S08 S11 S12 S14

lot A35 lot A36 lot A37

lot B1 lot B2 lot B3 lot B4 lot B5 lot B6

lot C709 lot C710

lot AB0182 lot AB0183 lot AB0184

SourceLot

RM ALot

RM BLot

RM CLot

S13

OutputLot

lot B7

S14 S15 S16 S17 S18

Perfect Separation vs. Ave. Perfect Separation vs. Ave. DataData

S10 S09 S08 S11 S12 S14

lot A35 lot A36 lot A37

lot B1 lot B2 lot B3 lot B4 lot B5 lot B6

lot C709 lot C710

lot AB0182 lot AB0183 lot AB0184

SourceLot

RM ALot

RM BLot

RM CLot

S13

OutputLot

lot B7

lot CD0532 lot CD0533 lot CD0534FinalProduct

Lot

lot CD0535 lot CD0536

S14 S15 S16 S17 S18

The Tools and Techniques for The Tools and Techniques for Analysis Vary GreatlyAnalysis Vary Greatly

Multivariate Data Acquisition and AnalysisMultivariate Data Acquisition and Analysis Process Analyzers or Process Analytical Process Analyzers or Process Analytical

Chemistry ToolsChemistry Tools Process Monitoring, Control, and End Process Monitoring, Control, and End

PointsPoints Continuous Improvement and Knowledge Continuous Improvement and Knowledge

Management Management

Business Needs Should Direct Business Needs Should Direct Analysis Analysis

If You are in High Market Business with If You are in High Market Business with little inventory – Focus on Capacitylittle inventory – Focus on Capacity

If Commodity Market and Have Excess If Commodity Market and Have Excess Capacity – Focus on CostsCapacity – Focus on Costs

If Highly Regulated – Focus on If Highly Regulated – Focus on Documenting Process UnderstandingDocumenting Process Understanding

Example for Pharmaceuticals : Example for Pharmaceuticals : FDA Definition of Process FDA Definition of Process

UnderstandingUnderstandingA process is generally considered well A process is generally considered well

understood when understood when (1)(1) all critical sources of variability are all critical sources of variability are

identified and explained;identified and explained;(2)(2) variability is managed by the process; variability is managed by the process;

and,and,(3)(3) product quality attributes accurately product quality attributes accurately

and reliably predictedand reliably predicted

Making the connectionMaking the connection

Exploratory analysisExploratory analysis It helps to examine the data graphically to see It helps to examine the data graphically to see

how and if things really do go together.how and if things really do go together. A poorly done analysis can make bad A poorly done analysis can make bad

results even worse. (Combining apples results even worse. (Combining apples and oranges, Garbage in Concentrated = and oranges, Garbage in Concentrated = Garbage out, etc.).Garbage out, etc.).

What’s the Payoff? Potential What’s the Payoff? Potential Areas for BenefitsAreas for Benefits

Increased understanding of manufacturing Increased understanding of manufacturing processes and variability by providing integrated processes and variability by providing integrated access to process and product data.access to process and product data.

Effectively demonstrate manufacturing Effectively demonstrate manufacturing processes are stable and capable.processes are stable and capable.

Efficiently disposition manufactured productEfficiently disposition manufactured product Other opportunitiesOther opportunities

Broad access to dataBroad access to data Auto-generation of key reportsAuto-generation of key reports Data sharing and comparison across sitesData sharing and comparison across sites

Understanding the SystemUnderstanding the System

Level of Sophistication

HIGH

MEDIUM

LOW

Details Resolved

HIGH

MEDIUM

LOW(HISTORICAL) DATA DERIVED FROMTRIAL-N-ERROR EXPERIMENTATION

HEURISTIC RULES

EMPIRICAL MODELS

MECHANISTICMODELS

1st Principles

The Need and the Opportunity for Improving Efficiency of U.S. Pharmaceutical Manufacturing:The Need and the Opportunity for Improving Efficiency of U.S. Pharmaceutical Manufacturing: …

References and References and AcknowledgementsAcknowledgements

I. Spiegler, Knowledge management: a new idea or a recycled concept, I. Spiegler, Knowledge management: a new idea or a recycled concept, Communications of the AIS 3 (14), 2000, pp. 1–24.Communications of the AIS 3 (14), 2000, pp. 1–24.

Israel Spiegler, Technology and knowledge: bridging a "generating" gap, Israel Spiegler, Technology and knowledge: bridging a "generating" gap, Information & Management, Volume 40, Issue 6, July 2003, Pages 533-539.Information & Management, Volume 40, Issue 6, July 2003, Pages 533-539.

FDA CDER Draft Guidance Document: PAT — A Framework for Innovative FDA CDER Draft Guidance Document: PAT — A Framework for Innovative Pharmaceutical Manufacturing and Quality Assurance, August 2003, Pharmaceutical Manufacturing and Quality Assurance, August 2003, Pharmaceutical cGMPsPharmaceutical cGMPs

The Need and the Opportunity for Improving Efficiency of U.S. The Need and the Opportunity for Improving Efficiency of U.S. Pharmaceutical Manufacturing: The Need and the Opportunity for Pharmaceutical Manufacturing: The Need and the Opportunity for Improving Efficiency of U.S. Pharmaceutical Manufacturing Technology Improving Efficiency of U.S. Pharmaceutical Manufacturing Technology Initiative, Ajaz S. Hussain, Ph.D., Deputy Director, Office of Pharmaceutical Initiative, Ajaz S. Hussain, Ph.D., Deputy Director, Office of Pharmaceutical Science, CDER, FDA, Science, CDER, FDA,

B. McGarvey, B. McGarvey, Eli Lilly and CompanyEli Lilly and Company R. Plapp, Eli Lilly and CompanyR. Plapp, Eli Lilly and Company W. Hendricks, Eli Lilly and CompanyW. Hendricks, Eli Lilly and Company

Pharma IT SummitPharma IT Summit

Making Sense of it All…Making Sense of it All…Rapidly Wringing Information From Rapidly Wringing Information From Apparently Indiscriminant Piles Of Apparently Indiscriminant Piles Of

NumbersNumbers

Robert H. McCaffertyRobert H. McCafferty

Beyond The Third DimensionBeyond The Third Dimension

Typical Industry PracticeTypical Industry Practice Few High Return Processes Fully UnderstoodFew High Return Processes Fully Understood Complex Chain/Hierarchy Of Intricate Unit ProcessesComplex Chain/Hierarchy Of Intricate Unit Processes Brute Force Numerical Analysis Characterization Method Of Brute Force Numerical Analysis Characterization Method Of

ChoiceChoice Human Intelligence Relegated To Back SeatHuman Intelligence Relegated To Back Seat Jungle Of N-Space ImpenetrableJungle Of N-Space Impenetrable

New Process Knowledge Latent In Existing DataNew Process Knowledge Latent In Existing Data Key To Extraction Engaging Human Mind… Native CuriosityKey To Extraction Engaging Human Mind… Native Curiosity Eyes Primary Path Of Information Input To Human BrainEyes Primary Path Of Information Input To Human Brain N-Dimensional Visualization Breakthrough TechnologyN-Dimensional Visualization Breakthrough Technology 3-Dimensional Status Quo Must Be Broken3-Dimensional Status Quo Must Be Broken

Unexpected ConsequencesUnexpected Consequences No Hypotheses, Modeling Assumptions Required… Only No Hypotheses, Modeling Assumptions Required… Only

CuriosityCuriosity

Increased Insight & Understanding - “It Makes Us Ask Better Increased Insight & Understanding - “It Makes Us Ask Better Questions”Questions”

More Engineering... Better Conclusions, With Less EffortMore Engineering... Better Conclusions, With Less Effort

Rapid Visual Learning From Existing Process DataRapid Visual Learning From Existing Process Data

Discovery Of “Black Holes”Discovery Of “Black Holes” Parameter Space Voids Where Desired Performance Never ObtainedParameter Space Voids Where Desired Performance Never Obtained Significant Issue For Process ControlSignificant Issue For Process Control Almost Generic In ExistenceAlmost Generic In Existence

Business Level BenefitBusiness Level Benefit Knowledge Sharing Mechanism Across OrganizationKnowledge Sharing Mechanism Across Organization

Traditional VisualizationTraditional Visualization

Time Trend Displays… Effective Limit Six VariablesTime Trend Displays… Effective Limit Six Variables

X-Y Plots, Contour Plots, 3-D Surface Views… Good For X-Y Plots, Contour Plots, 3-D Surface Views… Good For Up To Six VariablesUp To Six Variables

Radar Plots… Adequate For Many Variables, But Radar Plots… Adequate For Many Variables, But Visualization Only (popular in Japan)Visualization Only (popular in Japan)

Multiple Regression, PLS, PCA, Dimensionless Groups, Multiple Regression, PLS, PCA, Dimensionless Groups, Multivariate SPCMultivariate SPC

Reduce Dimensions To Allow Visualization (ideally 2-D) For Reduce Dimensions To Allow Visualization (ideally 2-D) For Lumped Variable/Reduced Parameter SpaceLumped Variable/Reduced Parameter Space

Parallel CoordinatesParallel Coordinates

Substantial Foundation In N-Dimensional GeometrySubstantial Foundation In N-Dimensional Geometry

Map N-D Into 2-D Through Coordinate TransformMap N-D Into 2-D Through Coordinate Transform

Allow Direct Data Visualization And ManipulationAllow Direct Data Visualization And Manipulation Many Process Variables Simultaneously (30+)Many Process Variables Simultaneously (30+) Mathematically Robust… Zero Information LossMathematically Robust… Zero Information Loss No Derived Quantities (Re, Nu, PC, etc.) RequiredNo Derived Quantities (Re, Nu, PC, etc.) Required

True VisualizationTrue Visualization Otherwise Unobservable Phenomena Easily SeenOtherwise Unobservable Phenomena Easily Seen Readily ExplainedReadily Explained

A Single 16-Dimensional Point In Parallel A Single 16-Dimensional Point In Parallel CoordinatesCoordinates

Visual AnalysisVisual Analysis

Patterns Formed When Many Points PlottedPatterns Formed When Many Points Plotted

Human Brain Superlative Pattern RecognizerHuman Brain Superlative Pattern Recognizer Very Good At Seeing “Bigger Picture”Very Good At Seeing “Bigger Picture” Eyes Better Than AlgorithmsEyes Better Than Algorithms

Knowledge Key To Understanding & Resolving Knowledge Key To Understanding & Resolving IssuesIssues

Specialized Training No Longer Gate To SolutionSpecialized Training No Longer Gate To Solution Process PhysicsProcess Physics MathematicsMathematics StatisticsStatistics

Anyone Can Use It… Anyone Can Use It…

Perfect Separation AnalysisPerfect Separation Analysis

Data Rich EnvironmentData Rich Environment

Oddities, Features, Relationships Readily VisibleOddities, Features, Relationships Readily Visible

Prone To Overstatement… But Excellent Spotter For More Prone To Overstatement… But Excellent Spotter For More Refined ExaminationRefined Examination

Applying Good, Better, Best Criteria Uncovers PatternsApplying Good, Better, Best Criteria Uncovers Patterns

Very Quick Form Of AnalysisVery Quick Form Of Analysis

Perfect Separation OverviewPerfect Separation Overview

Black Observations @ Top Of X27 Axis Weak Starting MaterialBlack Observations @ Top Of X27 Axis Weak Starting Material Curious Hole In Center Of X30 (Temporal Variable)Curious Hole In Center Of X30 (Temporal Variable) Clear Relationship Between X41 And X42Clear Relationship Between X41 And X42

Best Operating ZoneBest Operating Zone

““Sweet Spot”Sweet Spot”

Where To OperateWhere To Operate PlantPlant Process LineProcess Line Sector Within LineSector Within Line Individual Piece Of Manufacturing EquipmentIndividual Piece Of Manufacturing Equipment

How To Keep It ThereHow To Keep It There Comprehensive Engineering Analysis… One That Can See Comprehensive Engineering Analysis… One That Can See

EverythingEverything Visibility Across Entire Engineering Organization Visibility Across Entire Engineering Organization Right Tools In Operational HandsRight Tools In Operational Hands

Averaging Approach AnalysisAveraging Approach Analysis

Designed To Uncover Best Operating ZoneDesigned To Uncover Best Operating Zone

Based On Detailed Knowledge Of Lot GeneologyBased On Detailed Knowledge Of Lot Geneology

Averaged Contribution Of Pooled Sub-Lots CalculatedAveraged Contribution Of Pooled Sub-Lots Calculated

Substantial Compression Of Available Data… But Very Substantial Compression Of Available Data… But Very High Quality InformationHigh Quality Information

Investigation Keyed By Perfect Separation ObservationsInvestigation Keyed By Perfect Separation Observations

Applying Good, Better, Best Criteria Decorates Applying Good, Better, Best Criteria Decorates Gradients & Reveals Sweet SpotsGradients & Reveals Sweet Spots

Averaging Analysis OverviewAveraging Analysis Overview

Covers First Third Of Biosynthetic Insulin Manufacture… 50 Plus VariablesCovers First Third Of Biosynthetic Insulin Manufacture… 50 Plus Variables Note Hole In X2, High Limit For Premium Material On X12 (Temporal Vars)Note Hole In X2, High Limit For Premium Material On X12 (Temporal Vars) Possible Duality In Biosynthesis Mechanism Given Hole In X15Possible Duality In Biosynthesis Mechanism Given Hole In X15 Pronounced Sweet Spot In X14 (Environmental Variable)Pronounced Sweet Spot In X14 (Environmental Variable)

Geometric ModelGeometric Model

Derived From Best Operating Zone Uncovered During Data AnalysisDerived From Best Operating Zone Uncovered During Data Analysis Incorporates Variable Interactions Inherent In Desirable Operating RegionIncorporates Variable Interactions Inherent In Desirable Operating Region Excellent Vehicle For Response Surface Visualization… Process Excellent Vehicle For Response Surface Visualization… Process

Optimization, Inferential Measurement And ControlOptimization, Inferential Measurement And Control

Lessons LearnedLessons Learned

Leverage Standing IT InvestmentLeverage Standing IT Investment DatabasesDatabases Network InfrastructureNetwork Infrastructure

Harvest New Knowledge From Existing DataHarvest New Knowledge From Existing Data Engage Complementary Visualization TechnologyEngage Complementary Visualization Technology Analyze Full Span Of Process Data AvailableAnalyze Full Span Of Process Data Available Capitalize On Engineering KnowledgeCapitalize On Engineering Knowledge Effectively Mine Existing RecordsEffectively Mine Existing Records

Exploit GainsExploit Gains Process OptimizationProcess Optimization Problem ResolutionProblem Resolution Dynamic ControlDynamic Control