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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
2/00
11/
27/
001
2/1
2/00
01/
08/
010
1/2
3/01
02/
12/
010
2/2
7/01
03/
19/
010
4/0
3/01
04/
23/
010
5/0
8/01
05/
29/
01
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