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Turning Data Into Dollars John W. Rusher, Eli Lilly & Co. Robert H. McCafferty, Curvaceous Software Pharma – IT Summit March 18th, 2004.

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Presentation on theme: "Turning Data Into Dollars John W. Rusher, Eli Lilly & Co. Robert H. McCafferty, Curvaceous Software Pharma – IT Summit March 18th, 2004."— Presentation transcript:

1 Turning Data Into Dollars John W. Rusher, Eli Lilly & Co. Robert H. McCafferty, Curvaceous Software Pharma – IT Summit March 18th, 2004

2 Pharma IT Summit The Benefits of Integrating and Exploiting Data John W. Rusher

3 What is the purpose of collecting and analyzing data? To detect, interpret, and predict qualitative and quantitative patterns in data, leading to information and knowledge. To detect, interpret, and predict qualitative and quantitative patterns in data, leading to information and knowledge.

4 Now, whats the real objective? Reap the benefits of our infrastructure Reap the benefits of our infrastructure Improve quality, safety and/or efficiency Improve quality, safety and/or efficiency Process optimization Process optimization Minimize costsMinimize costs Maximize throughputMaximize throughput Minimize riskMinimize risk Rapid recovery from abnormal situations Rapid recovery from abnormal situations Ultimately, to increase revenue and/or decrease expenses Ultimately, to increase revenue and/or decrease expenses

5 Examples of Real-World Benefits Reducing production cycle times by using on-, in-, and/or at-line measurements and controls. Reducing production cycle times by using on-, in-, 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 efficiency and manage variability. Development of process knowledge to improve efficiency and manage variability. Using small-scale equipment (to eliminate certain scale-up issues) and dedicated manufacturing facilities. Using small-scale equipment (to eliminate certain scale-up issues) and dedicated manufacturing facilities. Improving energy and material use and increasing capacity. Improving energy and material use and increasing capacity.

6 I collect a bunch of data, isnt that enough? Data Does Not Equal Knowledge! Data Does Not Equal Knowledge! Data and technology are sometimes confused with knowledge. Data and technology are sometimes confused with knowledge. The computer, database management software, data warehouses, data marts are equated with information and knowledge. The computer, database management software, data warehouses, data marts are equated with information and knowledge. These are data access vehicles, they are information and not knowledge. These are data access vehicles, they are information and not knowledge.

7 So What is the Difference Among Data, Information and Knowledge I. Spiegler / Information & Management 40 (2003) 533–539

8 So, for The Techies Out There: The Transformation Algorithm If data becomes information when they are organized to add value, then information becomes knowledge when it is analyzed to add insight, abstraction, and better understanding. – I. Spiegler If data becomes information when they are organized to add value, then information becomes knowledge when it is analyzed to add insight, abstraction, and better understanding. – I. Spiegler Data Temperature = 39 DegC Flowrate = 45 lpm 3 deviations/lot Potency = 95% Information O = F(I 1, I 2, …I n ) Knowledge Thruput = 400 Bkgs/mth Organize Analyze

9 And, for you Non-Techies out there: The Restaurant Simile …data are the symbols on the menu, information is the understanding of the restaurants offerings, knowledge is the dinner. You dont go to the restaurant to lick the ink or eat the menu (by Lewis Perelman). …data are the symbols on the menu, information is the understanding of the restaurants offerings, knowledge is the dinner. You dont go to the restaurant to lick the ink or eat the menu (by Lewis Perelman).

10 OK, so it sounds great, but it can take lots of effort… Oceans of Data Oceans of Data Disparate Systems Disparate Systems Various ways to access Various ways to access Data May be Spread Across Various Processes Data May be Spread Across Various Processes Many Tools and Techniques for Data Analysis Many Tools and Techniques for Data Analysis Competing Business Priorities Competing Business Priorities

11 Oceans of data, Islands of knowledge In support of manufacturing pharmaceuticals, large volumes of data are collected to: In support of manufacturing pharmaceuticals, large volumes of data are collected to: Ensure compliance with cGMP, safety, purity, and quality standards Ensure compliance with cGMP, safety, purity, and quality standards Track lot history and genealogy Track lot history and genealogy Improve product quality, process reliability, and overall production performance Improve product quality, process reliability, and overall production performance Demonstrate to regulatory agencies that manufacturing systems are in control and reliable Demonstrate to regulatory agencies that manufacturing systems are in control and reliable

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

13 Data Aggregation Data Integration Data Acquisition Process Data Manufacturing Execution Lab 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-HT 1D 1NP 08/09/9911/01/99 01/10/00 03/27/0005/22/0007/31/00 10/17/0011/02/00 11/27/0012/12/0001/08/0101/23/0102/12/01 02/27/01 03/19/01 04/03/0104/23/01 05/08/0105/29/01 Control Charts Regulatory Reports Metrics Tables, Figures, Listings for Reg. Documents Technical Reports Ad-hoc queries

14 Why Integrate Disparate Data Systems? Accessing and organizing data for: Accessing and organizing data for: Lot disposition Lot disposition Production control Production control Root cause investigation Root cause investigation Process optimization/learning Process optimization/learning Without Integrated data we spend an inordinate amount of time extracting, collating and reformatting data prior to use. Without Integrated data we spend an inordinate amount of time extracting, collating and reformatting data prior to use.

15 What types of data are needed to integrate for effective analysis? Process Data Process Data Critical Process Parameters (CPPs) Critical Process Parameters (CPPs) Criteria for Forward Processing (CFPs) Criteria for Forward Processing (CFPs) Release Specifications Release Specifications Analytical results and controls Analytical results and controls Deviations Deviations Changes Changes Materials Materials Equipment & Maintenance Equipment & Maintenance

16 Analysis Of Data Across the Supply Chain: The Lot Genealogy Issue

17 Perfect Separation vs. Ave. Data

18 The Tools and Techniques for Analysis Vary Greatly Multivariate Data Acquisition and Analysis Multivariate Data Acquisition and Analysis Process Analyzers or Process Analytical Chemistry Tools Process Analyzers or Process Analytical Chemistry Tools Process Monitoring, Control, and End Points Process Monitoring, Control, and End Points Continuous Improvement and Knowledge Management Continuous Improvement and Knowledge Management

19 Business Needs Should Direct Analysis If You are in High Market Business with little inventory – Focus on Capacity If You are in High Market Business with little inventory – Focus on Capacity If Commodity Market and Have Excess Capacity – Focus on Costs If Commodity Market and Have Excess Capacity – Focus on Costs If Highly Regulated – Focus on Documenting Process Understanding If Highly Regulated – Focus on Documenting Process Understanding

20 Example for Pharmaceuticals : FDA Definition of Process Understanding A process is generally considered well understood when (1) all critical sources of variability are identified and explained; (2) variability is managed by the process; and, (3) product quality attributes accurately and reliably predicted

21 Making the connection Exploratory analysis Exploratory analysis It helps to examine the data graphically to see how and if things really do go together. It helps to examine the data graphically to see how and if things really do go together. A poorly done analysis can make bad results even worse. (Combining apples and oranges, Garbage in Concentrated = Garbage out, etc.). A poorly done analysis can make bad results even worse. (Combining apples and oranges, Garbage in Concentrated = Garbage out, etc.).

22 Whats the Payoff? Potential Areas for Benefits Increased understanding of manufacturing processes and variability by providing integrated access to process and product data. Increased understanding of manufacturing processes and variability by providing integrated access to process and product data. Effectively demonstrate manufacturing processes are stable and capable. Effectively demonstrate manufacturing processes are stable and capable. Efficiently disposition manufactured product Efficiently disposition manufactured product Other opportunities Other opportunities Broad access to data Broad access to data Auto-generation of key reports Auto-generation of key reports Data sharing and comparison across sites Data sharing and comparison across sites

23 Understanding the System Level of Sophistication HIGH MEDIUM LOW Details Resolved HIGH MEDIUM LOW (HISTORICAL) DATA DERIVED FROM TRIAL-N-ERROR EXPERIMENTATION HEURISTIC RULES EMPIRICAL MODELS MECHANISTIC MODELS 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: …

24 References and Acknowledgements I. Spiegler, Knowledge management: a new idea or a recycled concept, Communications of the AIS 3 (14), 2000, pp. 1–24. I. Spiegler, Knowledge management: a new idea or a recycled concept, Communications of the AIS 3 (14), 2000, pp. 1–24. Israel Spiegler, Technology and knowledge: bridging a "generating" gap, Information & Management, Volume 40, Issue 6, July 2003, Pages 533-539. Israel Spiegler, Technology and knowledge: bridging a "generating" gap, Information & Management, Volume 40, Issue 6, July 2003, Pages 533-539. FDA CDER Draft Guidance Document: PAT A Framework for Innovative Pharmaceutical Manufacturing and Quality Assurance, August 2003, Pharmaceutical cGMPs FDA CDER Draft Guidance Document: PAT A Framework for Innovative Pharmaceutical Manufacturing and Quality Assurance, August 2003, Pharmaceutical cGMPs 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 Technology Initiative, Ajaz S. Hussain, Ph.D., Deputy Director, Office of Pharmaceutical Science, CDER, FDA, 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 Technology Initiative, Ajaz S. Hussain, Ph.D., Deputy Director, Office of Pharmaceutical Science, CDER, FDA, B. McGarvey, Eli Lilly and Company B. McGarvey, Eli Lilly and Company R. Plapp, Eli Lilly and Company R. Plapp, Eli Lilly and Company W. Hendricks, Eli Lilly and Company W. Hendricks, Eli Lilly and Company

25 Pharma IT Summit Making Sense of it All… Rapidly Wringing Information From Apparently Indiscriminant Piles Of Numbers Robert H. McCafferty

26 Beyond The Third Dimension Typical Industry Practice Typical Industry Practice Few High Return Processes Fully Understood Few High Return Processes Fully Understood Complex Chain/Hierarchy Of Intricate Unit Processes Complex Chain/Hierarchy Of Intricate Unit Processes Brute Force Numerical Analysis Characterization Method Of Choice Brute Force Numerical Analysis Characterization Method Of Choice Human Intelligence Relegated To Back Seat Human Intelligence Relegated To Back Seat Jungle Of N-Space Impenetrable Jungle Of N-Space Impenetrable New Process Knowledge Latent In Existing Data New Process Knowledge Latent In Existing Data Key To Extraction Engaging Human Mind… Native Curiosity Key To Extraction Engaging Human Mind… Native Curiosity Eyes Primary Path Of Information Input To Human Brain Eyes Primary Path Of Information Input To Human Brain N-Dimensional Visualization Breakthrough Technology N-Dimensional Visualization Breakthrough Technology 3-Dimensional Status Quo Must Be Broken 3-Dimensional Status Quo Must Be Broken

27 Unexpected Consequences No Hypotheses, Modeling Assumptions Required… Only Curiosity No Hypotheses, Modeling Assumptions Required… Only Curiosity Increased Insight & Understanding - It Makes Us Ask Better Questions Increased Insight & Understanding - It Makes Us Ask Better Questions More Engineering... Better Conclusions, With Less Effort More Engineering... Better Conclusions, With Less Effort Rapid Visual Learning From Existing Process Data Rapid Visual Learning From Existing Process Data Discovery Of Black Holes Discovery Of Black Holes Parameter Space Voids Where Desired Performance Never Obtained Parameter Space Voids Where Desired Performance Never Obtained Significant Issue For Process Control Significant Issue For Process Control Almost Generic In Existence Almost Generic In Existence Business Level Benefit Business Level Benefit Knowledge Sharing Mechanism Across Organization Knowledge Sharing Mechanism Across Organization

28 Traditional Visualization Time Trend Displays… Effective Limit Six Variables Time Trend Displays… Effective Limit Six Variables X-Y Plots, Contour Plots, 3-D Surface Views… Good For Up To Six Variables X-Y Plots, Contour Plots, 3-D Surface Views… Good For Up To Six Variables Radar Plots… Adequate For Many Variables, But Visualization Only (popular in Japan) Radar Plots… Adequate For Many Variables, But Visualization Only (popular in Japan) Multiple Regression, PLS, PCA, Dimensionless Groups, Multivariate SPC Multiple Regression, PLS, PCA, Dimensionless Groups, Multivariate SPC Reduce Dimensions To Allow Visualization (ideally 2-D) For Lumped Variable/Reduced Parameter Space Reduce Dimensions To Allow Visualization (ideally 2-D) For Lumped Variable/Reduced Parameter Space

29 Parallel Coordinates Substantial Foundation In N-Dimensional Geometry Substantial Foundation In N-Dimensional Geometry Map N-D Into 2-D Through Coordinate Transform Map N-D Into 2-D Through Coordinate Transform Allow Direct Data Visualization And Manipulation Allow Direct Data Visualization And Manipulation Many Process Variables Simultaneously (30+) Many Process Variables Simultaneously (30+) Mathematically Robust… Zero Information Loss Mathematically Robust… Zero Information Loss No Derived Quantities (Re, Nu, PC, etc.) Required No Derived Quantities (Re, Nu, PC, etc.) Required True Visualization True Visualization Otherwise Unobservable Phenomena Easily Seen Otherwise Unobservable Phenomena Easily Seen Readily Explained Readily Explained

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31 A Single 16-Dimensional Point In Parallel Coordinates

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33 Visual Analysis Patterns Formed When Many Points Plotted Patterns Formed When Many Points Plotted Human Brain Superlative Pattern Recognizer Human Brain Superlative Pattern Recognizer Very Good At Seeing Bigger Picture Very Good At Seeing Bigger Picture Eyes Better Than Algorithms Eyes Better Than Algorithms Knowledge Key To Understanding & Resolving Issues Knowledge Key To Understanding & Resolving Issues Specialized Training No Longer Gate To Solution Specialized Training No Longer Gate To Solution Process Physics Process Physics Mathematics Mathematics Statistics Statistics Anyone Can Use It… Anyone Can Use It…

34 Perfect Separation Analysis Data Rich Environment Data Rich Environment Oddities, Features, Relationships Readily Visible Oddities, Features, Relationships Readily Visible Prone To Overstatement… But Excellent Spotter For More Refined Examination Prone To Overstatement… But Excellent Spotter For More Refined Examination Applying Good, Better, Best Criteria Uncovers Patterns Applying Good, Better, Best Criteria Uncovers Patterns Very Quick Form Of Analysis Very Quick Form Of Analysis

35 Perfect Separation Overview Black Observations @ Top Of X27 Axis Weak Starting Material Black 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 X42 Clear Relationship Between X41 And X42

36 Best Operating Zone Sweet Spot Sweet Spot Where To Operate Where To Operate Plant Plant Process Line Process Line Sector Within Line Sector Within Line Individual Piece Of Manufacturing Equipment Individual Piece Of Manufacturing Equipment How To Keep It There How To Keep It There Comprehensive Engineering Analysis… One That Can See Everything Comprehensive Engineering Analysis… One That Can See Everything Visibility Across Entire Engineering Organization Visibility Across Entire Engineering Organization Right Tools In Operational Hands Right Tools In Operational Hands

37 Averaging Approach Analysis Designed To Uncover Best Operating Zone Designed To Uncover Best Operating Zone Based On Detailed Knowledge Of Lot Geneology Based On Detailed Knowledge Of Lot Geneology Averaged Contribution Of Pooled Sub-Lots Calculated Averaged Contribution Of Pooled Sub-Lots Calculated Substantial Compression Of Available Data… But Very High Quality Information Substantial Compression Of Available Data… But Very High Quality Information Investigation Keyed By Perfect Separation Observations Investigation Keyed By Perfect Separation Observations Applying Good, Better, Best Criteria Decorates Gradients & Reveals Sweet Spots Applying Good, Better, Best Criteria Decorates Gradients & Reveals Sweet Spots

38 Averaging Analysis Overview Covers First Third Of Biosynthetic Insulin Manufacture… 50 Plus Variables Covers 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 X15 Possible Duality In Biosynthesis Mechanism Given Hole In X15 Pronounced Sweet Spot In X14 (Environmental Variable) Pronounced Sweet Spot In X14 (Environmental Variable)

39 Geometric Model Derived From Best Operating Zone Uncovered During Data Analysis Derived From Best Operating Zone Uncovered During Data Analysis Incorporates Variable Interactions Inherent In Desirable Operating Region Incorporates Variable Interactions Inherent In Desirable Operating Region Excellent Vehicle For Response Surface Visualization… Process Optimization, Inferential Measurement And Control Excellent Vehicle For Response Surface Visualization… Process Optimization, Inferential Measurement And Control

40 Lessons Learned Leverage Standing IT Investment Leverage Standing IT Investment Databases Databases Network Infrastructure Network Infrastructure Harvest New Knowledge From Existing Data Harvest New Knowledge From Existing Data Engage Complementary Visualization Technology Engage Complementary Visualization Technology Analyze Full Span Of Process Data Available Analyze Full Span Of Process Data Available Capitalize On Engineering Knowledge Capitalize On Engineering Knowledge Effectively Mine Existing Records Effectively Mine Existing Records Exploit Gains Exploit Gains Process Optimization Process Optimization Problem Resolution Problem Resolution Dynamic Control Dynamic Control


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