Biotechnology Manufacturing Analytics

Cory Brown Dermot McCaul Rob Kasprow, PhD SAP Adaptive Manufacturing Summit September 2009 Chicago, IL Biotechnology Manufacturing Analytics Vaccine Manufacturing Operations IT Account Manager Vaccine Manufacturing Vaccine Technology & Engineering

Company Background The Challenges The Solutions Implementation Highlights The Benefits Lessons Learned and Next Steps

Merck & Co., Inc. Global Pharmaceutical Manufacturing Merck is a global research-driven pharmaceutical company dedicated to putting patients first. Established in 1891, Merck currently discovers, develops, manufactures and markets vaccines and medicines to address unmet medical needs. SAP AG 2009, or Merck 2009; / 3

Merck & Co., Inc. > 55,000 employees worldwide Conducts research in nine priority disease areas Markets products in approximately 200 countries and regions Invested over $4 billion last year in research & development Present Vaccine product portfolio includes: GARDASIL (HPV, cervical cancer) M-M-R II (measles, mumps, rubella) PEDVAXHIB (Hib) COMVAX (Hib + hepatitis B) PROQUAD (MMR + chicken pox) RECOMBIVAX HB (hepatitis B) ROTATEQ (rotavirus) VARIVAX (chicken pox) VAQTA (hepatitis A) ZOSTAVAX (shingles) PNEUMOVAX 23 (Pneumococcal) SAP AG 2009, or Merck 2009; / 4

Business Challenges Pharmaceutical Industry Challenges in the 21 st Century Pipelines Production Constraints - Across Pharma Industry, R & D spending doubled in the last few years - Number of new compounds reduced by half - Patent Expirations Pricing Pressures - New Prescribing Criteria now include cost-effectiveness, in addition to efficacy and safety Responses by FDA and Industry Leaders FDA is Challenging the Industry to improve its manufacturing processes via the Continued Process Verification, and to reduce costs via Risk-based Validation Pharmaceutical Industry Leaders are investing in Operational Excellence Programs (PAT, Lean, Six Sigma, Right First Time), ERP and MES BUT, while data are abundant in many local systems, it is still difficult to collect information from all these different data sources for presentation, analysis and decision making SAP AG 2009, or Merck 2009; / 6

Manufacturing Challenges Biotech manufacturing presents difficult challenges for process analytics, given complexity of biological products and limitations of assays, relative to pharm processes Robust quality systems and in-depth process knowledge ensure safety and efficacy Further Optimize Manufacturing Process Consistency Increase throughput decrease cycle time, increase yields, without compromise to Quality More Real-Time data availability to drive faster business response to metrics Improve visibility across the business via single interface for all groups: Shop Floor Technology / Operations Decision Making Release Annual Review Across facilities / sites SAP AG 2009, or Merck 2009; / 7

Proactive Process Monitoring Process Map In general, the Yellow steps currently involve hands-on work by many people. Goal is to streamline the business process by leveraging IT tools where appropriate. Make Make Changes Changes prior prior to to running running next next batch? batch? Run Run the the Batch Batch Analytical Analytical Testing Testing Data Data recorded recorded in in Electronic Electronic and and Paper Paper Form Form Collect Collect Process Process Data Data Format Format Data Data for Analyze for Analyze Data Report / Data Report / (SPC Trend, etc.) Communicate Analysis (SPC Trend, etc.) Communicate Analysis Data for Data for Data Data for Process for Process Investigations Improvements Investigations Improvements SAP AG 2009, or Merck 2009; / 8

Manufacturing IT Challenges Technical challenges faced, across the industry: Integration: Need for facile, scalable and adaptable connectivity to various shop-floor systems Intelligence: Manufacturing information / metrics - Available, but often not in real-time Alignment: Legacy Technologies were designed to support functional (vertical) hierarchies, and are not well suited to facilitate (horizontal) customer demand driven supply-chain focus (business process orientation) Disconnects: Technology Vertical Disconnects Plant #1 Plant #2 Plant #3 Horizontal Disconnects SAP AG 2009, or Merck 2009; / 9

Desired Attributes of the Solution - IT Consistent with corporate technology road-map One-stop solution to connect to multiple shop floor systems Provide a common platform for visualization and usage Flexibility of the development tool Service-oriented architecture that enables rapid development and re-use of code when applicable Ease of integration with other data sources such as SQL Databases SAP AG 2009, or Merck 2009; / 11

Desired Attributes of the Solution - Business Dramatically reduce time required to monitor, compile and analyze process data via statistical trending Move toward real-time availability Exception based Manufacturing Push notification of issues rather than Pull Ability to discover relationships and key understandings from data Provide broad visibility to data and analysis results The capability to perform enhanced deep dive analysis into the way the product is manufactured. Flexible, scalable across the supply chain, between sites SAP AG 2009, or Merck 2009; / 12

SAP Mii: the bridge between ERP, MES and Shop Floor Using SAP Manufacturing Integration & Intelligence: Enables collection of data from multiple sources effectively, Reduces cycle time to wait for ERP and MES roll-outs to implement performance metrics and continuous improvement, Enables use as the engine for the manufacturing analytics framework that is forward compatible with future technology on our roadmap: Netweaver and SAP Portal, Satisfies implementing manufacturing performance management independently of ERP, Provides the capability to deploy a flexible platform at each site to service local needs, which are assumed to always be mixture of centralized and local systems. Enables implementing a solution that connects to a single central corporate ERP image, at the same time, which satisfy our architecture goal Mii selected as the Manufacturing Process Monitoring Engine SAP AG 2009, or Merck 2009; / 13

High Level MMD IT Architecture Mii SAP AG 2009, or Merck 2009; / 14

SAP Mii Core Functions/Services Data Services Bi-directional data access Metadata browsing XML-based data abstraction Connectors to external systems Business Logic Services Logic Integration services Notification services Calculation and data transformation Content creation Visualization Services Charts, grids, tickers, UI controls Dashboard components Analytic Services SPC/SQC (Six Sigma) analyses Statistical and other mathematical analyses Web Services All services exposed as web services Includes user-defined composite services SAP Mii SAP Mii Mfg. Intelligence Mfg. Integration Visualization KPI Alerts Analytics Business Logic Services Core Mfg Services S95/B2MML Standard Other Standards Web Services Data Services Connectors SAP AG 2009, or Merck 2009; / 15

SAP Mii System Overview: Data Aggregation & Analytics Display Reporting Analysis Business Results Presentation SAP Mii User Interfaces Graphs Trends Statistics SAP Mii Reports Batch Reports Lot Disposition GMP Reports SAP Mii Analytics Native Functions Export to Minitab, SAS, etc. SAP Mii Application SAP Mii Application Components (Aggregate, Align, Synchronize to create a Common Context) SAP Mii Connectors Data Sources ERP LIMS MMS etc MES DHS PI Legacy MRP etc Centralized Distributed Site Specific Infrastructure WAN, LAN s; Servers: Development, Test and Production SAP AG 2009, or Merck 2009; / 16

Merck SAP Mii Development A Modular Approach Manage Execution Risk Develop and Deploy functionality in stages Deliver modules that provide biggest return first Focus on flexible and scalable design Learn from early experience to refine before proceeding Better define business case cost vs. return / benefits Refine design principles based on end user experience using the tools live SAP AG 2009, or Merck 2009; / 17

SPC Data Refinement Trend - Unrefined Trend - Useful Information Contextual Trend 4 3 2 More Refined Data Time Base Only Lot Start Time Refined Information SPC Component 1 1 SAP AG 2009, or Merck 2009 / 18

SPC Trending Case Example: Is the component monitored changing, is the product trending upward? On the right is a control chart that would adequately enable a user to answer the request about the product trend. Mii equivalent of selecting lots on the left side of the DAT and selecting the Checkbox of the component source address. Benefit: Before: After: Produce Lot to Lot Validated Control Charts. 2 hours initial build, 1 hour peer review per chart, not validated <1 minute to build, set alerts, and add to a dashboard in order to monitor forever in a validated state. SAP AG 2009, or Merck 2009 / 19

Genealogy Case Example: A LOT of Product has been identified to contain a potential quality issue. We need to identify all Products that have been affected. On the right is the graphical representation of the potential magnitude of this request starting with the lot represented as a yellow triangle. Mii equivalent of selecting lot and checking the attribute box: @Everywhere LOT Went Benefit: Before: After: Produce a View of What Product/Material has gone into, or what product each raw material is consumed by. Many Man hours <1 minute to build SAP AG 2009, or Merck 2009 / 20

Process Consistency Case Example: The processing time for a component lot is inconsistent. An objective was set to bring the production process into consistent control, and maintain the processing duration permanently. Solution: Identify the production process required duration by exporting through Mii each process step s processing time. Establish the metric of 100 hours. Add the production processing time to the dashboard, and alert on deviations above 100 hours. Benefit: Before: After: Searching for the Golden Batch? Produce Lot to Lot Control Charts. Hours for initial build, hours of peer review, not validated <1 minute to build, set alerts, and add to a dashboard in order to monitor forever and validated SAP AG 2009, or Merck 2009 / 21

Dashboards Business Language Format. An opportunity to see what s working well, and what s not working. Know who s having what problems. SAP AG 2009, or Merck 2009 / 22

Merck Mii Roll-out Staged / Modular Deployment Plan Staged roll-out plan across supply chain and between sites Focus on re-use of core modules when possible Development will be required to accommodate source systems particular to sites / facilities Internal Supply Chain Suppliers Plant A Site #1 Plant B Site #1 Plant C Site #1 Pkg g Distributors Sites Plant A Site #2 Plant B Site #2 Plant C Site #3 Current Mii Project Potential Mii Project SAP AG 2009, or Merck 2009; / 23

Implementation highlights to date Process Monitoring Project #1 (Complete) Deployed in first facility: - Batch Record Data Collection - Data Aggregation and Data Export - Data analysis in third-party statistical package Process Monitoring Project #2 (in Progress) Deployed in second facility: - Batch Record Data Collection - Data Aggregation, Data Analysis, and Data Export in Mii - Query Scheduling for auto-generated SPC analysis - Dashboards - Integrated Data Analysis Functionality (export of data not required SAP AG 2009, or Merck 2009 / 25

SAP Mii Benefits Process Monitoring Project #1 IT Benefits: - Successfully connected to multiple source systems - Successfully aggregated data from many systems for export in single table for analysis - Created single web interface for Batch Record data entry Business Benefits: - New tools make large data sets available in minutes rather than days or weeks - Tools have led to deeper understanding of relationships between process parameters and quality attributes leading to improved process control / capability SAP AG 2009, or Merck 2009 / 27

SAP Mii Benefits Process Monitoring Project #2 (potential benefits) In addition to the above, also expect: - Further reduce time required to generate process analysis Drive shorter cycle times, higher throughput - Proactive awareness of process variations via: Auto-Gen SPC trend charts Process performance dashboards - Configurable release allow system to evolve as Engineers require SAP AG 2009, or Merck 2009 / 28

Lessons Learned - Challenges Mii Tools = great enablers but process knowledge is the key to benefits Projects must be led by the process owner Availability of subject matter experts is critical to project success Process changes to incorporate these capabilities are as important as building the tools fit for use and fit for purpose Source systems can present challenges to installation Inconsistent data standards across systems and sites is a major challenge Custom database views, or other source system modifications may be required to connect with Mii Completely defining requirements up-front Very complex system designs More training can assist users in better defining Mii requirements A unified strategy for process monitoring is required Alignment needed across Manufacturing (all sites), Quality, IT, and Engineering Each area owns source data components, but they all use the same data SAP AG 2009, or Merck 2009 / 30

What s Next? Current Project leveraging lessons learned from Project #1 Thorough assessment of source system structure and data limitations prior to design completion Greater time spent in Design stage to ensure user requirements are completely understood and accounted for Prototyping during both Requirements and Design Phases to expose risks and validate design approach Focus on flexible, configurable design during construction Leverage code / modules in future SAP Mii deployments within Merck And, Mii is also being adapted into other business areas including Predictive Analytics and Energy Management SAP AG 2009, or Merck 2009; / 31