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Best Practices in Data Collection for Successful Manufacturing Intelligence


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Robust Manufacturing Intelligence (MI) capabilities are fundamental to successful manufacturing enterprise management. And robust MI capabilities start with sound data collection practices.
The analytics feeding management dashboards will work with any properly formed data that can be drawn from manufacturing databases. However, if that data is compromised, the decisions made by management based on the compromised data may be faulty and put the organization at risk.
The key to world-class, sound decision making is a solid data collection foundation. This webinar examines data collection best practices:
• The requirements standards such as ISA 95, FDA Q10, and ISO 9001 and good manufacturing practices place upon the data collection process.
• The business implications of poor data collection.
• What data collection best practices should be implemented:
o Operator workflow support
o SOP enforcement
o Input error reduction
o Data integrity
• How data collection integrates with manufacturing management systems
The end result is compliant process and testing data collection that dependably provides high-quality data to feed the analytics that informs MI. Data collection with integrity is a core requirement to make MI work while keeping auditors and customers satisfied.

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Best Practices in Data Collection for Successful Manufacturing Intelligence

  1. 1. Best Practices in Data Collectionfor Successful Manufacturing Intelligence Jeffery Cawley VP Industry Leadership Northwest Analytics January 26, 2012
  2. 2. Agenda Manufacturing Intelligence Data Quality Data Collection Plant Floor Data Collection Best Practices
  3. 3. ISA 95 Activity Levels Business Planning & LogisticsLevel 4 Plant Production Scheduling, Operational Management, etc ManufacturingLevel 3 Operations & Control Dispatching Production, Detailed Production Scheduling, Reliability Assurance, ...Level 2 Batch Continuous Discrete Control Control ControlLevel 1
  4. 4. Manufacturing Intelligence (MI) Aggregation Contextualization Analysis Visualization Propagation*AMR/Gartner
  5. 5. Why MI Matters“The basic productivity gains in manufacturinghave been realized. The next level must beachieved through Manufacturing Intelligence.” Pierfrancesco Manenti IDC
  6. 6. Best-In-Class PerformanceStandardized KPIs Across Enterprise Aberdeen 2011
  7. 7. Minimum Ante: Good Data Operations Supply-chain compliance Regulatory compliance
  8. 8. Regulatory Ante: Good Data Organizations  Standards CGMP ICH Q10 ISA95 9001 GALP GFSI HACCP 22000 CAPA
  9. 9. Manufacturers’ #1 Barrier – Data Quality Deloitte, 2011
  10. 10. Top 5 Bad Practices No front-end design Workflow interruption Obscure, hard-to-use interface No best-practice enforcement Non-secure data handling
  11. 11. Top 5 Good Practices Well-defined operation Transparent, role-specific interface SOP support Immediate feedback Data-handling integrity
  12. 12. Well-Defined Operations Process definition Operator buy-in Role-specific interface Workflow support
  13. 13. Well-Defined Operations
  14. 14. SOP Control Access Currency Single-point maintenance
  15. 15. SOP ControlJL Pharmaceutical
  16. 16. Immediate feedback Intuitive operator interface Prompt & refresher Training compliance  Status alert from system-of-record  Link to test or refresher
  17. 17. Ease-Of-UseIMS Materials
  18. 18. Data Integrity Data read – ISA95 Enforce SOP workflow Monitor input data Transfer data to database
  19. 19. Top 5 Good Practices Well-defined operation Transparent, role-specific interface SOP support Immediate feedback Data-handling integrity
  20. 20. Summary Good data required for MI Must achieve effective, accurate collection Benefits are immediate, far reaching
  21. 21. Q&A
  22. 22. Manufacturing Intelligence for Intelligent Manufacturing.™