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. Recording at https://www1.gotomeeting.com/register/768216009

1. Best Practices in Data Collection
for Successful Manufacturing Intelligence
Jeffery Cawley
VP Industry Leadership
Northwest Analytics
January 26, 2012

2. Agenda
 Manufacturing Intelligence
 Data Quality
 Data Collection
 Plant Floor Data Collection Best Practices

3. ISA 95 Activity Levels
Business Planning & Logistics
Level 4 Plant Production Scheduling,
Operational Management, etc
Manufacturing
Level 3 Operations & Control
Dispatching Production, Detailed Production
Scheduling, Reliability Assurance, ...
Level 2
Batch Continuous Discrete
Control Control Control
Level 1

4. Manufacturing Intelligence (MI)
 Aggregation
 Contextualization
 Analysis
 Visualization
 Propagation
*AMR/Gartner

5. Why MI Matters
“The basic productivity gains in manufacturing
have been realized. The next level must be
achieved through Manufacturing Intelligence.”
Pierfrancesco Manenti
IDC

6. Best-In-Class Performance
Standardized KPIs Across Enterprise
Aberdeen 2011

7. Minimum Ante: Good Data
 Operations
 Supply-chain compliance
 Regulatory compliance

8. Regulatory Ante: Good Data
 Organizations  Standards
CGMP ICH Q10
ISA95 9001
GALP
GFSI HACCP
22000 CAPA

9. Manufacturers’ #1 Barrier – Data Quality
Deloitte, 2011

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. Top 5 Good Practices
 Well-defined operation
 Transparent, role-specific
interface
 SOP support
 Immediate feedback
 Data-handling integrity

12. Well-Defined Operations
 Process definition
 Operator buy-in
 Role-specific interface
 Workflow support

13. Well-Defined Operations

14. SOP Control
 Access
 Currency
 Single-point maintenance

15. SOP Control
JL Pharmaceutical

16. Immediate feedback
 Intuitive operator interface
 Prompt & refresher
 Training compliance
 Status alert from system-of-record
 Link to test or refresher

17. Ease-Of-Use
IMS Materials

18. Data Integrity
 Data read – ISA95
 Enforce SOP workflow
 Monitor input data
 Transfer data to database

19. Top 5 Good Practices
 Well-defined operation
 Transparent, role-specific
interface
 SOP support
 Immediate feedback
 Data-handling integrity

20. Summary
 Good data required for MI
 Must achieve effective,
accurate collection
 Benefits are immediate, far
reaching

21. Q&A

22. Manufacturing Intelligence for Intelligent Manufacturing.™