Best Practices in Data Collection for Successful Manufacturing Intelligence
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Best Practices in Data Collection for Successful Manufacturing Intelligence Jeffery Cawley VP Industry Leadership Northwest Analytics January 26, 2012
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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. Recording at https://www1.gotomeeting.com/register/768216009
Transcript of Best Practices in Data Collection for Successful Manufacturing Intelligence
Best Practices in Data Collection
for Successful Manufacturing Intelligence
Jeffery Cawley VP Industry Leadership
Northwest Analytics January 26, 2012
Agenda
Manufacturing Intelligence
Data Quality
Data Collection
Plant Floor Data Collection Best Practices
Business Planning & Logistics Plant Production Scheduling, Operational Management, etc
Manufacturing Operations & Control
Dispatching Production, Detailed Production Scheduling, Reliability Assurance, ...
Batch Control
Discrete Control
Continuous Control
Level 4
Level 3
Level 2
Level 1
ISA 95 Activity Levels
Aggregation
Contextualization
Analysis
Visualization
Propagation
Manufacturing Intelligence (MI)
*AMR/Gartner
Why MI Matters
“The basic productivity gains in manufacturing
have been realized. The next level must be
achieved through Manufacturing Intelligence.”
Pierfrancesco Manenti
IDC
Standardized KPIs Across Enterprise Aberdeen 2011
Best-In-Class Performance
Operations
Supply-chain compliance
Regulatory compliance
Minimum Ante: Good Data
CGMP
GALP
GFSI
9001
22000
ISA95
ICH Q10
HACCP
CAPA
Regulatory Ante: Good Data
Organizations
Standards
Manufacturers’ #1 Barrier – Data Quality Deloitte, 2011
No front-end design
Workflow interruption
Obscure, hard-to-use interface
No best-practice enforcement
Non-secure data handling
Top 5 Bad Practices
Well-defined operation
Transparent, role-specific
interface
SOP support
Immediate feedback
Data-handling integrity
Top 5 Good Practices
Process definition
Operator buy-in
Role-specific interface
Workflow support
Well-Defined Operations
Well-Defined Operations
Access
Currency
Single-point maintenance
SOP Control
JL Pharmaceutical
SOP Control
Intuitive operator interface
Prompt & refresher
Training compliance
Status alert from system-of-record
Link to test or refresher
Immediate feedback
IMS Materials
Ease-Of-Use
Data read – ISA95
Enforce SOP workflow
Monitor input data
Transfer data to database
Data Integrity
Well-defined operation
Transparent, role-specific
interface
SOP support
Immediate feedback
Data-handling integrity
Top 5 Good Practices
Good data required for MI
Must achieve effective,
accurate collection
Benefits are immediate, far
reaching
Summary
Q&A
Manufacturing Intelligence for Intelligent Manufacturing.™
