Driving Operational Excellence
with Data – Part 2
Katherine van Nes, P.Eng.
Director of Information Systems, JMP Engineering
Main Discussion Points
Trends in Manufacturing
Refresher in Concepts in Operational Excellence
Benchmarking the Pharmaceutical Industry
Driving Operational Excellence with Data
The Validation Hurdle
Driving Operational Excellence with Data – Pt. 2
Trends in Manufacturing
Challenges in the Industry
Global competition
Customer expectations
Shareholder expectations
Increasing standards and regulatory requirements
Trends in Manufacturing
The Canadian Scene
“Canada falls hard into deep, widespread recession in final
quarter of 2008 .”
Source: Canadian Press, March 2, 2009
"Despite facing increased adversity…(Manitoba) is
expected to maintain a positive growth rate, well above the
national average…the province will benefit from its strong
industrial diversification
Source: Canadian News Wire, March
12, 2009
Trends in Manufacturing
Pharmaceutical News
“Citigroup downgraded the pharmaceuticals and
biotechnology sector to "underweight" from "market weight"
on valuation and a worsening political environment for the
industry…
On the pharmaceuticals and biotechnology sector, analyst
Tobias Levkovich said U.S. President Barack Obama's
‘determination to move forward with his health care reform
proposals easily could weigh on long-term earnings
expectations’."
Source: Reuters, March 9, 2009
Trends in Manufacturing
Pharmaceutical Manufacturing Comparison
Measure
Pharmaceutical
Industry
A Winning
Pharma Factory
A World Class
Factory
Stockturn
3 to 5
14
50
OTIF
60% to 80%
97.4%
99.6%
RFT
85% to 95%
96.0%
99.4%
CpK
1 to 2
3.5
3.2
OEE
30.0%
74.0%
92.0%
720
48
8
0.100
0.050
0.001
Cycle Time (hrs)
Safety/100,000 hrs
Source: Benson, R. S. and D. J. MacCabe.
“From Good Manufacturing Practice to Good Manufacturing Performance.”
Pharmaceutical Engineering. July/August 2004. vol. 24, no. 4: 26-34.
Trends in Manufacturing
Refresher in Concepts in
Operational Excellence
“Those who are not dissatisfied will
never make any progress”
- Shigeo Shingo
The Way Forward - Operational Excellence
Objectives of Operational Excellence
Better Quality
Higher Throughput
Greater Availability
More Productive Operations and Maintenance
Streamlined Safety, Health and Environmental Compliance
Lower Utility Costs
Less Waste
Concepts in Operational Excellence
Supported by Regulatory Bodies
“A desired goal of the PAT framework is to design & develop
processes that can consistently ensure a predefined quality at the
end of the manufacturing process…Gains in quality, safety and/or
efficiency will vary…and are likely to come from:
Reducing production cycle times by using on-,in- and/or at-line
measurements and controls
Preventing rejects, scrap and reprocessing
Considering the possibility of real time release
Increasing automation and technology to improve operator safety
and reduce human errors
Facilitating continuous processing to improve efficiency and manage
variability
Improving energy and material use and increasing capacity”
Source: Guidance for Industry PAT – A Framework for Innovative Pharmaceutical
Manufacturing and Quality Assurance, Draft Guidance, U.S. Food and Drug Administration
Changing Pharmaceutical Landscape
“Operational Excellence and the Leadership Challenge”
“Lean Pharma: If Only It Were This Easy”
“Operational Excellence – Pharma’s Missed
Opportunities”
“Novartis Aims to Become Pharma’s Toyota by 2010”
“Operational Excellence – Walking the Talk”
“Lean Manufacturing and Pharma - An Interview with Phil
Emard”
Source: Pharmaceutical Manufacturing; Recent Articles
Concepts in Operational Excellence
Basic Op Ex Techniques
Lean
Six Sigma
Process Analytical Technology (PAT)
Overall Equipment Effectiveness (OEE)
Concepts in Operational Excellence
Lean
Lean manufacturing is a management philosophy
focusing on reduction of the seven (now nine) wastes to
improve overall customer value:
Overproduction
Transportation
Waiting
Inventory
Motion
Processing
Defects
Safety
Information
By eliminating waste (muda), quality is improved, and
production time and costs are reduced.
Reference Article - Lean Pharma: If Only It Were This Easy,
Pharmaceutical Manufacturing, June, 2008
Concepts in Operational Excellence
Six Sigma
System of practices originally developed by Motorola to
systematically improve processes by eliminating defects
Methodology to improve existing process: D.M.A.I.C.
Sigma
ppm Defects
Yield
Cost of Quality
2
308,537
69.2%
25-35%
3
66,807
93.3%
20-25%
4
6,210
99.4%
12-18%
5
233
99.98%
4-8%
6
3.4
99.99966%
1-3%
Source: Pharmaceutical Engineering, ‘From Good Manufacturing Practice to
Good Manufacturing Performance’, Benson, McCabe’
Concepts in Operational Excellence
Process Analytical Technology
Process Analytical Technology or PAT has been
defined by the United States Food and Drug
Administration (FDA) as a mechanism to design, analyze,
and control pharmaceutical manufacturing processes
through the measurement of critical process parameters
and quality attributes.
Design, analyze, and control manufacturing to improve
process understanding.
Concepts in Operational Excellence
Overall Equipment Effectiveness – Time Model
TOTAL TIME
(168 hrs)
Planned
Planned
Downtime
Downtime
AVAILABLE
AVAILABLE TIME
TIME
(120
(120 hrs)
hrs)
(48hrs)
(48hrs)
OPERATING
OPERATING TIME
TIME
Setup
Setup Breakdowns
Breakdowns
(6
(6 hrs)
hrs)
(102
(102 hrs)
hrs)
PRODUCTION
PRODUCTION TIME
TIME
(90
(90 hrs)
hrs)
(6
(6 hrs)
hrs)
GOOD
Rejects
GOOD PRODUCTION
PRODUCTION Rejects
(78
(78 hrs)
hrs)
OEE
OEE
Reduced
Reduced
Speed
Speed
(12
(12 hrs)
hrs)
Small
Small
Stops
Stops
(6
(6 hrs
hrs))
Performance
Quality
LOST
LOST CAPACITY
CAPACITY
Concepts in Operational Excellence
(12
(12 hrs)
hrs)
Availability
Overall Equipment Effectiveness – Time Model
OEE = Availability x
OEE =
Operating Time
Available Time
x
Quality
Good Output
Total Output
Concepts in Operational Excellence
x
x
Performance
Total Output
Potential Output at
Rated Speed
Benchmarking The Pharmaceutical
Industry
“You can always benchmark
within your own sector
and feel quite okay if
you’re amongst leading
companies, but if you look
outside that industry you’ll
find ways to improve”
Ralf Haefli
Head of Global Tech
Operations IT, Novartis
OEE Comparison
Semi-conductor Industry >85%
Pharmaceutical Industry <50%
Trends in Manufacturing
OEE Comparison Amongst Best-in-Class
70%
63%
60%
50%
40%
30%
44%
39%
30%
29%
22%
Best-in-Class
Average
Others
20%
10%
0%
Pharmaceutical
Food & Beverage
Source: www.informance.com/PharmaStudy/Default.aspx
Concepts in Operational Excellence
SPC in Pharma Industry
How often is SPC used to reduce process variance?
36% - Sometimes
34% - Rarely
Source: Agnes Shanley.
“Pharma Sharpens its Game: Results of Our First OpEx Survey.”
Pharmaceutical Manufacturing. May 2006. vol. 5, no. 5: p16.
Trends in Manufacturing
Driving Operational
Excellence with Data
Driving OpX With Data
“Drive thy business or it will drive thee.”
- Benjamin Franklin
Data as an Enabler
“Information technology and it’s use in…
Electronically and automatically reporting deviations
Tracking deviations by lot
Tracking deviations by type of issue
Tracking people assigned to resolving the deviation
Central data stores
…universally corresponds to superior
manufacturing performance metrics.”
Source: ‘Pharmaceutical Manufacturing Research Project
– Final Benchmarking Report’, Macher, Nickerson, September 2006
Driving Operational Excellence with Data
Automated Functionality
Document Management
Statistical Process Control
50%
39%
Testing Automation
49%
25%
Traceability and Geneology
48%
34%
Complaint Handling
32%
Supplier Quality Management
27%
Audit Management
Dashboards
56%
32%
NC/CAPA
Compliance Management
58%
44%
24%
Best-in-Class
All Others
31%
23%
0%
38%
36%
33%
20%
43%
30%
60%
Souce: Aberdeen Group "Compliance and Traceability in Real-Time"
Driving Operational Excellence with Data
Best in Class Manufacturers
53% more likely than Laggards to invest in MES
capabilities in support of their compliance and traceability
initiatives.
450% more likely than Laggards to invest in Enterprise
Manufacturing Intelligence to gain visibility
61% more likely to integrate MES with ERP.
93% of manufacturers still relying on manual processes
to manage compliance and traceability programs were
unable to achieve Best-in-Class status.
Source: Various Aberdeen, 2007 and 2008
Driving Operational Excellence with Data
Operational Excellence Challenges
Challenges
%
Selected
Responses to Challenges
%
Selected
1. Significant Culture change
required
68%
1. Train Employees
68%
2. Data Collection challenges
44%
2. Introduce change gradually
49%
3. Resistance from knowledge
workers and middle
management
28%
3. Assign senior management
champions accountable for
quantifiable results
44%
4. Continued commitment from
top mgmt after initial stage
26%
4. Engage Outside consultants
33%
5. Sustained company-wide
training and certification program
20%
5. Deploy IT solutions in
support of quality initiatives
27%
6. Cost of training and
certification programs
20%
6. Recruit external
qualified/certified individuals
25%
7. Excessive time spent
“scrubbing” data
19%
7. Implement automated data
collection
19%
Driving Operational Excellence with Data
Data Techniques
Manufacturing Intelligence
Manufacturing Execution Systems
Plant to ERP Integration
Driving Operational Excellence With Data
Manufacturing
Intelligence
Manufacturing Intelligence Defined
Manufacturing IT Challenges
Driving Operational Excellence with Data
Manufacturing Intelligence Defined
Driving Operational Excellence with Data
Manufacturing Intelligence Defined
Web and non-web reporting
Web-based portals and dashboards
Andon displays
Mobile hand-held displays
Emails, alerts
Driving Operational Excellence with Data
Mfg Intelligence Landscape of Pharmaceutical
Adoption
Are production, inventory and quality data collection automated
and available to the necessary job roles across the enterprise?
Pharma
BIC
Yes and in place for more than 1 year
29%
28%
In place for less than 1 year
5%
21%
Will be in place within 1 year
24%
31%
Will be in place in more than 1 year
38%
8%
No plans
5%
13%
www.pharmamanufacturing.com/articles/2007/087.html
Driving Operational Excellence with Data
Mfg Intelligence Case Study
Background:
Global medical products and services company
Required data captured via variety of ‘techniques’
System Monitoring and System Release for various equipment
not meeting corporate data.
FDA regulations met only through quasi-manual record
management
‘Snapshot’ critical parameters from separate system logged
via manually intensive procedures; no ability for analysis
Heavily dependent on operator interaction with inconsistent
data storage and access
Sanitization reports created through combination of manual
and HMI reporting.
Driving Operational Excellence with Data
Mfg Intelligence Case Study – Cont’d
Solution
Created a regulatory compliant and corporate compliant
data collection system connecting to variety of equipment
and sources.
Data concentrator used to collect data from sources with
minimal impact to existing computerized systems
Over 80 time-series trending reports for critical quality
variables, including time-series analysis and visualization
Rich event-based data captured for Sanitization, Systems
Monitoring and Systems Release reporting
Integrate business logic into Systems Release reporting to
correlate quality control and system status without manual
intervention
Driving Operational Excellence with Data
Driving Operational Excellence with Data
Manufacturing
Execution Systems
Driving Operational Excellence With Data
Granularity & rate of
timeliness becomes critical
Manufacturing
Systems Pyramid
Manufacturing Systems Pyramid
acquisition explodes
Demands for accuracy &
MES Defined
MES Defined
MESA-11
Functionalities
Driving Operational Excellence with Data
MES Op Ex Opportunities
Increase
Increase
•Throughput
•Throughput
•Product
•Productquality
quality
•Yield
•Yield
•Right
•Rightfirst
first time
time
•Equipment
•Equipmentutilization
utilization
•Material
•Materialutilization
utilization
•Energy
•Energyefficiency
efficiency
•Line
•Line uptime
uptime
•Plant
•Plant communication
communication
•Market
•Market response
response
Driving Operational Excellence with Data
Decrease
Decrease
•Inventory
•Inventory
•Regulatory
•Regulatorycosts
costs
•Waste
•Waste
•Time-to-volume
•Time-to-volume
•Cycle
•Cycle time
time
•Changeover
•Changeovertime
time
•Maintenance
•Maintenance costs
costs
•TCO
•TCOfor
forsystems
systems
MES Landscape of Pharmaceutical Adoption
Is there an enterprise-wide, coordinated MES implementation
and upgrade strategy?
Pharma
BIC
Yes and in place for more than 1 year
24%
23%
In place for less than 1 year
0%
18%
Will be in place within 1 year
14%
25%
Will be in place in more than 1 year
43%
13%
No plans
19%
23%
www.pharmamanufacturing.com/articles/2007/087.html
Driving Operational Excellence with Data
MES Case Study
Background
Full-service contract manufacturer supplying soft gel capsules
and encapsulation services for pharmaceutical industry
Compounding tank batch data recorded directly to printer with
PLC connection; or manually
Current system does not allow process data acquisition or
storage, only indication of process parameter value
Desire acquisition of critical parameters for analysis and
review at end of batch
Labour intensive batch correlation and analysis
Data insufficient for out of specification analysis
Driving Operational Excellence with Data
MES Case Study Cont’d
Solution
Monitor, store and report on critical process parameters
(temperatures, mixer speeds, pressures, weights, mixing duration,
etc.) and sequence of events to successfully produce compounding
batch record
Electronic validation of process setpoints by recipe including
dissolution times
Increased ‘richness’ of out of specification information for analysis
Batch Report for requirements and analysis
Batch Out-of-Spec Summary
Batch Out-of-Spec Details
Driving Operational Excellence with Data
“Bridging The Gap”
Plant To Enterprise Integration
Plant to ERP Integration Defined
Manufacturing IT Challenges
Driving Operational Excellence with Data
Plant to ERP Integration Defined
Planning
ERP to MES Data Flow Possibilities
Driving Operational Excellence with Data
Execution
Integration Op Ex Opportunities
1.
2.
3.
4.
5.
6.
7.
Improved production planning and scheduling
Improved inventory control
Improved visibility of inventory
Improved control of ingredients from a quality perspective.
Improved visibility of production
Improved pallet/ finished goods control
Improved product tracking for mock recalls/ shipment tracing
Driving Operational Excellence with Data
Integration Op Ex Opportunities
Driving Operational Excellence with Data
Integration Landscape of Pharmaceutical
Adoption
Is MES integrated with enterprise applications?
Pharma
BIC
Yes and in place for more than 1 year
19%
25%
In place for less than 1 year
0%
8%
Will be in place within 1 year
24%
33%
Will be in place in more than 1 year
38%
23%
No plans
19%
13%
www.pharmamanufacturing.com/articles/2007/087.html
Driving Operational Excellence with Data
The Validation Hurdle
V - Model
Process Control System Life Cycle
Compliance Strategy
Regulations
Guidelines
Company or site procedures and policies
Equipment procedures and policies
The Validation Hurdle
Determine Strategy for
Achieving Compliance
1. Scope and
Application
2. Assessment and
Categorization of
System Components
3. Risk Assessment
The Validation Hurdle
4. Supplier Assessment
Regulatory Drivers
U.S. Food and Drug Administration, Code of Federal
Regulations, Title 21, Subchapter C – Drugs; Most
Commonly Sited Document is:
21 CFR Part 11 – Electronic Records; Electronic
Signatures
Canada Health Protection Branch
The Validation Hurdle – Scope and Application
Scope and Application
“….concerns have been raised that some interpretations of the part 11
requirements would:
i.
unnecessarily restrict the use of electronic technology in a
manner that is inconsistent with the FDA’s stated intent in
issuing the rule,
ii.
significantly increase the costs of compliance to an extent that
was not contemplated at the time the rule was drafted, and
iii.
discourage innovation and technological advances with
providing a significant health benefit
Source: Guidance for Industry, Pt 11, Electronic Records, Scope and Application,
August 2003
The Validation Hurdle – Scope and Application
Scope and Application
Predicate rules applicable
Records required to be maintained under predicate rules or
submitted to FDA, when choice made to use records in electronic
format versus paper format
Records required to be maintained under predicate rules or are
submitted to FDA, that are maintained in electronic format in
addition to paper format and are relied on for regulated activities
Electronic signatures that are intended to be the equivalent of
handwritten signatures required by predicate rules
Systems with high impact on accuracy, reliability, integrity,
availability, and authenticity of records and signatures; even if no
predicate rule, in some instances it still may be important
Source: Guidance for Industry, Pt 11, Electronic Records, Scope and Application,
August 2003
The Validation Hurdle – Scope
Scope and Application
Predicate rules NOT applicable (or discretionary)
Records (and any associated signatures) that are not required to
be maintained under predicate rules, but are nonetheless
maintained in electronic form
Paper records generated by a computer system that meet the
requirements of the applicable predicate rules and paper records
are relied on regulated activities
Records that are not submitted, but are used in generating a
submission, unless maintained itself under a predicate rule
Source: Guidance for Industry, Pt 11, Electronic Records, Scope and Application,
August 2003
The Validation Hurdle – Scope and Application
Software Categories According to GAMP5
Category 1 – Infrastructure Software
Category 2 – No longer Used in GAMP5
Category 3 – Non-Configured Products (default COTS)
Category 4 – Configured Products (ERP, MES, SCADA)
Category 5 – Custom Applications (unique custom coded)
The Validation Hurdle – Assessment of System Components
Approach for a Non-Configured Product (Cat 3)
Source: “GAMP ® 5: A Risk-Based Approach to Compliant
GxP Computerized Systems.”
The Validation Hurdle – Assessment of System Components
Approach for a Non-Configured Product (Cat 4)
Source: “GAMP ® 5:
A Risk-Based Approach to
Compliant GxP Computerized
Systems.”
The Validation Hurdle – Assessment of System Components
Approach for a Non-Configured Product (Cat 5)
The Validation Hurdle – System Components
Source: “GAMP ® 5:
A Risk-Based Approach to
Compliant GxP Computerized
Systems.” ISPE 2007: p36.
Risk Assessment
“… recommend that the approach be based on a
justified and documented risk assessment and a
determination of the potential of the system to affect
product quality and safety, and record integrity.”
Source: “Guidance for Industry: Pt 11, Electronic Records; Electronic
Signatures - Scope and Application.” FDA et. al. August 2003: p9.
The Validation Hurdle – Risk
Science-Based Quality Risk Management
A computerized system involves a common and shared
understanding of:
Impact on patient safety, product quality and data integrity
Supported business processes
CQAs for systems that monitor or control CPPs
User requirements
Regulatory requirements
System components and architecture
Systems function
Supplier capability
The Validation Hurdle – Risk
Risk-Based Decisions During Test Planning
Function
Low Risk
Medium Risk
High Risk
Input
function with
acceptable
data range of
10.0 – 20.0
Verify normal data
is accepted
Boundary testing: 1 value below 10,
1 value in range, 1 value above 20
Boundary testing: 9.9, 10.0, 10.1,
19.9, 20.0, 20.1
Null value challenge
Null value challenge
Incorrect decimal precision
Alpha character
Temperature
control for
an
instrument
or vessel
Verify calibration
procedures
Interactive
voice
response
system
Verify that the
system is
connected
Verify accurate calibration
throughout operating range
Verify accurate calibration
throughout operation range
3-Point boundary testing for alarms
6-Point boundary testing for alarms
Challenge control precision
against defined process
parameters
Run test case to verify that an error
message is returned if the subject
is under 18 years old
Run test case to determine that
system can track and trace
availability of rescue drug kit for
specific subjects
Test data value entry & age calculation against local system date
The Validation Hurdle – Risk
Source: “GAMP ® 5: A Risk-Based Approach to Compliant GxP
Computerized Systems.” ISPE 2007: p128, table M3.5.
Managing Risks
Manage risks by:
Elimination by design
Reduction to an acceptable level
Verification to demonstrate that risks are managed to an
acceptable level
The Validation Hurdle – Risk
Managing Risk – Case Study 1
Segregation
Managing Risks – Case Study 2
Partial Segregation with Criticality Assessment
Managing Risks – Case Study 3
Full MES Implementation Discussion
Considerations
Full Life Cycle Approach
Software package selection
Software assessment and categorization
Supplier audits
Risk analysis by module
Architecture design features (redundancy, archiving, etc.)
Expansion
The Validation Hurdle – Risk Assessment
Supplier Assessment
Non-configured Product
(GAMP® Category 3)
Documentation
Training
Support &
maintenance
Configured Product
(GAMP® Category 4)
Specification,
configuration,
verification &
operation
Procedures agreed &
adopted per QMS
Software selection for
regulatory compliance
Supplier audit
The Validation Hurdle – Supplier Assessment
Custom Product
(GAMP® Category 5)
Full Life Cycle
involvement &
capability
Procedures agreed &
adopted per QMS
Supplier audit with org
capability & maturity
assessed
Industry & validation
experience
User-Supplier Relationship during
Specification and Testing
Source: ‘GAMP ® 4.’
Fig 8.2. ISPE 2007: p44.
“Improvement usually means doing something that
we have never done before.” - Shigeo Shingo
Driving Operational Excellence
with Data – Part 2
QUESTIONS?