1) The document discusses strategies for statistical process control (SPC) within a quality by design (QbD) framework for efficient manufacturing. 2) It covers univariate and multivariate SPC, process characterization post-commercialization using a multi-stage approach, and a cost-benefit analysis of real-time SPC and process analytical technology (PAT). 3) The benefits of real-time SPC/PAT include lower failure rates, higher productivity, improved robustness, lower costs, quicker investigations, and higher regulatory acceptability.

1. Modeling and Process Control
Strategies for Efficient
Manufacturing
IBC Asia BioProcess & Technology Summit 2015
28-29 October, Singapore
Mayank Garg
Shantha Biotechnics Private Limited (A Sanofi Company)
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2. Presentation snapshot
2
● Statistical Process Control (SPC) within Quality by Design (QbD) framework
● Driver for SPC
● SPC and beyond
● Univariate and Multivariate SPC
● Process Characterization post commercialization
● Cost-benefit analysis of Real-time SPC/PAT

3. SPC within QbD framework
3
● SPC within Quality by Design (QbD) framework
● Driver for SPC
● SPC and beyond
● Univariate to Multivariate SPC
● Process Characterization post commercialization
● Cost-benefit analysis of Real-time SPC/PAT
CQA
CpK
Design Space
(V1)
Design Space
(V2)

4. Critical Process
Parameters
Critical Raw MaterialsRaw Materials
Process Parameters
Driver for SPC
4
● SPC within Quality by Design (QbD) framework
● Driver for SPC
● SPC and beyond
● Univariate to Multivariate SPC
● Process Characterization post commercialization
x
y
z
Key Performance
Indicator
Input Output
Current Objective
CQA
CpK <1 CpK >1
Significant effect of input
on CQA
Strong relationship of output
parameters on CQA
● Cost-benefit analysis of Real-time SPC/PAT

5. 5
● SPC within Quality by Design (QbD) framework
● Driver for SPC
● SPC and beyond
● Univariate to Multivariate SPC
● Process Characterization post commercialization
W A Shewhart
Univariate Control charts1920s
Multivariate Control charts
H. Hotelling
Principal Component Analysis (PCA)
H. Hotelling
CUSUM & EWMA charts
E.S. Page and Roberts
Partial Least Square (PLS)
H. Wold
2004 Process Analytical Technology (PAT)
1930s
1940s
1950s
1970s
FDA
• DOE
• Process Analyzer
• Data Analysis
• Process Monitoring
• Process Controllers
• Knowledge/Risk Management
• Continuous Improvement
SPC and beyond
Major Milestones
● Cost-benefit analysis of Real-time SPC/PAT

6. 6
● SPC within Quality by Design (QbD) framework
● Driver for SPC
● SPC and beyond
● Univariate to Multivariate SPC
● Process Characterization post commercialization
Univariate to Multivariate
Out of Control
Univariate
● Cost-benefit analysis of Real-time SPC/PAT
1σ
1σ
2σ
2σ
3σ
3σ

7. CUSUM plot
CUSUM plot
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● SPC within Quality by Design (QbD) framework
● Driver for SPC
● SPC and beyond
● Univariate to Multivariate SPC
● Process Characterization post commercialization
Univariate
Univariate to Multivariate
Out of Control
● Cost-benefit analysis of Real-time SPC/PAT

8. 8
● SPC within Quality by Design (QbD) framework
● Driver for SPC
● SPC and beyond
● Univariate to Multivariate SPC
● Process Characterization post commercialization
Bivariate
Multivariate
Univariate <
<
<
<
Score plot
Univariate to Multivariate
Out of Control
● Cost-benefit analysis of Real-time SPC/PAT

9. 9
● SPC within Quality by Design (QbD) framework
● Driver for SPC
● SPC and beyond
● Univariate to Multivariate SPC
● Process Characterization post commercialization
Variables 
Age
Process Data
Univariate to Multivariate
Out of Control
Univariate Data Online Data Profile Data Analytical Data
● Cost-benefit analysis of Real-time SPC/PAT

10. Process Characterization
Post Commercial Launch
10
● SPC within Quality by Design (QbD) framework
● Driver for SPC
● SPC and beyond
● Univariate to Multivariate SPC
● Process Characterization post commercialization
Process Data Acquisition Data Modeling
Process control
Improvement
Trials/DOELiterature
Deviation/Failure
Investigations
CommercialScale
Lab/Pilot Scale
Multi-stage approach
● Cost-benefit analysis of Real-time SPC/PAT
Relationship of Input parameters with CQAs
Relationship of Output parameters with CQAs
Relationship of KPIs with Input parameters
Stage 1
Stage 2
Stage 3

11. Process Characterization
Post Commercial Launch
11
● SPC within Quality by Design (QbD) framework
● Driver for SPC
● SPC and beyond
● Univariate to Multivariate SPC
● Process Characterization post commercialization
Identification of CPPs
Severity
(Statistical Modeling)
y
n
Occurrence
(Screening analysis)
Univariate CpK
(against spec)
Multivariate One-time
(against golden batch/control limits)
Multivariate Time-series
(against golden batch/control limits)
Multivariate regression models
(relationship of Selected PPs with CQAs)
ControlStrategy
Design
Space
! It is preferred to study Multiple steps at once
Stage 1
Experience
(DOE + Literature)
Input variables
● Cost-benefit analysis of Real-time SPC/PAT
DOE CPPs
 Relationship of PPs with CQAs

12. Process Characterization
Post Commercial Launch
12
● SPC within Quality by Design (QbD) framework
● Driver for SPC
● SPC and beyond
● Univariate to Multivariate SPC
● Process Characterization post commercialization
Severity
(Statistical Modeling)
y
n
Occurrence
(Screening analysis)
Univariate CpK/OOT
(against spec/control limits)
Multivariate One-time
(against golden batch/control limits)
Multivariate Time-series
(against golden batch/control limits)
Multivariate regression models
(relationship of Selected PIs with CQAs)
Stage 2
● Cost-benefit analysis of Real-time SPC/PAT
Identification of KPIs
! It is preferred to study Multiple steps at once
 Relationship of Output with CQAs

13. Process Characterization
Post Commercial Launch
13
● SPC within Quality by Design (QbD) framework
● Driver for SPC
● SPC and beyond
● Univariate to Multivariate SPC
● Process Characterization post commercialization
y
n
Stage 3
Severity
(Mechanistic + MV Statistical modeling)
Mechanistic and MV predictive models
(relationship of Selected PPs with KPIs)
● Cost-benefit analysis of Real-time SPC/PAT
Identification of CPPs
! It is preferred to study Individual steps
Design
Space
DOE
CPPs
 Relationship of KPIs with PPs

14. Process Characterization
Post Commercial Launch
14
● SPC within Quality by Design (QbD) framework
● Driver for SPC
● SPC and beyond
● Univariate to Multivariate SPC
● Process Characterization post commercialization
y
n
Feed back to the controller
Real-time Analyzers for KPIs
(Direct or surrogate analytical methods)
Feedback loop
with Actuator
● Cost-benefit analysis of Real-time SPC/PAT
Identification of CPPs
Severity
(Mechanistic + MV Statistical modeling)
Mechanistic and MV predictive models
(relationship of Selected PPs with KPIs)
Design
Space
DOE
CPPs
Stage 3  Relationship of KPIs with PPs

15. 15
● SPC within Quality by Design (QbD) framework
● Driver for SPC
● SPC and beyond
● Univariate and Multivariate SPC
● Process Characterization post commercialization
● Cost benefit analysis of Real-time SPC/PAT
Cost benefit analysis
Capital Investment
Process Analyzers
NIR,IR, FTIR, NMR, MS, LCMS, GC etc
Data Acquisition/Aggregation
SCADA, PI System, LIMS, Middleware, Historian Server, etc
Data Analysis/Visualization
SIMCA, JMP, ProcessPad, PI Procesbook, etc
Process Controller/Automation
Feedback control loops, Control valve actuators, etc

16. 16
● SPC within Quality by Design (QbD) framework
● Driver for SPC
● SPC and beyond
● Univariate and Multivariate SPC
● Process Characterization post commercialization
● Cost benefit analysis of Real-time SPC/PAT
Cost benefit analysis
Advantages
• Lower failure rate
• Higher productivity
• Improved robustness
• Lower FTEs
• Cycle time reduction
• Higher plant efficiency
• Improved Product Quality
• Quicker investigations
• Lower QC testing
• Higher Regulatory acceptability

17. 17
Thank you