Effectively Applying Six Sigma in Biopharma

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Effectively Applying
Six Sigma in Biopharma
This presentation contains materials developed by Pharmatech Associates
and may not be used without expressed written consent
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Agenda
• Today’s Regulatory
Environment/Considerations
• Six Sigma and Operational
Excellence
• Tips for Success
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2007 ISPE SF Chapter Vendor Night
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Historical Framework-Patient Safety
• Prior to 2002: Oversight was based
upon adherence to pre-defined
specifications, record keeping, and
audit trails
• Quality Systems based upon
sampling , inspection within a
framework of specific systems
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2007 ISPE SF Chapter Vendor Night
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A Shift in Thinking
• David Graham testifies to Congress that
there are 5 approved drugs that in his
opinion represent a risk to public safety
• Flu-Vaccine shortage/Avian Flu Pandemic
• Part 11 Guidance-industry backlash
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2007 ISPE SF Chapter Vendor Night
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FDA Modernization Initiatives
• Critical Path Initiative – To “lean”
the drug and medical device
development process bringing
safer, more effective products to
market faster while reducing the
candidate failure rate
• Risk Based cGMPs – regulatory
oversight based on risk as well as
the use of scientific manufacturing
tools to reduce risk
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2007 ISPE SF Chapter Vendor Night
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FDA Critical Path Initiative
• Modernize the techniques and methods used to evaluate
the safety, efficacy and quality of medical products as they
move from candidate selection and design to mass
manufacture through better predictive and evaluative tools:
• Innovative trial design, new statistical tools and analytic
methods, use of modeling and simulation
• Establishing and qualifying predictive biomarkers for
specific conditions
• Less stringent cGxP regulations for IND exploratory
studies and clinical trials
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Risk-Based Pharmaceutical cGMPs
•
A change (shared worldwide) in regulatory philosophy
from a uniform application of regulations to regulatory
oversight based on the level of risk to the public:
Risk is mitigated through:
 The level of scientific understanding of how the
formulation and manufacturing process factors
affect safety, efficacy, and product quality
 The capability of process control strategies to
prevent or mitigate the risk of producing a poor
quality product
•
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2007 ISPE SF Chapter Vendor Night
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Timeline
• September 2003 – FDA Releases:
• Final report Pharmaceutical cGMPs for the 21st Century - A RiskBased Approach
• Guidance for Industry- Quality Systems Approach to
Pharmaceutical Current Good Manufacturing Practice
Regulations
• Guidance for Industry Sterile Drug Products Produced by Aseptic
Processing – cGMP
• Guidance for Industry PAT — A Framework for Innovative
Pharmaceutical Development, Manufacturing, and Quality
Assurance encourages industry to adopt online testing,
characterization and control
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2007 ISPE SF Chapter Vendor Night
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Timeline
• March 2005 - FDA Releases:
• Guidance for Industry Premarketing Risk Assessment
lays the groundwork for risk assessment process during
product development, with emphasis on Phase 3 clinical
trials
• Guidance for Industry - Development and Use of Risk
Minimization Action Plans documents additional steps
companies can take for patient risk that may not mitigated
by traditional product labeling
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2007 ISPE SF Chapter Vendor Night
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Timeline
• March 2005 - FDA Releases:
• Guidance for Industry Good Pharmacovigilance Practices
and Pharmacoepidemiologic Assessment documents
post-approval scientific methods to detect, assess,
understand and prevent adverse events, including rare
side effects, adverse reactions or drug interactions, and
other product-related problems
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Timeline
• November 2005 - ICH Releases:
• Q8 – Pharmaceutical provides guidance for the
Pharmaceutical Development section of an ICH regulatory
submission. Indicates areas where the effective application of
risk management and pharmaceutical and manufacturing
sciences can create a basis for flexible regulatory approaches
• Q9 – Quality Risk Management “provides guidance on the
principles and some of the tools of quality risk management
that can enable more effective and consistent risk based
decisions, both by regulators and industry, regarding the
quality of drug substances and drug (medicinal) products
across the product lifecycle “
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Quality
by
Design
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ICH Q9 Risk Management Process
Initiate Risk Management Process
Risk Assessment
Risk Identification
R
I
S
K
Risk Analysis
R
I
S
K
C
O
M
M
U
N
I
C
A
T
I
O
N
Risk Evaluation
M
A
N
A
G
E
M
E
N
T
Unacceptable
Risk Control
Risk Reduction
Risk Acceptance
Result /Output of the
Risk Management Process
Ongoing
Review
T
O
O
L
S
Risk Control
Risk Review
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The Challenge
14
Drug Development
Today
Drug Development
Tomorrow
R- Guidance Centric
Q- Inspection Centric
P- Product Centric
D- Subjective
R- Science/Risk Based
Q- Critical Parameters
P- Process Centric
D- Objective
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The Challenge
Drug Development
Today
•Product centric CMC
•Inspections based
validation
• Systems Based
Inspections
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2007 ISPE SF Chapter Vendor Night
Drug Development
Tomorrow
•PQAS- process based
CMC
•Risk Based Validation
•PAT
•Risk Based Inspections
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Why Six Sigma or Lean?
“The problems we face cannot be solved with
the same thinking that created them.”
-Albert Einstein
“We don’t know what we don’t know.”
-Anonymous
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Lean/Six Sigma Quality Relationship
Reduce
Complexity
Improve
Performance
LEAN
Reduce
Waste
LEAN/
SIX SIGMA
Sustain
Performance
Reduce
Variation
SIX SIGMA
Avoid
Variation
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Six Sigma* Improvement
A customer driven, process oriented, factbased method of improving the business by
reducing waste, cycle-time, and process
variation.
* typical name used for improvement by variation reduction
Six Sigma is a trademark of the Motorola Corporation
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Six Sigma History
• Motorola Introduced in 1986- Bill Smith- Sr Engineer and
Scientist was looking for a methodology that could produce
a 100 fold increase in process and product quality
• Allied Signal (Larry Bossidy) and GE (Jack Welch)
popularized in 1990’s
• Coca-Cola, Ford, DuPont, 3M, Dow Chemical
• Bank of America, JPMorgan Chase, American Express
• Sears, Home Depot
• Hospitals
• Pharmaceutical/Biotech- Big 10
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Use the Best
Improvement Process
... clearly define the problem , and
relate it to customer needs...
… measure what is key to the customer,
and know your measure is good...
… search for the root causes, and
identify the most likely causes
... find the root cause(s), and establish
methods to control them ...
…make sure the problem
doesn’t come back...
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Roots of the DMAIC Process
Change the Process
- Adopt the change or abandon it
- Standardize the change
- Gather additional data
Act
Plan the Improvement
Plan
- Identify process to improve
- Develop the test plan
Study
(Check)
Study the Results
Do
- Identify the likely root causes
- Determine how to improve
Conduct the Test
- Identify potential causes
- Compare causes to facts
Shewhart-Deming PDCA/PDSA Learning Cycle
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Right Process:
General DMAIC Approach






Determine how to measure improvement
Validate the measurement system
Determine the present process performance
Analyze


Identify the potential causes
Screen out unlikely causes
Improve


Identify root cause(s) of problem
Optimize the improvement



Standardize the improvement
Establish an ongoing process monitoring plan
Identify other areas to improve
Define
Measure
I
n
p
u
t
s
Process
O
u
t
p
u
t
s
Control
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Identify the process to improve
Assemble the team and describe problem
Identify the customers and their needs
2007 ISPE SF Chapter Vendor Night
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3M’s of Six Sigma Improvement
• Measures:
• Customer Driven – Business objectives and
measures that have the greatest impact on the
customer and bottom line.
• Management:
• Process Oriented – Uses dedicated resources that
focus on improving entire processes and value
chains rather than just the parts.
• Methods:
• Fact-Based – Uses a systematic fact-based
approach to process improvement aimed at
reducing waste, cycle time, and variation.
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A Customer
Focused Synergy
Me
as
ur
es
$tability
ds
ho
et
M
- Sigma Quality Level
- Roll-Throughput Yield
- DPMO
- Benchmarking
- Cycle Time
Management
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2007 ISPE SF Chapter Vendor Night
- Variance Reduction
- Passive Analysis
- MSA / Gauge R&R
- Hypothesis Testing
- Active Analysis
- Strategic Planning
- Project Charters
- DMAIC Projects
- Control Plans
- Process Control
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A Customer Focused Synergy
Me
as
ur
es
$tability
ds
ho
et
M
- Sigma Quality Level
- Roll-Throughput Yield
- DPMO
- Benchmarking
- Cycle Time
Management
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2007 ISPE SF Chapter Vendor Night
- Variance Reduction
- Passive Analysis
- MSA / Gauge R&R
- Hypothesis Testing
- Active Analysis
- Strategic Planning
- Project Charters
- DMAIC Projects
- Control Plans
- Process Control
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DMAIC Improvement (variation reduction)
Too Much Variation
Off-Target
LSL
USL
LSL
Center
Process
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Centered
On-Target
2007 ISPE SF Chapter Vendor Night
USL
Reduce
Spread
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Understanding
Process Variation
Most Process Measures Distribute Normally
NORMAL
CURVE
Number Measured
15
10
5
0
47
27
48
49
50 51 52 53
Continuous Measure
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Understanding
Process Variation
LSL
Anatomy of the
Normal Distribution USL
Average
We typically collect some data
from a process over
a short period of time.
Then, we compute the process
standard deviation.
σ
-3σ -2σ
-1σ
0
Next, we add the average to
+3SDs and set the process
operating tolerance.
1σ
2σ
Continuous Measure
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3σ
What assumptions do we make in
performing these actions?
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Measures:
All Processes Take Walks!
The Process Average
Wanders Over Time
Avg Target
LSL
-3σ -2σ
-1σ
0
USL
1σ
2σ
Continuous Measure
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Most processes do not remain
centered on the target...
In fact, over time they wander
about the target.
As they wander, there is greater
chance to produce unacceptable
results!
Predictable processes wander a
defined distance from the target.
3σ
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The Process
Operating Window
LSL
The Process Average
Wanders Over Time
Avg
Avg
Target
USL
-1.5σ +1.5σ
Process
Operating
Window
-3σ -2σ
30
-1σ
0
1σ
2σ
3σ
2007 ISPE SF Chapter Vendor Night
We now know most wellcontrolled processes
operate within a defined
window over time.
This operating window
for the average is +1.5SDs
using a short-term estimate
for the SD.
This process naturally has a
greater chance of producing
poor results when we
include an operating
window. (for this process
that chance is about 7%)
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Measuring Process Quality
6-Sigma Quality
-6σ
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<1% Defects
(Considered good
quality today...)
+4σ
3-Sigma Quality
-3σ
(Considered world-class
quality today!)
+6σ
4-Sigma Quality
-4σ
<0.01% Defects
<10% Defects
(Considered good
quality 20 years ago...)
+3σ
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About Sigma Quality Level
• Used to describe the relationship between process
variation and customer requirements, (this is also called
process capability).
• The further away from the specifications a process
operates, the less likely a defect is to occur.
• "Sigma Quality Level" (SQL) reflects the level of control
you have on a process - larger SQL's indicate a larger
level of process control.
• SQL is used for comparing the quality performance of
processes having different complexities.
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The Sigma Quality Scale
Sigma Level
DPMO
6.0
3.4
5.5
32
5.0
233
4.5
1 350
4.0
6 210
3.5
22 750
3.0
66 810
~70X
~30X
~10X
*using short-term data with a +1.5σ operating window
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Six Sigma Benchmarks
Defects per Million
1,000,000
IRS Tax Advice
(phone in)
Order Write-up
100,000
Doctor Prescription Writing
10,000
Restaurant Bills
Average
Company
1,000
Airline Baggage Handling
Domestic Airline
Fatality Rate
100
Best-in-Class
1
1
3
4
5
6
7
Sigma Level
Source: Six Sigma: A Breakthrough Strategy, Quality Progress, May 1998.
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How Good is Good Enough?
99% Good
99.99966% Good
Postal System
20,000 Lost Articles
Of Mail / Hr
Airline System
1 Short / Long
Per 5 Years
Two Short/Long
Landings / Day
Medical Profession
68 Wrong Drug
Prescriptions
200,000 Wrong Drug
Prescriptions / Year
35
7 Lost Articles / Hr
2007 ISPE SF Chapter Vendor Night
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A Customer
Focused Synergy
Me
as
ur
es
$tability
ds
ho
et
M
- Sigma Quality Level
- Roll-Throughput Yield
- DPMO
- Benchmarking
- Cycle Time
Management
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2007 ISPE SF Chapter Vendor Night
- Variance Reduction
- Passive Analysis
- MSA / Gauge R&R
- Hypothesis Testing
- Active Analysis
- Strategic Planning
- Project Charters
- DMAIC Projects
- Control Plans
- Process Control
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Passive vs. Active Data
Analysis
Passive Analysis
(Searching for Differences)
• Uses existing process data.
• Attempts to extract patterns
from the existing data to
uncover likely relationships
between inputs and outputs.
• Best used with processes that
are unstable in operation.
• Risks missing key process
inputs, because the
information is not contained in
the data!
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Active Analysis
(Studying Input/Output Relationships)
• Uses an organized plan for
changing process inputs to bring
about changes in the process
outputs. (called a "DOE")
• Develops new data used to
uncover relationships between
inputs and outputs from both
stable and unstable processes.
• Allows the experimenter to
quantify the relative contribution
of each input's effect on the
output(s).
2007 ISPE SF Chapter Vendor Night
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Identifying the Key Measures
● Cause & Effects diagrams and VOC Matrices
to identify the key focus area(s):
Voice of the Customer(VOC) Matrix
Top Level Process
Sub Step 3
Sub Step 1
Sub Step 2
Sub Step 2
Sub Step 1
Step 5
Sub Step 3
Sub Step 2
Sub Step 1
Step 4
Sub Step 3
Sub Step 1
Step 3
Sub Step 3
Sub Step 3
Importance (15)
Customer Needs
Man
Step 2
Sub Step 2
Material
Treat me like you want my business
Deliver products or services that meet my needs
Good
Measurement
Step 1
Sub Step 1
R elationship
9 Strong
3 Medium
1 Weak
Sub Step 2
Sub-Process
Steps
Process:
Provide products or services that work properly
Fill my needs accurately the first time
Make commitments that meet my needs
Fix it right the first time
Total
0
Total
0
Provide what want when I want it
Effect or
Problem
Meet your time commitments
Fast
I want fast and easy access to help
Don't waste my time
If it breaks, fix it fast
Deliver irresistable value
Cheap
Help me save money
Environment
Methods
Help me save time
Don't waste materials, and charge me for the waste
Total
Machine
● Process Flowcharts:
0
Customer*
The Process
Engineering
Tooling
The Sub-Process
Production
Field Service
The Micro-Process
Focus Area
● "Y" Matrices to the identify key
measures for this process:
Good
(quality)
Fast
(speed)
Cheap
(cost)
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2007 ISPE SF Chapter Vendor Night
Quality and Process
Indicators
Measure
Increment
Number of defects per unit
minute
Percent defectives per operation
hour
Total defects observed
day
Cycle time of operation or step
batch
# or % of Commitments missed
day
Wait or idle time
week
Ship or Total Lead Time
month
Build or process cost per unit
shift
Cost of waste per unit
batch
Cost of rework
transaction
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The 7MP Tools
for Soft Analysis
Affinity Diagram
ANALYZE
Interrelationship Digraph
Logical
Creative
Tree Diagram
Prioritization
Matrices
Criteria
A
Criteria
B
Criteria
C
Criteria
D
7
3
1
9
Issue 1
1
1
1
3
Issue 2
3
3
9
3
Issue 3
3
1
1
1
40
Issue 4
9
3
1
3
170
Issue 5
3
9
1
9
123
Issue 6
1
1
9
1
28
Total
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68
Matrix
Diagram
Tasks Known
Tasks
Unknown
TASK
Item ID
Completed?
Define.
1
2
3
4
IMPROVE
39
5
6
7
8
9
2
X
O
PDPC
Chart
2007 ISPE SF Chapter Vendor Night
X
O
3
Activity
Network
Diagram
4
FEB
MAR
APR
MAY
JUN
JUL
Yes
Yes
No
Yes
No
No
No
No
Yes
Measure.
1
JAN
Estimated Completion: 7-June-2003
Identify the product, process, or
system to improve or that has the
Determine the customers, and project
sponsor.
Determine the nature of the problem
from the perspective of the customer
Determine the customer's needs
and/or requirements
Translate the customer's need into
Critical to Quality characteristics,
Work with the project sponsor to
identify the team for the DMAIC
Develop the problem statement and
business case
Determine the expected "to be" state
of the process
Determine the project scope with the
sponsor, and prepare the PDF
Estimated Completion: 3-Aug-2003
Identify the measure(s) associated
with the key CTQ identified in the
Develop a more detailed map of the
process, drilling down into the subAssess the error of the measurement
system used to measure the project
Determine the requirements for the
project "Y"
No
No
No
No
Gantt Chart
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AUG
Shewart Cycle and Lean Six Sigma
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Operational Excellence Toolkit
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Process Capability Analysis
Process Capability Sixpack of Fill Values
Xbar Chart
Capability Histogram
Sample Mean
UCL=52.719
LSL
USL
S pecifications
LS L
50
U S L 55
52.5
_
_
X=51.994
52.0
51.5
LCL=51.269
1
3
5
7
9
11
13
15
17
19
50.25
R Chart
51.00
51.75
52.50
53.25
54.00
54.75
Normal Prob Plot
A D: 0.827, P : 0.032
Sample Range
UCL=2.658
2
_
R=1.257
1
0
LCL=0
1
3
5
7
9
11
13
15
17
19
50
Last 20 Subgroups
Values
Within
S tDev 0.540435
Cp
1.54
C pk
1.23
52
51
42
10
Sample
54
Capability Plot
53
5
52
15
20
2007 ISPE SF Chapter Vendor Night
Within
O v erall
O v erall
S tDev 0.590414
Pp
1.41
P pk
1.13
C pm
*
S pecs
42
Reducing Variation by
Robust Design
•
By tighter controls of the inputs ...
•
By Robust Process Design ...
Process "Y"
Transfer
Relationship
$$$
Input
Traditional Method of Reducing Variation
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2007 ISPE SF Chapter Vendor Night
Input
Alternate Method of Reducing Variation
43
A Customer Focused Synergy
Me
as
ur
es
$tability
ds
ho
et
M
- Sigma Quality Level
- Roll-Throughput Yield
- DPMO
- Benchmarking
- Cycle Time
Management
44
2007 ISPE SF Chapter Vendor Night
- Variance Reduction
- Passive Analysis
- MSA / Gauge R&R
- Hypothesis Testing
- Active Analysis
- Strategic Planning
- Project Charters
- DMAIC Projects
- Control Plans
- Process Control
44
Connect Customers to Improvement
Term
Critical to
Quality
Key Output
Measure
Critical
Customer
Requirements
VOC
CTQ
Key Y
CCR
Customer
concerns or
needs
Customer
needs
translated into
process terms
Key leading or
lagging
measure(s)
that reflects
the CTQ
Requirements
for the Key Y‘s
Explanation
Voice of the
Customer
Abbreviation
Start Here
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Involve Everyone in Improvement
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Functional
GreenBelts
BeltsBelts
Trained
Assigned
byCorporate,
Corporate,
Black
Trained
by
Blackbelt/Greenbelt Deployment
Functional
Placed
Back
Projects
in Business
Blackin
Belt
Deployment
Placed
Business
and in Six Sigma Org.
Functional and Pooled Blackbelts
Mktg Mfg Eng
HR
Company
Six Sigma Mgr
MBB
IT
Improvement Initiative
Function
Function
BU
Function
BU
BU
Improvement Initiative
MBB
BU
MBB
BU
Improvement
Initiative
Function
Function
BU
MBB
Training
BG
B
G
B
G
BU
Improvement
Initiative
BU
BU
BU
BU
B
B
G
47
Fin.
G
B
B
G
2007 ISPE SF Chapter Vendor Night
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BU
BU
BU
High
Low
Low
Med
High
Effort
Desirable Opportunities
Low
Low
Projects in upper left yield
the greatest value.
Med
High
Effort
Reasonable Opportunities
Least Desirable Opportunities
Projects in middle region are
reasonable, but will require
more effort to realize.
Projects in the lower region are
least desirable, because they
require the most effort and provide
the least benefit.
Benefits Include:
• Strategic Fit
• Revenue Growth
• Cost Reduction
• Capital Reduction
48
New Approach
Benefit
Med
Present Approach
Benefit
Med
High
Select the Right Projects
2007 ISPE SF Chapter Vendor Night
Effort Include:
• Personnel Needs
• Length of Project
• Capital Cost
• Project Risk
48
Drive Financial Performance
Increase Revenue “Grow the Business”
• Improve customer satisfaction, sales, throughput, and
competitive position
Decrease the Cost of Goods Sold
• Reduce process variation and defects, improve yield
• Identify and eliminate root causes of problems
• Develop systems robust to problems
• Streamline “workflow” processes
• Reduce waste, unnecessary costs, and excessive cycle time
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Drive Process Predictability
• Remove sources of variation
• Identify critical control parameters
(KPIVS)
• Identify critical control points (KPOVs)
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Applying Six Sigma to Biopharma
General
• Breakdown the silos
• Involve middle management
• Stick to the charter-drive accountability
• Grass Roots- Go after low hanging fruit
• Top Down- Choose projects that affect
peoples lives and have business impact
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Applying Six Sigma to Biopharma
General
• Define regulatory/quality constraints at the
outset “Better is not always good, it’s just
different”
• Utilize Lean Kaizen’s to build energy and
enthusiasm
• Don’t just focus on cost impact. We are a
quality driven industry
• Tie your solutions to your CAPA system
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Emerging Biopharma/Device/Diagnostics
• VC funded: SS provides the framework for
objective scientific inquiry. Better
milestone performance. Manage you burn
• Toll gate reviews keep everyone on the
same team
• Always keep the business objectives first
and foremost in your justification. Cash is
King , but so is COPQ (TTM, clinical
performance)
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2007 ISPE SF Chapter Vendor Night
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Summary
• The expectations for the regulatory
environment demands an objective
approach to drug development
• Six Sigma can provide the roadmap for
effectively satisfying the new science/risk
based cGMP’s
• Balancing financial/quality and business
requirements will ensure your SS initiative
will gain traction
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2007 ISPE SF Chapter Vendor Night
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Thank you for your
attention!
Bikash Chatterjee
President
Pharmatech Associates, Inc.
1098 Foster City Blvd., Suite 303
Foster City, California 94404
(510) 760-2456
[email protected]
Or visit our website at:
www.pharmatechassociates.com
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2007 ISPE SF Chapter Vendor Night
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