six sigma in biopharma - ispe boston · - dmaic - dmadv - dfss - 3.4 dpmo - c p, c pk - control ......
TRANSCRIPT
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Six Sigma in BioPharma
Niranjan S. Kulkarni, PhD CRB Consulting Engineers
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• Introduction to Six Sigma
• Categorizing Six Sigma Techniques
• Take-Away
Agenda
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Lean and Six Sigma
Lean Methodology “As-Is Flow” “To-Be Flow”
Increase efficiency
Simplify work flows
Focus on high-value steps
Eliminate waste
A Lean enterprise is one that delivers value to its
stakeholders with little or no wasteful consumption of
resources.
Product or Service Outputs
Six Sigma Methodologies
Increase consistency
Reduce variation
Eliminate defects
In a Six Sigma enterprise, everyone is focused on identifying
and eliminating defects.
Customer-driven Customer-driven
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Introduction to Six Sigma
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• Sigma (σ) – Standard deviation
Introduction to Six Sigma
Kubiak and Benbow, 2009
Philosophy Methodology Metrics Set of Tools
- Control the process, control the output
- Y = f(Xs)
- DMAIC - DMADV - DFSS
- 3.4 DPMO - Cp, Cpk
- Control charts
- FMEA - DOEs - Etc.
Variation & Defect Reduction
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• Types of variability
– Temporal
–Material
–Measurement
– Spatial
– Behavior
Introduction to Six Sigma (cont’d)
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• Process variability vs. flow variability
Introduction to Six Sigma (cont’d)
Avg. Lead Time
Process
variability
Flow variability
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• Increased variability increases effective cycle time
• Higher variability degrades system performance
• Systems with variability have to be buffered (Hopp and
Spearman, 2011)
– Time
– Capacity
– Inventory
Introduction to Six Sigma (cont’d)
ope TTUVCT
eCTTHWIP
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Categorizing Six Sigma Techniques
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• Six Sigma techniques can enable various degrees of
understanding variability at following levels:
– Descriptive
–Quantifying / Predictive
– Control
– Behavioral
Six Sigma Techniques
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• Histogram
• Stem & Leaf plot
• Box plots
• Gage R&R
• Etc.
Descriptive Techniques
Stem Leaf
1 2, 3, 7
2 1, 3, 3, 4, 7
3 0, 3, 9
4 5, 6
5 00-10 11-20 21-30 31-40 41-50 51+
0
3
5
3
2
0
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• Lengthy and highly variable changeover time impacted throughput
• 35% reduction in changeover times
Case Study – Descriptive Techniques
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• Design of Experiments
• Simulations
• Statistical and Probabilistic approaches
Quantifying / Predictive Techniques
X
Y
Z
X
Y
+ +
- -
2k Factorial Design
k = 2 k = 3
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Typical Process Simulation Outputs Feasible Schedule Inventory Sizing
Equipment Utilization
Utilities Consumption
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Typical DES Outputs
24%41%
17%18%
DES Model Snapshot Pareto Analysis to Understand Utilization
Headcount Requirements for Changing Demands Utilization Charts Capturing Different States
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• Given the constraints, recommend strategies to improve efficiency and throughput
• Increase in throughput
– Manufacturing (#
Batches) by 60%
– QC Labs (# Tests) by
43%
Case Study 1 – Quantifying Techniques
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Case Study 1 – Quantifying Techniques
Scenario – Increased throughput
Scenario – Base case
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• Identify variability inducing processes affecting quality of finished product
• Predictive model
• Capture probability of conformance
• Helped readjust process mean and develop sampling plan
Case Study 2 – Predictive Techniques
𝑃 𝑎 ≤ 𝑥 ≤ 𝑏 = Φ𝑏−𝜇
𝜎 - Φ
𝑎−𝜇
𝜎
Ideal Process Mean
Original process mean
Adjusted process mean
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• Process monitoring and control charts
• Process capability indices
Control Techniques
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• High fallout rate observed when raw material lot changed
• Raw material variability suspected
• Control charting to understand between lots OD variation
• Poor process capability revealed
• Adjustments initiated at supplier’s end
Case Study – Control Techniques
39153480304526102175174013058704351
0.450
0.425
0.400
0.375
0.350
Index
OD
av
g
Lot 8 start Lot 9 start Lot 10 startLot 7 start
Time Series Plot of OD avg
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• Behavior characteristics
– Behavior is an erratic, dynamic, continuous phenomenon
– Behavioral variability is a result of intrinsic / extrinsic
environmental influence
– Individual’s behavior may (or may not) be different than the
group’s behavior
Behavioral Techniques
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• Averaging responses of individual subjects within large groups not recommended – random nature of variability is cancelled out
• Capturing behavioral variability
– Three main concepts
» Basic Probability Assignment
» Belief (b)
» Plausibility (p) – Providing imprecise probability of an event
P(A) = [b, p]
Behavioral Techniques (cont’d)
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• FMEA for NPI
• Based on expert opinions only
• Combining responses
• Ranking failures modes
Case Study – Behavioral Techniques
Kulkarni and Johnson, 2012
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Take-Away
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Let’s use Six Sigma for everything!?!
Manufacturing
Supply Chain, Warehousing
Customer Service, Administrative Functions
Research and Development
Marketing and Sales
The reason: DMAIC is not well suited for some parts of the organization
Ramakrishnan, et al., 2007
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We Touched Just The Surface…
Processes
Technologies Tools and Techniques
Strategy and Alignment
Leadership
Behavior and Engagement
Visible
Enabling
Hines, et al., 2008
• We just discussed the ‘visible’ things
• Improving operations may necessitate cultural changes
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Don’t Forget the Human Element!
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Thank You
Niranjan S. Kulkarni • Sr. Operations Specialist
• CRB Consulting Engineers
• 617-475-3073