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InformationalBrief
2012, All Rights Reserved
Case Study #2Case Study #2
Presenter: Ken Myers, CQE, CSSBB, CMBBPresident, Ascendant Consulting Service, Inc.
This case study looks at a system designed to develop process understanding for
the product development team. This system uses design of experiment methods to develop that understanding.
The experimentation process used to build process understanding is slow and time
consuming. The Pilot Plant Ops team thought that Lean methodology could help them in improving process speed and throughput.
This case study starts with the unit of work and business case, and proceeds from
there.
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Case Study #2 Slide 2
The Unit of Work and Process
Separation
Discarded
Cells
Final
Product
GrowthMedia
Working
Cells
2L MiniFerm
Unit of Work
DO = Dissolved Oxygen
pH = acidity level
Low High
pH Probe
DO Probe
Fermentation
Experimental Pilot Plant Operations
All process and
results Data
Actual Work Product
Raw MaterialPurification
ChromatographyProcess
In this case study, we worked with a straight forward bio-pharmaceutical process.
The process shown in this slide is a scaled down version of the larger production process planned for future operation. The responsibility of the Pilot Plant team is to
develop and codify process understanding.
The “unit of work” for this process is the fermentation bioreactor shown in the upper
right side of this slide. In the fermentation process three control variables are used:
media temperature, media pH, and media dissolved oxygen concentration. The Pilot Plant conducts a number of experiments to learn which variables are dominant in
managing the fermentation process, which is where most of the value is created.
The operation of this process is similar to the fermentation of beer where yeast is the
active biological agent in the process. The yeast live in a nutritive growth media
called Wort which consists of the sugars of malted grains that the yeast consume and then excrete alcohol and carbon-dioxide as a by-product.
In our bio-pharmaceutical process genetically engineered animal cells live in an
artificial nutritive media similar to the Wort in beer fermentation. The cells in this
environment consume the provided sugars and excrete the “active drug substance”
used to make the end product.
The cells are separated from the drug and media mixture. The media is then purified
to extract the drug product which goes on to be sterilized and then packaged for sale.
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Case Study #2 Slide 3
The Problem and Business Case
� Problem:
� The present capacity of the Pilot Plant is not large enough to support the forecasted experimental work
planned for the next 5 years.
� Business Case:
� The Pilot Plant needs to expand capacity an additional
20% to meet the 5 year plan. If we cannot find this
additional capacity within the existing operations, then
we will need $100M of capital to implement the needed
expansion.
In this work we formalized both the problem statement and the business case in
much the same way as done in Six Sigma improvement efforts.
Because the problem statement contained the word “capacity” the management
team believed it prudent to focus on Lean improvement efforts and look at the cycle
times for processing work through the Pilot Production area. Given the customer “is
always right” we decided to follow the suggested strategy for improvement—this
was done without looking at any data or developing a clear understanding of the situation!
So, off we went to characterize the times and flows in the process and identify any
areas where we could remove waste and make efficiency improvements per
standard Lean practice…
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Case Study #2 Slide 4
Y8LowLowLowT8
Y7HighLowLowT7
Y6LowHighLowT6
Y5HighHighLowT5
Y4LowLowHighT4
Y3HighLowHighT3
Y2LowHighHighT2
Y1HighHighHighT1
DOpHTemp
Process Yield
Settings for Experimental FactorsTrial
Area Work Plan
� Each experimental plan consists of 8 base trials.
� With 50 base experimental sets per operational period*:
T1T1 T2T2 T3T3 T4T4
T5T5 T6T6 T7T7 T8T8
Base Set
*Operation period is approximately 19 days
The Work Plan shown in this slide is actually a set of defined experiments
requested by the Product Development area. The results from these experiments provide the foundation for understanding and managing this particular bio-chemical
process during the scale-up phases.
Each experimental plan consists of 8 runs allowing process understanding to be developed for 3 process variables. Each trial corresponds to one Miniferm (or one
unit of work). The original set of 8 experiments is labeled simply the “Base Set” of
experiments.
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Case Study #2 Slide 5
Y8LowLowLowT8
Y7HighLowLowT7
Y6LowHighLowT6
Y5HighHighLowT5
Y4LowLowHighT4
Y3HighLowHighT3
Y2LowHighHighT2
Y1HighHighHighT1
DOpHTemp
Process Yield
Settings for Experimental FactorsTrial
Experimental Loss
� Each experimental plan consists of 8 base trials.
� With 50 base experimental sets per operational period*:
T1T1 T2T2 T3T3 T4T4
T5T5 T6T6 T7T7 T8T8
Base Set
*Operation period is approximately 19 days
A loss of a single experimental trial renders the complete experimental set
unusable. We are left with running the entire set over again to obtain the desired process information.
The Pilot Plant has a history of periodic failures while running “Base Design Set” of
experiments or simply the “Base-set.” If one trial is lost during the experimentation period, (about 19 days), then the results for the entire “Base Set” are invalidated.
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Case Study #2 Slide 6
Y8LowLowLowT8
Y7HighLowLowT7
Y6LowHighLowT6
Y5HighHighLowT5
Y4LowLowHighT4
Y3HighLowHighT3
Y2LowHighHighT2
Y1HighHighHighT1
DOpHTemp
Process Yield
Settings for Experimental FactorsTrial
Replication became Standard Process
� Each experimental plan consists of 8 base trials.
� With 50 base experimental sets per operational period*:
T1T1 T2T2 T3T3 T4T4
T5T5 T6T6 T7T7 T8T8
Base Set
T1T1 T2T2 T3T3 T4T4
T5T5 T6T6 T7T7 T8T8
ReplicateSet
Redundant Experiments
*Operation period is approximately 19 days
To minimize the chance of a complete loss of work the Pilot Plant was instructed to
run a “replicate” set of experiments for each “base-set” as a fail-safe measure. This policy reduces the operational capacity of the Pilot Plant by about 50%.
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Case Study #2 Slide 7
Present-State Observations
� We conducted a present-state VSM and evaluated the stepwise Process Cycle Efficiency (PCE).
� Results follow, all times in minutes: (reviewed 10 ops)
0.332654176924268Data Prep and Delivery
60.396847281656021288Fermentation & Production
65.4477394901797027460*Summary
1.4228326394720Purification and Yield Measures
1.23777342419Separation(Harvest)
2.1275183582765Miniferm Prep
PCE, %Setup TimeNon-ValueAdd Time
Value AddTime
Cycle Time(mins)
Process Step
*CTtotal = 27460 * 1440 = 19 days/set
We began the improvement work by evaluating the stepwise cycle-times for the
“Base set” operation of experimental work. From this evaluation we determined the average Value-Add and Non-Value times for each step. Then we used the VA and
NVA averages to estimate the stepwise and total Process Cycle Efficiency (PCE)
as shown in the slide above. PCE is a quick way of determining the lean efficiency
of a process on both a stepwise and total value-stream basis.
Notice the Fermentation and Production step alone consumes over 75% of the total cycle time for this process, and this step has a PCE of over 60%. Most of the value
for this process is created at this single step.
Our initial findings indicated it would be challenging to improve the capacity of this process using only cycle time information. However, our client wanted us to
continue pushing through to find an answer using the best Lean approaches
available…
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Case Study #2 Slide 8
80%30%Continuous or One Piece Flow Assembly
35%15%Batch Transfer Assembly
50%10%Transactional Processes
25%5%Creative/Cognitive Processes
Process Cycle Efficiency Benchmarks
65.4%10027460
17970
PCT
CT VACEP
Total
Total=∗==
PCE Application Benchmarks*
*Based on experience with over 100 companies
ApplicationApplicationHighHigh--End PCE GoalEnd PCE Goal
(World(World--Class PCE)Class PCE)LowLow--End PCE GoalEnd PCE Goal
Source: George Group
This slide shows how PCE is calculated and provides some general reference to the identified estimates.
Note from this table, the fermentation and production step which contains about
75% of the total cycle time has a very high PCE.
We were told not to touch any aspect of this step or alter its operation any manner.
All other steps were open for change and improvement.
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Case Study #2 Slide 9
Present-State:
Estimates and Observations
� Initial system throughput*:
sets/day 2.5days 20.3
sets 50
CT
WIPTP
adj
initial ===
*Each set is replicated once for process reliability, which halves the available capacity
� Estimated System Capacity: (w/ 350 days/yr, and 1.5 day/setup)
sets/yr 807sets 50days 21.7
days 350apacityC year =∗=
� Results:
� At about 65% PCE we can process about 807 sets/yr
� The capacity goal is >20%, or about 968 sets/yr minimum
In order to get a bit closer to estimating the area capacity we called upon Little’s Law for some help. This slide shows both the calculation of process throughput
and the estimated system capacity.
In the throughput calculation please note the adjusted cycle time of 20.3 daysversus a mean of 19 days from the previous cycle time table. The 20.3 days
estimate adjusts for both flow and time variation in the calculation. Please see the Appendix for details on the area capacity calculation.
In the area capacity estimate we included the process setup time to insure we
accounted for all possible delays in the process.
We calculated a 65% PCE annual capacity supports about 800 base experimental design sets for the Pilot operation. The management team was looking for a 20%
increase in this value or about 970 sets/year.
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Case Study #2 Slide 10
Future-State Improvement
� Using 5S, setup reduction, and better ops planning we worked towards improving operational capacity.
� Results follow, all times in minutes: (reviewed 5 ops)
0.5161032621173379Data Prep and Delivery
63.358247281656021288Fermentation & Production
68.2246482931784926142Summary
1.5212245402647Purification and Yield Measures
1.33743349392Separation(Harvest)
1.62815421436Miniferm Prep
PCE, %Setup TimeNon-ValueAdd Time
Value AddTime
Cycle Time(mins)
Process Step
4773
765
419
720
4268
275
968
228
3265
582 183
342
394
92
77
326
27460 17970 9490
2.1
60.3
1.2
1.4
0.3
65.4
*CTtotal = 26142 * 1440 = 18.1 days/set
Using all of the basic Lean improvement tools we were able to make a minor improvement in the PCE for the value stream, i.e. from 65% to 68%.
Notice most of that improvement came at the Fermentation step by reducing the set-
up time by about 50%.
Because this one step dominates greatly in determining the value-add for the product
we were not able to see much improvement in any of the other steps.
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Case Study #2 Slide 11
Future-State 1:
Estimates and Observations
� Improved system throughput*:
sets/day 2.6days 19.3
sets 50
CT
WIPTP
adj
improved ===
*Each set is replicated once, which halves the available working capacity
� Estimated System Capacity: (w/ 350 days/yr, and 1.0 day/setup)
sets/yr 862sets 50days 20.3
days 350apacityC year ≈∗=
� Results:
� Initial-state at ~65% PCE had a capacity of ~807 sets/yr
� At ~68% PCE we can process ~862 sets/yr
� Achieved 6.8% capacity improvement with Lean work.
This slide shows the change in estimated area capacity due to the Lean
improvements made up to this point in time.
We essentially made a 7% improvement in the area capacity due to the combined
Lean improvement efforts.
We still did not achieve the desired capacity goal of 968 for base design sets/year.
Let’s look at the next slide to get a better picture of the results observed to this point
in the work.
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Case Study #2 Slide 12
750
800
850
950
1000
900
Capacity, sets/yr
Present
State
Capacity
Goal
Future
State 1
Future
State 2
Ideal
State
Summary:
Includes Lean Improvements
807
968
862
∆∆∆∆ over Present-State: 6.8%20%
So, as you can see from this slide we still have a ways to go with the capacity
improvement work.
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Case Study #2 Slide 13
Results of Lean Improvement Efforts
� Considerable work was done to improve operating capacity, and we achieved ~7% of the required 20% capacity improvement.
� But, we still fell short of the established goal.
� On the day we conducted the 5S work, I walked out of the lab and observed this trend in the measures display case:
Base Run Success Rate per Month
75%
80%
85%
90%
95%
100%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Time, month
Su
cc
es
s R
ate
Target
Average
As we observe in this slide, there appears to have been some quality related issues
in the Pilot operation prior to our involvement with the team.
The management team never mentioned these quality issues to us before or during the improvement effort. Instead, we just got lucky to find this trend information
posted on the business information board.
We realized from this trend that at least 10% of the runs were attributed to a variety
of process related issues. But, we did not know which issues to focus our attention
on.
Let’s look at the next slide to get an idea.
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Case Study #2 Slide 14
Ensuing Quality Issues
� Process was performing at about a 3-Sigma level.
� Upon review of the quality data, three causal areas were identified.
� Equipment malfunctionwas the greatest contributor to poor base-set performance.
� Most of the equipment malfunctions were due to failure of either the DO or pH probes in
So, now we realize the reason why the Pilot operation needed to conduct “replicate”
runs to support potential process failures in the “base experimental runs.”
We identified about three(3) primary issues with the process, as shown in this slide, that caused it to be unpredictable.
But, what were the underlying causes driving the areas listed above?
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Case Study #2 Slide 15
Quality Improvement Actions
Include Mistake Proofing and Setup-Time Reduction into M-Setup
1-2Miniferm Setup Error Reduction/Elimination
10-14Total Potential Benefit:
Recharacterize Autoclave Cycle using SS DMAICmethods
2-3Miniferm Contamination reduction, (improve sterilization process)
Develop a reliability change out plan for all process probes
7-9Dissolved oxygen and pH probe reliability improvement
Specific MethodsEstimated Capacity
Increase (Sets/Month)
Improvement Action
In this slide we show all of the root-causes for the observed process failures with the
individual MiniFerm probes located at the top of the list. Contamination of the
MiniFerm during operation is the next cause on the list. Together, these two causes comprised about 82% of all observed failures in operation.
When we looked closer we found there was no system in place for handling worn out
probes or probes reaching their end-of-life. Instead, the Pilot team continued to use
the probes until they failed. Considering the only time they failed was during use, the
team was in essence using the experimentation process as a means for sorting good from bad (or worn out) probes!
Concerning the contamination issue--it appears there were some problems with the
Miniferm storage procedure after cleaning and sterilization. We found that once the
Miniferms were prepared for the next experiments they were placed onto a shelf in the
area which was open to the laboratory environment. The cleaned Miniferms essentially got re-contaminated sitting on the lab shelves waiting for the next use. To
be fair they were wrapped in sealed plastic bags, but the seal was poor!
We suggested they store the newly prepared Miniferms in a clean and sterilized Autoclave oven until there was a need for their use.
Implementing the suggested changes along with developing a new process that
minimized set-up error significantly changed the process performance. See the next
slide for the supporting capacity improvement calculations which include both Lean
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Case Study #2 Slide 16
Future-State 2:
Estimates and Observations
� Adjusted system throughput*:
sets/mo 88 sets/mo 10days/mo 30*TPimproved =+
*Each set is replicated once, which halves the available capacity
� Estimated System Capacity: (w/ 350 days/yr, and 1.0 day/setup)
sets/yr 1056sets/mo 88 mo/yr 12apacityC adjyear =∗=−
� Results:
� 10 sets/month achievable with minimum quality improvement
� A small improvement in quality performance yields ~1,050 sets/yr
� Achieved a 30% capacity improvement with Lean and Six Sigma.
These are the similar calculations we have been making since the beginning of this
presentation.
Notice the large positive shift in the area capacity resulting from the quality improvement efforts.
If the Pilot team performed to the lowest part of the ranges shown in the previous slide,
then they may expect to exceed the desired capacity by 30% over nominal. This is
10% greater than required by the business management team.
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Case Study #2 Slide 17
750
800
850
950
1000
900
Capacity, sets/yr
Present
State
Capacity
Goal
Future
State 1
Future
State 2
Ideal
State
Summary:
Includes Quality Improvements
807
968
862
∆∆∆∆ over Present-State: 30%6.8%20%
1050
Here is the same bar plot as previously shown. This one includes the capacity
improvement with both the Lean and Six Sigma Quality approaches included into the Future State 2 bar on the far right.
Comparing this bar to the Capacity Goal we notice it exceeds the goal by 10%. So,
identifying and improving the quality issues in the process provided what was
needed to achieve the goal set by management.
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Case Study #2 Slide 18
Added Benefit
� Remember the base-set performance trend?
Base Run Success Rate per Month
75%
80%
85%
90%
95%
100%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Time, month
Su
cc
ess
Ra
te
Target
Average
Base Run Success Rate per Month
75%
80%
85%
90%
95%
100%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Time, month
Su
cc
ess
Ra
te
Target
Average
� Pilot Plant Ops was informed by the Product Development
Team that if they could achieve “targeted” performance on a
consistent basis, they could eliminate the use of “replicate”
runs – increasing available capacity by an additional 40%.
Given the findings shown in the previous slide, the Product Development Team
notified Pilot Plant Ops and suggested that if they could achieve the established targeted performance on a consistent basis in the future they could remove the
requirement for a “replicate” experimental set.
Removing this constraint would provide Pilot Plan Ops with an additional 40%
increase in area capacity. This capacity increase would allow the area to support the
business for at least the next 20 years.
The Pilot Plant team began work towards improving the targeted experimental yield
goal. This provided an excellent starting point for ongoing improvement.
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Case Study #2 Slide 19
750
800
850
950
1000
900
Capacity, sets/yr
Present
State
Capacity
Goal
Future
State 1
Future
State 2
Ideal
State
Summary:
All Potential Capacity Improvements
807
968
862
∆∆∆∆ over Present-State: 82%30%6.8%20%
1050 1470
Here is the final summary of all stepwise improvements and the final “Ideal” state for
future operations in the Pilot area.
As mentioned earlier, the Ideal state performance could provide uninterrupted Pilot
support for at least 20 years without any major investments in the operation.
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Case Study #2 Slide 20
Closing Observations
� Pilot Plant personnel initially believed that Lean methods would improve process throughput.
� Lean methods actually accounted for 7% of the total 20% improvement requirement.
� Upon review of process performance data it was found that equipment failure accounted for much of the throughput budget.
� Addressing the area quality issues resulted in 30% total improvement, exceeding the requirement by 10%.