Case Study #2 - ascendantconsulting.netascendantconsulting.net/ftpdocs/pdfs/Case Study2 -...

1

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.

2

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.

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…

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.

5


Y8LowLowLowT8

Y7HighLowLowT7

Y6LowHighLowT6

Y5HighHighLowT5

Y4LowLowHighT4

Y3HighLowHighT3

Y2LowHighHighT2

Y1HighHighHighT1

DOpHTemp

Process Yield


Experimental Loss



T1T1 T2T2 T3T3 T4T4

T5T5 T6T6 T7T7 T8T8

Base Set


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.

6


Y8LowLowLowT8

Y7HighLowLowT7

Y6LowHighLowT6

Y5HighHighLowT5

Y4LowLowHighT4

Y3HighLowHighT3

Y2LowHighHighT2

Y1HighHighHighT1

DOpHTemp

Process Yield


Replication became Standard Process



T1T1 T2T2 T3T3 T4T4

T5T5 T6T6 T7T7 T8T8

Base Set

T1T1 T2T2 T3T3 T4T4

T5T5 T6T6 T7T7 T8T8

ReplicateSet

Redundant Experiments


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%.

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…

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.

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.

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.

11


Future-State 1:


� 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


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.

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.

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.

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?

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

16


Future-State 2:


� Adjusted system throughput*:

sets/mo 88 sets/mo 10days/mo 30*TPimproved =+

*Each set is replicated once, which halves the available capacity


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.

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.

18


Added Benefit

� Remember the base-set performance trend?


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


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.

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.

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%.

Case Study #2 - ascendantconsulting.netascendantconsulting.net/ftpdocs/pdfs/Case Study2 -...

Documents

Transcript of Case Study #2 - ascendantconsulting.netascendantconsulting.net/ftpdocs/pdfs/Case Study2 -...