EXPANDING PRODUCTION AT

BIOLOGICS FACILITIES:

EFFECTIVE STRATEGIES AND

PLANNING

Biomanufacturing Conference

June 27-28 2013

Ken Hamilton, Genentech

AGENDA

Business Drivers

Strategic Considerations

Business Objectives

Financial Considerations

Processes and Tools

Risks & “Watch outs”

Summary

BUSINESS DRIVERS Required available, operating & planned capacity

Capacity is both titer &yield and runs per year

“Surge capacity” – who owns this? Network or site?

Duration of shutdown for plant changes

Capital cost v shutdown cost

Lost capacity – impact to network supply

Coordinate shutdown with other network shutdowns

Time to implement capacity increase

Scope v network capacity needs

Cost

Opportunity cost to network – can capacity be gained elsewhere in network?

Project cost & start up cost

STRATEGIC CONSIDERATIONS

Impact of changes on long range product demand:

Target fixed run rate increase or account for change in future demand?

Increase capacity for all products?

Operational reliability: Degree of redundancy for critical systems versus

lean based reliability engineering programs

Total cost of ownership:

Evaluate capital cost for new equipment versus cost of instituting operational

methods & increase to operations staffing levels

Prioritize, in advance, between scope of improvements, schedule and cost

Change Management for changes to GMP systems; account for partner

notifications during planning – impact to project timeline

PROJECT EXECUTION STRATEGY :

3. New Equipment Installation

1. Changes to Operational

Procedures

2. Optimizing Existing

Equipment & Automation

(Process, Matl’s Handling

& Utilities)

Optimum De-Bottlenecking Strategy is a combination of all three

Prime focus is on optimizing Operational improvements and existing equipment – least cost (but must achieve a robust operation)

Cost increases as rely more on new equipment

Three parts to de-bottlenecking :

BUSINESS OBJECTIVES

Define plan for run rate ramp up after plant changes in place

Required ‘white space’ between operations for preventive maintenance, routine environmental cleaning, revalidations, etc.

Materials management: re-order points may change

Impact to QC testing for capacity increase

Impact to lot release

Impact to environmental monitoring

Staffing: change to shift structure

Maintenance operations & shutdown planning

Account for variability in cycle time for manufacturing operations

Ergonomic assessment of more frequent operations

FINANCIAL CONSIDERATIONS - 1 Optimum between shutdown duration (period expense) and capital cost for

capacity increase - identify threshold for capital cost & shutdown duration

FINANCIAL CONSIDERATIONS – 2 A shorter shutdown better enables a manufacturing operation to modulate its

capacity without too much impact to operating cost. Look for optimum payback

BUSINESS PROCESS: CAPACITY INCREASE PROJECT – STRUCTURED APPROACH

Assess merits

of each option

Selection of

best option

Identify options for

solutions to each

bottleneck

Proof of

Concept

Final selection

for solutions

Project

Charter

Bottleneck Gap

Analysis / Brainstorming

Analysis Phase Enabling Phase Implementation Phase

De-bottlenecking teams consisting of experts and users

User team & experts / users

User team & Project Steering Team

User & design team

User teams & experts / users

Solut

ion Id

entifi

cation

Te

st &

Fin

alize

solut

ions

Project

Execution

Imple

ment

TOOLS TO BRING ABOUT CAPACITY INCREASE - WHY IS A SIMULATION TOOLSET IMPORTANT?

Biopharmaceutical manufacturing processes exhibit significant process variability

Connectivity can be a key constraint for Biomanufacturing processes (e.g. buffer operations & purification)

Adding capacity is expensive and accurate modeling of that process improvement is critical to “avoid surprises”

Build data models in Bio-G Real-Time Modeling System using historical manufacturing data

Visualize and understand the variability that is inherent in the process

Over 200 data sets were obtained

Over 200 resources, including all equipment & key utilities

over 300 activities were modeled, including durations, relationships with other tasks, e.g. predecessors

Verified the model accuracy

* No manufacturing specific data shown. Graphs

show sample data only.

Variability in

activity durations

Lot Number

Du

ratio

n fo

r g

ive

ste

p (

ho

urs

)

OUTPUTS Many alternatives to bottleneck solutions were evaluated,

alone and in combination with other alternatives:

Sequence of events for critical operations, to improve parallel operations

Shorten duration of fermentation & harvest operations

CIP & SIP optimizations

Reduced turn around time for product vessels

Significant improvements to plant connectivity for buffer operations

Removed constraints in sanitization operations

Shorten duration of formulation

Bio-G’s software was used to evaluate each of these options to achieve the run rate increases

OCEANSIDE’S APPROACH TO IMPROVING

RUN-RATE

Iteratively improve run rate by virtually “fixing” the run-rate limiting areas

of the facility

Used both one-factor-at-a-time analysis as well as design-of-experiment

type approaches

Critical areas to

improve; region of

greatest influence

on run rate

95% of activities are not

bottlenecks and can be ignored

* Not real data. Shown for clarification purposes only.

“X axis tick ” is each of the bottlenecks

Ru

n r

ate

(rp

w)

0

1

2

3

4

5

6

7

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49

KP

I (9

5%

ile o

f R

un

s p

er

week)

Number of Workflow Changes

Debottlenecking Efficient Frontier: (fewest changes to reach run

rate or capacity target)

GOAL: UNDERSTAND THE MINIMAL CHANGES

REQUIRED TO REACH A PARTICULAR RUN RATE

(THE “EFFICIENT FRONTIER”)

Analysis shows, for any target improvement in run rate, the fewest number

of engineering changes (or cost)

* Not real data. Shown for clarification purposes only.

Confidence Histogram - By Resource

0

5

10

15

20

25

30

3.355 3.379 3.402 3.426 3.450 3.478 3.503 3.528 3.554 3.580

Run Rate (rpw)

Nu

mb

er

of

ob

serv

ati

on

s

3 Upstream CIP Skids Upstream CIP Cycle Reductions

EXAMPLE OF A SIMPLIFED SOLUTION TO A KEY

BOTLENECK AND CAPITAL AVOIDANCE

Installing an additional CIP skid produced the same

result as optimizing existing equipment

No difference in the run rate

and distribution of probable run

rates between case for

additional skid v optimizing

exiting skid

“ROBUSTNESS” ANALYSIS

Examines the effect on run-rate of delays in manufacturing operations

Goal: to allow for a robust schedule that, despite inevitable delays, will

still allow us to reach our production targets

Robustness analyses look at varying levels of delays, typically from 1-8

hours (8 hours being an entire shift)

Can also be used to analyze the ‘white space’ available for preventative

maintenance and calibration activities

Gives engineering groups targets for further improvement and areas to

enhance operational efficiencies

ROBUSTNESS ANALYSIS BY UNIT OPERATION

7+ hour delay in Activity E

will significantly reduce

run rate 5+ hour delay in any activities in

Unit Operation F will reduce run

rate

Similar result for Unit Operation

G: 5+ hour delays reduce run

rate

Baseline run rate

Ru

ns

Per

Week

Activities, arranged by Unit Operation

Determine which operations have greatest adverse impact to run rate if their durations

extend

Unit operation E Unit operation F Unit operation G

RISKS AND “WATCH OUTS” Finding the optimum of likelihood of attaining target sustained capacity

increase, cost and any shutdown durations is key

Find balance between optimizing existing equipment versus installing back up

systems

Ensure capacity increase projects are always linked back to business needs

Business needs could change thru life of the project

Ensure scope of changes is thoroughly defined at the outset

Need to ensure operations groups and teams remain fully engaged thru

life of project

Ideally transition project to an operations group toward end of

implementation phase

Develop accurate cost estimates early in the project

Avoids recycle

SUMMARY

Seek alignment between business drivers and project execution

Ensure project continues to align to business drivers even once project

execution starts – capacity demands can change

Develop and apply a model to carefully identify solutions to bottlenecks

Account for variability in the manufacturing operation

ACKNOWLEDGEMENTS

Special recognition to:

Tiffany Barbour - Lead for BioG modeling at Genentech Oceanside site

Rick Johnson , Ph.D. – Principal, Bioproduction Group

David Zhang - Bioproduction Group