1. whitepaper
Biomanufacturing supply
chain optimization
Balancing risk against flexibility and just-in-time production
© Bioproduction Group. All Rights Reserved.

2. BIOMANUFACTURING SUPPLY CHAIN OPTIMIZATION
Biomanufacturing SUPPLY CHAIN OPTIMIZATION
Balancing risk against flexibility and just-in-time production
INTR ODUCTION
The biopharmaceutical supply chain presents unique challenges for supply chain
planners and their supporting technologies, primarily because of the need for very
high supply reliability. High costs and the life-preserving nature of biopharmaceutical
products mean that planners must avoid outages and ensure supply continuity. Such
imperatives make it difficult to meet the traditional goals for planners: flexible supply
chains that carry minimal inventory and manufacture ‘just-in-time’ to meet demand.
In this whitepaper, we discuss ways of modeling biomanufacturing networks that
explicitly account for supply chain risks. Capturing the likelihood and effect of
risks is a critical first step in determining how much inventory is needed to buffer
against adverse events, and therefore the level of safety stocks that are required.
In some cases, supply chain resilience and flexibility are not mutually exclusive,
and with careful planning, a network can be constructed that optimizes both.
© Bioproduction Group. All Rights Reserved. 1
“My boss tells me that ‘no patient
shall go without’. But what does
that mean in reality? Nothing can
be totally certain – we need to
be able to quantify that risk.”
Senior Supply Chain Planner, Large
Biopharmaceutical Manufacturer
Current Issues in the Biopharmaceutical Supply Chain
Biopharmaceutical supply chains represent a unique challenge for planners because
process variability, as well as contamination and other adverse events, need to be
explicitly considered in supply planning. The threat of the known unknown – risks
that cannot be accurately predicted – must also be incorporated into plans. Without
characterization of these issues, traditional inventory models produce results that
can appear to be optimal, but actually expose the business to unacceptably high
levels of risk. We outline some of the key factors that must be considered below.

3. BIOMANUFACTURING SUPPLY CHAIN OPTIMIZATION
1. Cell Growth Times and Yield Variability
Current cell lines are relatively slow-growing and require very tightly controlled
growing conditions in terms of temperature, pH, and growth medium. Up to 30
days is required to express sufficient material for a single batch. The effect of
this long growth phase is that manufacturers have focused on increasingly large
batch sizes and multiple serial production trains to ensure sufficient production
quantities. It also means the time required to react to adverse conditions is long.
In addition, current cell lines are extremely sensitive to growing conditions and
contamination by other cells such as bacteria or viruses. Cell growth is exponential
(since in a well-designed process it is only constrained by the doubling time of the
cell), but this growth pattern means that small variations in growth are magnified
over and over again. The result is highly variable output quantity and quality of 30%
or more (Shah, 2004). This variability is also highly auto-correlated, so that many
successive batches in a manufacturing campaign may be affected. This is a particular
problem for the biopharmaceutical industry where a small number of batches
are typically produced, and the time to react to manufacturing issues is long.
2. Contamination and Reject Rates
Biological-based manufacturing also requires exceptionally stringent levels of
cleanliness since cells grow in conditions well suited for many other bacteria
and viruses. Even with completely clean equipment, biological contamination
can be introduced from other sources including the feed stock (media),
water, operators, detection mechanisms, air handling systems, etc.
Once biological contamination is present, it can be extremely difficult to remove.
Typical biological protection mechanisms such as a protective thin film formation
(seen, for example, in slime) or spores (a reproductive mechanism designed
for survival in unfavorable conditions) are very difficult to chemically treat and
usually need to be physically removed from pipes (e.g. with an abrasive slurry).
The focus for biopharmaceutical manufacturers has therefore been to reduce bio-burden
(biological contamination) as much as possible using mechanisms like HTST
pasteurization of media, and to introduce purification steps that will filter out any
contamination. Specific types of bio-burden will render conditions unsuitable for the
fermentation process to continue; testing systems are designed to detect this and abort
the batch as early as possible. Even with such systems in place, contamination events
are common – as seen in the 2009 contamination event in Genzyme’s Allston facility.
3. Testing and Quality Assurance
Product and process-based testing puts a considerable burden on supply
chain cycle times and throughput. Hundreds of tests are done on each batch
as it progresses through production to ensure efficacy and safety of the final
product. A number of tests are done in-line and are relatively quick to complete,
while some (e.g. bioassays) can take up to 2 months to give results.
© Bioproduction Group. All Rights Reserved. 2
“What’s especially troubling
about the Allston facility event
is that it could happen to any
biomanufacturer, at any time.
Genzyme was just unlucky.”
Engineer, Cell Culture Operations Group

4. BIOMANUFACTURING SUPPLY CHAIN OPTIMIZATION
Target release cycle
time of 70 days.
Average cycle time
85 days, standard
deviation 30 days
No target release
cycle time. Average
time 60 days,
standard deviation
of 70 days
100-120
80% of batches
take less than
120 days
120-140
140-160
180-200
160-180
RELEASE TIME (days)
0-20
20-40
40-60
60-80
80-100
220-240
220-240
240+
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
PERCENT OF VALUE (%)
Some batches
require more
than 1/2 a year
Company 1
Company 2
© Bioproduction Group. All Rights Reserved. 3
FIGURE 1:
QA/QC Release Cycle Times
Overall biotechnology supply chain cycle times are currently between 41 days and 3
years; the value-added time (time when the material is actively produced) is between
0.3% and 5% of this (Shah, 2005). The single greatest cause for these very long cycle
times is the need to test batches at each stage of production. It is not uncommon
for batches to be delayed for 6 months or longer, if an irregularity is found.
In aggregate, the prolonged uncertainty of testing results and consequent delays
of production result in large stock levels of intermediate materials; stock levels
typically range from 30% to 90% of annual demand quantity (Shah, 2005), an
order of magnitude higher than traditional pharmaceutical products manufactured
using chemical processes. This is problematic because these intermediate
products can tie up millions (or hundreds of millions) of dollars in inventory.
4. Process-based Regulation
Since quality testing of the final product cannot provide a complete assurance
of product efficacy and safety, regulatory authorities around the world have
adopted a process-based approach to regulation. Agencies such as the
Food and Drug Administration (FDA) require licensure of the entire process
surrounding the production of biotechnology product. This means that any
changes to plant or process design must be certified in each country in which
it is sold, a process which may take up to 3 years for major projects.
Process-based regulation has an important effect on innovation in the industry,
since it provides a large disincentive for companies to change their processes.
Many companies have pursued a ‘license and leave’ approach to drug manufacture
where no significant changes to a plant are permitted after the facility has been
licensed to produce a product. This means that technological changes that
may have a dramatic impact on manufacturing cost, speed or even product
safety are not pursued since they would require re-licensure. Examples can be
seen in the slow adoption of continuous fermentation and purification systems,
disposables, and in-line testing systems. This is unlike other high-tech industries
such as the semiconductor industry, where technological innovation provides
competitive advantage and the only disincentive to innovate is cost-based.

5. BIOMANUFACTURING SUPPLY CHAIN OPTIMIZATION
2 MONTH OUTAGE
3 MO. 6 MO. 3 MO. 2 MO. 1 MO. 1 MO.
RESTART
CAMPAIGN JAN 2009 JUL 2009 JAN 2010 JUL 2010
4 MONTH OUTAGE
6 MO. 3 MO.
3 MO. + 3 MO. 1 MO. 1 MO.
PRODUCT 1
PRODUCT 2
PRODUCT 3
CONTAMINATION
REORDER CAMPAIGNS,
USE BACKUP FACILITY JAN 2009 JUL 2009 JAN 2010 JUL 2010
BACKUP FACILITY B
6 MONTH OUTAGE
6 MO.
2 MO.
© Bioproduction Group. All Rights Reserved. 4
FIGURE 2:
Recovery strategy for 3 different
con tamination severities
5. Process Design for Manufacturability
Most production facilities in existence are currently multi-product capable in the
sense that the basic site infrastructure allows for the manufacture of a range of
products. At least in principle, this means that new drugs can be produced in existing
facilities. In practice, however, it is quite difficult for facilities to change production
modes because of the sensitivity of culture growth and purification to hundreds
of production parameters. Almost all existing biopharmaceutical production is
‘product specific,’ that is, the facility is designed around the specific drug compound
being produced in order to maximize the titer and yield of that cell. This leads to
a highly irregular supply chain design in firms with more than one product.
One of the growing issues in process design is the need to match the capacity
of each of the major production steps. Fermentation, or cell growth, has
increased titers more quickly than the ability of purification processes to
manage the additional material produced. This evolution of titer has created
bottlenecks in recovery operations and the need to make significant changes
in downstream processing to accommodate these higher titers.
Case Study: Contamination of a Bulk Biomanufacturing Facility
One of the critical issues in supply chain planning is the need to model the effect
of adverse events like contamination. Bioproduction Group was asked by a large
biomanufacturer with a number of facilities to evaluate the likelihood and impact
of a contamination event in their facilities similar to Genzyme’s 2008/2009 events.
The team determined early on that it was not possible to ignore or ‘average out’
the above issues and instead constructed a high fidelity, high accuracy facility-network
model using Bio-G’s patented Simulation System technology. Using data
from plant managers and engineers, the model was designed to accurately depict
the production process and constraints of all the facilities in the network.
2 MO. BACKUP FACILITY A
2 MO. BACKUP FACILITY B
1 MO. + 3 MO. 1 MO. 1 MO.
REORDER CAMPAIGNS,
USE TWO BACKUP
JAN 2009 JUL 2009 JAN 2010 JUL 2010 FACILITIES
Through discussions with subject matter experts and key members of the
scheduling department, Bio-G determined several possible reactions to a facility
contamination to prevent product shortages. “When our client says ‘no patient shall
go without treatment,’ it is not corporate rhetoric: they mean it,” says Principal David
Zhang. “There is an intelligent way to utilize available network resources to keep
production flowing while avoiding unnecessary disruption to the supply chain.”

6. BIOMANUFACTURING SUPPLY CHAIN OPTIMIZATION
600
400
© Bioproduction Group. All Rights Reserved. 5
FIGURE 3:
INVENTORY RISK PROFILES ANALYZED
BY THE SIMULATION SYSTEM
Bio-G’s strategic model was used to evaluate a number of possible contamination
scenarios, depending on the severity of the contamination event. Low severity (2
month recovery time), medium severity (4 month recovery) and high severity (6
month recovery) were each considered to determine the range of effects on the
supply chain network. The diagram above shows the range of possible responses
to adverse events, depending on the event’s severity. Backup facilities were used in
more severe contamination cases to ensure stockout would not occur. The Simulation
System aided planners in quickly evaluating the best response in each case.
2 MONTH OUTAGE
4 MONTH OUTAGE
6 MONTH OUTAGE (BACKUP)
BASELINE (NO OUTAGE)
6 MONTH (NO BACKUP)
RELEASED INVENTORY LEVEL (KG)
INVENTORY LEVELS
TIME (HOURS)
500
300
100
0 2000 4000 6000 8000 10000 12000 14000
200
0
Bio-G’s strategic model provided a decision support tool that allowed for the
quantification and mitigation of risk for strategic decision-making. The results
showed that stockouts would occur with some of the response strategies being
considered by the manufacturer, leading to policies and accurate inventory
setting to prevent these from occurring. The tool was then used by the client to
determine the best responses amongst all possible scenarios, increasing confidence
that the manufacturer had sufficient agility to respond to adverse events.
Moving towards Lean and Just-In-Time
One of the most critical questions facing current supply chain planners today is
how to optimize their available capacity over the short and medium term (1-5
years) while also considering the impact of many of the key variables mentioned
above. New product versions, for example, bring higher titers which move
bottlenecks in the network. New technologies – disposables, HTST, modular skids,
etc. – are being adopted, but their impact on the supply chain is not yet clear.
While SAP and other traditional inventory planning toolsets have been applied
to this problem, they have been found in practice to grossly underestimate the
level of variability in the network and overestimate the feasibility of supply plans.
This is due to the fact that such toolsets are inherently deterministic, using a
single number to represent each parameter in the scenario rather than allowing a
range of possible values. Such systems are also inherently ‘push’-based, and are
inflexible to changing supply and demand conditions (Hopp & Spearman, 2000).
Bio-G’s approach to this problem is different. Instead of relying on single-point
estimates, the software gives planners the ability to generate and
explore ranges of values. This means the software produces results that
explicitly account for variability, risk and its effect in the supply chain –
analysis that is desperately needed in the biopharmaceutical industry.
“Bio-G’s unique software
plays a role in keeping U.S.
biomanufacturing capacity
globally competitive as well as
ensuring lower costs for patients.”
Industry Analyst, Biopharmaceutical/
Pharmaceutical Sector

7. BIOMANUFACTURING SUPPLY CHAIN OPTIMIZATION
40
35
30
25
20
15
10
© Bioproduction Group. All Rights Reserved. 6
FIGURE 4:
New protein-free formulation
at 60% lower observed
titers at the pilot scale
FIGURE 5:
INTRODUCING A NEW PRODUCT
VARIENT DOUBLES the number
of SKU’S IN THE SUPPLY CHAIN
Case Study: Examining the effect of a new product
variant on supply chain reliability
Bioproduction Group was asked by a large biopharmaceutical manufacturer to
implement a toolset to allow them more robust analysis of the effect of a new
product introduction. The new product used media that was not derived from
animal-based products, meaning a lower risk of supply outage due to raw material
contamination. However, the new product was exhibiting significantly lower harvest
titers than expected, leading them to re-evaluate manufacturing capacity.
In addition, the biomanufacturer’s supply chain was highly fragmented by the
need for a number of product variants for dosage, capping type and market. The
introduction of this additional new product variant required the manufacturer
to smoothly transition from the existing product to the new, while allowing
countries to purchase the old product variant until the new was approved. Such
an approach caused an explosion of product variants that the supply chain was
required to handle, with an uncertain effect on inventory levels and risk.
CAPPING TYPE
PRODUCT DOSAGE
FINAL MARKET
NEW PRODUCT
(NO ANIMAL-DERIVED
PEPTONES)
= 60 PRODUCT
VARIANTS
+
+
+
TITER (grams/liter)
1.0-1.2
1.2-1.4
1.4-1.6
1.6-1.8
1.8-2.0
2.0-2.2
2.2-2.4
2.4-2.6
2.6-2.8
2.8-3.0
3.0-3.2
5
0
LIKELIHOOD (%)
Existing Product
New Protien Free Product

8. BIOMANUFACTURING SUPPLY CHAIN OPTIMIZATION
2010 2011 2012
LAUNCH I N 2 MA RK ETS LAU NCH IN 4 MARKETS LAU NCH IN ALL MARKE TS
IN VE NTO RY FALLS
BELOW TARG ETS,
MANUFACTURING
AT CA PACIT Y
© Bioproduction Group. All Rights Reserved. 7
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FURTHER READING
Shah N (2004) Pharmaceutical supply
chains: key issues and strategies
for optimization. Computers and
Chemical Engineering 28: 929-941
Hopp and Spearman (2000)
Factory Physics, McGraw-Hill
MORE INFORMATION
BIOPRODUCTION GROUP
CONTACT@BIO-G.COM
WWW .BIO-G.COM
Bioproduction Group’s approach was to build a high speed supply chain
simulator that could evaluate the capacity of each of the manufacturing facilities
used by the manufacturer, along with hundreds of rules and constraints on
the manufacturing process. The result was a model that was both a highly
accurate representation of the manufacturing network, and one that could
easily be altered by planners to perform sophisticated what-if analysis.
The approach allowed the biomanufacturer to confirm that a world-wide introduction of
the new product posed an unacceptably high risk to their supply chain. Using data from
over 10,000 years of simulated time and hundreds of automatically generated scenarios,
the Simulation System suggested a staggered product introduction that focused on
markets that were likely to approve the product quickly, while leaving countries like
Japan and Canada (with long and highly variable approval times) for a later date.
The figure above shows the effect of implementing the staggered production
introduction strategy vs. the original strategy where the firm launched simultaneously
in all markets. The difference between the two strategies is not visible to the supply
chain organization until nearly 9 months after launch when inventory starts falling
below target levels and manufacturing reacts by shifting to higher capacity. But
this capacity increase is not sufficient to bring inventory levels back to target values
for nearly 2 years after launch, posing an unacceptable risk to the business.
In this graph, the use of a simple yet powerful aggregated metric ‘Supply Chain Risk’
based on a Value at Risk or VaR calculation, allows the business to see with one
view real comparisons between supply scenarios. The ability to perform this analysis
allowed the business to bring concrete analytics to their supply chains, providing a
‘virtual supply chain’ that analysts could quickly alter to evaluate what-if scenarios.
Conclusions
Biomanufacturing Supply Chains represent unique challenges for planners: the need for
high service levels in a heavily constrained industry with long lead times, high levels of
regulation, and increasing focus on inventory and cost reduction. Bioproduction Group’s
Simulation System provides the means to model such complex supply chains in an easy-to-
use, powerful framework. The patented technology provides a 100-fold increase
in speed over existing simulation toolsets, allowing planners to perform sophisticated
design-of-experiment and what-if optimization easily and quickly. This approach has
been used to quantify millions of dollars in direct cost savings to biomanufacturers,
while aiding risk avoidance and increasing supply reliability to customers.
“The ability to rapidly and
confidently model and analyze
a complex supply chain could
have enormous impact on a firms’
ability to remain competitive and
to continuously improve high
technology production processes.”
National Science Foundation Evaluation
of Bio-G Technology, 2010
Staggered Product Introduction Strategy
SUPPLY CHAIN RISK
ORIGINAL STRATEGY STAGGERED INTRODUCTION S TRATEGY
FIGURE 6:
VAR CALCULATION FOR THE
MANUFACTURER’s original supply
chain strategy, contrasted
against a staggered product
introduction strategy