Manufacturing Economics: Process Modeling
Garbage In, Garbage Out:
the Case for More Accurate
Process Modeling in
Manufacturing Economics
Comstock Images/Getty Images
Rick Johnston, David Zhang
abstract
Current biotech manufacturing plants are capital intensive, with yearly depreciation
costs as high as raw materials costs at a facility. In such an environment, where indirect
costs dominate any direct charges because of labor, raw materials, or utilities, a single
metric becomes important: throughput of sale-able product. In this article, we examine
a return-on-investment case study for processing a high-titer product in a largescale biopharmaceutical plant. In this case, modeling the altered unit operation in
the context of other (existing) unit operations was essential to establish accurate
throughput metrics, and therefore, valuation. We argue that such process-focused
economics models are essential in the biopharmaceutical industry.
T
Rick Johnston is NEED TITLE at the
Center for Biopharmaceutical Operations,
University of California, Berkeley, and
David Zhang is NEED TITLE at Bioproduction
Group, Inc. Berkeley, CA, NEED TELEPHONE
(Who is the corresponding author?),
[email protected].
BioPharm International
he key goal of any manufacturing economics study is the
accurate evaluation of the
processing costs for a particular manufacturing facility against the
projected demand stream for the products it will produce. Typical economic
analysis in the 1980s and 1990s was
based around the value of custom-built
plants for a single or small number of
products. Such economic evaluations
were relatively simple, because they
were based on building a ‘greenfield’
facility that matched the required
process specifications and projected
demand with the minimum capital
outlay. However, with a proliferation
of capacity in biopharmaceutical production, manufacturers have shifted
focus away from building large-scale
single-use facilities and more toward
retrofitting existing facilities to produce new or process-improved products. Economic evaluations are more
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difficult in this context for a number
of reasons.
First, retrofit options typically involve
value destruction as well as value creation. While value destruction is typically thought of as the damage caused
by construction, in biotech the most
significant modes of value destruction
are the downtime associated with plant
shutdown and requalification.
Second, greenfield facilities are inherently right-sized, whereas retrofitted
facilities are not. Retrofitting requires the
comparison of legacy equipment in the
plant, for example, steam and clean in
place (CIP/SIP) skids and existing media,
and buffer preparation tanks, with new
technology. Such decisions inevitably
involve design compromises, and these
can have unintended effects on throughput and equipment utilization.
Finally, cost-saving retrofits are not
typically justified solely on direct cost
reductions, but on metrics like flex-
Process Modeling Manufacturing Economics
ibility or capacity increase. As an example,
the adoption of disposables in many existing
plants is either based around process standardization, decreased contamination risk,
or increased flexibility (either in changeover
or in utilities). Given existing tanks and CIP
or SIP handlers, there is little or no motivation in direct cost-savings to move to new
technologies, and thus retrofits typically use
one or more indirect cost savings metrics in
their justification. Such metrics also may be
more qualitative than quantitative, such as
the flexibility of a plant to produce multiple
products in the future.
Figure 1 shows the cost allocations in a
typical large-scale mammalian cell culture
production facility. Raw materials, direct support costs, and direct production labor costs
account for around 33–50% of the total yearly
operating cost of such a facility. Indirect labor,
quality, corporate overheads, and depreciation account for more than 50% of the total
costs. Such indirect costs are semi-variable
in the sense that they are not directly runrate–dependent and are difficult to control
in the short-term. Indirect labor, for example,
includes project work that may be an integral
part of the plant even if its run-rate is zero.
Such a fixed-cost infrastructure is uncommon in most other manufacturing sectors,
where raw materials costs and direct-support
costs far outweigh indirect cost considerations. One of the few exceptions to this is
in semiconductor manufacturing, where significant infrastructure costs and the requirement for a controlled environment create
higher indirect costs on a scale similar to
biopharmaceuticals.
In such an environment where indirect
costs dominate the direct cost of manufacturing, only relatively small improvements in performance can be achieved through direct cost
reduction. For example, a 15% reduction in
raw materials cost—roughly equivalent to completely removing the most expensive recovery step in most biopharmaceutical process
operations, Protein A—reduces overall operating expenses by less than 2%. Such direct
cost reductions are typically outweighed by
the need to shutdown the plant to perform
installation and testing of retrofit options: an
expensive proposition because most costs are
not run-rate dependent. Far more important
in most economic studies is the ability for bio-
Figure 1. Yearly operating cost breakdown for a typical
mammalian cell-based biotech plant
12%
12%
Raw materials
Direct and support costs
12%
12%
Direct labor
Indirect labor
Depreciation
12%
12%
Corporate overhead
Quality
12%
12%
Administration
pharmaceutical manufacturers to maximize
the number of kilograms of material they can
manufacture (and subsequently sell) in a year.
The cost of getting it wrong
Figure 2 shows the outcome of an analysis done for a retrofit project at a large biotech manufacturer interested in installing a
new perfusion-based downstream processing
technology. The net present value (NPV) of
the scenario was $35 million over five years.
Figure 2 compares the three most significant causes of error in economic evaluations:
incorrect raw materials costing, construction and installation costs, and incorrect
estimation of the quantity produced by that
scenario. Each of these three categories is
altered by 5%, 10%, and 20% respectively, to
see their relative effect on the NPV.
As shown, the cost category that is most
sensitive to errors is the quantity produced, or
supply of material to the production network.
Small inaccuracies in this metric produce significant negative impact on the NPV of the scenario in the case of a 20% inaccuracy, actually
causing the project to have a negative return.
Accurate supply-based models are essential to
establish accurate metrics for the value of retrofit scenarios. In the next section, we discuss
this type of modeling in a case study involving
a major biopharmaceutical manufacturer.
Retrofitting plants for
higher titer products
Supply-based planning can be seen in retrofit
projects that aim to allow higher titer products
to be produced in existing biopharmaceutical
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Manufacturing Economics Process Modeling
Figure 2. A comparison of the economic effect (NPV: net present value) of calculation errors
Percent of values
NPV ($ million)
for the downstream capacity.
Both required similar infra40
structure investments and
NPV: $35 million
resulted in similar downtimes
30
for retrofit.
The economic compari20
son of these two scenarios
was therefore predicated on
10
5% wrong
their comparative run rates
and manufacturing produc10% wrong
0
tion profiles. However, there
20%
wrong
Raw materials costs
Raw materials costs
Quantity produced
was considerable disagree–10
ment from subject matter
experts on which of the two
–20
scenarios was better. Batchsplitting could be performed
–30
after the Protein A recovery
Cost category
step, a possible plant bottleneck, but it was unclear
manufacturing plants. With the rapid advances whether the material would exceed time at
in cell culture and fermentation technology, ambient specifications if required to wait for
many plants suffer from a bottleneck in down- the other half of the “split” batch to comstream (purification) operations. A project plete processing. Partial batching produced
with a major biopharmaceutical company was less material per batch, but seemed to maxiundertaken to establish the most cost-effec- mize the downstream plant’s capacity for
tive means of producing 4 g per liter (g/L) each batch.
products in a facility designed for the 1–2 g/L
range. Although a number of alternatives were Production-based Analysis
considered, the two most practical options The University of California at Berkeley and
were determined to be split batching, in which the Bioproduction Group, Inc., a company
the fermentation volume is split into two at specializing in quantitative biotech process
harvest and processed in two separate lots and models, worked in conjunction with the manpartial batching, in which a reduced fermen- ufacturer to produce a process-based analtation volume is prepared, exactly enough ysis. Rather than focusing on data mining
ac c u r ate c o st i n g
Figure 3. Variability in unit operation processing times
for raw materials or
construction costs,
the approach was to
Median
0 .18
processing time
build a highly accu~ 5.5 h
rate v irt ual-plant
0 .16
models of bot h
7.2
h
(+
30%):
0 .14
scenarios.
~35% of values
11 h (+ 100%):
above this
0 .12
Fig ure 3 shows
~10% of values above this
indicative data of
0.1
what Bioproduction
Company A
0 .08
Group calls “characCompany B
0 .06
teristic variability”
in operating times
0 .04
of biopharmaceuti0 .02
cal plant operations.
0
S u c h v a r i a b i l it y
1– 2 2– 3 3– 4 4–5 5 –6 6– 7 7– 8 8– 9 9–10 10–11 11–12 More
is common in bioStandardized processing time (h)
pharmaceutical
manufacturing and
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Process Modeling Manufacturing Economics
Quantity output (kg)
confounds process improvement Figure 4. Comparison of split versus partial (4 g/L) outputs over time
efforts: reductions in the variability
80
of a processing time are often more
important than altering the process80
Partial batch
ing time itself. Accurately modeling
Split batch
70
such variability is critical to establish
60
accurate metrics around what a plant
can produce.
50
One of the key issues with vari40
ability in operating times is that
30
changes to one or more manufacturing unit operations may have unex20
pected changes in the performance
10
of other (untouched) areas of the
0
plant. A unit operation requiring
0
20
40
60
80
100
120
140
160
180
additional cleaning, for example,
Time (days)
may exhaust existing CIP/SIP capability, reducing the total capacity of
the facility. These unforeseen bottlenecks are common in almost all biophar- and 160 days. (This output also includes the
maceutical processing plants, and to get an fermentation time, e.g., it takes nearly 30 days
accurate estimate of the run-rate possible to produce the first batch.) Note that the lines
with a new unit operation, a detailed analysis cross at small campaign lengths, because the
of the facility must be performed that incor- partial batch scenario requires shorter fermenporates the variability data seen above.
tation times. However, for significant camThe production-based analysis performed paign lengths of 40 days or more, the split
used a technology known as process simula- batch produces 10–15% more output for an
tion. This technique produces detailed process equivalent campaign. The return on investmodels that incorporate variability in unit ment (ROI) of this scenario was nearly $200
operations, media, and buffer preparation million over four years .
activities, CIP and SIP activities, pre-use and
post-use operations as well as quality test- Valuing Capacity Increases
ing. Process simulations mirror plant automa- One of the final questions in an economic
tion systems in the sense that they will not analysis is the value of increasing throughput
start an operation until
all the required resources Figure 5. A traditional valuation of a retrofit scenario, producing additional material at a lower
are present, which is cost per gram in an upgraded facility.
important in the case of
200
Ability to divest a
time-sensitive protein
more costly plant
substances. This technique confirmed that
Apply to use existing
expiration times were
plant for CMP
Traditional valuation: each
production
not a risk for the splitadditional kg reduces
the cost if production
batch scenario, as well
by a fixed amount
(not applicable when
as to quantify the exact
demand is fixed)
profile of the quantity
and timing of the mate0
rial to be produced in the
Linear valuation
altered plants.
Non-linear
valuation
Figure 4 shows a comNo value to additional
parison of the split versus
capacity: fixed plant
partial batch outputs over
investment
time, for varying campaign lengths between 0 *Assumes additional material produced in upgraded facility at a lower variable cost per gram than existing capacity
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Manufacturing Economics Process Modeling
at a plant. As we have discussed, most largescale retrofit projects do not justify themselves on the basis of direct cost savings alone
because indirect costs make up such a high
percentage of total plant operating costs. As
such, one of the key issues for biopharmaceutical manufacturers is how to increase operating throughput or to configure plants in such
a way to make them more flexible. This is
only the first part of the analysis, however:
the increased production must be balanced
against the manufacturer’s ability to sell the
additional material they produce.
Figure 5 shows a traditional valuation of a
retrofit scenario, producing additional material at a lower cost per gram in an upgraded
facility. A typical ROI calculation will use
the blue line (linear valuation) to calculate
return; a capital investment delivers positive
return if sufficient additional material can
be produced. However, in a manufacturing
environment where the additional grams
produced cannot be sold, there is actually no
direct economic benefit to investing in additional plant capacity or flexibility projects.
Benefit is only derived when the flexibility or
capacity allows the plant to be used for other
purposes (such as production as a contract
manufacturer) or where capacity increases
allow savings in other places in the network
(such as the divestment of a more costly
plant). This non-linear tradeoff makes manufacturing economic analysis in the biopharmaceutical industry’s current environment
an even more difficult proposition. Valuation
of a new scenario must therefore be carefully
aligned with the strategy of a company.
of vital importance in establishing correct
metrics around the economic benefits of
any scenarios considered. ◆
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Conclusion
In conclusion, biopharmaceutical manufacturing’s current proliferation of capacity
and the trend toward an increasing number
of small-volume products creates unique
challenges in manufacturing economics
studies. The high indirect costs of biopharmaceutical manufacturing means that any
plant downtime for retrofitting raise the
bar for performance improvement efforts
to overcome. Typically, improvements with
significant benefit to the organization focus
on creating additional capacity (or additional effective capacity, such as reduced
changeover times). In such analyses, however, producing accurate process models is
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