The University of California at Berkeley and the Bioproduction Group, Inc., a company specializing in quantitative biotech
process models, worked in conjunction with the manufacturer to produce a process-based analysis. Rather than focusing on data
mining accurate costing for raw materials or construction costs, the approach was to build highly accurate virtual-plant models
of both scenarios.
Figure 3 shows indicative data of what Bioproduction Group calls "characteristic variability" in operating times of biopharmaceutical
plant operations.3 Such variability is common in biopharmaceutical manufacturing and confounds process improvement efforts: reductions in the
variability of a processing time are often more important than altering the processing time itself. Accurately modeling such
variability is critical to establish accurate metrics around what a plant can produce.
Figure 3. Variability in unit operation processing times3
One of the key issues with variability in operating times is that changes to one or more manufacturing unit operations may
have unexpected changes in the performance of other (untouched) areas of the plant. A unit operation requiring additional
cleaning, for example, may exhaust existing CIP/SIP capability, reducing the total capacity of the facility. These unforeseen
bottlenecks are common in almost all biopharmaceutical processing plants, and to get an accurate estimate of the run-rate
possible with a new unit operation, a detailed analysis of the facility must be performed that incorporates the variability
data seen above.
The production-based analysis performed used a technology known as process simulation. This technique produces detailed process
models that incorporate variability in unit operations, media, and buffer preparation activities, CIP and SIP activities,
pre-use and post-use operations as well as quality testing. Process simulations mirror plant automation systems in the sense
that they will not start an operation until all the required resources are present, which is important in the case of time-sensitive
protein substances. This technique confirmed that expiration times were not a risk for the split-batch scenario, and made
it possible to quantify the exact profile of the quantity and timing of the material to be produced in the altered plants.
Figure 4 shows a comparison of the split versus partial batch outputs over time, for varying campaign lengths between 0 and
160 days. (This output also includes the fermentation time, e.g., it takes nearly 30 days to produce the first batch.) Note
that the lines cross at small campaign lengths, because the partial batch scenario requires shorter fermentation times. However,
for significant campaign lengths of 40 days or more, the split batch produces 10–15% more output for an equivalent campaign.
The return on investment (ROI) of this scenario was nearly $200 million over four years.4
Figure 4. Comparison of outputs over time in a 4 g/L process using split and partial batches.
VALUING CAPACITY INCREASES
One of the final questions in an economic analysis is the value of increasing throughput at a plant. As we have discussed,
most large-scale 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 it produces.
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.
Figure 5. A traditional valuation of a retrofit scenario, producing additional material at a lower cost per gram in an upgraded