Garbage In, Garbage Out: The Case for More Accurate Process Modeling in Manufacturing Economics - A case study in capturing indirect costs and benefits. - BioPharm International

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Garbage In, Garbage Out: The Case for More Accurate Process Modeling in Manufacturing Economics
A case study in capturing indirect costs and benefits.


BioPharm International
Volume 22, Issue 8

PRODUCTION-BASED ANALYSIS

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. Variability in unit operation processing times3
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.

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. Comparison of outputs over time in a 4 g/L process using split and partial batches.
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

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. A traditional valuation of a retrofit scenario, producing additional material at a lower cost per gram in an upgraded facility.
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.


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