Garbage In, Garbage Out: The Case for More Accurate Process Modeling in Manufacturing Economics

ABSTRACT

Current biotech manufacturing plants are capital intensive, with yearly depreciation costs sometimes as high as raw materials costs. In such an environment where indirect costs dominate direct charges, 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 large-scale biopharmaceutical plant. In this case, modeling the altered unit operation in the context of the existing unit operations was essential to establish accurate throughput metrics and overall valuation. We argue that such process-focused economics models are essential in the biopharmaceutical industry.

The 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 a 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 difficult in this context for a number of reasons.

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First, retrofit options typically involve value destruction as well as value creation. Although 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, such as, steam and clean in place (SIP/CIP) 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 flexibility 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 run-rate–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.

Figure 1. Yearly operating cost breakdown for a typical mammalian cell-based biotech plant1

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 shut down 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 biopharmaceutical 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.² 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.

Figure 2. A comparison of the economic effect (NPV: net present value) of calculation errors2

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 manufacturing plants. With the rapid advances in cell culture and fermentation technology, many plants suffer from a bottleneck in downstream (purification) operations. A project with a major biopharmaceutical company was undertaken to establish the most cost-effective means of producing 4 g/L products in a facility designed for the 1–2 g/L range. Although a number of alternatives were considered, the two most practical options were determined to be split batching, in which the fermentation volume is split in two at harvest and processed in two separate lots and partial batching, in which a reduced fermentation volume is prepared, exactly enough for the downstream capacity. Both required similar infrastructure investments and resulted in similar downtimes for retrofit.

The economic comparison of these two scenarios was therefore predicated on their comparative run rates and manufacturing production profiles. However, there was considerable disagreement from subject matter experts on which of the two scenarios was better. Batch-splitting could be performed after the Protein A recovery step, a possible plant bottleneck, but it was unclear whether the material would exceed time at ambient specifications if required to wait for the other half of the "split" batch to complete processing. Partial batching produced less material per batch, but seemed to maximize the downstream plant's capacity for each batch.

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 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 facility.

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 of vital importance in establishing correct metrics around the economic benefits of any scenarios considered.

Rick Johnston is co-director at the Center for Biopharmaceutical Operations, University of California, Berkeley, and David Zhang is principal at Bioproduction Group, Inc. Berkeley, CA, 650.823.7533, rickj@berkeley.edu

REFERENCES

1. The University of California at Berkeley. Center for Biopharmaceutical Operations; 2009. Available frome: http://cbo.berkeley.edu.

2. Numbers indicative only.

3. Bioproduction Group. Indicative times only, not from any specific single biopharmaceutical manufacturer. Comparative durations for times retained, X and Y scales normalized. Processing step includes inline testing. Rejected batches removed.

4. Yang A. Genentech Simulation System. CBO Workshop; 21 May 2008. Available from: http://cbo.berkeley.edu.