Biopharmaceutical. Planning Pharmaceutical Manufacturing Strategies in an Uncertain World. William C. Brastow, Jr., and Craig W.

Transcription

1 Planning Pharmaceutical Manufacturing Strategies in an Uncertain World William C. Brastow, Jr., and Craig W. Rice Biopharmaceutical manufacturers face one of the most uncertain planning environments in the manufacturing field due to long lead times for adding new capacity and the uncertainly surrounding new product approvals. Coupled with the high cost of biopharmaceutical facilities, this makes forming the best strategic manufacturing decisions a formidable task. The key to addressing this task lies in carefully characterizing the uncertainties and in providing an orderly way to explore their impact. In this article we discuss such an approach that we have developed for analyzing bulk, fill, and packaging capacity strategies at Genentech. Worldwide capacity shortages have forced the biopharmaceutical industry to face new challenges in capacity planning decisions. Roland Andersson, head of Arthur D. Little s North America Health PRODUCT FOCUS: ALL BIOPHARMACEUTICALS PROCESS FOCUS: MANUFACTURING STRATEGY ANALYSIS WHO SHOULD READ: STRATEGIC PLANNING MANAGERS KEYWORDS: STRATEGIC PLANNING, SIMULATION LEVEL: ADVANCED 46 BioProcess International JUNE 2003 Figure 1: Two possible strategies for expanding capacity Industries Practice, writes, The growth of the biopharmaceutical market is going to create critical shortages in some areas of protein manufacturing capacity, with severe consequences. Says David Molowa, managing director, JP Morgan, The main reason we re in this biocapacity crunch is [that] too many people still remember the overinvestment in capacity that led to the crash in the early 1990s. Whether or not there is an oversupply or shortfall of capacity industrywide still remains to be seen. Nevertheless, each individual company must ascertain how much capacity it needs and define its strategy for securing that capacity. In the early 1990s, forecasts of clinical successes and of demand failed to materialize. Executives who made the decision to expand capacity based on more optimistic results were left with unused capacity and little chance of recovering the large amount of capital needed to build it. Clearly, the costs of overestimation can be high, but what about the costs of lost opportunities if you don t have enough capacity? Can a balance point be found somewhere between the extremes? Where is it? SOURCES OF UNCERTAINTY Some of the uncertainties that underlie those questions include evaluating clinical trial results, predicting launch dates, defining market size and share, controlling manufacturing variables, obtaining licensing approvals, and finding available outsourcing capacity.

2 Evaluating Clinical Trial Results: From initial drug discovery through to FDA approvals, many opportunities arise for some or all of a drug s indications to fall out. On the other hand, new indications may be discovered along the way. Predicting Launch Dates for Approved Products: Even though a product is approved, the actual launch date may be uncertain for example, because of marketing considerations. Defining Market Size and Share for New Products: Making accurate predictions about market sizes for new products is notoriously difficult. Furthermore, pinning down the market share for a company s own product is difficult because of the large number of influencing factors such as pricing, sales efforts, and competitive moves. Efficiency of New or Improved Manufacturing Processes: Projecting the process yields and ranges for future products or the yields and ranges for improvements to existing processes are additional sources of uncertainty. Obtaining Facility Licensing Approvals: The length of time needed Figure 2: An example output from one model of bulk demand by product over time superimposed over the total capacity available from all plants to obtain requisite FDA approval is almost always uncertain, particularly for new facilities and processes. Finding Available, High-Quality Outsourcing Capacity: A manufacturer may consider outsourcing as part of an expansion strategy to lower the capital cost of expansion, but the outsourcer s capabilities may not be entirely visible, so this represents an added uncertainty. The risks and stakes for being wrong are high in both directions. The risks due to overbuilding tend to be the most easily quantified because they involve specific costs. They include large capital investments that result in little return; supporting idle facilities with continued overhead dollars; selling or leasing excess capacity not directly part of the manufacturing business; and eventual sale of facilities at a loss. The risks due to underbuilding can be just as great, but they are often harder to quantify because they involve not only lost revenues, but also impacts on intangibles, such as from lost sales; from share gains by the competition affecting long-term sales; from reduced patient survival Figure 3: An example showing that two demand distributions can imply very different risk profiles JUNE 2003 BioProcess International 47

3 Figure 4: A sample diagram developed as a result of individual interviews with team members and others to identify sources of uncertainty, define how to characterize manufacturing strategies, define appropriate levels of detail and metrics, and access probabilities for the uncertain variables from data or from expert judgments to meet demand; and from longterm impact on company reputation and brand equity. rates through inability to meet demand (with the resulting damage to brand image and long-term sales); from delays in expansion of product lines, which can lead to reduced market share; from delays in bringing product to market if clinical capacity is constrained; from lower profitability when outside manufacturing sources must be used QUESTIONS FOR DECISION MAKERS To make appropriate manufacturing capacity decisions, management needs to answer these questions: What is the likelihood of falling short of meeting demand in n years if we don t start building more capacity now? How much additional capacity is needed to ensure that demand can be met? What products are most affected by shortfalls? How variable is excess capacity? How do factory characteristics contribute to capacity variability? How much capacity will be needed for a target of meeting x% of the demand with high confidence? Figure 5: A two-stage Monte Carlo with an embedded assignment of products to plants 48 BioProcess International JUNE 2003

4 How and where should capacity be changed to meet targets and reduce supply uncertainty? In this context of uncertainty, management faces many decision options and concomitantly, many possible strategies for expanding capacity. Figure 1 shows two possible strategies defined by choosing among the available options. Each strategy has different costs, revenues, and risks. To achieve the delicate balance between overbuilding and underbuilding, you need to consider many questions and evaluate each strategy properly. In this context, our task at Genentech was to design a tool that would provide an orderly approach to sorting out the strategic alternatives. GETTING STARTED The first step in addressing the uncertainties and attendant risks is to develop a deterministic model of manufacturing capacity for a given strategy. An important purpose of the deterministic model is to clearly establish all quantitative relationships involved. Typically, a strategy is characterized by numeric assumptions about the facilities that would be open, their operating costs, efficiencies, and capacities for various products. Numeric Figure 6: Example of an Excel wizard allowing a user to modify worksheets, adding or deleting plants and products without damaging the integrity of the worksheets. assumptions are also made about nonmanufacturing elements such as products available, product demand, and pricing. If more than one plant can produce the same product, some method of assigning products to plants must be chosen. This assignment can be made manually when only a few alternatives are being examined; however, manual assignment severely limits the ability to draw a real picture of risk. Figure 2 shows an example of one output from such a model that provides a picture of the amount of bulk demand by product over time superimposed over the total capacity available from all plants. This picture represents the analysis of one particular capacity strategy and one value for each of the numeric assumptions. Often deterministic models are used with quantitative assumptions based on the expected or average values of the numeric data. Using only the expected values for such a model can be misleading when attempting to understand the level of risk, because the expected value hides the uncertainty and therefore the degree of risk in a strategy. Figure 3 shows an example of this problem. Each graph in the figure shows the uncertain demand for a drug. In both cases, the expected demand (23.2 kgs) is the same, but the shapes of the two probability distributions are decidedly different. In the top Figure 7: First graph show output relating to demand: the distribution of demand for all products expressed in a common metric, runs, for The second graph summarizes the probability that the overall demand will not be met. JUNE 2003 BioProcess International 49

5 Figure 8: Showing how changing capacity affects unmet demand by combining the overall demand distribution with the expected capacity for a run Figure 9: An analysis automatically generated by the authors model that shows, for the strategy being analyzed, what the probability is of making a particular Service Level and the probability information about demand distributions and probabilities of demand fulfillment for individual products graph, the probability that demand is only 10 kgs is 10%. In the bottom graph, the probability of a 10-kg demand is 25%. If capacity is designed to meet the expected demand in both cases, the resulting risk profiles are very different. If the demand distribution looks like the top case, then the probability of a 23% shortfall is about 35%. If the demand distribution looks like the bottom case, then the probability of a 23% shortfall is over 50%. Similarly, the probability of ending up with more than 130% excess capacity is 10% in the top graph, but 25% in the bottom graph. Clearly the two demand distributions imply very different risk profiles. Of course, the uncertainties could be explored by running the model many times with different choices for the numeric assumptions. It is 50 BioProcess International JUNE 2003 difficult to organize multiple model runs into a meaningful whole with respect to the questions at hand. In addition, if product/plant assignments have to be made manually for each run, the process becomes tedious and error-prone. To address these issues, we chose a deterministic model as the starting point to design a Monte Carlo model. DEVELOPING A MONTE CARLO MODEL A Monte Carlo model is an extension of the deterministic approach in which each uncertain variable is represented not by a single number such as the average, but by the probability distribution for that variable. A probability distribution includes two pieces of information: a list of values that the variable can take on, and the probability of its taking on that value. The two demand distributions shown in Figure 3 are examples. Because this is a model for manufacturing decisions, it must properly reflect the chain of events that comes before the fermentors begin their work. To this end we formed a team of representatives from product development, marketing, finance, and manufacturing. The team worked together closely to discuss the approach and desired outcome and to provide data needed by the model. So that the team meetings would be productive, a series of individual interviews was conducted with team members and others to identify sources of uncertainty, define how to characterize manufacturing strategies meaningfully, define appropriate levels of detail and metrics, and access probabilities for the uncertain variables from data or from expert judgments. Figure 4 shows a simple diagram of this process. The resulting model had to be organized to take account of the uncertain variables. In addition, our team discussed the type of output analyses that would be most useful for decision-makers. It was extremely important that the simulation model produce a set of orderly, easily interpretable results. ORGANIZING THE MODEL A Monte Carlo works like a collection of roulette wheels, one wheel for each uncertain variable. A model run proceeds by spinning each of the wheels, recording the number where each wheel stops, and using that number as the value for each associated uncertain variable. Calculations are then carried out and their results recorded. If we make only one run, we have a deterministic model. A Monte Carlo model makes many runs, spinning the wheels many times and keeping track of all the results for all the runs. In addition to the questions already discussed, another important question in structuring a Monte Carlo model is, What do we know,

6 and when do we know it? The answer tells us in what order to spin the wheels and in what order to make some of the calculations. For example, because of the timing of when decisions have to be made, you are most likely to know which products have obtained FDA approval before decisions need to be made about assignment of products to the plant for production. It will also generally be the case that the demand for those same products will not be known before initial assignment and production runs must be planned. This timing is accounted for by structuring the model so that at the beginning of each run, the model determines which products and plants will be available for that run. At this point, the model automatically assigns products to plants. This assignment can be done using a method that tries to maximize gross margin or to minimize costs subject to demand and supply constraints that are not known when the initial assignment must be made. In the Genentech project, we used a heuristic method to make this initial assignment and allowed the constraints to be taken from anywhere on the distributions for these variables. Even though these assignments initially must be made before various demand and supply uncertainties are resolved, as time goes by, we typically get more information about the state of the demand and about the operation of plants. The initial assignment is not cast in concrete, so as uncertain information is updated, the model adjusts the assignment using rules of thumb that came from the initial assignment. As in the real world, the model cannot remake the assignments from scratch, because some production has occurred before resolution of the uncertainties. The rules of thumb attempt to mimic, at a high level, what production managers would do as they receive new information about demand and about the performance of their production facilities. Figure 5 shows the structure of this model, formerly known as a two-stage Monte Carlo, with an 52 BioProcess International JUNE 2003 Figure 10: The average overall production at each plant over time Figure 11: An example of capacity analysis aggregated over all plants Figure 12: The average capacity excess(+) or shortage(-) by year. In this example, the capacity strategy is clearly failing to keep up with demand over time. embedded assignment of products to plants. The first stage is the outer loop, and the second stage is the inner loop. Each outer loop typically contains many inner loops. IMPLEMENTATION The model was implemented as a Microsoft Excel add-in. The strategy definition and all the data for a given run were defined on a series of worksheets within an Excel workbook formatted for the purpose. A wizard (see Figure 6) is provided so that a user can easily modify the various worksheets, adding or deleting plants and products, for example, without damaging the integrity of the worksheets. Once the data are edited, the model is run from an added submenu on the Excel menu. As the

7 Figure 13: Input variables that have had the highest impact on total unmet demand for the year 2005 model runs, information is provided to the user about the model s progress. The Monte Carlo model may execute thousands of loops in the course of its analysis. LOOKING AT MODEL RESULTS DEMAND The purpose of the Monte Carlo was to systematically explore the interactions of many probability distributions and to display the results in a way that could clearly answer the questions that we posed earlier. Each run of the model concludes by creating a large number of charts that have been designed with the initial questions in mind. Figure 7 shows output relating to demand: the distribution of demand for all products expressed in a common metric, runs, for This distribution comes directly from the inputs, but observing how this distribution changes over time can be enlightening. The second chart shown in Figure 7 summarizes the probability that the overall demand will not be met. Instead of the single number for unmet demand that would come from a deterministic model, we have a 54 BioProcess International JUNE 2003 distribution that results from the interactions of all the uncertainties we accounted for. One way to approach how changing capacity affects unmet demand is to combine the overall demand distribution with the expected capacity for a run. Figure 8 shows an example of such analysis. The green area of the chart is that portion of the distribution of overall demand for all products that was less than or equal to the expected overall capacity. Both demand and capacity are expressed in common units of runs. In this case the manufacturing strategy was designed so that the expected overall capacity would equal the expected overall demand. Note, however, that even though this is the case, there is greater than a 60% probability that demand will exceed capacity. The concept of service level is a very useful tool for establishing manufacturing commitments in an uncertain environment such as this one. Service level is defined here as the percentage of demand that will be met by manufacturing. Significant cost and revenue implications are associated with service-level commitments, and given the uncertainties, a particular service level cannot necessarily be met with certainty. A key question is, therefore, How much uncertainty is associated with a particular service level? The chart in Figure 9 illustrates an analysis automatically generated by our model that shows, for the strategy being analyzed, what the probability is of making a particular service level. For example, in the chart, the probability of meeting just 50% of the demand is about 82%. The chance of meeting 80% of the demand is only about 25% probably not a satisfactory strategy. Revenue and cost information can be used to convert the service level axis to a gross margin, thus providing a powerful picture of the financial impact of the strategy. In addition to the analyses of overall demand shown here, the model also provides probability information about demand distributions and probabilities of demand fulfillment for individual products. LOOKING AT MODEL RESULTS CAPACITY The model outputs also include detailed analyses of the impact of uncertainty on capacity use in the various plants involved. Just as in the demand analysis, results are available at aggregate levels or for individual plants and products. Figure 10 shows the average overall production at each plant over time. Although average value analyses contain no information about risks, they can be useful for summarizing the behavior of the strategy over time. We have discussed analyses of the probability that the strategy can meet demand. Another perspective from the capacity point of view is the probability that plants run under or over capacity. Figure 11 shows an example of such analysis for capacity aggregated over all plants. In this example for the year 2008, capacity, on the average, fails to meet demand by about 42 runs. On the other hand, some degree of

8 overcapacity occurs about 50% of the time. Another feature of this particular example is that the probability of shortfalls in capacity (the negative values) above 159 runs is about 8%, with most of the shortfall falling below 94 runs. Attaching costs and revenue or lost revenue opportunities to these results can provide important financial guidance. We mentioned that average values, although hiding risk information, can be useful. Figure 12 shows the average capacity excess(+) or shortage(-) by year. In this example, the capacity strategy is clearly failing to keep up with demand over time. As in the demand charts discussed previously, the simulation model also provides more detailed information by product and by plant on the capacity side. Figure 14: A sensitivity analysis illustrating the variables to which total capacity excess/shortfalls are most sensitive LOOKING AT MODEL RESULTS SENSITIVITY In our example, the capacity strategy has an increasing risk of failure to meet demand as time goes by. The strategy planner now has to answer the question, What aspects of this strategy could be changed to lower its risk of not meeting demand targets? Given the number of variables and interactions embodied in the model, this would seem to be a formidable task. Indeed, it is a formidable task when you try to address it with a deterministic model by changing inputs and making additional runs. The simulation model, however, has already generated many runs, automatically selecting many different inputs to make the probability statements. By saving these data, the model has a rich database that planners can exploit to help find their way through the thicket. Some of the changes have little or no effect on the outcomes. Others have significant impact. The model performs a statistical analysis of the data to determine which are most correlated with the outcomes we are interested in and the degree of their impact. Figures 13 and 14 show examples of this. In Figure 13, the model displays those input variables that have had the highest impact on total unmet demand for the year The length and direction of the bar represent the degree and direction of the impact on total unmet demand for that year. In this case, the largest impacts come from supply-side parameters, suggesting that adjustments could be made to the manufacturing strategy to reduce risk. Figure 14 shows a similar sensitivity analysis illustrating the variables to which total capacity excess/shortfalls are most sensitive. Such tornado charts are very useful tools for directing planners to those aspects of a current strategy that can be changed to improve its performance and reduce its risk of failure. HOW IS THE MODEL BEING USED AT GENENTECH? The strategy simulation model described here was designed to answer questions such as those we asked at the beginning of the article. It has proved to be a flexible tool for analyzing alternative capacity strategies. The model has provided us with improved insight into the impact of uncertainty in capacity decisions. During the time this work was undertaken, corresponding author William C. Brastow, Jr., PhD, was with Standard & Poor s Applied Decision Analysis; he is currently principal of Planalytics, 1175 Greenwood Avenue, Palo Alto, CA 94301, , wbrastow@earthlink.net. Craig W. Rice, PhD, is senior director of strategic planning in product development, Genentech, 1 DNA Way, South San Francisco, CA 94080, rice.craig@gene.com JUNE 2003 BioProcess International 55