When a biologics company prepares to launch a new product, it must forecast the manufacturing capacity it will need. To create this forecast, it must factor in its estimate of the size of future sales, the timing of the launch, the dosage of the product, its strategy for building its market and a host of other variables. Variations in any one of those factors can lead to drastically different demand scenarios. If a company overestimates demand, it may end up investing in too much capacity, and therefore find itself paying more per unit of the product than it needs to, thus impacting its margins. If it underestimates demand, it risks not being able to satisfy demand, therefore losing revenue.
Forecasting demand is a complex endeavor. For instance, it’s not unusual for the forecasted and actual dosage of a product to vary by a factor of as much as three. Obviously, that makes a big difference to a demand forecast. If a manufacturer has built capacity in anticipation of a new product and its clinical trial is delayed (for any number of reasons), that manufacturer’s capital is tied up in a fallow facility. For a small company for which liquidity is critical, that can be catastrophic.
Why forecasting is so hard
When planning for capacity, a manufacturer must consider both volume and scale. Both are important, the effect of scale being to make costs non-linear with volume. For example, say a manufacturer makes a 2,000-liter subculture batch, at a cost of about $1.5 million to $2 million (including raw materials). For a titer of 1 gram per liter, that’s about 1.6 kilos of active pharmaceutical ingredient (API), with yield losses, at a cost of $1,250 a gram. A 20,000-liter batch, which would cost $4 million, would yield about 16 kilos of product at $250 a gram. That’s 80% less than the 2,000-liter batch. Much more cost effective.
However, at the 20,000-liter batch size, the sponsor could end up with too much product, some of which may expire before it can be sold. That’s a loss. Further, the commitment to large-scale capacity is very expensive, whether in-house or via a contract development and manufacturing organization (CDMO). Large-scale plants cost hundreds of millions of dollars, and many CDMOs require long-term commitments for large scale.
On the other hand, the manufacturer could run a batch only every few years at large scale to guard against producing too much product. However, that might create scheduling issues and degrade the effectiveness of the manufacturing organization; changeovers in large-scale plants can be very expensive. The manufacturer also risks losing inventory, not just due to product expiration, but also due to latent defects, issues that may only become apparent after several years. Plus, one would still need three to five validation batches, which being large, would be expensive.
Of course, at a smaller scale of production, each unit costs more.
Large or small, the approach one chooses will have a ripple effect throughout the business, affecting hiring decisions, cash flow, schedule duration, available capacity, cost of goods and on and on. And the scarcity of outsourceable capacity at certain scales (for example, 20,000-liters) further complicates the process – all of which sometimes leads companies to choose a solution that is suboptimal.
Click here for the prescription to navigating demand complexity in biologics production.