Scott Chizzo, President and Chief Consultant at Maxiom Group (http://www.maxiomgroup.com/), has seen plenty of uncertainty in pharma and biopharma production planning, and answers our questions about how to deal with it.
PhM: When we talk about “uncertainty” in regards to production planning at pharma and biopharma companies, what are we talking about?
S.C.: Great question. First, I would like to differentiate between “uncertainty” and “risk” as they are often used interchangeably but they are not the same. Risk refers to negative events that could potentially happen, to which we can assign probabilities and likely impact. Uncertainty, on the other hand, refers to a lack of knowledge about the future (and isn’t necessarily negative). Risk can be mitigated, avoided and prevented by actions we take, whereas uncertainty is inherently out of our control. The best we can do with uncertainty is to plan for it. Now, they are both related, in the sense risks are often outcomes of uncertainty.
We like to think about uncertainty in the life sciences industry in four buckets—clinical, commercial, operational and regulatory uncertainty (see Box, for examples). The common thread among them is that business managers are forced to make decisions on these topics without having all of the information. In fact, it is impossible for them to have the information because they are dealing with future outcomes which are inherently uncertain, unknowable and usually far beyond their immediate control.
|Categorizing Production Planning Uncertainty
By Scott Chizzo, Maxiom Group
• Will the FDA approve our IND/NDA/etc., and by when?
• Will the product end up being safe and effective after clinical testing?
• What will it take to achieve our projected enrollment targets and schedule?
• How will global regulatory requirements impact our trial?
• Will the expected market demand materialize?
• How accurately can we forecast demand in collaboration with Commercial Operations?
• Do we have adequate demand and supply planning processes?
• What unforeseen market (upside or downside) events could impact our supply planning?
• How do we plan and budget given the uncertainty?
• Do we have the manufacturing capacity (either in house or outsourced) to meet demand?
• Can production be scaled effectively to meet demand?
• How stable are our suppliers (raw material to production)?
• What if unexpected manufacturing or supply issues arise?
• How can changes in global regulations change the production planning picture?
PhM: For those charged with production planning, what are the risks in ignoring these uncertainties, or perhaps not understanding them fully?
S.C.: Uncertainty results in risk because decisions must be either made earlier, with less information (at the risk of making poor decisions), or delayed, until more information is available (at the risk of missing opportunities). So, let’s talk about three typical production planning scenarios which make it difficult for life sciences companies to manage through uncertain times.
Unlike some industries, biopharma companies traditionally have long supply lead times, starting with raw material sourcing and continuing through API/drug substance production, drug product production, and packaging/labeling/distribution. Long lead times create a slow response to market changes (ultimately a business risk) such as fluctuations in demand (a type of commercial uncertainty).
One common strategy to mitigate demand or supply uncertainty is to hold large inventories at various stages along the supply chain, essentially reducing the risk of stock-outs and shortages. However, if not managed appropriately, this conservative strategy can lead to high cost of inventory and misallocation of resources (e.g. a financial risk).
For longer-term planning, making decisions about manufacturing capacity is a great example. Building out new capacity requires significant lead time and upfront investment and these decisions, by nature, must be taken amid high levels of uncertainty. It is hard to foresee what your market demand will be by the time the new plant is up and running and therefore make smart, risk-based investment decisions.
PhM: Have you done any work to quantify the negative impact of these scenarios? Or could you provide us with some idea of just how costly uncertainty can be?
S.C.: The short answer is yes. Life sciences companies struggle with decision-making under the uncertainties described above all the time. Uncertainty can be very costly. Consider an emerging pharma company we recently worked with to prepare for commercial launch of their first product. As typical for a company in their situation, they had built infrastructure, staffed up and established supply partnerships, not to mention the expenditures for clinical trials. So what happened? Their product was unexpectedly not approved by the FDA. Ultimately people were laid off, much of the work abandoned, shareholder value disintegrated and the company acquired. And that’s just one example.
PhM: How does product supply uncertainty vary across the product development lifecycle?
S.C.: In the clinical development stage, uncertainty is primarily driven by unknown clinical trial and regulatory decision outcomes. Many investments and commitments must be made prior to proof of concept and before market approval in order to be ready for an uncertain product launch, including process development, analytical testing, clinical supply, commercial product configurations, and investment in commercial production facilities and/or partners.
Commercial uncertainty, on the other hand, is primarily driven by market conditions. The commercial organization must be ready to quickly respond to scenarios such as competitor products suddenly gaining or losing market share, impact of patent expiration and generic competition, adverse events, off-label usage and follow-on indications. Some of these situations are more predictable than others, but all contain inherent uncertainties. A secondary source of uncertainty might be around stability of their suppliers.
Ultimately, neither of these observations should be all that surprising—R&D is speculative and dependent on safety, efficacy, and regulatory decisions, whereas commercial operations depend on actual market conditions and ability to maintain supply. Once you’ve recognized this, the challenge lies in what to do about it.
PhM: You’ve worked with various small and large drug manufacturers—how do challenges differ for companies in different stages of maturity?
S.C.: The primary differences between these different types of companies is how equipped they are to plan for uncertainty and how nimbly they can adapt to changing conditions. Emerging companies may lack experience and robust internal processes for understanding and managing through these uncertainties. Three examples we frequently encounter include lack of adequate processes for accurate production planning, limited control or visibility over supply chain (e.g. due to significant outsourcing), and smaller portfolios of lead candidates to fall back on if one fails.
Established companies, on the other hand, often have these systems in place but may lack the flexibility to quickly respond to unexpected change. For example, often these companies may feel locked into technologies, facilities, staffing models, decision-making processes, and cultural norms. Also, over time, processes and systems can become outdated, inefficient and difficult to improve.
PhM: What are basic strategies that all production planning professionals can adhere to in handling these uncertainties?
S.C.: There are several essential steps that we recommend. First and foremost is to acknowledge the uncertainty facing the company and the challenges it has historically experienced in dealing with them. Then you can get down to business. Start by defining the scenarios and identify key variables and unknowns and then quantify the risks and opportunities. Next, include uncertainty considerations in strategy development and decision-making. In doing that, consciously build in flexibility for adapting to changing conditions. Finally, it is important to periodically re-evaluate strategies and decisions as more information becomes available.
PhM: What are some broader operational best practices that companies can adopt for minimizing the impact of uncertainty?
S.C.: Readiness for uncertainty and change goes far beyond the planning function—it transcends to the organization’s ability to deliver according to plan and react to changing conditions. With that in mind, there are many smart ways that companies can prepare. We often encourage our clients to focus in three key areas: supply chain reliability and flexibility, responsive internal sales and operations planning (S&OP) processes, and proactive risk monitoring. In particular, we often recommend applying Operational Excellence principles (Lean, Six Sigma, industry best practices, etc) for cycle time reduction, yield improvement and efficiency gains. Excellence in S&OP requires strong internal process oversight, real-time supply visibility, timely market information, frequent re-planning, and strong supplier partnerships. In addition to the focus on executing the daily work well, we are seeing an increasing interest in formal risk management programs, as companies recognize the need to proactively prepare for potential damaging events.
PhM: Any final thoughts?
S.C.: Planning under uncertainty really boils down to “how can I make good decisions without having all of the information?” and “how can I make myself the most prepared for a variety of potential outcomes?” Start addressing these questions today to improve the forward-looking strength of your business.
|Tools for Tackling Uncertainty in Production Planning
By Scott Chizzo, Maxiom Group
Scenario Planning: This considers several possible future states and enables comparison without the need for sophisticated analytics and resources
Decision Tree Analysis (DTA): Tree-like analysis of decisions and their possible consequences, including chance event outcomes, resource costs, and utility
Discounted Cash Flow (DCF) and Net Present Value (NPV): Methods of valuing a project, company, or asset using the concepts of the time value of money, estimated and discounted to give their present values
Expected NPV (eNPV): Scenarios are defined to represent different outcomes, and each scenario is assigned a probability. A net, combined expected value is computed.
Simulation Modeling: Uses sophisticated analytics and resources to quantify the degree and range of uncertainty when multiple variables interact. Monte Carlo simulation is one such example.
Real Options Analysis: Moves beyond understanding risks, to develop flexible plans that can shift as conditions change, assuming a dynamic series of future decisions