Recently, Blue Matter conducted a series of one-on-one interviews with 14 biopharma executives and R&D leaders. The primary goal was to identify the key success factors that they think are essential for building a “Best Practice Organization” (BPO) in R&D. We summarized their insights in a report. The interviewees identified five key success factors. One of those factors was the need for “smart risk-taking and decision making,” which was closely intertwined with other success factors, such as the need for more strategic portfolio prioritization. Incidentally, we also conducted a similar set of interviews with 24 commercial leaders. They provided similar insights, ranking better, faster, and more insight-driven decision making in their top five most important factors.
Their input reinforced the rather common-sense notion that good decision making is critical to success. So, doesn’t it stand to reason that biopharma leaders should strive to elevate their approaches to decision making, thereby maximizing the likelihood of achieving their goals and generating the best possible outcomes? Of course it does, and decision science (DS) is a field of discipline that aims to do that.
In this article (part 1 of a 2-part series), we provide an overview of decision science within the context of the biopharma industry. Specifically, we define “decision,” contrast good and bad decision-making approaches, define DS and explore how its two main branches interrelate, and outline key ways DS can be applied within the biopharma enterprise. In part 2 we describe a framework for using DS to enhance decision making, sharing some details and illustrative examples along the way.
What Is a Decision?
As we dive in, it’s essential to define what a decision is. Simply put, a decision is the act of choosing among alternatives. A person or organization can sometimes make a decision about something and then change it later. However, no decision is truly final until the resources to implement it have been committed.
In most cases, the entity making a decision is attempting to achieve some desired (“good”) outcome. Anyone with any degree of life experience knows that decisions do not always turn out as hoped or expected. The decision maker typically tries to make a “good” decision that will lead to the desired outcome. But sometimes, good decisions turn out badly. The flip-side is also true, as poorly made decisions can sometimes result in positive outcomes. This is because there’s usually some level of uncertainty associated with any decision and there are almost always elements that are beyond the decision-maker’s control.
The key is to employ an approach to decision making that yields the best decision possible, given the information that was available at the time. That should maximize the likelihood of success. All this leads to an important question…
What Is a Good Decision?
Here, it’s important to articulate what “good” looks like. In other words, what are the differences between good and bad decision-making processes? The table below compares two different approaches: a bad one (advocacy-based decision making) and a good one.
| Decision Problem – Stakeholders view the decision as addressing a problem that must be overcome. | Decision Opportunity – Stakeholders view the decision as offering a potential opportunity to advance, improve, or succeed. They begin by articulating the opportunity. |
| Positioning – Stakeholders focus on how to solve the problem, formulating potential solutions. They may develop multiple opinions and positions on the matter. Psychological biases—which all humans have on some level—can cause stakeholders to miss or ignore key information that might contradict their own views. | Define Objectives – Based on the opportunity before them, stakeholders discuss and align around a common set of objectives. At this stage, they focus on outlining what they want to achieve rather than how to achieve it. This is where they define what success looks like. |
| Arguing – As stakeholders’ ideas take shape more clearly, they begin to argue for their respective positions. | Identify Alternatives – Stakeholders identify and articulate multiple potential pathways for achieving their stated objectives. |
| Camps – Stakeholders generally congregate into two or more opposing camps, each with its own preferred solution. | Assess Consequences – By drawing insights from the available information, stakeholders attempt to understand the risks, as well as the pros and cons of each alternative pathway. |
| Compromise / Power Play – Ultimately, the camps engage in a process of “give and take” to arrive at a final decision. That decision is most often sub-optimal and based more on political considerations, the strength of the personalities involved, and “back-room deals” rather than an objective review of the available facts. | Evaluate and Decide – Decision makers reflect on their own preferences and the sacrifices they are willing to make. Leveraging an objective, data-driven evaluation of the alternatives, they decide on the one that is most likely to deliver an outcome that aligns with their priorities. |
The table should not give the impression that a “good” approach to decision making is devoid of disagreements and debate. After all, there can be spirited debate even at an early stage, such as when defining the objectives. However, the general approach is a logical and systematic one that is designed to facilitate a much more objective and data-driven process. It serves as a forcing function to make the team align at several points: such as defining the objectives, identifying the alternatives, and so on. It’s also designed to account for—and help overcome—common human biases that can prevent objective consideration of the facts at hand.
How can Decision Science Help Achieve a Good Decision?
Decision Science is a broad term that refers to the use of scientific principles to help make smarter, faster, and more data-driven decisions. As mentioned earlier, the goal is to maximize the likelihood of achieving desired outcomes. There is no single or standardized way to “do” decision science, so there can be significant variability in the processes, tools, and approaches that different people and organizations use.
Broadly speaking, there are two main branches in DS: Descriptive and Prescriptive.
Descriptive DS is highly observational and focuses on building a detailed understanding of how decisions are actually made. It relies heavily on psychology to gain insights into the impact of human biases and other factors on real-world decision-making patterns.
Prescriptive DS is a bit different. Whereas Descriptive DS is concerned with how decisions get made, Prescriptive DS focuses on how they should be made. It leverages a range of methods and tools such as mathematical models, simulations, optimization models, and decision frameworks to provide practical recommendations for making better decisions.
While we’ve described the two branches as if they are separate disciplines, it’s important to understand that they are closely interrelated. Decision Science actually thrives on the synergy between them. The goal is to leverage psychological and behavioral insights regarding biases and human judgment—which descriptive approaches provide—to enhance the more analytical, data-driven prescriptive methods. That synergy is what ultimately leads to the best decisions.
Applying Decision Science Within Biopharma
Within biopharma companies, many decisions get made every day and at all levels. While any decision can benefit from a more systematic and objective decision-making process, there are several areas in which it can be extremely valuable.
Corporate, Portfolio, and Product Strategy
A DS-driven approach can be critical when defining a company’s overarching vision and strategic priorities, as well as identifying the most appropriate pathway for building corporate value and long-term success. This is especially important for emerging companies who may be seeking to determine whether to partner out their core assets, become a fully integrated company with commercial capabilities, or to follow a combination of pathways across different markets.
Likewise, a systematic approach is imperative when formulating portfolio strategies and / or therapeutic area and franchise strategies. Such efforts require a detailed understanding of corporate priorities, coupled with a keen awareness of the trade-offs that are required when allocating finite resources across a range of assets. A dispassionate, data-driven assessment of the implications of those trade-offs is essential to optimizing overall value.
Even at the product strategy level, DS can be essential. After all, decision makers must optimize the potential of each product or asset by identifying the right development, commercialization, and lifecycle strategies. Doing that effectively requires a “good” approach to decision making.
Finance and Decision Support
Even after the broad strategic decisions are made, a scientific approach can be extremely valuable when it comes to supporting downstream decisions. For example, if a company has decided to focus on being an R&D engine, out-licensing its assets as they reach a certain point in development, then it still must make key decisions around evaluating potential partners, developing or considering deal terms, and so on. DS can support a wide range of decisions across business development and licensing, forecasting and scenario planning, risk management and mitigation, and capital investment.
Operational Excellence and Governance
Of course, effective decision-making processes are needed for individual business decisions. But those processes don’t just pop into existence out of thin air, with leaders automatically knowing what they are and how to use them. Instead, they must be defined, enabled, and supported within an organization. That way, decision makers can tap into them and apply them effectively.
That’s why it’s critical that they get “baked into” a company’s governance, business planning, and portfolio management processes (just to name a few). Of course, for any given strategic decision, a third-party consultant can introduce such DS-driven approaches. However, companies should incorporate the right approaches and tools into their processes for the long-term. Doing so requires a concerted effort and some change management work, but it can pay big dividends.
Coming Next
To this point, we’ve taken a general, high-level look at decision science and its applications within biopharma. In the next installment, we’ll go into more detail regarding a specific framework for applying decision science. We’ll share some concrete examples to make these general concepts come alive within a relevant context. To read part 2 when it becomes available, check our blog regularly or follow us on LinkedIn. If you have specific questions or would like to discuss how decision science might support your own company’s strategic needs, then please contact us via our website.
