Case Article—Bayer New Drug Development Decision Making

1. Introduction

“Bayer New Drug Development Decision Making” is an interactive case study developed from a real application of deciding whether to develop a new drug to treat blood clots in legs (Stonebraker 2002). The goal of the case is to improve a student’s analytical, modeling, and consulting skills by engaging students with a complex, unstructured decision under uncertainty. Student engagement or active learning is a key pedagogical underpinning of the case. The interactive case study is the consummation of a semester-long elective MBA course, entitled Decision Making Under Uncertainty. The interactive case study requires student teams to apply a structured analytical process to evaluate the case. Each student team serves as decision consultants, whereas the instructor serves as the decision maker, subject matter expert (SME), and coach for the student teams.

New drug development is time consuming, resource intensive, risky, and heavily regulated. On average, it takes 10–15 years to research and develop a drug with a cost of approximately $1 billion (DiMasi and Grabowski 2007). Drug development is a stage-gate process consisting of a sequence of decision gates or points. During a stage, information is gathered prior to the next decision point. At each decision point, the technical feasibility and market potential of the new drug are evaluated and these results are used in deciding whether development should continue. Decision analysis has proven to be of tremendous value in resolving complex real business decisions (Corner and Kirkwood 1991, Keefer et al. 2004), especially in drug development (Sharpe and Keelin 1998, Beccue 2001, Stonebraker 2002, Johnson and Petty 2003, Poland 2004, Viswanathan and Bayney 2004). To be most effective, the application of decision analysis to drug development requires a structured interaction between a cross-functional project team and its senior management, with periodic exchanges of key deliverables (Bodily and Allen 1999).

This case focuses on whether Bayer should begin development of its new drug BAY 57-9602 or “Plasmin” (Stonebraker 2002). Bayer anticipates that Plasmin will offer a new paradigm in thrombolytic drug therapy of peripheral arterial occlusion by directly dissolving blood clots in legs, resulting in potentially safer, more-effective treatment than currently available. To ensure that it makes the best new product development decisions, Bayer uses a structured process based on the principles of decision analysis, called the decision analysis cycle (McNamee and Celona 2008), to evaluate the technical feasibility and market potential of its new drugs. The four steps of the decision analysis cycle are (1) basis development, (2) deterministic structuring, (3) probabilistic evaluation, and (4) basis appraisal. The interactive case study requires student teams to apply the decision analysis cycle to evaluate Bayer’s Decision Point 1 of Plasmin (Stonebraker 2002). Student teams are responsible for managing the decision analysis cycle, facilitating the framing of the project, collecting data from the SME (instructor), constructing the Excel model, assessing probabilities for the most-sensitive uncertainties from the SME, evaluating the Excel-based decision-tree model using Decision Programming Language (DPL) (Syncopation Software 2020), and presenting evaluation results and recommendations to the decision maker (instructor). I purchase an annual academic department site license for DPL from Syncopation Software. This is no cost to the students. Other decision analysis software (e.g., Palisade @Risk, Oracle Crystal Ball) are alternatives to DPL.

2. Teaching Objectives

The goal of the interactive case study is to improve a student’s analytical, modeling, and consulting skills by providing students with the opportunity to apply these skills to a real-world decision under uncertainty. By the end of the interactive case study, students will learn how to (1) identify and frame key issues of decision; (2) obtain decision-relevant information in an unbiased manner; (3) create equations in Excel that capture the key issues of the decision; (4) analyze, evaluate, and compare each alternative; and (5) present a cogent case to gain commitment to action.

3. Classroom Experience

The interactive case study culminates a semester-long elective MBA course in which the students learn the principles of decision analysis in the first half of the course using simple problems and practice these principles in the second half of the course using realistic problems, with the last four weeks of the semester dedicated to the case (Table 1). All course materials (lectures, problem sets, and labs) are available upon request. The textbook is Decision Analysis for the Professional by McNamee and Celona (2008). The interactive case study simulates an actual consulting engagement (Stonebraker 2002) where each team of two or three students interacts with the instructor using the decision analysis cycle. Since 2010, I have offered Decision Making Under Uncertainty 31 times to 870 graduate (mostly MBA) students face-to-face and online in the fall, spring, and summer semesters. I have used the interactive case study in Decision Making Under Uncertainty for 18 face-to-face offerings to 449 students and 3 online offerings for 85 students. I have not used the interactive case study in 10 online offerings for 336 students because most of these offerings were during the shortened summer session of 10 weeks.

Table 1. Course Schedule for Decision Making Under Uncertainty

Table 2 is a roadmap of case activities to guide the instructor and student teams. I discovered that providing students with a roadmap of the project schedule is extremely helpful for student teams to complete the case in a timely manner (four weeks). The actual application that this case is based on was completed in three weeks by a senior consultant who was given unlimited access to the schedules of the SMEs on the cross-functional project team (Stonebraker 2002). Typically, this type of consulting engagement in the biopharmaceutical industry would take three to four months to complete (Stonebraker 2013). In the roadmap (Table 2), each step of the decision analysis cycle is organized by the timing of the activities (premeeting, meeting, and postmeeting). All premeetings require student teams to provide an agenda and schedule the meeting with the decision maker and/or SME (instructor). All postmeetings require student teams to summarize key takeaways from the meeting to the decision maker and/or SME.

Table 2. Roadmap of Activities for the Interactive Case Study

Instead of a classroom lecture, the instructor meets with each student team weekly for 15 minutes during weeks 1, 2, and 3 of the interactive case study (corresponding to the first three steps of the decision analysis cycle). There can be additional meetings if necessary. In week 4 of the case (corresponding to the last step of the decision analysis cycle), student teams submit case deliverables electronically to the instructor. I found that the 15-minute meeting provides a good assessment of students’ progress. Like an oral exam, the 15-minute meeting helps the instructor assess the students’ depth and breadth of knowledge of the concepts learned in the course and is particularly effective in distinguishing the analytical, modeling, and consulting skills of a student. The 15-minute meeting is also practical training ground for the real world. Besides the roles of decision maker and SME for the case, the instructor mentors students on how to analyze, model, and consult much like the relationship of a senior consultant mentoring a junior colleague during a consulting engagement.

In week 1 of the interactive case study, students seek to understand the decision basis (decision alternatives, uncertainties, constants or assumptions, determined values or equations, and the value proposition) and obtain decision-relevant data. Before Meeting 1, the instructor provides students the case description, PowerPoint template, and Excel template. The description of the case encourages discussion with student teams in clarifying the complexity of the case and the variables and their interrelationships. Most students are ready to jump right into data collection before understanding the decision context. By design, the case description is an overview of the decision with little data provided to ensure students seek to first understand the context of the decision. The PowerPoint template provides instructions for the student teams to complete the case. The Excel template is a framework that promotes model consistency among student teams. These templates are extremely useful for learning and keeping grading manageable. After Meeting 1, the instructor provides student teams with the data and equations because student teams will not have enough time to collect all of the data and ask questions to clarify equations.

In week 2 of the interactive case study, student teams construct an Excel model that logically describes the interrelationship of the decision basis, enter the collected data into the Excel model, and determine the most-sensitive uncertainties from one-way sensitivity analysis (tornado diagram) by linking their Excel model to DPL. Given the complexity of the case, constructing the Excel model is the most time-consuming part of the case. After revising their Excel model, students conduct sensitivity analysis and generate a tornado diagram using their Excel-based DPL model.

In week 3 of the interactive case study, student teams review the tornado diagram with the decision maker (instructor) at Meeting 3 and obtain probability assessments on the top two most-sensitive uncertainties from the SME (instructor) using probability-encoding methods (Spetzler and Staël von Holstein 1975, Shephard and Kirkwood 1994). After obtaining the encoded probabilities, students revise the Excel model with the new 10-50-90 percentile values (Keefer and Bodily 1983) for the most-sensitive variables, create a decision tree in DPL, evaluate the decision tree to determine an expected net present value for the go and no-go alternatives in the case, and generate a cumulative distribution function for each alternative.

In week 4 of the interactive case study, student teams make recommendations to the decision maker (instructor) and provide insight. Student teams electronically submit their case deliverables (PowerPoint, Excel, and DPL files) to the instructor. Case deliverables are due during final-exam week.

4. Teaching Suggestions

The target audience for the interactive case study in the Decision Making Under Uncertainty course is MBA students, both full-time and part-time, who take the course face-to-face or online. Graduate students in industrial and systems engineering have also taken the course. Over the years, a few PhD students have taken the course. In fact, one doctoral student in the Department of Forest Biomaterials (College of Natural Resources) took the MBA course and later the author joined her dissertation committee as a coadvisor. I have not offered the interactive case study in an online summer term because it is only 10 weeks. However, Regan (2005) has offered a five-week course in professional decision modeling. I have taught an undergraduate version of Decision Making Under Uncertainty without the interactive case study five times to 94 undergraduate students, mostly business management students but a few industrial and systems engineering students. Undergraduate business and engineering students would require a scaling down of the current interactive case study (Kopcso et al. 2016).