Innovation is at the heart of the life sciences where firms spend up to 25% of their revenues on R&D. The last decades R&D productivity has declined, and open innovation has become a way to curb costs, shorten development times and reduce uncertainty in R&D through collaborations between large firms and university-based scientists (Sarah Gilbert’s Oxford team and Astra Zeneca) or biotech firms (BioNTech and Pfizer). Decisions about what technologies to develop in-house and which ones to out-source, and what partners to choose are essential for established firms; while intellectual property protection and commercialization partner choice are critical for smaller firms. Other essential decisions concern digitization and AI that offer cost savings both in R&D and manufacturing, but there is uncertainty about their exact benefits and when and what version to invest in.
This course is addressing innovation decisions from a strategic perspective. Learning about empirical regularities in how technologies evolve under competition will help you understand and make better-informed decisions in fast-paced R&D environments, both from a producer and a customer perspective.
The course addresses four large areas relevant for innovation in the life sciences: (1) Technology models that explain how technology evolves from discontinuities, into design competition and incremental stages with the objective to make better decisions about whether, when and what version of new technology to invest in. Life-sciences examples we discuss are machine learning and digitization’s roles in lab work and clinical testing, and technologies for production scaling. (2) Eco systems and different actors’ roles in the system. We discuss innovation sources such as university-based scientists, using the crowd, and collaboration between large and small firms. (3) Firm strategies with a special focus on the combination of intellectual property and the control of market access. How do you combine multiple IP tools to maximize invention protection? What is the best small firm entry option for a new product or service in, for example, MedTech? What possible collaboration strategies are there in your eco system? (4) Forecasting is essential when making investment decisions and we explore a broad portfolio of traditional and recent models (prediction markets and AI).
Each day you apply course models to a burning problem your face at work or an innovation-related issue you want to explore more deeply in a tutorial session.
Key themes in the course
- Open vs. closed innovation approaches
- Open innovation management and processes
- Distributed sources of innovation
- Open innovation methods for outbound, inbound and coupled innovation processes
- Organizational antecedents of open innovation.
- Calculation of the value of innovation
Your learning outcome
- Develop a sound understanding of the open innovation approach
- How to effectively search across and collaborate with different sources of innovation (internal and external to an organization) to develop innovations
- Create and capture value in open innovation
- Design and implement open innovation activities in firms.
Who should attend this course
The course is relevant to managers from Pharma-, BioTech-, and MedTech-business, who are interested in influencing their workplaces and careers by gaining insights into different innovation practices.
Participants signed up for the specialization business development of the lifesciences are eligible for the course.
