Open innovation – what does it mean to you? Shared resources? Open data? More cost-effective workflows?
Across the science and technology sectors, the concept of open innovation (OI) is gaining currency. However, individual expectations of what it means in practice vary, and in a world of intellectual property, legal and safety frameworks, that is a potential problem.
To move the discussion on, a recent paper on the life sciences sector by Danish OI specialists Niclas Nillson and Timo Minssen propose a simple classification system to help codify expectations. This system offers a practical handle on the problem by providing five ready-defined levels of OI based on the degree of openness appropriate to need.
What is open innovation?
Henry Chesbrough, the father of the concept, defined OI as: “Purposive inflows and outflows of knowledge to accelerate internal innovation.” Meanwhile, the Centre for the Advancement of Sustainable Medical Innovation considers it as: “The process of innovating with others for shared risk and reward to produce mutual benefits.”
Those two definitions have slightly different emphases – Chesbrough’s definition is focused on OI as a mechanism to speed up internal processes, while the CASMI focus is more outward looking, emphasizing mutual risk and outcomes. What they both share is the concept of breaking the knowledge silos found in traditional research and development (R&D).
Traditional R&D ecosystems are linear, with the strategic science rationale, basic research, development of specific drugs or technologies, and implementation of developments all hived off into closed silos. Only need-to-know information is passed between each link in the chain, let alone externally.
This protectionist approach was deemed necessary to preserve intellectual property (IP) and financial interests, especially in high-cost/high-value industries such as pharmaceuticals. However, to society and increasingly to industries themselves, it is generating substantial costs.
An obvious problem is replication – if no one knows what anyone else is working on, then costly research work may be undertaken for no innovation gain. As many industries find their margins are narrowing, such wasted investment is a major negative.
The benefits and risks of OI
The primary benefits of OI are the spreading of knowledge, outputs, risks, and costs. Sharing resources (open access), methods (open source), strategic rationales (open science), and ultimately results (open data), reduces costs, speeds up access to new technologies, and stimulates new and unexpected avenues of R&D.
However, for each partner in a project, OI also brings risks. Negative aspects include reduced control over research and outputs, legal issues such as IP and legacy ownership, and contractual complications from working in an OI environment.
Although these can be navigated and assessed on a case-by-case basis, Nillson and Minssen argue that a standardized OI framework simplifies negotiations around these issues, reducing resources spent on contracts, and maximizing the focus on innovation and research.
Degrees of openness
At its most basic (Nillson and Minssen’s level 1), OI is simply the disclosure of research or innovation needs and the creation of shared risks and benefits between R&D partners. This shares more than a traditional external collaboration but allows partners to keep business-critical detail confidential. An example would be Novo Nordisk working with InnoCentive to crowdsource a small-molecule glucose binder.
Nillson and Minssen identify level 2 as being open access to tools or resources. This is more restricted than open source, as the IP behind the resources is not provided, and their use, while open, can still be subject to conditions. Benefits to businesses sharing their resources may include first refusal on patentable technologies that arise – providing a return on investment while stimulating innovation that wouldn’t be possible in-house. An example would be Eli Lilly allowing scientists to submit compounds to be tested using Lilly’s proprietary biological assays.
Ramping up a notch, the proposed level 3 requires both open science and open sources. The scientific rationale behind the research need and the detail of methods and techniques are openly shared, either between partners or publicly. An example would be AstraZeneca offering access to their clinical compound bank. Benefits to this approach stem from a greater level of understanding of the scientific need, context, and approaches than in level 2, allowing more ideas to be generated. However, it can prove impractical if business-critical science is involved.
For levels 4 and 5, near-complete openness is required. Nillson and Minssen’s level 4 is a scientific collaboration unrestricted by business terms, while their level 5 embraces all the previous levels, and adds in a requirement for publicly open data. This fully open approach offers the greatest level of innovation opportunity from a big picture viewpoint but rules out the potential for IP ownership. An example of level 4 would be LEO Pharma’s OI platform, while a level 5 example would be the Open Source Malaria program, which openly shares protocols and results.
There is no one right way to undertake an OI collaboration – each case will have unique needs. Nillson and Minssen caution against viewing their framework as a hierarchy with level 5 representing an ideal state. Where a large publicly funded health project may achieve the best social outcomes via a level 5 open data approach, a group of businesses may find themselves better served by a level 2 sharing of technical resources.
Weighing an approach
To assess whether OI is right for a project or business and if so, to identify the most appropriate level of openness is a complex issue, involving cost-benefit analyses, legal and IP issues. In a 2016 paper, André Ullrich of the University of Potsdam, and his colleague Gergana Vladova proposed a decision-making framework and software tool aimed at simplifying this process. Their concern was particularly focused on smaller businesses and start-ups who might be the junior partner in a collaboration and have limited expertise in performing such an assessment.
The business expectations and regulatory frameworks that must be negotiated in a global market may influence both the degree of openness in a particular collaboration and how far a fully standardized approach can be implemented. However, the use of a reference framework, along with potential tools for assessing the risk/reward balance of an OI approach, is a valuable building block in the discussion and implementation of openness in STEM innovation.