Abstract

The European Bioinformatics Institute (EMBL-EBI), a host site of the European Molecular Biology Laboratory, attracts approximately two thirds of its total budget from external funders, both through regular funding calls and commercial & industrial partnerships. As expected, these projects are diverse in budget and duration with disparate T&Cs. Each team and each staff member is often simultaneously involved in multiple linked projects, giving rise to complex and varied portfolios, in which internally distributed funds are also factored in.
The sustainability of the work undertaken at the institute is therefore reliant on careful research management, encompassing all aspects of the grant life cycle. This ranges from the preparation of enticing proposals to the impactful presentation of outcomes, including watchful resource allocation throughout the projects’ lifespan. At EMBL-EBI, however, the Research Management Officer’s (RMOer) role is not only to assist in monitoring the progress of individual projects, but primarily to provide an overview of the team’s portfolio as a whole, considering financial and staff-related variables, as well as risk factors that impact several projects at once.
This all-encompassing perspective allows the RMOer to deliver multiple scenario forecasts, which have proved vital in ensuring adequate resource allocation, informing the decision-making process, and allowing team leads to design their team strategy confidently. Ultimately, it maximises the value of the awarded funds, assisting EMBL-EBI in fulfilling its mission of providing bioinformatics services to the world-wide scientific community.
However, the currently available ERP system at EMBL-EBI lacks the flexibility to factor in the numerous variables involved in scenario modelling. Therefore, an MS Excel-based tool was developed that builds on the ERP system data and allows for its manipulation, enabling the projection of diverse scenarios.
In this poster, we demonstrate how portfolio-wide forecasting is accomplished, and discuss how we mitigate for sources of uncertainty. Additionally, we address the possibilities that the offline forecasting tool presents, along with its risks and opportunities for improvement.