Topics covered to illustrate the role of PMO and tools available after late-stage setbacks, including forecasting resources in growth phase, scenario planning, managing steep decrease in demand due to portfolio changes, toolbox needed to support transition, and lessons learned.

Machine Learning techniques are playing an ever-increasing role in resource forecasting. Our evolution spans from early days of using ML to identify the most impactful drivers used in parametric algorithms, to ML-enabled modulation of algorithm output, and finally ML calculated forecast without any algorithm at all.  While this approach is inherently more nimble and self-calibrating, it requires new ways of communicating how the model works to stakeholders and fundamentally changes the model maintenance required.

Whether creating a new therapeutic area or executing lifecycle management on a mature brand, R&D organizations are faced with unique decision making opportunities to efficiently deliver their portfolios from a cost and resource perspective, all while mitigating risk. This talk will focus on options and tactics to consider when evaluating a range of opportunities in the context of an overarching portfolio strategy.

Proactive constraint identification is a key deliverable of any forecasting model, but that’s not the end of the story. Organizations have a variety of avenues to resolve these constraints including staff augmentation, demand prioritization, process efficiency initiatives, and more. We recently implemented a tool to help business leaders think through those options, arm them with historical references/benchmarks, and quantify the likelihood of a given plan to actually resolve the constraint.

What happens when an organization makes the decision to internalize the entire development portfolio over the course of one year? As biotechnology organizations evolve and mature, the operating model for running the clinical trial portfolio should be continually assessed for fit to company culture and vision. This talk will explore how BeiGene, a global biotechnology company, internalized the clinical operations workforce (people, process, technology) over a short period of time.

The enormous growth of decentralized trials brought on by the pandemic brought with it a need quickly adapt and evolve capacity planning and resource management in clinical operations.  Explore some of the innovative approaches and new capabilities rapidly deployed by Eli Lilly to enable clinical trials to progress, and impact resource planning and forecasting going forward.