Meet Pegasi, DHC Quant’s new proprietary data-modeling application. Designed to model uncertainty, it applies customized input assumptions to quantitatively describe a range of probable outcomes. It can be used for capacity (and/or facility) planning, COGs analysis, or build vs. buy analytics.
One simulation gives you a number. Multiple simulations gives you a model. Pegasi’s Monte Carlo modeling capabilities offer a versatile and adaptable method that uses repetition to effectively address variability. The below box whisker plot, for example, represents the likely success/failure variability of therapy candidates in each phase over time.
The unique aspect of Pegasi is that it can facilitate decision making on questions in environments of high uncertainty. Examples of the types of modeling questions that may be relevant to you are:
Pegasi’s functionality includes Monte Carlo algorithms, allowing a capture of inherent variability in outcomes to enable enhanced statistical sampling. The following Market Forecast is an example, forecasting of the CGT market space through 2030.
Project requests may range from build vs. buy decisions to COGs assessments and market analyses; deliverables include a dynamic dashboard capable of capturing changes to key assumptions in real time across all connected models. The image below is a dashboard view. The Pegasi dashboard allows the viewer to stay at a top-level view or drill down into the details. This sample demonstrates a view of the benefits of different manufacturing strategies using real-time navigation through facility timelines and requirements.
This example shows the image-as-filter capability, in this case enabling dynamic navigation of costs based on clinical manufacturing phase.
Our first Pegasi case study is now available! In this example, an international tools and technology company requested a market analysis of the gene-editing and advanced therapies space as well as a facility buildout plan to manufacture a critical component for gene therapy applications.
Predict facility size requirements, instrumentation needs, and FTE by department over time under the full range of reasonable input assumptions to enable robust scenario planning even in environments of great uncertainty.
Evaluate the range of likely product COGs over time and evaluate key levers for cost reduction based on the impact of various input assumption on overall product COGs.
Forecast CapEx and OpEx over time and over a wide range of input assumptions to identify the most cost effective solution for your manufacturing needs.
Determine current, and project future market size for your product or services based on inputs such as patient/user/customer type, indication prevalence, and assumed market penetration.
Model profitability scenarios based on COGs, OpEx, CapEx, and market size inputs.