DHC’s quantitative modeling service supports clients in scenario analysis of processes with a high degree of uncertainty, avoiding ‘back of the napkin’ calculations that can lead to costly mistakes. Our expert team works with you to craft input-assumption ranges based on process specifics and industry standards, then uses Monte Carlo simulations to enable enhanced statistical sampling: simulating the full range of probable outcomes based on current process knowledge. As the program advances and more accurate data becomes available, this information can be fed into the model to increase the accuracy of the model predictions.

This unique, proprietary quantitative modeling platform can enable more data-driven decision making for a variety of strategic planning activities, including:

Capacity planning: 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.

Cost of Goods analysis: 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.

Build vs. Buy analytics: 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.

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a quant model example
a quant modeling example of clinic demand
A quant model example of demand modeled over time

DHC’s quantitative modeling service supports clients in scenario analysis of processes with a high degree of uncertainty, avoiding ‘back of the napkin’ calculations that can lead to costly mistakes. Our expert team works with you to craft input-assumption ranges based on process specifics and industry standards, then uses Monte Carlo simulations to enable enhanced statistical sampling: simulating the full range of probable outcomes based on current process knowledge.

a quant model example

As the program advances and more accurate data becomes available, this information can be fed into the model to increase the accuracy of the model predictions.

This unique, proprietary quantitative modeling platform can enable more data-driven decision making for a variety of strategic planning activities, including:

Capacity planning: 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.

a quant modeling example of clinic demand

Cost of Goods analysis: 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.

Build vs. Buy analytics: 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.

A quant model example of demand modeled over time

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