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Approach in Detail

For example, Dynamic Modeling can be used to look at pricing/inventory/demand dynamics in a particular industry. Items such as Inventory and Capacity which accumulate and can be counted at any single timepoint are represented as stocks. The actions which directly affect these accumulations (such as production, sales, and capacity starts) are shows as flows which occur over a period of time. Variables housed in the circles are called auxiliaries and can hold inputs (such as base demand per consumer), show key relationships (operating rate is based on level of Inventory), and house calculations (actual demand per consumer is a function of base demand per consumer and the effect of Price.) Finally, the thin arrows or connectors show the dependent relationships between variables (the inputs for Price are Inventory and base demand per consumer.) This operational structure provides a framework which can be expanded for more detailed analyses.

Aggregate stock/flow structure can get more specific in one of two ways.

1. Model elements can get segmented into categories with group-specific behavioral patterns. For example, decision makers might want to look at more detail around Consumers. By disaggregating the stock into categories, the Dynamic Model can track the specifics related to each individual consumer segment.

2. Overall model structure can be populated with individual simulation agents, each having specific characteristics and behavior rules. For example, Capacity in this industry might be in the form of a small number of individual production facilities. The Dynamic Model could use an agent-based approach to show the behavior of each plant individually, as well as apply specific behavior rules for each plant’s decision regarding operating rate. In an agent-based model, activities such as capacity coming online become discrete events rather than continuous flows of activity.

Much of Lexidyne’s client work focuses on quantifying the movement of patients in various therapeutic areas. For example, a manufacturer of a prosthetic device might be interested in the pools of patients and the associated flows of activity in a particular marketplace. A Dynamic Model could identify the stages patients go through (At Hospital, In Acute Phase, In Post-Acute Phase) as well as quantify the annual (or monthly) number of patients that experience various events (advancing between stages, requiring additional procedures, deaths, etc.) based on specific data estimates (death rate in acute phase, average life of prosthesis, etc.) Such a model would help a client team understand the structure of the existing marketplace as well as identify high-leverage intervention points for formulating more effective strategies.

Patient flow dynamics can be integrated with:

  • Structure describing physician adoption of available treatment options.
  • Metrics regarding the components and evaluation of the attractiveness of those therapy options.
  • Utility functions and associated choice models using the resulting Treatment Attractiveness scores.
  • Line of therapy ( LOT) progressions that replicate Markov processes to get at an overall picture of marketplace dynamics and subsequent revenue generation.

These types of integrated models result in a “virtual marketplace” in which various strategic intervention points can be tested and evaluated for cost effectiveness. These types of “what if” analyses often provide insight into questions that are difficult to address without an operational representation of the relevant dynamic processes.