In this paper we will go over several case studies that show how simulation and optimization helps in overcoming many of the challenges associated with data science techniques. The methodology we use is easily extended to a wide range of industries and use cases and enables an organization to improve its decision making and generate business value.
In this talk, I go over some interesting case studies from the past to highlight the challenges and what lessons we can learn from them.
While there are many predictions about how the future of analytics will unfold, we don’t have to always wait for them to come true for analytics to be a value producing component of the business.
Most predictive analytics and AI efforts fail to reach their full potential because it is not always easy to understand how different strategic choices and operational tactics will impact business KPIs in the long run.
My perspectives on big data and data science / analytics. Interview conducted by Dr. Swain.
Describes methods to create deep models by combining analytics with simulation methods to help the business make operational and strategic decisions.
This presentation describes a digital twin environment that represents the entire addressable market with data-driven characteristics and behaviors of individuals.
BI and Analytics add value when they inform business decisions. In this presentation I describe how we are informing business decisions to drive significant value to the enterprise.
Tackling fraud is difficult because fraudsters are continually adapting their tactics to any actions that organizations put in place. This leads to an ongoing escalation that can be called a red queen trap.
Use of model based dynamic enterprise architecture concepts to inform business decisions and prioritize capabilities / investments.
Commonly used analytics techniques are not sufficient by themselves to fully realize business value. In this presentation I describe how we use analytics combined with operations research techniques to inform business decisions and realize business value for the enterprise.
Classic approaches to business decision making are both over simplified and insufficient to account for the dynamic complexities of reality.
Describes how ABM can be used to inform strategic decisions and understand their implications in a wide range of scenarios.
How Agent Based Models can be used to analyze broad financial implications of product development decisions
Describes several industrial applications of Swarm Intelligence and Agent Based Modeling
ABSTRACT The past three decades have seen phenomenal growth in investments in the area of product lifecycle management (PLM) as companies exploit opportunities in streamlining product lifecycle processes, and fully harnessing their data assets.
