One of the key challenges that businesses are facing is how to leverage the emerging tools and methods of data science to improve their business performance. Although there are many examples of the use of data science or machine learning for business, it is often not easy to translate that success into other industries or use cases.
In this paper, researchers introduce two case studies that show how simulation and optimization help in overcoming many of the challenges associated with data science techniques. The methodology 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.
First case study involves an end-to-end product acquisition process. The model was built to address capacity problems after a demand shock. The process was modeled using the system dynamics modeling method and it captured the process flow as well as causal factors that drove key aspects of the process.
Second case study involves the strategic planning process of an organization. Researchers developed a system-level strategic simulation model that represents the entire market of tens of millions of potential customers. The model was built at an individual level using an agent-based modeling methodology. Data was analyzed and several individualized behavior models were developed to predict how a person behaves under certain circumstances.