This tutorial teaches you how to use the statistical programming language R to develop a
business case simulation and analysis. It presents a methodology for conducting business case
analysis that minimizes decision delay by focusing stakeholders on what matters most and
suggests pathways for minimizing the risk in strategic and capital allocation decisions.
Business case analysis often conducted in spreadsheets exposes decision makers to additional
risks that arise just from the use of the spreadsheet environment. R has become one of the most
widely used tools for reproducible quantitative analysis and analysts fluent in this language
are in high demand. The R language traditionally used for statistical analysis provides a
more explicit flexible and extensible environment than spreadsheets for conducting business
case analysis. The main tutorial follows the case in which a chemical manufacturing company
considers constructing a chemical reactor and production facility to bring a new compound to
market. There are numerous uncertainties and risks involved including the possibility that a
competitor brings a similar product online. The company must determine the value of making the
decision to move forward and where they might prioritize their attention to make a more
informed and robust decision. While the example used is a chemical company the analysis
structure it presents can be applied to just about any business decision from IT projects to
new product development to commercial real estate. The supporting tutorials include the
perspective of the founder of a professional service firm who wants to grow his business and a
member of a strategic planning group in a biomedical device company who wants to know how much
to budget in order to refine the quality of information about critical uncertainties that might
affect the value of a chosen product development pathway. What You'll Learn Set up a business
case abstraction in an influence diagram to communicate the essence of the problem to other
stakeholders Model the inherent uncertainties in the problem with Monte Carlo simulation using
the R language Communicate the results graphically Draw appropriate insights from the results
Develop creative decision strategies for thorough opportunity cost analysis Calculate the value
of information on critical uncertainties between competing decision strategies to set the
budget for deeper data analysis Construct appropriate information to satisfy the parameters for
the Monte Carlo simulation when little or no empirical data are available Who This Book Is For
Financial analysts data practitioners and risk business professionals also appropriate for
graduate level finance business or data science students
