國立高雄大學圖資館 |

Language: English

Back

Business case analysis with Rsimulat...

Brown, Robert D.

Business case analysis with Rsimulation tutorials to support complex business decisions /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Business case analysis with Rby Robert D. Brown III.
Reminder of title:	simulation tutorials to support complex business decisions /
Author:	Brown, Robert D.
Published:	Berkeley, CA :Apress :2018.
Description:	xviii, 282 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	R (Computer program language)
Online resource:	http://dx.doi.org/10.1007/978-1-4842-3495-2
ISBN:	9781484234952$q(electronic bk.)

Business case analysis with Rsimulation tutorials to support complex business decisions /
Brown, Robert D.

Business case analysis with Rsimulation tutorials to support complex business decisions /[electronic resource] :by Robert D. Brown III. - Berkeley, CA :Apress :2018. - xviii, 282 p. :ill. (some col.), digital ;24 cm.

Part 1: Business Case Analysis with R -- Chapter 1: A Relief from Spreadsheet Misery -- Chapter 2: Setting up the Analysis -- Chapter 3: Include Uncertainty in the Financial Analysis -- Chapter 4: Interpreting and Communicating Insights -- Part 2: It's Your Move -- Chapter 5: "What Should I Do?" -- Chapter 6: Use a Decision Hierarchy to Categorize Decision Types -- Chapter 7: Tame Decision Complexity by Creating a Strategy Table -- Chapter 8: Clearly Communicate the Intentions of Decision Strategies -- Chapter 9: What Comes Next -- Part 3: Subject Matter Expert Elicitation Guide -- Chapter 10: "What's Your Number, Pardner?" -- Chapter 11: Conducting SME Elicitations -- Chapter 12: Kinds of Biases -- Part 4: Information Espresso -- Chapter 13: Setting a Budget for Making Decisions Clearly -- Chapter 14: A More Refined Explanation of VOI -- Chapter 15: Building the Simulation in R -- Appendix A: Deterministic Model -- Appendix B: Risk Model -- Appendix C: Simulation and Finance Functions -- Appendix D: Decision Hierarchy and Strategy Table Templates -- Appendix E: VOI Code Samples.

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.

ISBN: 9781484234952$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-3495-2doiSubjects--Topical Terms:

210846
R (Computer program language)

LC Class. No.: QA276.45.R3

Dewey Class. No.: 005.133

Business case analysis with Rsimulation tutorials to support complex business decisions /
LDR:04632nmm a2200289 a 4500 001 534181
003 DE-He213
005 20180305122159.0
006 m d
007 cr nn 008maaau
008 181205s2018 cau s 0 eng d
020 $a 9781484234952$q(electronic bk.)
020 $a 9781484234945$q(paper)
024 7 $a 10.1007/978-1-4842-3495-2 $2 doi
035 $a 978-1-4842-3495-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276.45.R3
082 0 4 $a 005.133 $2 23
090 $a QA276.45.R3 $b B879 2018
100 1 $a Brown, Robert D. $3 810299
245 1 0 $a Business case analysis with R $h [electronic resource] : $b simulation tutorials to support complex business decisions / $c by Robert D. Brown III.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2018.
300 $a xviii, 282 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Part 1: Business Case Analysis with R -- Chapter 1: A Relief from Spreadsheet Misery -- Chapter 2: Setting up the Analysis -- Chapter 3: Include Uncertainty in the Financial Analysis -- Chapter 4: Interpreting and Communicating Insights -- Part 2: It's Your Move -- Chapter 5: "What Should I Do?" -- Chapter 6: Use a Decision Hierarchy to Categorize Decision Types -- Chapter 7: Tame Decision Complexity by Creating a Strategy Table -- Chapter 8: Clearly Communicate the Intentions of Decision Strategies -- Chapter 9: What Comes Next -- Part 3: Subject Matter Expert Elicitation Guide -- Chapter 10: "What's Your Number, Pardner?" -- Chapter 11: Conducting SME Elicitations -- Chapter 12: Kinds of Biases -- Part 4: Information Espresso -- Chapter 13: Setting a Budget for Making Decisions Clearly -- Chapter 14: A More Refined Explanation of VOI -- Chapter 15: Building the Simulation in R -- Appendix A: Deterministic Model -- Appendix B: Risk Model -- Appendix C: Simulation and Finance Functions -- Appendix D: Decision Hierarchy and Strategy Table Templates -- Appendix E: VOI Code Samples.
520 $a 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.
650 0 $a R (Computer program language) $3 210846
650 1 4 $a Computer Science. $3 212513
650 2 4 $a Big Data. $3 760530
650 2 4 $a Big Data/Analytics. $3 742047
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-1-4842-3495-2
950 $a Professional and Applied Computing (Springer-12059)