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000826730 020__ $$a9781484234952$$q(electronic book)
000826730 020__ $$a1484234952$$q(electronic book)
000826730 020__ $$z9781484234945
000826730 0247_ $$a10.1007/978-1-4842-3495-2$$2doi
000826730 035__ $$aSP(OCoLC)on1027724368
000826730 035__ $$aSP(OCoLC)1027724368
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000826730 049__ $$aISEA
000826730 050_4 $$aQA276.45.R3
000826730 08204 $$a005.13/3$$223
000826730 1001_ $$aBrown, Robert D.$$eauthor.
000826730 24510 $$aBusiness case analysis with R :$$bsimulation tutorials to support complex business decisions /$$cRobert D. Brown III.
000826730 264_1 $$a[Berkeley, CA] :$$bApress,$$c2018.
000826730 264_2 $$aNew York, NY :$$bDistributed to the book trade worldwide by Springer,
000826730 264_4 $$c©2018
000826730 300__ $$a1 online resource (xviii, 282 pages) :$$billustrations
000826730 336__ $$atext$$btxt$$2rdacontent
000826730 337__ $$acomputer$$bc$$2rdamedia
000826730 338__ $$aonline resource$$bcr$$2rdacarrier
000826730 500__ $$aIncludes index.
000826730 5050_ $$aPart 1: Business Case Analysis with R -- Chapter 1: A Relief from Spreadsheet Misery -- Chapter 2: Setting up the Analysis -- Chapter 3: Include Uncertainty in the Financial Analysis -- Chapter 4: Interpreting and Communicating Insights -- Part 2: It?s Your Move -- Chapter 5: "What Should I Do?" -- Chapter 6: Use a Decision Hierarchy to Categorize Decision Types -- Chapter 7: Tame Decision Complexity by Creating a Strategy Table -- Chapter 8: Clearly Communicate the Intentions of Decision Strategies -- Chapter 9: What Comes Next -- Part 3: Subject Matter Expert Elicitation Guide -- Chapter 10: “What?s Your Number, Pardner?” -- Chapter 11: Conducting SME Elicitations -- Chapter 12: Kinds of Biases -- Part 4: Information Espresso -- Chapter 13: Setting a Budget for Making Decisions Clearly -- Chapter 14: A More Refined Explanation of VOI -- Chapter 15: Building the Simulation in R -- Appendix A: Deterministic Model -- Appendix B: Risk Model -- Appendix C: Simulation and Finance Functions -- Appendix D: Decision Hierarchy and Strategy Table Templates -- Appendix E: VOI Code Samples.
000826730 506__ $$aAccess limited to authorized users.
000826730 520__ $$aThis 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.
000826730 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 7, 2018).
000826730 650_0 $$aR (Computer program language)
000826730 852__ $$bebk
000826730 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-1-4842-3495-2$$zOnline Access$$91397441.1
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000826730 980__ $$aEBOOK
000826730 980__ $$aBIB
000826730 982__ $$aEbook
000826730 983__ $$aOnline
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