Supply chain operations face many risks, including political, environmental, and economic. The past five years have seen major challenges, from pandemic, impacts of global warming, wars, and tariff impositions. In this rapidly changing world, risks appear in every aspect of operations. This book presents data mining and analytics tools with R programming as well as a brief presentation of Monte Carlo simulation that can be used to anticipate and manage these risks. RStudio software and R programming language are widely used ...
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Supply chain operations face many risks, including political, environmental, and economic. The past five years have seen major challenges, from pandemic, impacts of global warming, wars, and tariff impositions. In this rapidly changing world, risks appear in every aspect of operations. This book presents data mining and analytics tools with R programming as well as a brief presentation of Monte Carlo simulation that can be used to anticipate and manage these risks. RStudio software and R programming language are widely used in data mining. For Monte Carlo simulation applications we cover Crystal Ball software, one of a number of commercially available Monte Carlo simulation tools. Chapter 1 of this book deals with classification of risks. It includes a typical supply chain example published in academic literature. Chapter 2 gives a brief introduction to R programming. It is not intended to be comprehensive, but sufficient for a user to get started using this free open source and highly popular analytics tool. Chapter 3 discusses risks commonly found in finance, to include basic data mining tools applied to analysis of credit card fraud data. Like the other datasets used in the book, this data comes from the Kaggle.com site, a free site loaded with realistic datasets. The remainder of the book covers risk analytics tools. Chapter 4 presents R association rule modeling using a supply chain related dataset. Chapter 5 presents Monte Carlo simulation of some supply chain risk situations. Chapter 6 gives both time series and multiple regression prediction models as well as autoregressive integrated moving average (ARIMA; Box-Jenkins) models in SAS and R. Chapter 7 covers classification models demonstrated with credit risk data. Chapter 8 deals with fraud detection and the common problem of modeling imbalanced datasets. Chapter 9 introduces Na�ve Bayes modeling with categorical data using an employee attrition dataset. Features: Overview of predictive analytics presented in an understandable manner Presentation of useful business applications of predictive data mining Coverage of risk management in finance, insurance, and supply chain contexts Presentation of predictive models Demonstration of using these predictive models in R Screenshots enabling readers to develop their own models The purpose of the book is to present tools useful to analyze risks, especially those faced in supply chain management and finance.
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Add this copy of Risk and Predictive Analytics in Business with R to cart. £88.00, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2025 by CRC Press.
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New. Print on demand Sewn binding. Cloth over boards. 176 p. Contains: Unspecified, Illustrations, black & white, Line drawings, black & white, Tables, black & white. Chapman and Hall/CRC Statistics in Business and Economics.
Add this copy of Risk and Predictive Analytics in Business with R to cart. £94.07, new condition, Sold by Booksplease rated 4.0 out of 5 stars, ships from Southport, MERSEYSIDE, UNITED KINGDOM, published 2025 by CRC Press.
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New. Sewn binding. Cloth over boards. 176 p. Contains: Unspecified, Illustrations, black & white, Line drawings, black & white, Tables, black & white. Chapman and Hall/CRC Statistics in Business and Economics.
