Bøger af David L. Olson

The objective of the book is to provide materials to demonstrate the development of TOPSIS and to serve as a handbook. It contains the basic process of TOPSIS, numerous variant processes, property explanations, theoretical developments, and illustrative examples with real-world cases. Possible readers would be graduate students, researchers, analysts, and professionals who are interested in TOPSIS, a distance-based algorithm, and who would like to compare TOPSIS with other MCDM methods. The book serves as a research reference as well as a self-learning book with step-by-step illustrations for the MCDM community.

Dieses Buch bietet einen Überblick über Data-Mining-Methoden, die durch Software veranschaulicht werden. Beim Wissensmanagement geht es um die Anwendung von menschlichem Wissen (Erkenntnistheorie) mit den technologischen Fortschritten unserer heutigen Gesellschaft (Computersysteme) und Big Data, sowohl bei der Datenerfassung als auch bei der Datenanalyse. Es gibt drei Arten von Analyseinstrumenten. Die deskriptive Analyse konzentriert sich auf Berichte über das, was passiert ist. Bei der prädiktiven Analyse werden statistische und/oder künstliche Intelligenz eingesetzt, um Vorhersagen treffen zu können. Dazu gehört auch die Modellierung von Klassifizierungen. Die diagnostische Analytik kann die Analyse von Sensoreingaben anwenden, um Kontrollsysteme automatisch zu steuern. Die präskriptive Analytik wendet quantitative Modelle an, um Systeme zu optimieren oder zumindest verbesserte Systeme zu identifizieren. Data Mining umfasst deskriptive und prädiktive Modellierung. Operations Research umfasst alle drei Bereiche. Dieses Buch konzentriert sich auf die deskriptive Analytik.Das Buch versucht, einfache Erklärungen und Demonstrationen einiger deskriptiver Werkzeuge zu liefern. Es bietet Beispiele für die Auswirkungen von Big Data und erweitert die Abdeckung von Assoziationsregeln und Clusteranalysen. Kapitel 1 gibt einen Überblick im Kontext des Wissensmanagements. Kapitel 2 erörtert einige grundlegende Softwareunterstützung für die Datenvisualisierung. Kapitel 3 befasst sich mit den Grundlagen der Warenkorbanalyse, und Kapitel 4 demonstriert die RFM-Modellierung, ein grundlegendes Marketing-Data-Mining-Tool. Kapitel 5 demonstriert das Assoziationsregel-Mining. Kapitel 6 befasst sich eingehender mit der Clusteranalyse. Kapitel 7 befasst sich mit der Link-Analyse. Die Modelle werden anhand geschäftsbezogener Daten demonstriert. Der Stil des Buches ist beschreibend und versucht zu erklären, wie die Methoden funktionieren, mit einigen Zitaten, aber ohne tiefgehende wissenschaftliche Referenzen. Die Datensätze und die Software wurden so ausgewählt, dass sie für jeden Leser, der über einen Computeranschluss verfügt, weithin verfügbar und zugänglich sind.

This book presents key concepts related to quantitative analysis in business.Business analytics has grown to be a key topic in business curricula, and there is a need for stronger quantitative skills and understanding of fundamental concepts.This book is aimed at business students, undergraduate and graduate, taking an introductory core course. Topics covered include knowledge management, visualization, sampling and hypothesis testing, regression (simple, multiple, and logistic), as well as optimization modeling. It concludes with a brief overview of data mining. Concepts are demonstrated with worked examples.

This book is an e¿ort that focuses on a very vast cluster of Enterprises and their digitising technology involvement and take us through the road map of the implementation process in themNote: T& F does not sell or distribute the Hardback in India, Pakistan, Nepal, Bhutan, Bangladesh and Sri Lanka.

Data mining has become the fastest growing topic of interest in business programs in the past decade. This book is intended to describe the benefits of data mining in business, the process and typical business applications, the workings of basic data mining models, and demonstrate each with widely available free software.

This book addresses the use of quantitative tools to support general project management.Part I of the book deals with critical path modeling. Part II discusses risk modeling tools to include Program Evaluation and Review Technique (PERT), critical chain modeling, and agile/scrum approaches. Project control through earned value analysis is also covered. Part III is a Microsoft Project orientation. A feature of the book is an effort to tie content to that of the Project Management Body of Knowledge (PMBOK).Each chapter includes reference to how each chapter relates to the PMBOK structure and its relationship to the 2020 Project Management Professional (PMP) Exam Outline.

This book addresses project management in the context of general project management.An introductory chapter discusses project features in general. Part I of the book focuses attention on the important human element in project management. Part II discusses two processes involved in the initial project definition stage, as well as covering estimation. Part III involves planning and project risk and implementation.A feature of the book is an effort to tie content to that of the Project Management Body of Knowledge (PMBOK). Each chapter includes reference to how each chapter relates to the PMBOK structure, and relationship to the 2020 PMP Exam Outline.

This book brings a unique combination of years of experience in academics research and studies in regards to "e;ERP systems"e; with years of experience from a practitioner's perspective. Each year billions of dollars are spend by organizations to implement, manage, and maintain ERP systems. A simple browse through the Internet will demonstrate how challenging ERP implementations can be. Success rates are seen as quite low with time, costs, and effort typically being above planned and often significantly. Law firms make a living from ERP's gone badly. Academia is investing more and more time and research into developing success models that not only attempt to objectively determine ERP success or failure but also attempt to be a proactive in that effort. But why? If ERP systems (and all their inherent functionality) can bring a true ROI to business, why are they so challenging? Why do they often deliver as advertised? And, why are they often seen as failing?

This book reviews forecasting data mining models, from basic tools for stable data through causal models, to more advanced models using trends and cycles. These models are demonstrated on the basis of business-related data, including stock indices, crude oil prices, and the price of gold.

Further, it examines models related to pandemic planning, such as evaluation of financial contagion, debt risk analysis, and health system efficiency performance, and addresses specific models of pandemic parameters. The book demonstrates various tools using available data on the ongoing COVID-19 pandemic.

Chapter 2 covers data visualization, including directions for accessing R open source software (described through Rattle). Chapter 7 goes on to describe link analysis, social network metrics, and open source NodeXL software, and demonstrates link analysis application using PolyAnalyst output.

Describes the megatrends that shape how people behave and organizations work. This title also describes how globalization, digitization, changing demographics, changing industry mix, deregulation and privatization, commoditization of processes, new value chains, and cultural conflicts have led to what they define as a convergence revolution.

In recent years, a number of interesting decision aids have become available to assist in such decisions. The aim of this book is to provide a comparative survey of many of the decision aids currently available. Subsequent chapters then focus on specific decision aids and demonstrate some of the software which implement these ideas.

Enterprise risk management has always been important. However, the events of the 21st Century have made it even more critical. The top level of business management became suspect after scandals at ENRON, WorldCom, and other business entities. Financially, many firms experienced difficulties from bubbles. The problems of interacting cultures demonstrated risk from terrorism as well, with numerous terrorist attacks, to include 9/11 in the U.S. Risks can arise in many facets of business. Businesses in fact exist to cope with risk in their area of specialization. Financial risk management has focused on banking, accounting, and finance. We have discussed several aspects of risk, to include information systems, disaster management, and supply chain perspectives. The bulk of this book is devoted to presenting a number of operations research models that have been (or could be) applied to enterprise supply risk management, especially from the supply chain perspective.

Enterprise Risk Management in Finance is a guide to measuring and managing Enterprise-wide risks in financial institutions. Technical and rigorous, this book will be a welcome addition to the literature in this area, and will appeal to risk managers, actuaries, regulators and senior managers in banks and financial institutions.

1 Facility Location Problems The location problem has been with humans for all of their history. The development of industry involved location problems for production facilities and trade outlets. In the 19 century, there seemed to be a focus on the cost of transporting raw materials versus the cost of transporting goods to consumers.

1 Facility Location Problems The location problem has been with humans for all of their history. The development of industry involved location problems for production facilities and trade outlets. In the 19 century, there seemed to be a focus on the cost of transporting raw materials versus the cost of transporting goods to consumers.

This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. Part II describes and demonstrates basic data mining algorithms. Part III focuses on business applications of data mining.