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Digitising Enterprise in an Information Age is an effort 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 them, some of them being ICT, Banking,
Stock Markets, Textile Industry & ICT, Social Media, Software
Quality Assurance, Information Systems Security and Risk
Management, Employee Resource Planning etc. It delves on increased
instances of cyber spamming and the threat that poses to e-Commerce
and Banking and tools that help and Enterprise toward of such
threats. To quote Confucius, "As the water shapes itself to the
vessel that contains it, so does a wise man adapts himself to
circumstances." And the journey of evolution and progression will
continue and institutions and enterprises will continue to become
smarter and more and more technology savvy. Enterprises and
businesses across all genre and spectrum are trying their level
best to adopt to change and move on with the changing requirements
of technology and as enterprises and companies upgrade and speed up
their digital transformations and move their outdate heirloom
systems to the cloud, archaic partners that don't keep up will be
left behind. Note: T&F does not sell or distribute the Hardback
in India, Pakistan, Nepal, Bhutan, Bangladesh and Sri Lanka.
Convergenomics is about the megatrends that are shaping how people
behave and organizations work. In this insightful analysis, Sang
Lee and David Olson describe how globalization, digitization,
changing demographics, changing industry mix, deregulation and
privatization, commoditization of processes, new value chains,
emerging new economies, deteriorating environment, and cultural
conflicts have led to what they define as a convergence revolution.
Lee and Olson discuss this convergence revolution from the
perspectives of technology, industry, knowledge, open-source
networking and bio-artificial convergence, and they explain how
human systems are transformed by what they have named
convergenomics. Understanding convergenomics can lead to innovative
strategic approaches and, the authors contend, more agile
businesses are already employing these approaches to become and
remain competitive and to generate greater value in a world
radically changed by e-commerce. Business leaders and 'students' of
strategy at all levels will learn from this book how revolutionary
developments can be embraced rather than feared, and how technology
that is potentially frightening in its complexity can be harnessed
and used to enable productive collaboration and gain competitive
advantage.
Convergenomics is about the megatrends that are shaping how people
behave and organizations work. In this insightful analysis, Sang
Lee and David Olson describe how globalization, digitization,
changing demographics, changing industry mix, deregulation and
privatization, commoditization of processes, new value chains,
emerging new economies, deteriorating environment, and cultural
conflicts have led to what they define as a convergence revolution.
Lee and Olson discuss this convergence revolution from the
perspectives of technology, industry, knowledge, open-source
networking and bio-artificial convergence, and they explain how
human systems are transformed by what they have named
convergenomics. Understanding convergenomics can lead to innovative
strategic approaches and, the authors contend, more agile
businesses are already employing these approaches to become and
remain competitive and to generate greater value in a world
radically changed by e-commerce. Business leaders and 'students' of
strategy at all levels will learn from this book how revolutionary
developments can be embraced rather than feared, and how technology
that is potentially frightening in its complexity can be harnessed
and used to enable productive collaboration and gain competitive
advantage.
Enterprise Risk Management in Finance is a guide to measuring and
managing Enterprise-wide risks in financial institutions. Financial
institutions operate in a unique manner when compared to other
businesses. They are, by the nature of their business, highly
exposed to risk at every level, and indeed employ their own risk
management functions to manage many of these risks. However,
financial firms are also highly exposed at enterprise level.
Traditional approaches and frameworks for ERM are flawed when
applied to banks, asset managers or insurance houses, and a
different approach is needed. This new book provides a
comprehensive, technical guide to ERM for financial institutions.
Split into three parts, it first sets the scene, putting ERM in the
context of finance houses. It will examine the financial risks
already inherent in banking, and then insurance operations, and how
these need to be accounted for at a floor and enterprise level. The
book then provides the necessary tools to implement ERM in these
environments, including performance analysis, credit analysis and
forecasting applications. Finally, the book provides real life
cases of successful and not so successful ERM 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.
COVID-19 has spread around the world, causing tremendous structural
change, and severely affecting global supply chains and financial
operations. As such there is a need for analytic tools help deal
with the impact of the pandemic on the world's economies; these
tools are not panaceas and certainly won't cure the problems faced,
but they offer a means to aid governments, firms, and individuals
in coping with specific problems. This book provides an overview of
the COVID-19 pandemic and evaluates its effect on financial and
supply chain operations. It then discusses epidemic modeling,
presenting sources of quantitative and text data, and describing
how models are used to illustrate the pandemic impact on supply
chains, macroeconomic performance on financial operations. It
highlights the specific experiences of the banking system, which
offers predictions of the impact on the Swedish banking sector.
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. While it includes
some citations, it focuses on describing the methods and explaining
how they work, rather than on theory. The data sets and software
presented were all selected on the basis of their widespread
availability to any reader with computer links.
This book presents key concepts related to quantitative analysis in
business. It is targeted at business students (both undergraduate
and graduate) taking an introductory core course. 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 second edition adds material on
Tableau, a very useful software for business analytics. This
supplements the tools from Excel covered in the first edition, to
include Data Analysis Toolpak and SOLVER.
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. Part III
deals with 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 provides an overview of predictive methods demonstrated
by open source software modeling with Rattle (R') and WEKA.
Knowledge management involves application of human knowledge
(epistemology) with the technological advances of our current
society (computer systems) and big data, both in terms of
collecting data and in analyzing it. We see three types of analytic
tools. Descriptive analytics focus on reports of what has happened.
Predictive analytics extend statistical and/or artificial
intelligence to provide forecasting capability. It also includes
classification modeling. Prescriptive analytics applies
quantitative models to optimize systems, or at least to identify
improved systems. Data mining includes descriptive and predictive
modeling. Operations research includes all three. This book focuses
on prescriptive analytics. The book seeks to provide simple
explanations and demonstration of some descriptive tools. This
second edition provides more examples of big data impact, updates
the content on visualization, clarifies some points, and expands
coverage of association rules and cluster analysis. Chapter 1 gives
an overview in the context of knowledge management. Chapter 2
discusses some basic data types. Chapter 3 covers fundamentals time
series modeling tools, and Chapter 4 provides demonstration of
multiple regression modeling. Chapter 5 demonstrates regression
tree modeling. Chapter 6 presents autoregressive/integrated/moving
average models, as well as GARCH models. Chapter 7 covers the set
of data mining tools used in classification, to include special
variants support vector machines, random forests, and boosting.
Models are demonstrated using business related data. The style of
the book is intended to be descriptive, seeking to explain how
methods work, with some citations, but without deep scholarly
reference. The data sets and software are all selected for
widespread availability and access by any reader with computer
links.
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 intended to
present key concepts related to quantitative analysis in business.
It is targeted to 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 a comprehensive guide to several aspects of risk,
including information systems, disaster management, supply chain
and disaster management perspectives. A major portion 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. Each
chapter of this book can be used as a unique module on a different
topics with dedicated examples, definitions and discussion notes.
This book comes at a time when the world is increasingly challenged
by different forms of risk and how to manage them. Events of the
21st Century have made enterprise risk management even more
critical. Risks such as suspicions surrounding top-management
structures, financial and technology bubbles (especially since
2008), as well as the demonstrated risk from terrorism, such as the
9/11 attack in the U.S. as well as more recent events in France,
Belgium, and other locations in Europe, have a tremendous impact on
many facets of business. Businesses, in fact, exist to cope with
risk in their area of specialization.
This book offers an overview of knowledge management. It starts
with an introduction to the subject, placing descriptive models in
the context of the overall field as well as within the more
specific field of data mining analysis. Chapter 2 covers data
visualization, including directions for accessing R open source
software (described through Rattle). Both R and Rattle are free to
students. Chapter 3 then describes market basket analysis,
comparing it with more advanced models, and addresses the concept
of lift. Subsequently, Chapter 4 describes smarketing RFM models
and compares it with more advanced predictive models. Next, Chapter
5 describes association rules, including the APriori algorithm and
provides software support from R. Chapter 6 covers cluster
analysis, including software support from R (Rattle), KNIME, and
WEKA, all of which are open source. Chapter 7 goes on to describe
link analysis, social network metrics, and open source NodeXL
software, and demonstrates link analysis application using
PolyAnalyst output. Chapter 8 concludes the monograph. Using
business-related data to demonstrate models, this descriptive book
explains how methods work with some citations, but without detailed
references. The data sets and software selected are widely
available and can easily be accessed.
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. The book's main approach is above
all descriptive, seeking to explain how the methods concretely
work; as such, it includes selected citations, but does not go into
deep scholarly reference. The data sets and software reviewed were
selected for their widespread availability to all readers with
internet access.
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.
The book focuses on demonstrating common business data mining
applications. It provides exposure to the data mining process, to
include problem identification, data management, and available
modeling tools. The book takes the approach of demonstrating
typical business data sets with open source software. KNIME is a
very easy-to-use tool, and is used as the primary means of
demonstration. R is much more powerful and is a commercially viable
data mining tool. We also demonstrate WEKA, which is a highly
useful academic software, although it is difficult to manipulate
test sets and new cases, making it problematic for commercial use.
This book brings a unique combination of years of experience in
academics research and studies in regards to "ERP systems" with
years of experience from a practitioner's perspective. Each year
billions of dollars are spent 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?
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.
One of the most important tasks faced by decision-makers in
business and government is that of selection. Selection problems
are challenging in that they require the balancing of multiple,
often conflicting, criteria. 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. The first chapters present
general ideas which underpin the methodologies used to design these
aids. Subsequent chapters then focus on specific decision aids and
demonstrate some of the software which implement these ideas. A
final chapter provides a comparative analysis of their strengths
and weaknesses.
A comprehensive guide to credit repair and enhancement. Includes
individual letters for almost any scenario. Easy to follow step by
step instructions. Credit repair guidelines that every one needs to
know. No fluff, no filler, REAL ANSWERS for REAL P
1 Facility Location Problems The location problem has been with
humans for all of their history. In the past, many rulers had the
decision of locating their capital. Reasons for selecting various
locations included central location, transportation benefits to
foster trade, and defensibility. The development of industry
involved location problems for production facilities and trade
outlets. Obvious th criteria for location ofbusiness facilities
includedprofit impact. 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. Location decisions were made
considering all potential gains and expenses. Some judgment was
required, because while most benefits and costs could be measured
accurately, not all could be. Successful business practice depended
on the soundjudgment of the decision-maker in solvinglocation
problems. Each of these enterprises produced some wastes. Finding a
location to dispose of these wastes was not a difficult task. In
less-enlightened times, governments resorted to fiat and
land-condemnationto take the sites needed th for disposal. In the
19 century, industry grew rapidly in Great Britain and elsewhere as
mass production served expanding populations of consumers. The
by-products of mass-production were often simply discarded in the
most expeditious manner. There are still mountains in the United
States Introduction 2 with artificial facades created from the
excess material discarded from mining activity. We have developed
the ability to create waste of lethal toxicity
A step by step proven turnkey system that will walk you through
buying and selling residential real estate in today's turbulent
economy at FULL MARKET VALUE. Great for first time investors and
seasoned professionals. Our system is unique for today's market. We
have the secrets to tapping into a huge population of families that
the real estate world does not know how to service. Become part of
the changing of an industry while building incredible wealth.
Risk management has become a critical part of doing business in the
twenty-first century. This book is a collection of material about
enterprise risk management, and the role of risk in decision
making. Part I introduces the topic of enterprise risk management.
Part II presents enterprise risk management from perspectives of
finance, accounting, insurance, supply chain operations, and
project management. Technology tools are addressed in Part III,
including financial models of risk as well as accounting aspects,
using data envelopment analysis, neural network tools for credit
risk evaluation, and real option analysis applied to information
techn- ogy outsourcing. In Part IV, three chapters present
enterprise risk management experience in China, including banking,
chemical plant operations, and information technology. Lincoln, USA
David L. Olson Toronto, Canada Desheng Wu February 2008 v Contents
Part I Preliminary 1 Introduction . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
David L. Olson & Desheng Wu 2 The Human Reaction to Risk and
Opportunity . . . . . . . . . . . . . . . . . . . 7 David R. Koenig
Part II ERM Perspectives 3 Enterprise Risk Management: Financial
and Accounting Perspectives . . . . . . . . . . . . . . . . . . . .
. . . . . . 25 Desheng Wu & David L. Olson 4 An Empirical Study
on Enterprise Risk Management in Insurance . . 39 Madhusudan
Acharyya 5 Supply Chain Risk Management . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 57 David L. Olson & Desheng
Wu 6 Two Polar Concept of Project Risk Management. . . . . . . . .
. . . . . . . . . 69 Seyed Mohammad Seyedhoseini, Siamak Noori
& Mohammed AliHatefi Part III ERM Technologies 7 The
Mathematics of Risk Transfer. . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 95 Marcos Escobar & Luis Seco 8 Stable
Models in Risk Management. . . . . . . . . . . . . . . . . . . . .
. . . . . . . .
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