This book presents the Multiple Criteria Decision Making (MCDM) paradigm for modelling agricultural decision-making in three parts. The first part, comprising two chapters, is philosophical in nature and deals with the concepts that define the underlying structure of the MCDM paradigm. The second part is the largest part consisting of five chapters, each of which presents the logic of a specific MCDM technique, and demonstrates how it can be used to model a particular decision problem. In the final part, some selected applications of the MCDM techniques to agricultural problems are presented and thus reinforce the development of an understanding of the MCDM paradigm.

The book has been designed for use at different levels: as a textbook for final year undergraduate and postgraduate courses in modelling for decision-making; as a manual for researchers and practising modellers; and, as general reference on the application of MCDM techniques. Readers with basic appreciation of algebra and linear programming can easily follow the contents of this book.

This masterly book substantially extends Howard Raiffa's earlier classic, "The Art and Science of Negotiation," It does so by incorporating three additional supporting strands of inquiry: individual decision analysis, judgmental decision making, and game theory. Each strand is introduced and used in analyzing negotiations.

The book starts by considering how analytically minded parties can generate joint gains and distribute them equitably by negotiating with full, open, truthful exchanges. The book then examines models that disengage step by step from that ideal. It also shows how a neutral outsider (intervenor) can help all negotiators by providing joint, neutral analysis of their problem.

Although analytical in its approach--building from simple hypothetical examples--the book can be understood by those with only a high school background in mathematics. It therefore will have a broad relevance for both the theory and practice of negotiation analysis as it is applied to disputes that range from those between family members, business partners, and business competitors to those involving labor and management, environmentalists and developers, and nations.

This book constitutes the refereed proceedings of the 2021 International Conference on Business Intelligence and Information Technology (BIIT 2021) held in Harbin, China, during December 18-20, 2021. BIIT 2021 is organized by the School of Computer and Information Engineering, Harbin University of Commerce, and supported by Scientific Research Group in Egypt (SRGE), Egypt. The papers cover current research in electronic commerce technology and application, business intelligence and decision making, digital economy, accounting informatization, intelligent information processing, image processing and multimedia technology, signal detection and processing, communication engineering and technology, information security, automatic control technique, data mining, software development, and design, blockchain technology, big data technology, artificial intelligence technology.

'Full of compelling advice on how to lead more effectively by choosing your words more wisely' - ADAM GRANT, author of Originals and Give and Take FT Book of the Month Your words matter more than you think Most of us use the language we inherited from a time when workers worked with their hands and managers worked with their heads. Today, your people do much more than simply follow orders. They contribute to performance and solve problems, and it's time we updated our language to reflect that. In Leadership Is Language, former US Navy captain L. David Marquet offers a radical playbook to empower your people and put your team on a path to continuous improvement. The framework will help you achieve the right balance between deliberation and action, and take bold risks without endangering your mission. Among other things, you'll learn: * How to avoid the seven common sins of questioning, from binary questions (should we do A or B?) to self-affirming questions (B is the better option, right?) * Why you should vote first, then discuss, when deciding on a plan with your team, rather than voting after discussion * Why it's better to give your people information instead of instructions As a submarine captain, Marquet used his counterintuitive model of leadership to turn the worst-performing submarine crew into the best-performing one in the fleet, a story he recounted in his bestselling book Turn the Ship Around! Now, in Leadership Is Language, he draws on a wide range of examples, from the 2017 Oscars Best Picture mishap to the tragic sinking of the SS El Faro, to show you exactly how the words you use (and don't use) impact how your people contribute.

OLAP enables users to access information from multidimensional data warehouses almost instantly, to view information in any way they like, and to cleanly specify and carry out sophisticated calculations. Although many commercial OLAP tools and products are now available, OLAP is still a difficult and complex technology to master.
* Substantially updated with expanded coverage of implementation methods for data storage, access, and calculation; also, new chapters added to combine OLAP with data warehouse, mining, and decision support tools
* Teaches the best practices for building OLAP models that improve business and organizational decision-making, completely independent of commercial tools, using revised case studies
* Companion Web site provides updates on OLAP standards and tools, code examples, and links to valuable resources

This book gives a comprehensive and thorough insight into all of today's common methods of modern market risk management including coverage of variance, co-variance, historical simulation, Monte Carlo, 'Greek' ratios, and statistical concepts such as volatility and correlation. In addition, all the important modern derivatives and their pricing methods (i.e. present value, Black Scholes, binomial trees, Monte Carlo) are presented and guidelines are given as to exactly which method can be used for which instruments.

This book aims to provide relevant theoretical frameworks and the latest empirical research findings in Internet of Things (IoT) in Management Science and Operations Research. It starts with basic concept and present cases, applications, theory, and potential future. The contributed chapters to the book cover wide array of topics as space permits. Examples are from smart industry; city; transportation; home and smart devices. They present future applications, trends, and potential future of this new discipline. Specifically, this book provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning capabilities of managing IoT. This book deals with the implementation of latest IoT research findings in practice at the global economy level, at networks and organizations, at teams and work groups and, finally, IoT at the level of players in the networked environments. This book is intended for professionals in the field of engineering, information science, mathematics, economics, and researchers who wish to develop new skills in IoT, or who employ the IoT discipline as part of their work. It will improve their understanding of the strategic role of IoT at various levels of the information and knowledge organization. The book is complemented by a second volume of the same editors with practical cases.

This book mainly introduces some techniques of decision-making, uncertain reasoning and regression analysis under the hesitant fuzzy environment and expands the applications of hesitant fuzzy sets in solving practical problems. The book pursues three major objectives: (1) to introduce some techniques about decision-making, uncertain reasoning and regression analysis under the hesitant fuzzy environment, (2) to prove these techniques theoretically and (3) to apply the involved techniques to practical problems. The book is especially valuable for readers to understand how hesitant fuzzy set could be employed in decision-making, uncertain reasoning and regression analysis and motivates researchers to expand more application fields of hesitant fuzzy set.

Originally published in 1972. Managers at all levels and management students may all expect to become involved increasingly in the development of computer-based information systems. This book, based upon practical training given to systems analysts, is designed to help managers achieve a route to successful implementation of computer systems, or to prepare them for involvement in computer projects.

This research-oriented book presents key contributions on architecting the digital transformation. It includes the following main sections covering 20 chapters: * Digital Transformation * Digital Business * Digital Architecture * Decision Support * Digital Applications Focusing on digital architectures for smart digital products and services, it is a valuable resource for researchers, doctoral students, postgraduates, graduates, undergraduates, academics and practitioners interested in digital transformation.

This book combines two distinctive topics: data science/image analysis and materials science. The purpose of this book is to show what type of nano material problems can be better solved by which set of data science methods. The majority of material science research is thus far carried out by domain-specific experts in material engineering, chemistry/chemical engineering, and mechanical & aerospace engineering. The book could benefit materials scientists and manufacturing engineers who were not exposed to systematic data science training while in schools, or data scientists in computer science or statistics disciplines who want to work on material image problems or contribute to materials discovery and optimization. This book provides in-depth discussions of how data science and operations research methods can help and improve nano image analysis, automating the otherwise manual and time-consuming operations for material engineering and enhancing decision making for nano material exploration. A broad set of data science methods are covered, including the representations of images, shape analysis, image pattern analysis, and analysis of streaming images, change points detection, graphical methods, and real-time dynamic modeling and object tracking. The data science methods are described in the context of nano image applications, with specific material science case studies.

A Text on the Foundation Processes, Analytical Principles, and Implementation Practices of Engineering Risk Management Drawing from the author's many years of hands-on experience in the field, Analytical Methods for Risk Management: A Systems Engineering Perspectivepresents the foundation processes and analytical practices for identifying, analyzing, measuring, and managing risk in traditional systems, systems-of-systems, and enterprise systems. Balances Risk and Decision Theory with Case Studies and Exercises After an introduction to engineering risk management, the book covers the fundamental axioms and properties of probability as well as key aspects of decision analysis, such as preference theory and risk/utility functions. It concludes with a series of essays on major analytical topics, including how to identify, write, and represent risks; prioritize risks in terms of their potential impacts on a systems project; and monitor progress when mitigating a risk's potential adverse effects. The author also examines technical performance measures and how they can combine into an index to track an engineering system's overall performance risk. In addition, he discusses risk management in the context of engineering complex, large-scale enterprise systems. Applies Various Methods to Risk Engineering and Analysis Problems This practical guide enables an understanding of which processes and analytical techniques are valid and how they are best applied to specific systems engineering environments. After reading this book, you will be on your way to managing risk on both traditional and advanced engineering systems.

Working through 7 practical steps, you will be able to identify and reduce your biggest risks so you can get evidence-based confidence to move forward:

• Do you have a new product or business idea but don’t know where to start?
• Do you want to know if your idea works before you’ve wasted too much time or money?
• Do you want to harness the power of lean thinking to move quicker and faster?

The Really Good Idea Test gives you a step-by-step plan gather evidence, so you can develop and improve your idea.

Step 1. My hypothesis is that ...
Step 2. I already have evidence that ….
Step 3. The most crucial evidence I now need is ...
Step 4. To get that evidence I am going to …
Step 5. And get the answers to these questions …
Step 6. My hypothesis is right if …
Step 7. I now know that …

Follow the 7 steps to talk to customers, work through the riskiest assumptions and work out if your idea is worth pursuing.

We've outsourced too much of our thinking. How do we get it back? Have you ever followed your GPS device to a deserted parking lot? Or unquestioningly followed the advice of an expert&#8212perhaps a doctor or financial adviser&#8212only to learn later that your own thoughts and doubts were correct? And what about the stories we've all heard over the years about sick patients&#8212whether infected with Ebola or COVID-19&#8212who were sent home or allowed to travel because busy staff people were following a protocol to the letter rather than using common sense? Why and how do these kinds of things happen? As Harvard lecturer and global trend watcher Vikram Mansharamani shows in this eye-opening and perspective-shifting book, our complex, data-flooded world has made us ever more reliant on experts, protocols, and technology. Too often, we've stopped thinking for ourselves. With stark and compelling examples drawn from business, sports, and everyday life, Mansharamani illustrates how in a very real sense we have outsourced our thinking to a troubling degree, relinquishing our autonomy. Of course, experts, protocols, and computer-based systems are essential to helping us make informed decisions. What we need is a new approach for integrating these information sources more effectively, harnessing the value they provide without undermining our ability to think for ourselves. The author provides principles and techniques for doing just that, empowering readers with a more critical and nuanced approach to making decisions. Think for Yourself is an indispensable guide for those looking to restore self-reliant thinking in a data-driven and technology-dependent yet overwhelmingly uncertain world.

This book has resulted from the activities of IFAC TC 5.2 "Manufacturing Modelling for Management and Control". The book offers an introduction and advanced techniques of scheduling applications to cloud manufacturing and Industry 4.0 systems for larger audience. This book uncovers fundamental principles and recent developments in the theory and application of scheduling methodology to cloud manufacturing and Industry 4.0. The purpose of this book is to present recent developments in scheduling in cloud manufacturing and Industry 4.0 and to systemize these developments in new taxonomies and methodological principles to shape this new research domain. This book addresses the needs of both researchers and practitioners to uncover the challenges and opportunities of scheduling techniques' applications to cloud manufacturing and Industry 4.0. For the first time, it comprehensively conceptualizes scheduling in cloud manufacturing and Industry 4.0 systems as a new research domain. The chapters of the book are written by the leading international experts and utilize methods of operations research, industrial engineering and computer science. Such a multi-disciplinary combination is unique and comprehensively deciphers major problem taxonomies, methodologies, and applications to scheduling in cloud manufacturing and Industry 4.0.

The customer problem in the public sector appears when too many processes are in place and staff volumes are too large to adapt to sudden change. As situations evolve and solutions are required, public managers are faced with an overload of information for decision-making, as normal day-to-day policy is overlooked to accommodate management by crisis. Generally, emergency situations call for effective steps to be taken, constrained by short time frames and a dispersed public workforce. Managing teams require structure in their response to an evolving crises, which is generally a difficult position to attain when information and resources are limited. Protocol and response plans are only activated in extreme crises, leaving a gap in response when overload has been reached but is not within the stipulated margins. Recognition at this stage is important if successful outcomes are to be achieved. This book proposes an 8-point model, which it labels the DALI Model, for responding to these situations, to simplify and synthesize decision-making processes.

This book deals with stochastic combinatorial optimization problems in supply chain disruption management, with a particular focus on management of disrupted flows in customer-driven supply chains. The problems are modeled using a scenario based stochastic mixed integer programming to address riskneutral, risk-averse and mean-risk decision-making in the presence of supply chain disruption risks. The book focuses on integrated disruption mitigation and recovery decision-making and innovative, computationally efficient multi-portfolio approach to supply chain disruption management, e.g., selection of primary and recovery supply portfolios, demand portfolios, capacity portfolios, etc. Numerous computational examples throughout the book, modeled in part on realworld supply chain disruption management problems, illustrate the material presented and provide managerial insights. Many propositions formulated in the book lead to a deep understanding of the properties of developed stochastic mixed integer programs and optimal solutions. In the computational examples, the proposed mathematical programming models are solved using an advanced algebraic modeling language such as AMPL and CPLEX, GUROBI and XPRESS solvers. The knowledge and tools provided in the book allow the reader to model and solve supply chain disruption management problems using commercially available software for mixed integer programming. Using the end-of chapter problems and exercises, the monograph can also be used as a textbook for an advanced course in supply chain risk management. After an introductory chapter, the book is then divided into six main parts. Part I addresses selection of a supply portfolio; Part II considers integrated selection of supply portfolio and scheduling; Part III looks at integrated, equitably efficient selection of supply portfolio and scheduling; Part IV examines integrated selection of primary and recovery supply and demand portfolios and production and inventory scheduling, Part V deals with selection of resilient supply portfolio in multitier supply chain networks; and Part VI addresses selection of cybersecurity safequards portfolio for disruption management of information flows in supply chains.

This book presents a range of qualitative and quantitative analyses in areas such as cybersecurity, sustainability, multivariate analysis, customer satisfaction, parametric programming, software reliability growth modeling, and blockchain technology, to name but a few. It also highlights integrated methods and practices in the areas of machine learning and genetic algorithms. After discussing applications in supply chains and logistics, cloud computing, six sigma, production management, big data analysis, satellite imaging, game theory, biometric systems, quality, and system performance, the book examines the latest developments and breakthroughs in the field of science and technology, and provides novel problem-solving methods. The themes discussed in the book link contributions by researchers and practitioners from different branches of engineering and management, and hailing from around the globe. These contributions provide scholars with a platform to derive maximum utility in the area of analytics by subscribing to the idea of managing business through system sciences, operations, and management. Managers and decision-makers can learn a great deal from the respective chapters, which will help them devise their own business strategies and find real-world solutions to complex industrial problems.

This book includes a spectrum of concepts, such as performance, productivity, operations research, econometrics, and data science, for the practically and theoretically important areas of 'productivity analysis/data envelopment analysis' and 'data science/big data'. Data science is defined as the collection of scientific methods, processes, and systems dedicated to extracting knowledge or insights from data and it develops on concepts from various domains, containing mathematics and statistical methods, operations research, machine learning, computer programming, pattern recognition, and data visualisation, among others. Examples of data science techniques include linear and logistic regressions, decision trees, Naive Bayesian classifier, principal component analysis, neural networks, predictive modelling, deep learning, text analysis, survival analysis, and so on, all of which allow using the data to make more intelligent decisions. On the other hand, it is without a doubt that nowadays the amount of data is exponentially increasing, and analysing large data sets has become a key basis of competition and innovation, underpinning new waves of productivity growth. This book aims to bring a fresh look onto the various ways that data science techniques could unleash value and drive productivity from these mountains of data. Researchers working in productivity analysis/data envelopment analysis will benefit from learning about the tools available in data science/big data that can be used in their current research analyses and endeavours. The data scientists, on the other hand, will also get benefit from learning about the plethora of applications available in productivity analysis/data envelopment analysis.

"Energy Budgets at Risk" "(EBar)"(R) provides everyone from facility energy managers and financial managers to government policy-makers and electric utilities program planners with the background information required to understand energy cost, price, efficiency, and related issues important in developing a balanced approach to facility energy risk management. Throughout the book, respected energy economist Dr. Jerry Jackson clearly shows how to reduce energy costs and increase cash flows by using risk management concepts developed in the financial industry.

In Collaboration, author Morten Hansen takes aim at what many leaders inherently know: in today's competitive environment, companywide collaboration is an imperative for successful strategy execution, yet the sought-after synergies are rarely, if ever, realized. In fact, most cross-unit collaborative efforts end up wasting time, money, and resources. How can managers avoid the costly traps of collaboration and instead start getting the results they need? In this book, Hansen shows managers how to get collaboration right through "disciplined collaboration"-- a practical framework and set of tools managers can use to: * Assess when--and when not--to pursue collaboration across units to achieve goals * Identify and overcome the four barriers to collaboration * Get people to buy into the larger picture, even when they own only a small piece of it * Be a "T-Shaped Manager," collaborating across divisions while still working deeply in your own unit * Create networks across the organization that are not large, but nimble and effective Based on the author's long-running research, in-depth case studies, and company interviews, Collaboration delivers practical advice and tools to help your organization collaborate--for real results.

This book offers a comprehensive and readable introduction to modern business and data analytics. It is based on the use of Excel, a tool that virtually all students and professionals have access to. The explanations are focused on understanding the techniques and their proper application, and are supplemented by a wealth of in-chapter and end-of-chapter exercises. In addition to the general statistical methods, the book also includes Monte Carlo simulation and optimization. The second edition has been thoroughly revised: new topics, exercises and examples have been added, and the readability has been further improved. The book is primarily intended for students in business, economics and government, as well as professionals, who need a more rigorous introduction to business and data analytics - yet also need to learn the topic quickly and without overly academic explanations.