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Systems and Decision Making A Management Science Approach Hans G Daellenbach University of Canterbury, Christchurch, New Zealand Traditional methods of problem solving, based on the cause-and-effect model, can no longer cope with the complex situations in which decisions have to be made today. These problem situations occur within a systems context. Most of these systems are created and controlled by humans and it is, therefore, important that decision making is guided by a systematic and comprehensive methodology that helps the decision maker to make effective use of his/her extensive but limited powers of reasoning. Systems and Decision Making combines contemporary systems work with Operations Research (OR). Daellenbach places an emphasis on developing a methodology for decision situations that lend themselves to quantitative approaches rather than give an elementary survey of many OR/MS techniques. It incorporates some of the learnings of soft systems methodology for more practical problem solving, particularly at the problem identification and formulation stages. The text also shows that the scientific component of modelling can be considerably enhanced by the use of various diagrammatic devices. The second part of the book studies a number of topics important for the analyst, such as how to deal with the time element, with constraints, with uncertainty, and with multiple goals. These are demonstrated by various OR/MS techniques. Systems and Decision Making is an excellent core text for undergraduate and graduate students of systems, management science and MBA courses.
Combines topics from two traditionally distinct quantitative subjects, probability/statistics and management science/optimization, in a unified treatment of quantitative methods and models for management. Stresses those fundamental concepts that are most important for the practical analysis of management decisions: modeling and evaluating uncertainty explicitly, understanding the dynamic nature of decision-making, using historical data and limited information effectively, simulating complex systems, and allocating scarce resources optimally.
The focus of this book is on using data and spreadsheet models effectively for the analysis of business problems and decision making. Included are discussions of building good spreadsheet models; data collection, visualization, and statistical analysis; forecasting; optimization using Excel Solver; decision and risk analysis; and simulation using Crystal Ball add-in for Excel and Arena BE. The principal focus is on gaining insight and intuition for better decisions, with applications in operations planning, finance, and marketing.
Accessible and concise, this exciting new textbook examines data analytics from a managerial and organizational perspective and looks at how they can help managers become more effective decision-makers. The book successfully combines theory with practical application, featuring case studies, examples and a ‘critical incidents’ feature that make these topics engaging and relevant for students of business and management. The book features chapters on cutting-edge topics, including: • Big data • Analytics • Managing emerging technologies and decision-making • Managing the ethics, security, privacy and legal aspects of data-driven decision-making The book is accompanied by an Instructor’s Manual, PowerPoint slides and access to journal articles. Suitable for management students studying business analytics and decision-making at undergraduate, postgraduate and MBA levels.
Application of Decision Science in Business and Management is a book where each chapter has been contributed by a different author(s). The chapters introduce and demonstrate a decision-making theory to practice case studies. It demonstrates key results for each sector with diverse real-world case studies. Theory is accompanied by relevant analysis techniques, with a progressive approach building from simple theory to complex and dynamic decisions with multiple data points, including big data, lot of data, etc. Computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques are expertly blended to support analysis of multi-criteria decision-making problems with defined constraints and requirements. The book provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning abilities of decision making. It is complementary to other sub-disciplines such as economics, finance, marketing, decision and risk analysis, etc.
Written for a wide-range of mathematical abilities, this comprehensive, accessible overview emphasizes the conceptual aspects of decision-making rather than mathematical techniques or computer methods. Structured around a clear framework, the text first builds up the basic ideas of systems and shows how hard "OR" incorporates these concepts into its modeling process. It also incorporates important real-world aspects such as time, decision-making over time, constraints, uncertainty and multiple objectives into this framework, and shows how conflicts and ambiguities of world views lead to fundamental changes in the aims and models of decision-making approaches. A companion website is located at: http://www.palgrave.com/business/daellenbach/index.asp
This book contains international perspectives that unifies the themes of strategic management, decision theory, and data science. It contains thought-provoking presentations of case studies backed by adequate analysis adding significance to the discussions. Most of the decision-making models in use do take due advantage of collection and processing of relevant data using appropriate analytics oriented to provide inputs into effective decision-making. The book showcases applications in diverse fields including banking and insurance, portfolio management, inventory analysis, performance assessment of comparable economic agents, managing utilities in a health-care facility, reducing traffic snarls on highways, monitoring achievement of some of the sustainable development goals in a country or state, and similar other areas that showcase policy implications. It holds immense value for researchers as well as professionals responsible for organizational decisions.
Multicriterion Decision in Management: Principles and Practice is the first multicriterion analysis book devoted exclusively to discrete multicriterion decision making. Typically, multicriterion analysis is used in two distinct frameworks: Firstly, there is multiple criteria linear programming, which is an extension of the results of linear programming and its associated algorithms. Secondly, there is discrete multicriterion decision making, which is concerned with choices among a finite number of possible alternatives such as projects, investments, decisions, etc. This is the focus of this book. The book concentrates on the basic principles in the domain of discrete multicriterion analysis, and examines each of these principles in terms of their properties and their implications. In multicriterion decision analysis, any optimum in the strict sense of the term does not exist. Rather, multicriterion decision making utilizes tools, methods, and thinking to examine several solutions, each having their advantages and disadvantages, depending on one's point of view. Actually, various methods exist for reaching a good choice in a multicriterion setting and even a complete ranking of the alternatives. The book describes and compares these methods, so-called `aggregation methods', with their advantages and their shortcomings. Clearly, organizations are becoming more complex, and it is becoming harder and harder to disregard complexity of points of view, motivations, and objectives. The day of the single objective (profit, social environment, etc. ) is over and the wishes of all those involved in all their diversity must be taken into account. To do this, a basic knowledge of multicriterion decision analysis is necessary. The objective of this book is to supply that knowledge and enable it to be applied. The book is intended for use by practitioners (managers, consultants), researchers, and students in engineering and business.
This book is about prescriptive analytics. It provides business practitioners and students with a selected set of management science and optimization techniques and discusses the fundamental concepts, methods, and models needed to understand and implement these techniques in the era of Big Data. A large number of management science models exist in the body of literature today. These models include optimization techniques or heuristics, static or dynamic programming, and deterministic or stochastic modeling. The topics selected in this book, mathematical programming and simulation modeling, are believed to be among the most popular management science tools, as they can be used to solve a majority of business optimization problems. Over the years, these techniques have become the weapon of choice for decision makers and practitioners when dealing with complex business systems.