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Decision support systems have experienced a marked increase in attention and importance over the past 25 years. The aim of this book is to survey the decision support system (DSS) field – covering both developed territory and emergent frontiers. It will give the reader a clear understanding of fundamental DSS concepts, methods, technologies, trends, and issues. It will serve as a basic reference work for DSS research, practice, and instruction. To achieve these goals, the book has been designed according to a ten-part structure, divided in two volumes with chapters authored by well-known, well-versed scholars and practitioners from the DSS community.
A decision support system (DSS) is an intelligent information system that uses data, models it, processes or analyzes it using problem-specific methodologies, and assists the user in the decision-making process through a graphical user interface (GUI). Developing Spreadsheet-Based Decision Support Systems is a comprehensive book that describes how to build decision support systems using the Excel spreadsheet framework and the VBA programming language. This book illustrates complete decision support development applications through several case studies arising in operations research, industrial engineering, management, and business administration.
CD-ROM contains: Crystal Ball -- TreePlan -- AnimaLP -- Queue -- ExcelWorkbooks.
For MIS specialists and non-specialists alike, this text is a comprehensive, readable, understandable guide to the concepts and applications of decision support systems.
Excel Basics to Blackbelt is intended to serve as an accelerated guide to decision support designs. Its structure is designed to enhance the skills in Excel of those who have never used it for anything but possibly storing phone numbers, enabling them to reach a level of mastery that will allow them to develop user interfaces and automated applications. To accomplish this, the major theme of the text is 'the integration of the basic'; as a result readers will be able to develop decision support tools that are at once highly intuitive from a working-components perspective but also highly significant from the perspective of practical use and distribution. Applications integration discussed includes the use of MS MapPoint, XLStat and RISKOptimizer, as well as how to leverage Excel's iteration mode, web queries, visual basic code, and interface development. There are ample examples throughout the text.
Quantitative Methods for Decision Making is a comprehensive guide that provides students with the key techniques and methodology they will need to successfully engage with all aspects of quantitative analysis and decision making; both on their undergraduate course, and in the larger context of their future business environments. Organized in accordance with the enterprise functional structure where the decision making takes place, the textbook encompasses a broad range of functions, each detailed with clear examples illustrated through the single application tool Microsoft Excel. The authors approach a range of methods which are divided into major enterprise functions such as marketing, sales, business development, manufacturing, quality control and finance; illustrating how the methods can be applied in practice and translated into a working environment. Each chapter is packed with short case studies to exemplify the practical use of techniques, and contains a wealth of exercises after key sections and concepts, giving students the opportunity to monitor their own progress using the solutions at the back of the book. An Online Resource Centre accompanies the text and includes: For students: - Numerical skills workbook with additional exercises, questions and content - Data from the examples and exercises in the book - Online glossary of terms - Revision tips - Visual walkthrough videos covering the application of a range of quantitative methods - Appendices to the book For lecturers: - Instructor's manual including solutions from the text and a guide to structuring lectures and seminars - PowerPoint presentations - Test bank with questions for each chapter - Suggested assignment and examination questions
Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all factors that affect a business, such as customers, competitors, business partners, economic environment, and internal operations, therefore enabling optimal decisions to be made. Business Intelligence provides readers with an introduction and practical guide to the mathematical models and analysis methodologies vital to business intelligence. This book: Combines detailed coverage with a practical guide to the mathematical models and analysis methodologies of business intelligence. Covers all the hot topics such as data warehousing, data mining and its applications, machine learning, classification, supply optimization models, decision support systems, and analytical methods for performance evaluation. Is made accessible to readers through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies. Explains how to utilise mathematical models and analysis models to make effective and good quality business decisions. This book is aimed at postgraduate students following data analysis and data mining courses. Researchers looking for a systematic and broad coverage of topics in operations research and mathematical models for decision-making will find this an invaluable guide.