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In Decision Modelling And Information Systems: The Information Value Chain the authors explain the interrelationships between the decision support, decision modelling, and information systems. The first two parts of the book focus on the interdisciplinary decision support framework, in which mathematical programming (optimization) is taken as the inference engine. The role of business analytics and its relationship with recent developments in organisational theory, decision modelling, information systems and information technology are considered in depth. Part three of the book includes a carefully chosen selection of invited contributions from internationally-known researchers. These contributions are thought-provoking and cover key decision modelling and information systems issues. The final part of the book covers contemporary developments in the related area of business intelligence considered within an organizational context. The topics cover computing delivered across the web, management decision-making, and socio-economic challenges that lie ahead. It is now well accepted that globalisation and the impact of digital economy are profound; and the role of e-business and the delivery of decision models (business analytics) across the net lead to a challenging business environment. In this dynamic setting, decision support is one of the few interdisciplinary frameworks that can be rapidly adopted and deployed to so that businesses can survive and prosper by meeting these new challenges.
Providing a comprehensive overview of various methods and applications in decision engineering, this book presents chapters written by a range experts in the field. It presents conceptual aspects of decision support applications in various areas including finance, vendor selection, construction, process management, water management and energy, agribusiness , production scheduling and control, and waste management. In addition to this, a special focus is given to methods of multi-criteria decision analysis. Decision making in organizations is a recurrent theme and is essential for business continuity. Managers from various fields including public, private, industrial, trading or service sectors are required to make decisions. Consequently managers need the support of these structured methods in order to engage in effective decision making. This book provides a valuable resource for graduate students, professors and researchers of decision analysis, multi-criteria decision analysis and group decision analysis. It is also intended for production engineers, civil engineers and engineering consultants.
Asset Condition, Information Systems and Decision Models, is the second volume of the Engineering Asset Management Review Series. The manuscripts provide examples of implementations of asset information systems as well as some practical applications of condition data for diagnostics and prognostics. The increasing trend is towards prognostics rather than diagnostics, hence the need for assessment and decision models that promote the conversion of condition data into prognostic information to improve life-cycle planning for engineered assets. The research papers included here serve to support the on-going development of Condition Monitoring standards. This volume comprises selected papers from the 1st, 2nd, and 3rd World Congresses on Engineering Asset Management, which were convened under the auspices of ISEAM in collaboration with a number of organisations, including CIEAM Australia, Asset Management Council Australia, BINDT UK, and Chinese Academy of Sciences, Beijing University of Chemical Technology, China. Asset Condition, Information Systems and Decision Models will be of particular interest to finance, maintenance, and operations personnel whose roles directly affect the capability value of engineering asset base, as well as asset managers in both industry and government.
In the current fast-paced and constantly changing business environment, it is more important than ever for organizations to be agile, monitor business performance, and meet with increasingly stringent compliance requirements. Written by pioneering consultants and bestselling authors with track records of international success, The Decision Model: A
In financially constrained health systems across the world, increasing emphasis is being placed on the ability to demonstrate that health care interventions are not only effective, but also cost-effective. This book deals with decision modelling techniques that can be used to estimate the value for money of various interventions including medical devices, surgical procedures, diagnostic technologies, and pharmaceuticals. Particular emphasis is placed on the importance of the appropriate representation of uncertainty in the evaluative process and the implication this uncertainty has for decision making and the need for future research. This highly practical guide takes the reader through the key principles and approaches of modelling techniques. It begins with the basics of constructing different forms of the model, the population of the model with input parameter estimates, analysis of the results, and progression to the holistic view of models as a valuable tool for informing future research exercises. Case studies and exercises are supported with online templates and solutions. This book will help analysts understand the contribution of decision-analytic modelling to the evaluation of health care programmes. ABOUT THE SERIES: Economic evaluation of health interventions is a growing specialist field, and this series of practical handbooks will tackle, in-depth, topics superficially addressed in more general health economics books. Each volume will include illustrative material, case histories and worked examples to encourage the reader to apply the methods discussed, with supporting material provided online. This series is aimed at health economists in academia, the pharmaceutical industry and the health sector, those on advanced health economics courses, and health researchers in associated fields.
"A very rich book sprinkled with real-life examples as well as battle-tested advice.” —Pierre Haren, VP ILOG, IBM "James does a thorough job of explaining Decision Management Systems as enablers of a formidable business transformation.” —Deepak Advani, Vice President, Business Analytics Products and SPSS, IBM Build Systems That Work Actively to Help You Maximize Growth and Profits Most companies rely on operational systems that are largely passive. But what if you could make your systems active participants in optimizing your business? What if your systems could act intelligently on their own? Learn, not just report? Empower users to take action instead of simply escalating their problems? Evolve without massive IT investments? Decision Management Systems can do all that and more. In this book, the field’s leading expert demonstrates how to use them to drive unprecedented levels of business value. James Taylor shows how to integrate operational and analytic technologies to create systems that are more agile, more analytic, and more adaptive. Through actual case studies, you’ll learn how to combine technologies such as predictive analytics, optimization, and business rules—improving customer service, reducing fraud, managing risk, increasing agility, and driving growth. Both a practical how-to guide and a framework for planning, Decision Management Systems focuses on mainstream business challenges. Coverage includes Understanding how Decision Management Systems can transform your business Planning your systems “with the decision in mind” Identifying, modeling, and prioritizing the decisions you need to optimize Designing and implementing robust decision services Monitoring your ongoing decision-making and learning how to improve it Proven enablers of effective Decision Management Systems: people, process, and technology Identifying and overcoming obstacles that can derail your Decision Management Systems initiative
This volume, developed in honor of Dr. Dundar F. Kocaoglu, aims to demonstrate the applications of the Hierarchical Decision Model (HDM) in different sectors and its capacity in decision analysis. It is comprised of essays from noted scholars, academics and researchers of engineering and technology management around the world. This book is organized into five parts: Technology Policy Planning, Strategic Technology Planning, Technology Assessment, Application Extensions, and Methodology Extensions. Dr. Dundar F. Kocaoglu is one of the pioneers of multiple decision models using hierarchies, and creator of the HDM in decision analysis. HDM is a mission-oriented method for evaluation and/or selection among alternatives. A wide range of alternatives can be considered, including but not limited to, different technologies, projects, markets, jobs, products, cities to live in, houses to buy, apartments to rent, and schools to attend. Dr. Kocaoglu’s approach has been adopted for decision problems in many industrial sectors, including electronics research and development, education, government planning, agriculture, energy, technology transfer, semiconductor manufacturing, and has influenced policy locally, nationally, and internationally. Moreover, his students developed advanced tools and software applications to further improve and enhance the robustness of the HDM approach. Dr. Kocaoglu has made many contributions to the field of Engineering and Technology Management. During his tenure at Portland State University, he founded the Engineering and Technology Management program, where he served as Program Director and later, Department Chair. He also started the Portland International Conference on Management of Engineering and Technology (PICMET), which organizes an annual conference in international locations such as Korea, Turkey, South Africa, Thailand, and Japan. His teaching has won awards and resulted in a strong sense of student loyalty among his students even decades later. Through his academic work and research, Dr. Kocaoglu has strongly supported researchers of engineering management and has provided tremendous service to the field. This volume recognizes and celebrates Dr. Kocaoglu’s profound contributions to the field, and will serve as a resource for generations of researchers, practitioners and students.
As technology continues to advance, it is critical for businesses to implement systems that can support the transformation of data into information that is crucial for the success of the company. Without the integration of data (both structured and unstructured) mining in business intelligence systems, invaluable knowledge is lost. However, there are currently many different models and approaches that must be explored to determine the best method of integration. Integration Challenges for Analytics, Business Intelligence, and Data Mining is a relevant academic book that provides empirical research findings on increasing the understanding of using data mining in the context of business intelligence and analytics systems. Covering topics that include big data, artificial intelligence, and decision making, this book is an ideal reference source for professionals working in the areas of data mining, business intelligence, and analytics; data scientists; IT specialists; managers; researchers; academicians; practitioners; and graduate students.
Annotation The book presents state-of-the-art knowledge about decision-making support systems (DMSS). Its main goals are to provide a compendium of quality chapters on decision-making support systems that help diffuse scarce knowledge about effective methods and strategies for successfully designing, developing, implementing, and evaluating decision-making support systems, and to create an awareness among readers about the relevance of decision-making support systems in the current complex and dynamic management environment.