In today's competitive, always-on global marketplace, businesses need to be able to make better decisions more quickly. And they need to be able to change those decisions immediately in order to adapt to this increasingly dynamic business environment. Whether it is a regulatory change in your industry, a new product introduction by a competitor that your organization needs to react to, or a new market opportunity that you want to quickly capture by changing your product pricing. Decisions like these lie at the heart of your organization's key business processes. In this IBM® RedpaperTM publication, we explore the benefits of identifying and documenting decisions within the context of your business processes. We describe a straightforward approach for doing this by using a business process and decision discovery tool called IBM Blueworks LiveTM, and we apply these techniques to a fictitious example from the auto insurance industry to help you better understand the concepts. This paper was written with a non-technical audience in mind. It is intended to help business users, subject matter experts, business analysts, and business managers get started discovering and documenting the decisions that are key to their company's business operations.

Systems of record (SORs) are engines that generates value for your business. Systems of engagement (SOE) are always evolving and generating new customer-centric experiences and new opportunities to capitalize on the value in the systems of record. The highest value is gained when systems of record and systems of engagement are brought together to deliver insight. Systems of insight (SOI) monitor and analyze what is going on with various behaviors in the systems of engagement and information being stored or transacted in the systems of record. SOIs seek new opportunities, risks, and operational behavior that needs to be reported or have action taken to optimize business outcomes. Systems of insight are at the core of the Digital Experience, which tries to derive insights from the enormous amount of data generated by automated processes and customer interactions. Systems of Insight can also provide the ability to apply analytics and rules to real-time data as it flows within, throughout, and beyond the enterprise (applications, databases, mobile, social, Internet of Things) to gain the wanted insight. Deriving this insight is a key step toward being able to make the best decisions and take the most appropriate actions. Examples of such actions are to improve the number of satisfied clients, identify clients at risk of leaving and incentivize them to stay loyal, identify patterns of risk or fraudulent behavior and take action to minimize it as early as possible, and detect patterns of behavior in operational systems and transportation that lead to failures, delays, and maintenance and take early action to minimize risks and costs. IBM® Operational Decision Manager is a decision management platform that provides capabilities that support both event-driven insight patterns, and business-rule-driven scenarios. It also can easily be used in combination with other IBM Analytics solutions, as the detailed examples will show. IBM Operational Decision Manager Advanced, along with complementary IBM software offerings that also provide capability for systems of insight, provides a way to deliver the greatest value to your customers and your business. IBM Operational Decision Manager Advanced brings together data from different sources to recognize meaningful trends and patterns. It empowers business users to define, manage, and automate repeatable operational decisions. As a result, organizations can create and shape customer-centric business moments. This IBM Redbooks® publication explains the key concepts of systems of insight and how to implement a system of insight solution with examples. It is intended for IT architects and professionals who are responsible for implementing a systems of insights solution requiring event-based context pattern detection and deterministic decision services to enhance other analytics solution components with IBM Operational Decision Manager Advanced.

Decision-Making for Biomass-Based Production Chains: The Basic Concepts and Medothologies presents a comprehensive study of key-issues surrounding the integration of strategic, tactical and operational decision levels for supply chains in the biomass, biofuels and biorefining sectors. Comprehensive sections cover biomass resources, harvesting, collection, storage and distribution systems, along with the necessary technical and technological background of production systems. In addition, the basics of decision-making, problems and decision levels encountered in design, management and operation phases are covered. Case studies are supplied in each chapter, along with a discussion and comparative analysis of topics. The book presents a clear vision of advances in the field. Graduate students and those starting in this line of research will also find the necessary information on how to model this kind of complex system. Finally, this comprehensive resource can be used as a guide for non-expert industry decision-makers and government policymakers who need a thorough overview on the industry. - Examines analytic methodologies for complex decision-making when designing, deploying and managing biomass and bio-based products supply chains - Includes real-life examples of main sustainability indicators, standards and certification schemes from the European Union, United States and worldwide - Explores the progress of decision-making procedures to provide a detailed perspective for effective selection of the most reliable solutions for each kind of problem - Provides detailed, in-depth analyses of various models and frameworks for their implementation, challenges and solutions - Presents multi-criteria and multi-objective decision-making and modeling approaches, including mathematical modeling, simulation-based modeling, and artificial intelligence-based modeling

Within traditional decision theory, common decision principles -- e.g. the principle to maximize utility -- generally invoke idealization; they govern ideal agents in ideal circumstances. In Realistic Decision Theory, Paul Weirch adds practicality to decision theory by formulating principles applying to nonideal agents in nonideal circumstances, such as real people coping with complex decisions. Bridging the gap between normative demands and psychological resources, Realistic Decision Theory is essential reading for theorists seeking precise normative decision principles that acknowledge the limits and difficulties of human decision-making.

This book provides accounting students in post-secondary institutions with an advanced level understanding of how to use MS-Excel to make business decisions. It reflects real-life applications of this important analytical tool, which has become the accepted industry standard for spreadsheet software.

This book discusses whether and to what extent there are widespread injustices and inequities caused by the distribution of environmental hazards in America today.