Since the major goal of business intelligence is to help managers to make faster and smarter decisions, they need systems that can predict, optimize and adapt the right decisions. The authors define the Adaptive Business Intelligence (hereafter ABI) as the discipline of combining prediction, optimization, and adaptability into a system capable of answering the following two fundamental questions: “What is likely to happen in the future?” and “What is the best decision right now?” The authors’ bet is that the future of the industry lies in ABI.
The purpose of this book is threefold: (a) The authors want to explain why the business intelligence industry can rely on systems that can make good decisions, rather than tools that produce detailed reports. (b) The second aim is to point out the principles behind many prediction methods and optimization techniques in simple terms, so that any business manager could grasp and apply them. (c) The authors’ third goal is to underscore the enormous applicability of ABI to many real-word business problems, ranging from demand forecasting and scheduling, to fraud detection, and investment strategies.
The twelve chapters of the book are organized into three parts, which correspond mainly to the three objectives mentioned above. Part I comprises the first four chapters: the introductory Chapter 1; Chapter 2: Characteristics of Complex Business Problems; Chapter 3: An Extended Example: Car Distribution; and Chapter 4: Adaptive Business Intelligence. This part presents the fundamental ideas behind ABI, and explains the various roles that prediction, optimization, and adaptability play for producing near-optimal decisions. The characteristics of many business problems are discussed, and a particular distribution problem is introduced, which is used throughout the text as a running example since it can be extrapolated to many other business domains.
Part II of the book discusses various prediction methods and optimization techniques that can be used to develop an ABI system. The sections are: Chapter 5: Prediction Methods and Models; Chapter 6: Modern Optimization Techniques; Chapter 7: Fuzzy Logic; Chapter 8: Artificial Neural Networks; Chapter 9: Other Methods and Techniques. The distribution example introduced in Part I is continued throughout these chapters, effectively highlighting the strengths and weaknesses of each method and technique. Each chapter in Part II is concluded by a Recommended Reading section, which provides suggestions for readers who want to learn more about particular issues discussed.
Part III (containing Chapters 10–12) begins with Chapter 10 (Hybrid Systems and Adaptability), which explains how to combine different methods (exposed in Part II) and how to involve the component of adaptability to the final design. Chapter 11: (Car Distribution System) discusses the definitive solution to the distribution problem for the example used throughout the text, while Chapter 12 (Applying Adaptive Business Intelligence) illustrates the ABI application to several complex business problems. The final Chapter 13 (Conclusions) outlines the main features, characteristic aspects, and useful arguments that make this book a novelty work and valuable textbook.