Fuzzy logic in management

In this monograph we want to show that the complexity of management can be reduced and that the changes of the environment can be more easily handled by bringing fuzzy logic into the management models and into the practice of management. The book begins with a short survey of leadership and management activities in order to set the stage for what should be expected from support technologies and progresses through an analysis of the potentials and the limitations of these technologies. After providing a short introduction to fuzzy sets, fuzzy relations, the extension principle, fuzzy implications and linguistic variables, the book addresses some issues related to group decision making describing some models for supporting the consensus reaching processes, by introducing the principle of a soft degree of consensus, some elements of commonsense knowledge and natural language, which are brought together in an interactive mode for communications. Then new series of models for evaluating very large investment projects are introduced, based on the so-called for fuzzy real option valuation (FROV) method. The models have been tested with real life data and the impact of the innovations have been traced and evaluated against both the traditional ROV-models and the classical net present value (NPV) models. The fuzzy real options were found to offer more flexibility than the traditional models; both versions of real option valuation were found to give better guidance than the classical NPV models. The models are being run from a platform built by standard Excel components, but the platform was enhanced with an adapted user interface to guide the users to both a proper use of the tools and better insight. Another part has been devoted to the outcomes of the research work focused on the demand fluctuations in paper mills caused by the frictions of information handling in the supply chain and worked out means to reduce or eliminate the fluctuations with the help of information technology. The program enhanced existing theoretical frameworks with fuzzy logic modeling and built a hyperknowledge platform for fast implementation of the theoretical results. Knowledge management is worked out as an area for implementing intelligent information systems and it is shown that there are some possibilities to work on the collection, storing, transfer and management of knowledge with fuzzy logic. The principles are implemented in detail with software agents, which are used for searching and scanning in data sources and for organizing and storing material in data warehouses. In the last part of the book we introduce some applications of mobile technology by first showing empirical facts about the use of mobile technology in Finland, Hong Kong and Singapore and, secondly, showing that the applications are information systems, which can be supported with software agents. There is gradually emerging some consensus in the field that advanced, value-adding mobile services will need intelligent support both in the user interface and in the infrastructure, and that this support would much benefit from fuzzy logic.