Admitting risks towards circular economy practices and strategies: An empirical test from supply chain perspective

The paper is focused on the analysis of the key aspects of sustainability projects, namely advanced risk management and project knowledge. These aspects are recommended to the attention of institutions and project managers when designing and executing new projects simultaneously with quality and project status management. The aim of the paper is to point out the critical factors that have recently affected the success of sustainability projects, which is also its contribution. Empirical research focused on the identification of the application level of the post-project phases in project management in the Czech Republic in 2016 and 2017 was performed. The research was performed as qualitative research employing observation and inquiry methods in the form of a controlled semistructured interview. The research identified 21 most common reasons for not executing post-project phases. Ensuring good and efficient progress of post-project phases, in particular by the means of post-implementation system analysis and compilation of a set of improvement suggestions for subsequent project management, forms the practical background for application of knowledge management and project management principles. A case study focused on the application of fuzzy logic in project risk assessment has been elaborated. In practice, current project management requires the application of advanced risk analysis methods that will replace the simple risk values estimated by calculations of separate risk components.

Risk Management and Knowledge Management as Critical Success Factors of Sustainability Projects

Data from National Statistical Institutes is generally considered an important source of credible evidence for a variety of users. Summarization and dissemination via traditional methods is a convenient approach for providing this evidence. However, this is usually comprehensible only for users with a considerable level of statistical literacy. A promising alternative lies in augmenting the summarization linguistically. Less statistically literate users (e.g., domain experts and the general public), as well as disabled people can benefit from such a summarization. This article studies the potential of summaries expressed in short quantified sentences. Summaries including, for example, “most visits from remote countries are of a short duration” can be immediately understood by diverse users. Linguistic summaries are not intended to replace existing dissemination approaches, but can augment them by providing alternatives for the benefit of diverse users of official statistics. Linguistic summarization can be achieved via mathematical formalization of linguistic terms and relative quantifiers by fuzzy sets. To avoid summaries based on outliers or data with low coverage, a quality criterion is applied. The concept based on linguistic summaries is demonstrated on test interfaces, interpreting summaries from real municipal statistical data. The article identifies a number of further research opportunities, and demonstrates ways to explore those.

/pdf/augmenting-statistical-data-dissemination-by-short-5edfegqfno.pdf

Augmenting Statistical Data Dissemination by Short Quantified Sentences of Natural Language

Now-a-days insurance companies have its significance in the society. Customers are interested in claiming the insurance on their property, vehicles. All the vehicle users approach different insurance companies which provide better security to their vehicles. In the same manner the Fraudulent cases also increases. There are different mining techniques in detecting fraud and analyse the data. In this literature survey we present some techniques to fraud analysis, classification and prediction which we consider important to handle fraud detection. Among those The Naive Bayesian model is more powerful fraud detection in automobile insurance. Bayesian visualization is selected to analyse and interpret the classifier predictions. However this visualization technique is not suitable for abundant data with little frauds. To avoid this limitation, we are using Fuzzy Logic by framing fuzzy rules to improve the Fraud Detection. This technique will be implemented on more number of datasets and variables. By using this technique time complexity will be decreased and implementation is easy and interprets the results accurately.

Robust fuzzy rule based technique to detect frauds in vehicle insurance

Manufacturing enterprises are collaborating among each other in manufacturing service ecosystems (MSE) with the objective to compose and provision numerous product---services (P---S) on the market. However, many paramount processes outset much before the actual composition, like the strategy planning of those P---S. Such decisions are usually full of ambiguities with complex sets of decisional possibilities, which are extremely hard to encompass even within a decision support system. Thus, the aim of this article is to undergird the development of an effective decision support system (DSS) for solving the challenge of planning a P---S strategy within a MSE, as well to present and apply a relative novel fuzzy inference technique, in order to build the DSS in question. This is achieved by first designing the logical data model that conceptualizes the context of planning a P---S strategy within a MSE, secondly by designing the actual business intelligence (BI) sets of rules and thirdly to build a DSS and test its data. As the input data needed to plan a strategy are often intangible, without a clear delineation among classes (e.g. "Market_1 is more competitive than Market_2"), with more than just binary values that can also overlap among each other and can be expressed using human language, a fuzzy based inference system is used to build the BI rules set. The DSS provides answers to three central uncertainties in P---S strategy planning expressed in the article as performance questions.

An evolving fuzzy inference system for extraction of rule set for planning a product---service strategy

Fuzzy Set Theory has been developed during the last decades to a demanding mathematical theory. There exist more than 50,000 publications in this area by now. Unluckily the number of reports on applications of fuzzy technology has become very scarce. The reasons for that are manifold: Real applications are normally not single-method-applications but rather complex combinations of different techniques, which are not suited for a publication in a journal. Sometimes considerations of competition my play a role, and sometimes the theoretical core of an application is not suited for publication. In this paper we shall focus on applications of fuzzy technology on real problems in business management. Two versions of fuzzy technology will be used: Fuzzy Knowledge based systems and fuzzy clustering. It is assumed that the reader is familiar with basic fuzzy set theory and the goal of the paper is, to show that the potential of applying fuzzy technology in management is still very large and hardly exploited so far.

/pdf/applications-of-fuzzy-technology-in-business-intelligence-1eqz2svdge.pdf

Applications of Fuzzy Technology in Business Intelligence

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Applications of Fuzzy Technology in Business Intelligence