Forecasting of Intellectual Capital by Measuring Innovation Using Adaptive Neuro-Fuzzy Inference System

Purpose – The aim of every organization is to achieve its set goals and objectives as well as secure competitive advantage over its competitors. However, these cannot be achieved or actualized if staff or workers act independently and do not share ideas. Today prominent businesses are becoming more aware that the knowledge of their employees is one of their primary assets. Sometimes organizational decisions cannot be effectively made with information alone; there is need for knowledge application. An effective Knowledge Management System can give a company the competitive edge it needs to be successful, and, for that reason, knowledge Management projects should be high priority. This means that for any organization to be competitive in today’s global world there is need for combination or pooling together of ideas by employees in order to achieve teamwork; this is in support of the saying that ‘two good heads are better than one’. Due to the advent of the knowledge-based economy and the developments in activity nature of the companies at international level, intellectual capital is taken to be one of the fundamental pillars of the companies for achieving efficiency. The aim of this study is to predict the amount and effectiveness of intellectual capital or intangible assets on the basis of innovation ability of the companies using an integrated artificial neural networks fuzzy logic analysis approach in order to cope with future challenges of strategic management. Design/methodology/approach – This paper suggests some guidelines for setting up the development of valuation approach based on application and adaption of selected financial and non-financial indicators by means of artificial neural networks and fuzzy logic. The artificial neural network model is highly accurate in predicting intellectual capital of the companies. This research paper presents the construction and design of Hybrid Application using Neural Network and Fuzzy Logic. This proposed system uses a simplified algorithmic design approach with wide range of input and output membership functions. In this research a hybrid Neuro-Fuzzy systems modelling methodology is developed and applied to an empirical data set in order to determine the hidden fuzzy if-then rules. Furthermore, the proposed methodology is a valuable tool for successful knowledge management. Findings – The findings show the opinion of that the complexity of development has been improved by expansion in the amount of knowledge available to organizations. Future research should contain of high degree of study to analytically examine the successful project knowledge management in different types of plans, companies and commences. Learning comes through creating and applying knowledge, whilst learning increases an individual's and organization's knowledge asset. Both learning and knowledge management feed off the same root: learning, improved capacity to perform work tasks, ability to make effective decisions, predict future parameters on the basis of some certain parameters and positively impact the world around us. Challenges – Identification and evaluation of the significant factors that create and determine enterprise value in industry is based on complex calculations involving many variables. Regardless of this reason, existing business valuation methods for such companies have to be improved with taking into account a numerous qualitative and even additional quantitative factors.Therefore, economic experts and scientists in the field of business valuation are confronted with new challenges in determination of appropriate approaches that should be able to eliminate the disadvantages of existing valuation methods. The environment in which businesses operate is ever changing. The market has become global and the technological advancement has changed the way business is done. The resulting impact of globalization is fierce competition that has altered the business landscape. Firms are increasingly employing various techniques in order to remain relevant and competitive. Since decision making is considered as the management main elements and sometimes equivalent to management itself, it is essential that researchers pay a specific attention to this field because if decisions are made in an optimized and effective form in an organization. This work is motivated by the need for a model that addresses the study of Knowledge in specific environments such as Business and Management, where several situations are very difficult to be analyze in conventional ways and therefore is insufficient in describing the complications of represent a realistic social phenomena and their social actors. Distributed Agency methodology will be used that requires the use of all available computational techniques and interdisciplinary theories as an approach to describe the interactions between agents in the development of social phenomena. Data Mining and Neuro-Fuzzy System are also used as part of the methodology to discover and assign rules on agents that represent real-world companies and employees. Practical implications – Today most organizations have discovered that advanced trainings can be considered as the key asset for modern organizations. This study presents a forecasting model that predicts intangible assets on the basis of innovation performance in organizational training using widely applied innovation criteria. The research focused on criteria, such as organization culture, ability to respond to organizational technology changes, relationship with other organizations in the training process and the use of high technology in education. The adaptive neuro-fuzzy inference systems (ANFIS) approach has been used to verify the proposed model. It is possible to predict innovation performance and it can also adjust allocated resources to organizational training system for its innovation objectives with this method. Originality/value – A great deal of work has been published over the past decade on the application of neural networks in diverse fields. This paper presents a model that measure and forecasts the intangible assets by the development of an Adaptive Neural Network with Fuzzy Inference system (ANFIS), using data that concern human capital, organizational support and innovativeness. The results indicate that fuzzy neural networks could be an efficient system that is easy to apply in order to accurately measure and forecast the intangible assets by measuring innovation capabilities of an organization or firm.