Yeni ürünler için gelecekteki müşteri ihtiyaçlarının tahminlenmesi

The way of doing business and being successful in it evolves within time. With the increasing speed of technological developments, innovation has become a more important topic than it ever was. Competition in markets has become tougher due to globalization. Product life cycles have become shorter due to the race among companies to produce the better and newer product. Making customers happy has become a differentiating factor in business. As a result of these developments, business environment demands high customer satisfaction rate for success in long term. Thus to be successful, there is a need for companies to know what customers expect from them. A sector in which the technology is changing very fast, also needs faster adaptation or for a better chance, requires companies to be a leading innovator. However, with each innovation, customers' expectations are also effected. So, companies need not only understand their customers' needs but they should also anticipate the change in their needs in future. Thus, there is a need for combining a forecast system that is able to detect the changes in customer needs, extracted from a QFD study or similar analyses. This study primarily focuses on finding a conceptual framework which can be used to predict the weights of future customer requirements (CR) of the target market segment for new product development. The lack of historical data is a problem for forecasting when it comes to new products, so existing forecasting methods are carefully examined. QFD tools are used in the first step to understand the different categories and importance of customer requirements. The, Kano questionnaire is applied to use Kano categories to modify weights and predict the changes of states for each CR. A modified version of Kano questionnaire is conducted; that can be analyzed to find out transition probabilities between Kano categories. With the help of Markov Chain, the probabilities of states for each CR are predicted to generate four data points. Grey Theory Forecasting is a suitable tool, as it only requires four data points for a robust forecast. GM (1,1) methodology is applied to the data to predict the change in weight of customer requirements. The suggested framework has been applied with a case study, in which the target product was selected as notebook. Customer requirements have been forecasted for four periods. The results indicate that importance of CRs do change within time for customers. In fact, 20 out of 24 CRs selected for this study changed in importance rankings after four periods. This highlights the necessary effort on companies' behalf to be able to predict future importance of CRs, especially in the early design phase to produce more successful products. The output of this model can be much valuable for management or decision makers, in the process of design for engineers. This will also help preventing unnecessary R&D efforts and budget spending on features which can become obsolete in future; while directing the energy to an area which will be more valuable in the eyes of the customers.