Methodology Qualitative research are used to fulfilled the objectives of the research where the main characteristics are suitable in small sample, which the outcomes or results are not measurable so that the aim of the research are complete and detailed, the data is in the form of words, individuals interpretation of events required, qualitative data more detailed, time consuming and less able to generalized and the research become more intense. In other way, the qualitative research can helps to offer the complete details and analysis of the project without limitations. Besides that, quantitative research are also used in this project as it helps to classify features, construct the statistical models in the attempt on explanation what have been analysed, all aspects of the study are well planned and the objective are remain separated from the subject matter based on the skills and abilities where the outcome cannot be changed compared to qualitative research. The approaches of the research are following the main purpose of this project. On the beginning a specific observation have been conducted that result the generalized theories and conclusions that can be produce in this project. The main weakness of this approach is that most of the research can only produced the general theory and conclusions resulted from small number of data that can be used for the research where the result reliability are questionable. For the main purpose of data by using the data of sports car sales in Europe, the built in data analysis from Excel are used to calculate the moving average and exponential smoothing. The forecast times period are January 2017 to September 2017.The section of the report are split into sub sections: The result of forecasting model for new product performance using time series methods The analysis of variable demand Other than that, different time series are used to test the efficiency of the models. This is because time series are simple to understand and use, good for short-term forecasting that will benefits the introduction of new products that are huge amount, small fluctuations can be smoothed out and there also various software for the models. The main three disadvantages of time series is its requires a long term of historical data, having forecasts that requires weights and the forecast can give out errors if many fluctuations.