Electric Fan Industry Case Study Example | Topics and Well Written Essays - 250 words

Electric Fan Industry - Case Study ExampleUnfortunately, a relevant data could not be found and had to use a monthly production by organized firmament since 2000 A.D. Anyway, this study give be analyzed to all(prenominal)ow the manager to make a decision to favor which method is better in decision making. The methods in question involve regression, and additive and multiplicative. After thorough scrutiny of all the methods, it was concluded that regression model was the most efficient method due to slight errors. In order to reach this conclusion, a myriad of analysis tools such as, StatTools 6, MS Solver, were used in the analysis of sample data and results using spreadsheets.The Bhagyanagar Fans Limited Company has been experiencing a reduction in the components show by many units in the last peak season. So, the association decided to do something about it by hiring Ravi Kumar as a market explore executive to come forward with a model that will stop the company from losin g money and help them better understand the short term demand pattern. only according to David W. Stockburger, in order to make this forecast, or the info which needs to be predicted, must be obtained from some kind of sample data, then transform this information into the predicted.The young executive, Ravi Kumar, despite his unsuccessful research data, was accepting the challenge to work with the only available data he could find, the monthly production from 2000 A.D., in order to come up with a short term forecast for the next six months as requested by the owner, MR. Tibrewala. Different tools were used in the analysis of the electric fans production such as, MS Excel Solver, and StatTools 6 to determine the best forecasting model where the properties of regression, additive and multiplicative models were discussed. After all of these models were analyzed using the historical data, the trend and the seasonality which had the most impact on the data. The regression model