Digital Cell Phone, Inc. Case Study
Digital Cell Phone, Inc. Case Study
Paul Jordan's boss gave him an assignment where he was required to look at cell phone orders for the past three years. With this information, he is to use new planning techniques to project expected orders for the next 6 to 12 months, as well as advise his boss on what actions he should take for future production. The method we used to forecast the cell phone orders for the upcoming year is regression analysis; we calculated the linear regression formula from the given data, and then applied the formula to the later months. Based on the linear regression equation, we anticipate the cell phone industry to continue to grow over the next 12 months, but Jordan's boos should feel free to stray away from actual forecasts for certain months. Method II is used to forecast the cell phone orders for the upcoming year is seasonal analysis. Seasonal variations in data are regular up and down movements in a time series that relate to recurring events such as weather or holiday. Analyzing and adjusting the data to seasonal indexes allows the forecast to be a more accurate estimate of demand. We are here to learn how to use linear regression to forecast how an industry will perform as well as use these calculations to recommend real life actions, like Paul Jordan is required to do. We are also here to learn how to use a variety of methods to forecast how an industry is expected to perform and provide analysis for capacity planning in
Paul Jordan's boss gave him an assignment where he was required to look at cell phone orders for the past three years. With this information, he is to use new planning techniques to project expected orders for the next 6 to 12 months, as well as advise his boss on what actions he should take for future production. The method we used to forecast the cell phone orders for the upcoming year is regression analysis; we calculated the linear regression formula from the given data, and then applied the formula to the later months. Based on the linear regression equation, we anticipate the cell phone industry to continue to grow over the next 12 months, but Jordan's boos should feel free to stray away from actual forecasts for certain months. Method II is used to forecast the cell phone orders for the upcoming year is seasonal analysis. Seasonal variations in data are regular up and down movements in a time series that relate to recurring events such as weather or holiday. Analyzing and adjusting the data to seasonal indexes allows the forecast to be a more accurate estimate of demand. We are here to learn how to use linear regression to forecast how an industry will perform as well as use these calculations to recommend real life actions, like Paul Jordan is required to do. We are also here to learn how to use a variety of methods to forecast how an industry is expected to perform and provide analysis for capacity planning in