Paul Jordan was given the assignment by his supervisor to analyze cell phone orders from the past three years. The goal was to use innovative planning techniques to predict future orders for a period of 6 to 12 months and suggest production recommendations. We chose regression analysis as our approach for forecasting cell phone orders in the upcoming year. Using the data at hand, we developed a linear regression equation and implemented it in the following months.
Based on the linear regression equation, there is an anticipated growth in the cell phone industry over the next 12 months. However, it is advisable for managers in Jordan to remain adaptable when making predictions for individual months. To forecast cell phone orders for the upcoming year, Method II incorporates seasonal analysis. Seasonal variations in data encompass periodic upward and downward shifts within a time series, influenced by recurring factors such as weather conditions or holidays.
Analyzing and adjusting the data to seasonal indexes enables a more accurate estimation of demand for forecasting purposes. In this course, we aim to learn how to utilize linear regression for predicting industry performance and leveraging these calculations to make practical recommendations, similar to what Paul Jordan is mandated to do. Additionally, we seek to acquire proficiency in employing diverse forecasting methods to anticipate industry performance and furnish capacity planning analysis for organizations.
