Fedexx Inc. is a logistics providing a wide variety of services to companies throughout the world. Q Public, the recently hired assistant controller, has been asked to develop a cost function to forecast shipping costs. The previous assistant controller had provided the forecast shipping department costs each year by plotting cost data against; direct laborhours for the most recent 12 months and visually fitting a straight line through the points. The results were not satisfactory.
After discussions with the shipping department personnel, Q decided that shipping costs could be more closely related to the number of orders filled. He based his conclusion on the fact that 10 months ago the shipping department added some automated equipment.
Furthermore, Q believes that using linear regression analysis will improve the forecasts of shipping costs. Cost data for the shipping department have been accumulated for the last 25 weeks. Q ran two regression analysis of the data, one using direct labor-hours, and one using the number of cartons shipped. The information from the two linear regressions is the following:
Regression 1 Regression 2
Equation SC = 804.3 + 15.68 DL SC = 642.9 + 3.92 NR
R-squared 0.365 0.729
Standard error of the 2.652 1.884 estimate
Where SC = Total shipping department costs
DL = Total direct labour hours
NR = Number of cartons shipped
Required:
Identify which regression equation (Regression 1 or Regression 2) that Fedexx Inc. should adopt for forecasting total shipping department costs and explain your reasons.
If Fedexx estimates that 600 orders will be filled in the coming week, calculate the total shipping department costs using the regression that you have selected in part (a).
Are there any limitations to the regression that you have selected in part (a) above? If so, identify ways to address the limitations. Include in your answer the effect, if any, of the global nature of Fedexx's business.