Below is a table containing data on product demand for the most recent five months along with the forecasts that had been made for those 5 previous months. Use the 3-period weighted moving average method to forecast the number of sales to expect next month. Use the following weights: 0.4 for the most recent, 0.3 for the 2nd most recent, and 0.3 for the 3rd most recent. Month Demand Forecast 1 308 337 388 341.4 344 368 4. 400 342.8 341 378.4 2)

Below is a table containing data on product demand for the most recent five months along with the forecasts that had been made for those 5 previous months. Use the 3-period weighted moving average method to forecast the number of sales to expect next month. Use the following weights: 0.4 for the most recent, 0.3 for the 2nd most recent, and 0.3 for the 3rd most recent. Month Demand Forecast 1 308 337 388 341.4 344 368 4. 400 342.8 341 378.4 2)

Understanding Business

12th Edition

ISBN:9781259929434

Author:William Nickels

Publisher:William Nickels

Chapter1: Taking Risks And Making Profits Within The Dynamic Business Environment

Section: Chapter Questions

Problem 1CE

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4.
A Use a simple moving average model. Experiment with the models using five weeks and three weeks past data. 3 week MA Week ATL BOS CHI DAL LA TOTAL 1 2 3 4 5 6 7 8 9 10 11 12 13 5 week MA Week ATL BOS CHI DAL LA TOTAL 1 2 3 4 5 6 7 8 9 10 11 12 13 B evaluate the forecast that would have been made over the 13 weeks using overall mean absolutely deviation, mean absolute percent error, and tracking signal as criteria.
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Given the following data for demand at the XYZ Company, calculate the monthly forecast for 2003 using a 3-month moving average. Calculate the Forecast, Error, MAD (mean absolute percentage error), Bias and the TS (tracking signal) . Period Demand Forecast Error MAD Bias TS 2-Oct 2012 800 　　　　　 2-Nov 2012 1000 　　　　　 2-Dec 2012 950 　　　　　 2-Jan 2013 1100 2-Feb 2013 930 2-Mar 2013 1020 2-Apr 2013 870
a) Apply moving average and exponential smoothing to generate two forecasts of the closing price for the entire data period. Set appropriate values for the forecast parameters (e.g., k and a). b) Plot and compare the actual and predicted data values you calculated in part (b). Provide one chart that displays actual, moving average, and exponential smoothing lines. Hint: Calculate MSE, MAD, or MAPE to compare the different forecasts. Develop your own forecast from scratch. Do NOT use Excel’s Data Analysis or Forecast Sheets.
Below is a table containing data on product demand for the most recent five months along with the forecasts that had been made for those 5 previous months. Use the 3-period weighted moving average method to forecast the number of sales to expect next month. Use the following weights: 0.4 for the most recent, 0.3 for the 2nd most recent, and 0.3 for the 3rd most recent. Month Demand Forecast 1 308 337 2 388 341.4 3 344 368 4 400 342.8 5 341 378.4
4. The sales (in millions of dollars) for an 18-month period are as follows. Month Sales Month Sales 1 600 600 775 10 775 3 600 11 600 4 650 12 575 700 13 625 800 14 650 550 15 600 8 775 a. Compare a three-month moving average forecast with an exponential smoothing forecast. Use a = 0.1. Which provides the better forecasts based on MSE? b. Find the forecast for the next month using the best forecast method. 2.
Below is the actual demand for electrical boxes for Abco Electric for the last nine periods along with a forecast. A) Complete the Mean Absolute Deviation for this forecast. B) Complete an additional forecast for periods 4-12 using a weighted moving average of your choice. Change the title to describe your method. C) Calculate the MAD for your forecast. D) In the text box below put your comment comparing the accuracy of your forecast versus the one given in A. Was your method more accurate? Why or why not? Use whole numbers for all forecasts. Use ONE decimal place for the MAD calculations. Period Demand Naïve Method Error (e) MAD Your Title here Your MAD 1 855 Abs e Abs e 2 910 3 930 4 890 930 5 935 890 6 980 935 7 1020 980 8 960 1020 9 905 960 10 895 905 11 955 895 12 820 955 MAD D. Your comment on Forecast Accuracy:
The following represents the actual demand and two different sets of forecasts using different methods. Compute the MAD for each forecasting method and determine the best method for this set of data. Year-Qtr Actual demand Forecast methos 1 MAD1 (A-F) Forecast Method 2 MAD2 (A-F) 2020-3 98 97 89 2020-4 108 110 106 2021-1 83 68 70 2021-2 100 101 102 2021-3 95 86 87 2021-4 90 94 92 2022-1 71 68 72 2022-2 104 105 106 2022-3 84 91 88 2022-4 80 93 91 2023-1 78 68 70 2023-2 120 118 122
The director of the Riley County, Kansas, library systemwould like to forecast evening patron usage for next week.Below are the data for the past 4 weeks: a) Calculate a seasonal index for each day of the week.b) If the trend equation for this problem is y = 201. 74 + .18x,what is the forecast for each day of week 5? Round your forecastto the nearest whole number.
After using your forecasting model for six months, you decide to test it using MAD and a tracking signal. Here are the forecast and actual demands for the six-month period: PERIOD May June July August September October Period FORECAST 450 500 550 615 635 710 a. Find the tracking signal. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.) May June July August September October ACTUAL 495 545 395 460 675 625 Tracking Signal 1.00 2.00 (0.80) (2.20) (2.38) (3.50)
Below is a table containing data on product demand for the most recent three months along with the forecasts that had been made for those three previous months. Calculate the MAE (or MAD). Month Demand Forecast 1 308 310 2 388 390 3 344 342