Operations Management CH 3 Question 14 Harlen Industries has a simple forecasting model: Take the actual demand for the same month last year and divide that by the number of fractional weeks in that month. This gives the average weekly demand for that month. This weekly average is used as the weekly forecast for the same month this year. This technique was used to forecast eight weeks for this year, which are shown in the following tables along with the actual demand that occurred. The following eight weeks show the forecast (based on last year) and the demand that actually occurred: WEEK FORECAST DEMAND ACTUAL DEMAND 1 135 132 2 135 128 3 135 150 4 134 160 5 134 180 6 144 170 7 155 185 8 156 205

Operations Management CH 3 Question 14 Harlen Industries has a simple forecasting model: Take the actual demand for the same month last year and divide that by the number of fractional weeks in that month. This gives the average weekly demand for that month. This weekly average is used as the weekly forecast for the same month this year. This technique was used to forecast eight weeks for this year, which are shown in the following tables along with the actual demand that occurred. The following eight weeks show the forecast (based on last year) and the demand that actually occurred: WEEK FORECAST DEMAND ACTUAL DEMAND 1 135 132 2 135 128 3 135 150 4 134 160 5 134 180 6 144 170 7 155 185 8 156 205

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter14: Data Mining

Section14.2: Classification Methods

Problem 2P

See similar textbooks

WEEK	FORECAST DEMAND	ACTUAL DEMAND
1	135	132
2	135	128
3	135	150
4	134	160
5	134	180
6	144	170
7	155	185
8	156	205

Similar questions

The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six months of observations for validation purposes, use the method of moving averages with a carefully chosen span to forecast U.S. retail sales in the next year. Comment on the performance of your model. What makes this time series more challenging to forecast?
The Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?
The file P13_19.xlsx contains the weekly sales of a particular brand of paper towels at a supermarket for a one-year period. a. Using a span of 3, forecast the sales of this product for the next 10 weeks with the moving averages method. How well does this method with span 3 forecast the known observations in this series? b. Repeat part a with a span of 10. c. Which of these two spans appears to be more appropriate? Justify your choice.
The file P13_02.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise, its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the companys suspicion. b. By what percentage are sales increasing each month? c. What is the MAPE for the forecast model in part b? In words, what does it measure? Considering its magnitude, does the model seem to be doing a good job? d. In words, how does the model make forecasts for future months? Specifically, given the forecast value for the last month in the data set, what simple arithmetic could you use to obtain forecasts for the next few months?
The file P13_26.xlsx contains the monthly number of airline tickets sold by the CareFree Travel Agency. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Does it make much of an improvement over the model in part b?
Management of a home appliance store wants to understand the growth pattern of the monthly sales of a new technology device over the past two years. The managers have recorded the relevant data in the file P13_05.xlsx. Have the sales of this device been growing linearly over the past 24 months? By examining the results of a linear trend line, explain why or why not.
Do the sales prices of houses in a given community vary systematically with their sizes (as measured in square feet)? Answer this question by estimating a simple regression equation where the sales price of the house is the dependent variable, and the size of the house is the explanatory variable. Use the sample data given in P13_06.xlsx. Interpret your estimated equation, the associated R-square value, and the associated standard error of estimate.
DFC Company has recorded the past years sales for the company: Year(t) Sales(x) (in Million Pesos) 2011(1) 2012(2) 2013(3) 2014(4) 2015(5) 2016(6) 2017(7) 2018(8) 2019(9) 2020(10) 219 224 268 272 253 284 254 278 282 298 Use the naïve model. Compute for MAE and MSE Use a three period moving average. Compute for the MAE and MSE Use the simple exponential smoothing to make a forecasting table. Compute the MAE and MSE of the forecasts. Alpha = 0.1 Use the least square method to make the forecasting table. Compute the MAE and MSE *** Use excel or r programming
Choose the correct option that best represents the pattern observed in the time-series graph. Ocean Waves 45 35- 30- 20- 154 10- V Time Series Plot of Ocean Waves 50 Index 60 70 The trend is increasing over time. The trend is decreasing over time. O The trend is relatively stable with no significant changes. The trend exhibits periodic fluctuations. None of the above are correct. 80 90 99 20.2
Which of the following is not a similarity between seasonal and cycle factors? They both model variability in the dependent variable. They both sum to the number of data points in the averaging process. They are both calculated as ratios. They both use the actual data series in their calculation. All of the options are correct.
The following table shows a company's annual revenue (in billions of dollars) for 2009 to 2014. Year Period (t) Revenue ($ billions) 2009 1 23.8 2010 29.2 2011 37.9 2012 4 50.3 2013 59.8 2014 6 66.6 (a) Construct a time series plot. 80 80 70 70 70 70 60 60 50 50 50 30 30 30 30- 20 20 20 20 10 10 10 - 10- 4. 6 7 4 6 4 5 6 7 1 2. 4 6 7 Period Period Period Period What type of pattern exists in the data? O The time series plot shows an upward linear trend. O The time series plot shows a downward curvilinear trend. O The time series plot shows a downward linear trend. O The time series plot shows an upward curvilinear trend. (b) Develop a linear trend equation for this time series to forecast revenue (in billions of dollars). (Round your numerical values to three decimal places.) T = (c) What is the average revenue increase per year (in billions of dollars) that this company has been realizing? (Round your answer to three decimal places.) billion Revenue ($ billions) Revenue ($ billions)…
calculate x-bar for the data shown, to two decimal places, 9,7.9,10,21.8,7