EBK OM
6th Edition
ISBN: 9781305888210
Author: Collier
Publisher: YUZU
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Chapter 9, Problem 2PA
Summary Introduction
Interpretation: 2-month moving average needs to be calculated based on the given data.
Concept Introduction: Moving average method is suitable for short term planning and is simply based on the mean of demand fluctuations in the time series.
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Students have asked these similar questions
Given the following historical data, what is the simple three-period moving average forecast for
period 6?
Period
1
2
3
4
5
67.67
68.2
67.3
68.4
Value
73
66
64
73
66
In a retail store, the actual sales of a particular product (in thousands of units) over the past few months are as follows:
Month
Sales
1
16
2
22
3
18
4
20
5
23
Using exponential smoothing method with α (smoothing constant) of 0.75 and the given forecast for month 1 equal to 10, what is the forecast for month 6?
1) A supermarket has experienced weekly demand of milk of D1 = 120, D2 = 127, D3 = 114, and
D4 = 122 gallons over the past four weeks. Forecast demand for Period 5 using a four-period
moving average. What is the forecast error if demand in Period 5 turns out to be 125 gallons?
2)
Consider the supermarket in Example 7-1, in which weekly demand for milk has been D1 = 120,
D2 = 127, D3 = 114, and D4 = 122 gallons over the past four weeks. Forecast demand for Period
5 using weighted average method. Assign weights yourself.
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- The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?arrow_forwardUse simple exponential smoothing with a -0.8 to forecast electric scooter sales at Guelph- Humber Inc. for July. Assume that the forecast for May was for 45 electric scooters. Round your answer to 1 decimal Electric Scooter Sales Month May 42 June 47 July 45 August 40 Weekly demand for medical masks for the last five weeks at Chopper Drug Store has been as follows: 92, 96, 100, 100 and 102 (listed from the oldest to most recent). Suppose a naive forecast was used to forecast demand. What would the MAD be for this situation? Enrollment in a particular Yoga class at Guelph Fitness Centre for the last five months has been 80, 86, 88, 88 and 90(listed from the oldest to most recent). Suppose a two-month mov average was used to forecast enrollment. What would the MSE be for this situation?arrow_forwardActual demand for a product for the past three months was: Three months ago 400 units Two months ago 350 units Last month 325 units Using a simple three-month moving average, make a forecast for this month. Note: Round your answer to the nearest whole number. If 300 units were actually demanded this month, what would your forecast be for next month, again using a three-month moving average? Note: Round your answer to the nearest whole number. Using simple exponential smoothing, what would your forecast be for this month if the exponentially smoothed forecast for three months ago was 450 units and the smoothing constant was 0.20? Note: Round your answer to the nearest whole number.arrow_forward
- Amazon finds that sales in July are much greater relative to other months because of the Amazon Prime days in that month. Assuming that an exponential smoothing model is used for forecasting sales, what should the company best do to make the forecast for the next month (Aug) more reflective of reality? Please pick from the provided options. F t+1 = αDt + (1-α) Ft increase value of alpha decrease value of alpha stay with the same alpha decrease the expected forecast for the current month (Ft) On hearing our forecast, our clients probe - how sure are we about the forecast? Which measure of errror (among those stated below) should we best use to respond? a. Root MSE (RMSE) b. Mean absolute percentage error (MAPE) c. Mean forecast error (Avg. error) d. Cumulative forecast error (CFE)arrow_forwardUsing linear regression analysis, what would you estimate demand to be for each month next year? Month Forecast January February March April May June July August September October November Decemberarrow_forwardThe Yummy Ice Cream Company projects the demand for ice cream by using first-order exponential smoothing. Last week the forecast was 100,000 gallons of ice cream, and 90,000 gallons was actually sold. Using alpha=.1, prepare a forecast for next week. Calculate the forecast using Alpha=.2 and Alpha=.3 for this problem. Which values of Alpha gave the best forecast, assuming actual demand for next week ends up being 95,000 gallons?arrow_forward
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