EBK PRODUCTION AND OPERATIONS ANALYSIS
7th Edition
ISBN: 8220102480681
Author: Olsen
Publisher: WAVELAND
expand_more
expand_more
format_list_bulleted
Concept explainers
Videos
Question
Chapter 2.6, Problem 11P
a.
Summary Introduction
To determine: The one step ahead forecast for period 9.
Introduction:
b.
Summary Introduction
To determine:The one step ahead forecast that was made for period 6.
Introduction: Forecasting is the main function of predicting the future using the information available for decision making. It is a mechanism for planning decisions based on the predicted information.
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solution
Students have asked these similar questions
The monthly sales for Yazici Batteries, Inc., were as follows:
Month
Jan
Feb
Mar
Apr May
Jun
Jul
Aug Sept
Oct
Nov
Dec
Sales
20
21
16
15
15
16
17
19
19
20
23
22
This exercise contains only parts b and c.
b) The forecast for the next month (Jan) using the naive method = 22 sales (round your response to a whole number).
The forecast for the next period (Jan) using a 3-month moving average approach = 21.67 sales (round your response to two decimal places)
The forecast for the next period (Jan) using a 6-month weighted average with weights of 0.10, 0.10, 0.10, 0.20, 0.20, and 0.30, where the
heaviest weights are applied to the most recent month = 20.7 sales (round your response to one decimal place).
Using exponential smoothing with a = 0.40 and a September forecast of 18.00, the forecast for the next period (Jan) :
sales (round your
response to two decimal places).
Using a method of trend projection, the forecast for the next month (Jan) =
sales (round your response to two decimal places).…
The monthly sales for Yazici Batteries, Inc., were as follows:
Month
Jan
Feb
Mar
Apr
May Jun
Jul
Aug Sept
Oct
Nov
Dec
Sales
19
21
17
15
15
18
16
19
22
20
20
24
This exercise contains only parts b and c.
b) The forecast for the next month (Jan) using the naive method = 24 sales (round your response to a whole number).
The forecast for the next period (Jan) using a 3-month moving average approach = 21.33 sales (round your response to two decimal places).
The forecast for the next period (Jan) using a 6-month weighted average with weights of 0.10, 0.10, 0.10, 0.20, 0.20, and 0.30, where the heaviest weights are
applied to the most recent month = 20.9 sales (round your response to one decimal place).
Using exponential smoothing with a = 0.40 and a September forecast of 18.00, the forecast for the next period (Jan) = sales (round your response to two decimal
places).
1. The number of bushels of apples sold at a roadside fruit stand over a 12-day period were
PROBLEMS
as follows:
Day
Numbar Sold Day Number Sold
25
35
31
29
33
32
38
10
40
37
32
34
11
37
12
If a two-period moving average has been used to forecast sales, what were the daily
forecasts starting with the forecast for day 3?
If a four-period moving average has been used, what were the forecasts for eacn uay
starting with day 5?
Plot the original data and each set of forecasts on the same graph. Which forecast
has the greater tendency to smooth? Which forecast has the better ability to respond
quickly to changes?
What does use of the term sales instead of demand imply?
b.
C.
2. If exponential smoothing with a = .4 had been used to forecast daily sales for apples in
Problem 1, determine what the daily forecasts would have been. Then, plot the original
data, the exponential forecasts, and a set of naive forecasts on the same graph. Based
on a visual comparison, is the naive more accurate or…
Chapter 2 Solutions
EBK PRODUCTION AND OPERATIONS ANALYSIS
Ch. 2.4 - Prob. 1PCh. 2.4 - Prob. 2PCh. 2.4 - Prob. 3PCh. 2.4 - Prob. 4PCh. 2.4 - Prob. 5PCh. 2.4 - Prob. 6PCh. 2.4 - Prob. 7PCh. 2.4 - Prob. 8PCh. 2.4 - Prob. 9PCh. 2.6 - Prob. 10P
Ch. 2.6 - Prob. 11PCh. 2.6 - Prob. 12PCh. 2.6 - Prob. 13PCh. 2.6 - Prob. 14PCh. 2.6 - Prob. 15PCh. 2.7 - Prob. 16PCh. 2.7 - Prob. 17PCh. 2.7 - Prob. 18PCh. 2.7 - Prob. 19PCh. 2.7 - Prob. 20PCh. 2.7 - Prob. 21PCh. 2.7 - Prob. 22PCh. 2.7 - Prob. 23PCh. 2.7 - Prob. 24PCh. 2.7 - Prob. 25PCh. 2.7 - Prob. 26PCh. 2.7 - Prob. 27PCh. 2.8 - Prob. 28PCh. 2.8 - Prob. 29PCh. 2.8 - Prob. 30PCh. 2.8 - Prob. 31PCh. 2.8 - Prob. 32PCh. 2.9 - Prob. 33PCh. 2.9 - Prob. 34PCh. 2.9 - Prob. 35PCh. 2.9 - Prob. 36PCh. 2.9 - Prob. 37PCh. 2.10 - Prob. 38PCh. 2.10 - Prob. 42PCh. 2.10 - Prob. 43PCh. 2.10 - Prob. 44PCh. 2.10 - Prob. 45PCh. 2 - Prob. 47APCh. 2 - Prob. 48APCh. 2 - Prob. 49APCh. 2 - Prob. 50APCh. 2 - Prob. 51APCh. 2 - Prob. 52APCh. 2 - Prob. 53APCh. 2 - Prob. 54APCh. 2 - Prob. 55APCh. 2 - Prob. 56APCh. 2 - Prob. 57APCh. 2 - Prob. 58AP
Knowledge Booster
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, operations-management and related others by exploring similar questions and additional content below.Similar questions
- Under what conditions might a firm use multiple forecasting methods?arrow_forwardThe file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.arrow_forwardThe 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_forward
- 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?arrow_forwardThe file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building materials (which includes retail sales of building materials, hardware and garden supply stores, and mobile home dealers). a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?arrow_forwardThe 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?arrow_forward
- 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?arrow_forwardThe file P13_28.xlsx contains monthly retail sales of U.S. liquor stores. a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?arrow_forwardDo 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.arrow_forward
- 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.arrow_forwardThe file P13_25.xlsx contains the quarterly numbers of applications for home mortgage loans at a branch office of Northern Central Bank. 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? Is it guaranteed to produce better forecasts for the future?arrow_forwardThe monthly sales for Yazici Batteries, Inc., were as follows: Month Sales Jan 19 Feb Mar 17 21 Apr May Jun Jul Aug Sept Oct 12 15 18 15 19 20 20 This exercise contains only parts b and c. b) The forecast for the next month (Jan) using the naive method = 22 sales (round your response to a whole number). Nov Dec 20 22 The forecast for the next period (Jan) using a 3-month moving average approach = 20.67 sales (round your response to two decimal places). The forecast for the next period (Jan) using a 6-month weighted average with weights of 0.10, 0.10, 0.10, 0.20, 0.20, and 0.30, where the heaviest weights are applied to the most recent month = sales (round your response to one decimal place).arrow_forward
arrow_back_ios
SEE MORE QUESTIONS
arrow_forward_ios
Recommended textbooks for you
Practical Management ScienceOperations ManagementISBN:9781337406659Author:WINSTON, Wayne L.Publisher:Cengage,
Contemporary MarketingMarketingISBN:9780357033777Author:Louis E. Boone, David L. KurtzPublisher:Cengage Learning
MarketingMarketingISBN:9780357033791Author:Pride, William MPublisher:South Western Educational Publishing
Practical Management Science
Operations Management
ISBN:9781337406659
Author:WINSTON, Wayne L.
Publisher:Cengage,
Contemporary Marketing
Marketing
ISBN:9780357033777
Author:Louis E. Boone, David L. Kurtz
Publisher:Cengage Learning
Marketing
Marketing
ISBN:9780357033791
Author:Pride, William M
Publisher:South Western Educational Publishing
Single Exponential Smoothing & Weighted Moving Average Time Series Forecasting; Author: Matt Macarty;https://www.youtube.com/watch?v=IjETktmL4Kg;License: Standard YouTube License, CC-BY
Introduction to Forecasting - with Examples; Author: Dr. Bharatendra Rai;https://www.youtube.com/watch?v=98K7AG32qv8;License: Standard Youtube License