Operations chapter 4
demand forecasts, also called __ forecasts, are projections of demand for a company's products or services
sales
__ is a forecasting technique based upon salespersons' estimates of expected sales
sales force composite
if a barbershop operator notes that tuesday's business was typically twice as heavy as wednesday's, and that friday's business was typically the business of the week, the business at the barbershop is subject to __.
seasonal variations
__ forecasts are concerned with rates of technological progress, which can result in the birth of exciting new products, requiring new plants and equipment
technological
which of the following uses 3 types of participants: decision makers, staff personnel, and respondents
the Delphi metho
__ forecasts use a series of past data point to make a forecast
time-series
__ is a time-series forecasting method that fits a trend line to a series of historical data points and then projects the line into the future for forecasts
trend projection
a naive forecast for september sales of a product would be equal to the sales in august
true
a time-series model uses a series of past data points to make the forecast
true
a trend projection equation with a slope of 0.78 means that there is a 0.78 unit rise in Y for every unit of time that passes
true
cycles and random variations are both components of time series
true
demand (sales) forecasts serve as input to financial, marketing, and personnel planning
true
demand cycles for individual products can be driven by product life cycles
true
focus forecasting tries a variety of computer models and selects the best one for a particular application.
true
forecast including trend is an exponential smoothing technique that utilizes two smoothing constant: one for the average level of the forecast and one for its trend
true
if a forecast is consistently greater than (or less than) actual values, the forecast is said to be biased.
true
in trend projection, the trend component is the slope of the regression equation
true
linear-regression analysis is a straight-line mathematical model to describe the functional relationships between independent and dependent variables
true
many service firms use point-of-sale computers to collect detailed records needed for accurate short-term forecasts
true
most forecasting techniques assume that there is some underlying stability in the system
true
one advantage of exponential smoothing is the limited amount go record keeping involved
true
seasonal indexes adjust raw data for patterns that repeat at regular time intervals
true
the best way to forecast a business is by finding a leading variable
true
the forecasting time horizon and the forecasting techniques used tend to vary over the life cycle of a product
true
the quarterly "make meeting" of lexus dealers is an example of a sales force composite forecast
true
the sales force composite forecasting method relies on salespersons' estimates of expected sales
true
a 6-month moving average forecast is better than a 3-month moving average forecast if demand a. is rather stable b. has been changing due to recent promotional efforts c. follows a downward trend d. follows a seasonal pattern that repeats itself twice a year e. follows an upward trend
a
forecasts are usually classified by time horizon into three categories a. short-range, medium-range, long-range b. finance/accounting, marketing, and operations c. strategic, tactical, and operational d. exponential smoothing, regression, and time series e. departmental, organizational, and industrial
a
the fundamental difference between cycles and seasonality is the a. duration of repeating patterns b. magnitude of the variation c. ability to attribute the pattern to a cause d. all of the above e. none of the above
a
which of the following techniques uses variables such as price and promotional expenditures, which are related to product demand, to predict demand? a. associative models b. exponential smoothing c. weighted moving average d. simple moving average e. time series
a
which of the following values of alpha would cause exponential smoothing to respond the most slowly to forecast errors? a. 0.10 b. 0.20 c. 0.40 d. 0.80 e. cannot be determined
a
linear regression is known as a(n) __ because it incorporates variables or factors that might influence the quantity being forecast
associative model
Computer monitoring of tracking signals and self-adjustment if a signal passes a preset limit is characteristic of a. exponential smoothing including trend b. adaptive smoothing c. trend projection d. focus forecasting e. multiple regression analysis
b
forecasts a. become more accurate with longer time horizons b. are rarely perfect c. are more accurate for individual items than for groups of items d. all of the above e. none of the above
b
which of the following is not a step in the forecasting process? a. determine the use of the forecast b. eliminate any assumptions c. determine the time horizon d. select forecasting method e. validate and implement the results
b
which of the following is not present in a time series a. seasonality b. operational variations c. trend d. cycles e. random variations
b
Yamamha manufactures which set of products with complementary demands to address seasonal fluctuations?a. gold clubs and skis b. swimming suits and winter jackets c. jet skis and snowmobiles d. pianos and guitars e. ice skates and winter skis
c
in a regression equation where Y is demand and X is advertising, a coefficient of determination (R squared) of 0.70 means that 70% of the variance in advertising is explained by demand
false
in trend projection, a negative regression slope is mathematically impossible
false
mean square error and coefficient of correlation are two measures of the overall error of a forecasting method
false
the larger the number of periods in the simple moving average forecasting method, the greater the method's responsiveness to changes in demand
false
the larger the standard error of the estimate, the more accurate the forecasting method
false
__ forecasting tries a variety of computer models and selects the best one for a particular application
focus
a __ forecast uses an average of the most recent periods of data to forecast the next period
moving average
what 2 numbers are contained in the daily report to the CEO of Disney regarding the 6 Orlando parks?
yesterday's forecast and yesterday's actual attendance
which time series model assumes that demand in the next period will be equal to the most recent period's demand?
naive approach
the 2 generic approaches to forecasting are
qualitative and quantitative
__ forecasts employ one more mathematical models that rely on historical data and/or casual variables to forecast demand
quantitative
using an exponential smoothing model with smoothing constant alpha=0.20, how much weight would be assigned to the 2nd most recent period
0.16
a seasonal index for a monthly series is about to be calculated on the basis of 3 years' accumulation of data. the three previous July values were 110, 150, and 130. the average over all months is 190. the approximate seasonal index for july is
0.684 (130/190--> most recent/average)
which smoothing constant would make an exponential smoothing forecast equivalent to a naive forecast?
1.0
given an actual demand of 103, a previous forecast value of 99, and an alpha of 0.5, the exponential smoothing forecast for the next period would be?
100.6
demand for certain product is forecast to be 800 units per month, averaged over all 12 months of the year. the product follows a seasonal pattern, for which the january monthly index is 1.25. what is the seasonally-adjusted sales forecast for january?
1000 units (800*1.25)
john's house of pancakes uses a weighted moving average method to forecast pancake sales. it assigns a weight of 5 to the pervious month's demand, 3 to demand 2 months ago, and 1 to demand 3 months ago. if sales amounted to 1000 pancakes in may, 2200 pancakes in june, and 3000 pancakes in july, what should the forecast for august be?
2511
the last four months of sales were 8, 10, 15, and 9 units. the last four forecasts were 5, 6, 11, and 12 units. MAD is...
3.5
Given the forecast errors of -1, 4, 8, and -3, what is the MAD?
4
a time series trend equation is 25.3+2.1x. what is your forecast for period 7?
40.0
given an actual demand of 61, a previous forecast of 58, and a smoothing constant of 0.3, what would the forecast for the next period be using simple exponential smoothing?
58.9
a fundamental distinction between trend projection and linear regression is that a. trend projection uses least squares while linear regression does not b. only linear regression can have a negative slope c. in trend projection the independent variable is time, in linear regression the independent variable need not be time, but can be any variable with explanatory power d. linear regression tends to work better on data that lacks trends e. trend projection uses 2 smoothing constants, not just one
c
for a given product demand, the time series trend equation is 53-4x. the negative sign on the slope of the equation a. is a mathematical impossibility b. is an indication that the forecast is biased, with forecast values lower than actual sales c. is an indication that product demand is declining d. implies that the coefficient of determination will also be negative e. implies that the RSFE will be negative
c
gradual, long-term movement in time series data is called a. seasonal variation b. cycles c. trends d. exponential variation e. random variation
c
the percent of variation in the dependent variable that is explained by the regression equation is measured by the a. mean absolute deviation b. slope c. coefficient of determination d. correlation coefficient e. intercept
c
the primary purpose of MAD in forecasting is to a. estimate the trend line b. eliminate forecast errors c. measure forecast accuracy d. seasonally adjust the forecast e. all of the above
c
which is not a characteristic of exponential smoothing? a. smoothes random variations in the data b. easily altered weighting scheme c. weights each historical value equally d. has minimal data storage requirements e. none of the above; they are all characteristics of exponential smoothing
c
which of the following statements about time series forecasting is true? a. it is based on the assumption that future demand will be the same as past demand b. it makes extensive use of the data collected in the qualitative approach c. the analysis of past demand helps predict future demand d. because it accounts for trends, cycles, ands seasonal patterns, it is more powerful than casual forecasting e. all of the above
c
the __ measures the strength of the relationship between two variables
coefficient of correlation
if two variables were perfectly correlated, the correlation coefficient r would equal a. 0 b. -1 c. 1 d. b or c e. none of the above
d
in time series, which of the following cannot be predicted a. large increases in demand b. technological trends c. seasonal fluctuations d. random fluctuations e. large decreases in demand
d
increasing the number of periods in a moving average will accomplish greater smoothing, but at the expense of a. manager understanding b. accuracy c. stability d. responsiveness to changes e. all of the above are diminished when the number of periods increases
d
the degree or strength of a linear relationship is shown by the a. alpha b. mean c. mean absolute deviation d. correlation coefficient e. RSFE
d
the last four weekly values of sales were 80, 100, 105, and 90 units. the last four forecasts were 60, 80,95, and 75 units. these forecasts illustrate a. qualitative methods b. adaptive smoothing c. slope d. bias e. trend projection
d
the tracking signal is the a. standard error of the estimate b. running sum of the forecast errors (RSFE) c. mean absolute deviation (MAD) d. ratio RSFE/MAD e. mean absolute percent error (MAPE)
d
which of the following statements comparing the weighted moving average technique and exponential smoothing is true? a. exponential smoothing is more easily used in combination with the delphi method b. more emphasis can be placed on recent values using the weighted moving average c. exponential smoothing is considering more difficult to implement on a computer d. exponential smoothing typically requires less record keeping of past data e. exponential smoothing allows one to develop forecasts for multiple periods, whereas weighted moving averages does not
d
many services maintain records of sales noting a. the day of the week b. unusual events c. weather d. holidays e. all of the above
e
one use of short-range forecasts it to determine a. production planning b. inventory budgets c. research and development plans d. facility location e. job assignments
e
taco bell's unique employee scheduling practices are partly the result of using a. point-of-sale computers to track food sales in 15 minute intervals b. focus forecasting c. a 6-week moving average forecasting technique d. multiple regression e. a and c are correct
e
time series data may exhibit which of the following behaviors? a. trend b. random variations c. seasonality d. cycles e. all of the above
e
which of the following is not a type of qualitative forecasting? a. executive opinions b. sales force composites c. consumer surveys d. the Delphi method e. moving average
e
which of the following is true regarding the two smoothing constants of the forecasting including trend model? a. one constant is positive, while the other is negative b. they are called MAD and RSFE c. alpha is always smaller than beta d. one constant smoothes the regression intercept, whereas the other smoothes the regression slope e. their values are determined independently
e
__ forecasts address the business cycle by predicting inflation rates, money supplies, housing starts, and other planning indicators
economic
the three major types of forecasts used by business organizations are
economic, technological, and demand
the smoothing constant is a weighting factor used in __
exponential smoothing
which time series model uses past forecasts and past demand data to generate a new forecast?
exponential smoothing
when one constant is used to smooth the forecast average and a sec on constant is sued to smooth the trend, the forecasting method is __
exponential smoothing with trend adjustment or trend-adjusted smoothing or second-order smoothing or double smoothing
a forecast based on the previous forecast plus a percentage of the forecast error is...
exponentially smoothes forecast
a naive forecast for September sales of a product would be equal to the forecast for August
false
forecasts of individual products tend to be more accurate than forecasts of product families
false
if a quarterly seasonal index has been calculated at 1.55 for the october-december quarter, then raw data for that quarter must be multiplied by 1.55 so that the quarter can be fairly compared to other quarters
false
the forecasting model that pools the opinions of a group of experts or managers is known as the
jury of executive opinion model
forecasts used for new product planning, capital expenditures, facility location or expansion, and R&D typically utilize
long-range time horizon
__ is a measure of overall forecast error for a model
mean absolute deviation (MAD)
a measure of forecast error that does not depend on the magnitude of the item being forecast is the __
mean absolute percent error (MAPE)
a forecast with a time horizon of about 3 months to 3 years is typically called a
medium-range forecast