Deck 10: Demand Forecasting: Building the Foundation for Resource Planning

Question 1

Planning benefits have a direct impact on profit margin and return on assets.

Answer 1

True

Question 2

All plans have a direct impact on net income through net sales.

Answer 2

False

Question 3

The minimum-planning horizon is determined by the lead-time associated with obtaining resources necessary to meet objectives.

Answer 3

True

Question 4

There is no single planning horizon for a business.

Question 5

More changes in the service sector are sure to come as a result of product innovation and service obsolescence.

Question 6

Consumer services have short life cycles, and consumer products have very long ones.

Question 7

When using causal forecasting models, the appearances of a relationship between demand and an external variable may be due to correlation.

Question 8

In some cases, random fluctuation can be significant enough to make it difficult to see trend or seasonality.

Question 9

In simple exponential smoothing, if the alpha was one, the forecast would be equal to the last period's forecast.

Question 10

Which of the following statements is NOT true about the financial benefits of effective planning?

A) Planning benefits have a direct impact on profit margin and return on assets
B) All plans have a direct impact of net income though net sales
C) Plans related to resources that are used to create value provide a way to improve the reliability of obtaining those resources
D) An advance plan to carry a new item has the potential to reduce cost and increase net sales, yielding a . substantial boost to profit margin.
E) All of the above are true.

Question 11

A good guide to follow regarding planning horizons is

A) We should plan as far into the future as possible
B) The shorter the lead-time, the farther out our planning horizon should be
C) Each business has an optimal planning horizon
D) The planning horizon is dictated by the lead times associated with obtaining resources.

Question 12

Product life cycles:

A) Describe demand over time for products.
B) Are easier to manage is they are extremely short.
C) Are long for consumer products, but tend to be shorter for consumer services.
D) Have gotten longer in recent years

Question 13

In what stage of the product life are costs reduced through standardization?

A) Introduction
B) Growth
C) Maturation
D) Decline
E) Saturation

Question 14

The forecasting technique that allows managers to use external quantitative data for forecasting is referred to as:

A) Qualitative technique
B) Simple exponential smoothing
C) Simple Moving Average
D) Causal forecasting
E) Weighted moving average

Question 15

Which of the components of time series cannot, by definition, be forecast?

A) Cycle
B) Trend
C) Seasonality
D) Random fluctuation
E) None of the above

Question 16

Which of the components of a time series is usually of little importance in demand forecasting?

A) Cycle
B) Trend
C) Seasonality
D) Random fluctuation
E) None of the above

Question 17

The greater the responsiveness of a forecast

A) The greater the error
B) The greater the correction when error exists
C) The greater the weight placed on the most recent demand
D) The more accurate the forecast when randomness is high

Question 18

Using simple exponential smoothing, if we want the forecast to be very responsive to recent demand, the value of alpha should be:

A) High
B) Low
C) Moderate
D) Zero
E) The value of alpha doesn't matter

Question 19

A time series is

A) A demand cycle that repeats itself at least once a year
B) A correlation between time and demand
C) A series of demand values from the past
D) A measure of forecast bias

Question 20

When using simple linear regression as a causal forecasting tool :

A) A linear relationship is identified between the demand and an external variable
B) A linear relationship is identified between the demand and the time period
C) An external variable is used as the dependent variable
D) Qualitative data is preferred over quantitative data

Question 21

A time series is believed to consist of random fluctuation, but no evidence of a trend or seasonality is present. Which forecasting tool would be most appropriate?

A) Simple moving average
B) Double exponential smoothing
C) Simple linear regression
D) Seasonal indices

Question 22

A time series is believed to consist of random fluctuation, as well as seasonality, but no evidence of a trend is present. Which forecasting tool would be most appropriate?

A) Simple exponential smoothing
B) Simple moving average
C) Simple linear regression
D) Seasonal indices

Question 23

When using a simple moving average to forecast demand, the responsiveness will be increased:

A) As more periods are added to the average
B) As weights for most recent demands are increased
C) As fewer periods are used in the average
D) As weights for past demands are increased

Question 24

In simple exponential smoothing, what value of alpha would make the forecast equal to the naive forecast?

A) Alpha = 1
B) Alpha = 0.5
C) Alpha = 0
D) Alpha = 2
E) None of the above

Question 25

Forecast error

A) Is calculated as forecast minus actual demand.
B) Is comprised of absolute error and forecast bias.
C) Is comprised of absolute error and trend.
D) Only results from randomness.

Question 26

Which of the following statements is accurate regarding simple exponential smoothing?

A) The smoothing constant, beta, determines the weight assigned to most recent demand
B) The higher the smoothing constant, the more responsive the forecast to demand changes
C) If the smoothing constant was 1, the forecast would be equal to last period's forecast.
D) If the smoothing constant was 0, the forecast would equal to last period's demand.

Question 27

What smoothing constants are necessary for forecast including trend?

A) Alpha
B) Beta
C) Alpha and Beta
D) Alpha, Beta, and Sigma

Question 28

The development of a multiplicative model for forecasting seasonality and trend together includes

A) Computing a seasonal index for each period.
B) Seasonalizing the data.
C) Determination of alpha and beta.
D) Identifying bias in the forecast.

Question 29

The most popular measure of absolute error is:

A) MSE
B) MAD
C) MAPE
D) MFE
E) RSFE

Question 30

A method for measuring forecast bias that requires the error to be summed periodically as each new forecast, actual demand, and error become available is referred to as:

A) MSE
B) MAD
C) MAPE
D) MFE
E) RSFE

Question 31

If the MFE had a negative sign

A) The forecast method used inappropriate.
B) The distribution of randomness was skewed to the right.
C) The forecast tended to be too high.
D) Demand was higher than expected.

Question 32

A tracking signal is used

A) To state the error in terms of MADS
B) To identify random fluctuation in error
C) To control the alpha value
D) To identify seasonal patterns in the time series.

Question 33

In order to provide a relative measure of the absolute error, one should use the

A) MSE
B) MAD
C) MAPE
D) RSFE

Question 34

Which of the following statements is/are true about ERP?

A) In ERP, planning for all resources is accomplished from one common database
B) The techniques used to guide ERP system design are considered to be "best practice" techniques
C) ERP systems, for the most part, are customized for a particular business
D) Both A and B
E) Both B and C
F) A, B, and C

Question 35

During the _____ stage of the product life cycle, demand begins to fall because alternatives have begun to appear and take away demand.

Question 36

Use of time-series as the basis of demand forecasting requires an understanding of the ______ of the time series.

Question 37

Forecasts should be ____ to changes in demand when changes in demand are not indicative of anything in the future.

Question 38

If the ____ value was equal to ____, the simple exponential smoothing forecast would equal to the naive forecast.

Question 39

In trend-adjusted exponential smoothing, the _____ value is used to include the trend component.

Question 40

The _____ model combines the linear regression approach to forecasting trend with the seasonal indices used to forecast seasonality.

Question 41

______ and _____ approaches measure forecasting bias.

Question 42

A positive MFE indicates that the forecast tends to be too ____. On the other hand, a negative number is an indication that the forecast is too___.

Question 43

The techniques used to guide ERP system design are considered to be ____ _____ techniques.

Question 44

ERP systems are designed to integrate all aspects of an organization through one ______ _________.

Question 45

Match these items.

-Mean forecast error

A) Uses alpha and beta
B) Included in the multiplicative model
C) A qualitative forecasting technique
D) A measure forecast bias
E) A measure of absolute forecast error
F) Demand that repeats over long period of time
G) A type of forecast error
H) Uses only alpha as a smoothing constant
I) Includes a saturation stage
J) Past demand data

Question 46

Match these items.

-Product life cycle

A) Uses alpha and beta
B) Included in the multiplicative model
C) A qualitative forecasting technique
D) A measure forecast bias
E) A measure of absolute forecast error
F) Demand that repeats over long period of time
G) A type of forecast error
H) Uses only alpha as a smoothing constant
I) Includes a saturation stage
J) Past demand data

Question 47

Match these items.

-Forecast including trend

A) Uses alpha and beta
B) Included in the multiplicative model
C) A qualitative forecasting technique
D) A measure forecast bias
E) A measure of absolute forecast error
F) Demand that repeats over long period of time
G) A type of forecast error
H) Uses only alpha as a smoothing constant
I) Includes a saturation stage
J) Past demand data

Question 48

Match these items.

-Mean squared error

A) Uses alpha and beta
B) Included in the multiplicative model
C) A qualitative forecasting technique
D) A measure forecast bias
E) A measure of absolute forecast error
F) Demand that repeats over long period of time
G) A type of forecast error
H) Uses only alpha as a smoothing constant
I) Includes a saturation stage
J) Past demand data

Question 49

Match these items.

-Forecast bias

A) Uses alpha and beta
B) Included in the multiplicative model
C) A qualitative forecasting technique
D) A measure forecast bias
E) A measure of absolute forecast error
F) Demand that repeats over long period of time
G) A type of forecast error
H) Uses only alpha as a smoothing constant
I) Includes a saturation stage
J) Past demand data

Question 50

Match these items.

-Time series

A) Uses alpha and beta
B) Included in the multiplicative model
C) A qualitative forecasting technique
D) A measure forecast bias
E) A measure of absolute forecast error
F) Demand that repeats over long period of time
G) A type of forecast error
H) Uses only alpha as a smoothing constant
I) Includes a saturation stage
J) Past demand data

Question 51

Match these items.

-Simple exponential smoothing

A) Uses alpha and beta
B) Included in the multiplicative model
C) A qualitative forecasting technique
D) A measure forecast bias
E) A measure of absolute forecast error
F) Demand that repeats over long period of time
G) A type of forecast error
H) Uses only alpha as a smoothing constant
I) Includes a saturation stage
J) Past demand data

Question 52

Match these items.

-Expert opinion

A) Uses alpha and beta
B) Included in the multiplicative model
C) A qualitative forecasting technique
D) A measure forecast bias
E) A measure of absolute forecast error
F) Demand that repeats over long period of time
G) A type of forecast error
H) Uses only alpha as a smoothing constant
I) Includes a saturation stage
J) Past demand data

Question 53

Match these items.

-Seasonality and trend

A) Uses alpha and beta
B) Included in the multiplicative model
C) A qualitative forecasting technique
D) A measure forecast bias
E) A measure of absolute forecast error
F) Demand that repeats over long period of time
G) A type of forecast error
H) Uses only alpha as a smoothing constant
I) Includes a saturation stage
J) Past demand data

Question 54

Match these items.

-Cycle

A) Uses alpha and beta
B) Included in the multiplicative model
C) A qualitative forecasting technique
D) A measure forecast bias
E) A measure of absolute forecast error
F) Demand that repeats over long period of time
G) A type of forecast error
H) Uses only alpha as a smoothing constant
I) Includes a saturation stage
J) Past demand data

Question 55

The weekly demand for French fries at a local restaurant during the past six weeks has been.
$\begin{array}{lcccccc} \text {Week} & 1 & 2 & 3 & 4 & 5 & 6 \\\text {Demand} & 600 & 550 & 560 & 730 & 500 & 555\end{array}$
Forecast the demand for week 7 using a five-period moving average. a. Forecast the demand for week 7 using a three-period weighted moving average. Use the following weights to obtain your forecast: W1=.5, W2=.3, W3=.2. b. Forecast the demand for week 7 using exponential smoothing. Use an avalue of .2 and assume that the forecast for week 6 was 500 units.

Question 56

Compute the MAD and MFE for the following data:
$\begin{array}{ccc} \text {Period }& \text {Demand }& \text {Forecast}\\1 & 120 & 105 \\2 & 106 & 117 \\3 & 140 & 131 \\4 & 130 & 138 \\5 & 145 & 140\end{array}$

Question 57

This period's demand was 146 units, even though it was forecast to be 132. Use simple exponential smoothing with an alpha of .3 to forecast demand for next period.

Question 58

The quarterly demands for a popular model of backpacking tent are provided below:
Fall: 112
Winter: 80
Spring: 210
Summer: 164
Compute the seasonal indices for each quarter.

Question 59

A toy manufacturer wants to predict quarterly demand for a certain product line for periods 13 and 14, which happen to be the second and third quarters of a particular year. The series consists of both trend and seasonality. The trend portion of demand is projected using the equation g t= 130 + 7.8t. Quarterly seasonal indices are Q1= 1.10, Q2= 1.20, Q3= 0.95, and Q4= 0.75. Use this information to predict demand for periods 13 and 14.

Question 60

In 2004, the Board of Control of Public Schools hired a consultant to develop a series of forecasting models to project the future enrollments of each school in the district. After examining the historical data and trends, the consulting firm felt that exponential smoothing would be the most appropriate time series technique for projecting enrollment. An exponential smoothing model was then developed. The Board members determined the alpha level (smoothing constant) for each school based on their gut feeling. The forecast enrollments helped the board set the budget for each school for the upcoming year. Based on the above situation, discuss the pros and cons of using the exponential smoothing technique for this forecasting purpose. How can the model be abused? What could the board do to improve the accuracy of enrollment information?

Question 61

Discuss the relationship between a retailer's demand forecast accuracy and the lead time associated with replenishing inventory.