Cpim-Bscm Session 2

Front 1

1. Which of the following is a characteristic (component) of demand?
A. Mean absolute variation
B. Seasonal variation
C. Average variation
D. Standard deviation

Back 1

B

Front 2

2. Product A is made from components B and C. Component B is made from parts D and E. Which items should be forecast?
A. Only A
B. A, B, and C
C. D and E
D. B, C, D, and E

Back 2

A

Front 3

3. Which of the following is the best statement about the general principles of forecasting? (Estimate of future demand)
A. Forecasts are more accurate for individual items than for groups of items.
B. Forecasts are more accurate for distant periods of time.
C. Every forecast should include an estimate of error.
D. Forecasts usually are accurate.

Back 3

C

Front 4

4. What important assumption is made about quantitative forecasting methods?
A. The past is a valid indicator of the future.
B. Demand trend is seldom linear.
C. Seasonal variations are small.
D. Random variations are small.

Back 4

A

Front 5

5. Which forecasting technique takes the average demand for some past number of periods?
A. Trend time average
B. Moving average
C. Demand smoothing
D. Qualitative analysis

Back 5

B

Front 6

6. Why is it important to monitor the forecast?
A. To compare the actual sales with the forecast
B. To improve our forecasting methods
C. To utilize actual sales data
D. To satisfy marketing’s need to know

Back 6

B

Front 7

7. Which of the following statements is most accurate?
A. Demand fluctuations that depend on the time of the year, week, or day are called trend.
B. The seasonal index is an estimate of how much demand during the season will be above or below the average demand.
C. Seasonality always occurs in summer, winter, spring or fall.
D. Random variation is constant from period to period.

Back 7

B

Front 8

8. Which of the following causes forecast error?
A. Random variation from the average demand
B. Errors in monitoring the forecast
C. Differences in lead times
D. Differences between sales and demand

Back 8

A

Front 9

9. Which of the following statements is most accurate?
A. Independent demand items should be forecast.
B. A forecast for sales next year will not be as accurate as a forecast for a year from now.
C. Forecasts for families of products should be built up from individual product forecasts.
D. Forecasts are almost always accurate.

Back 9

A

Front 10

10. Which of the following statements is most accurate?
A. If we wish to forecast demand, past sales must be used for the forecast.
B. Forecasts made in dollars for total sales should be used by manufacturing.
C. Forecasts should be made for individual items in a group.
D. The circumstances relating to demand data should be recorded.

Back 10

D

Front 11

Those competitive characteristics that a firm must exhibit to be a viable competitor in the marketplace?

Back 11

ORDER QUALIFIERS

Front 12

_______variation; a fluctuation in data that is caused by uncertain or random occurrences?

Back 12

RANDOM

Front 13

An arithmetic average of a certain number (n) of the most recent observations; as each new observation is added, the oldest observation is dropped?

Back 13

MOVING AVERAGE

Front 14

The average of the absolute values of the deviations of observed values from some expected value?

Back 14

MEAN ABSOLUTE DEVIATION

Front 15

Demand that is directly related to or derived from the bill of material structure for other items or end products.

Back 15

DEPENDENT

Front 16

Methods for forecasting sales data when a definite upward or downward pattern exists?

Back 16

TREND

Front 17

Inventory built up to smooth production in anticipation of a peak seasonal demand?

Back 17

SEASONAL

Front 18

An estimate of future demand?

Back 18

FORECAST

Front 19

A forecast based on internal factors, such as an average of past sales?

Back 19

INTRINSIC

Front 20

A type of moving-average forecasting technique in which planners can choose the relative weighting of the latest period’s actual demand and its forecast in developing the forecast for the next period?

Back 20

EXPONENTIAL SMOOTHING

Front 21

An approach to forecasting that is based on intuitive or judgmental evaluation?

Back 21

QUALITATIVE

Front 22

Those competitive characteristics that cause a firm’s customers to choose that firm’s goods and services over those of its competitors?

Back 22

ORDER WINNERS

Front 23

The demand for an item that is unrelated to the demand for other items?

Back 23

INDEPENDENT

Front 24

A consistent deviation from the mean in one direction (high or low)?

Back 24

BIAS

Front 25

An approach to forecasting where historical demand data is used to project future demand?

Back 25

QUANTITATIVE

Front 26

A forecast method on a correlated leading indicator, such as estimating furniture sales based on housing starts?

Back 26

EXTRINSIC

Front 27

A need for a particular product or component?

Back 27

DEMAND

Front 28

What is the weighted average calculation for exponential smoothing?

Back 28

New Forecast = (α)(latest demand) + (1-α)(previous forecast)

Front 29

A low alpha value (such as .2) will give much more weight to which forecast - the old or the new?

Back 29

A low alpha value, such as .2, would mean: (1-.2 = .8) which would be multiplied by the old or previous forecast.

Front 30

If there is an upward or downward demand trend, would a lower or higher alpha value be more appropriate?

Back 30

Higher. If there is a recent trend then a higher alpha value would have greater weight being applied against the latest demand.

New Forecast = (α)(latest demand) + (1-α)(previous forecast)

Front 31

How do you determine Seasonality?

Back 31

1st: Calculate the overall average for all the periods involved (rows and column).

2nd: Calculate the average for each period (column)

3rd: Calculate the seasonal index for each period by;
seasonal Index = Individual period average
divided by the average demand for all the
periods.

Front 32

How do you De-seaonalize demand and create a forecast for the next year?

Back 32

Take the forecasted demand for the next year (420 for example) and divide it by the number of periods (4 for example) to get your period average (420/4 = 105 for example). Then you take this average and multiply it by the seasonal index for each period. (1.28 x 105 = 134.4 units for example)

Front 33

An estimate of how much demand during the season will be above or below the average demand.

Back 33

The Seasonal Index

Front 34

The difference between actual demand and forecast demand?

Back 34

Forecast error

Front 35

Random variation from the average demand causes____________?

Back 35

Forecast error

Front 36

Why is it important to monitor the forecast?

Back 36

To improve our forecasting methods.

Front 37

Should the circumstances relating to demand data be recorded?

Back 37

Yes

Front 38

What are the four components of demand?

Back 38

1. Cyclical
2. Random
3. Seasonal
4. Trend

Front 39

Seasonal variation is a characteristic of ____________?

Back 39

Demand

Front 40

Every forecast should include an estimate of __________?

Back 40

Error

Front 41

_________ forecasting techniques are based on judgement or intuition and informed opinion; therefore they tend to be subjective.

Back 41

Qualitative

Front 42

Forecasting technique used for medium to long-range planning, where conditions and trends are likely to change.

Back 42

Qualitative

Front 43

________ forecasting techniques are based on the idea of correlation and cause and effect. They rely on external factors to make projections or forecasts of demand. For example - Housing Starts

Back 43

Extrinsic

Front 44

_______ forecasting techniques use time-sequenced historical data, or time series data, for an item as the source for projecting future demand.

Back 44

Intrinsic

Front 45

Two examples of basic intrinsic forecasting techniques?

Back 45

1. Moving averages
2. Exponential smoothing