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SCM Exam 2 > Chapter 9 Vocabulary > Flashcards

Chapter 9 Vocabulary Flashcards

1
Q

Two types of demand

A

Independent

Dependent

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2
Q

Demand that’s coming from your customer; you don’t know exactly how to forecast it

A

Independent Demand

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3
Q

Demand that you can actually calculate

A

Dependent Demand

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4
Q

An estimate of the future level of some variable. Looks at things like demand levels, supply levels and prices

A

Forecast

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5
Q

Four Laws of Forecasting

A

Forecasts are almost always wrong (but they are still useful)
Forecasts for the near term tend to be more accurate
Forecasts for groups of products or services tend to be more accurate
Forecasts are no substitute for calculated values

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6
Q

Forecasts are almost always

A

Wrong

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7
Q

Forecasts for the near term tend to be

A

More accurate

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8
Q

Forecasts for groups of products or services tend to be

A

More accurate

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9
Q

Forecasts are no substitute for

A

Calculated values

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10
Q

Seven Steps in Forecasting

A 
Determine the use of the forecast 
Select the items to be forecasted 
Determine the time horizon of the forecast 
Select the forecasting model(s) 
Gather the data (HARDEST STEP)
Make the forecast 
Validate and implement results
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11
Q

Forecasting models that use measurable, historical data to generate forecasts

A

Quantitative forecasting

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12
Q

Two models of quantitative forecasting

A

Time series models

Causal models

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13
Q

Forecasting techniques based on intuition or informed opinion. Used when data is scarce, not available or irrelevant

A

Qualitative forecasting techniques

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14
Q

Five qualitative forecasting techniques

A 
Market surveys 
Build-up forecasts 
Life cycle analogy method 
Panel consensus forecasting 
Delphi method
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15
Q

time series models

A 
Naive approach (Last Period) 
Moving average 
Weighted moving average 
Exponential smoothing 
Seasonal Adjustment
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16
Q

Two causal models

A

Linear regression

Multiple regression

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17
Q

A structured questionnaire submitted to potential customers, often to gauge potential demand

A

Market survey

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18
Q

Brings experts together to discuss and develop a forecast

A

Panel consensus forecasting

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19
Q

Experts work individually to develop forecasts. The individual forecasts are then shared among the group, and then each participant is allowed to modify his or her forecast based on the information from the other experts; repeated until consensus is reached

A

Delphi method

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20
Q

Three types of participants in the Delphi method

A

Decision makers (evaluate responses and make decisions)
Staff (administering survey)
Respondents (people who can make valuable judgements)

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21
Q

Attempts to identify the time frames and demand levels for the introduction, growth, maturity and decline life cycle stages for a new product or service; looks at demand for a similar product to see trajectory

A

Life Cycle Analogy Method

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22
Q

Individuals familiar with specific market segments estimate demand within these segments. These individual forecasts are then added up to get an overall forecast

A

Build-up forecast

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23
Q

A series of observations arranged in chronological order

A

Time series

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24
Q

With a time series model, this is important in developing forecasts

A

The chronology of the observations and their values

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25
Q

In the context of forecasting, unpredictable movement from one time period to the next is

A

Randomness

26
Q

Long term movement up or down in a time series is a

A

Trend

27
Q

A repeated pattern of spikes, or drops in a time series associated with certain times of the year

A

Seasonality

28
Q

Assumes demand in next period is the same as demand in most recent period; sometime cost effective and efficient

A

Naive Forcast (Last Period Model)

29
Q

Derives a forecast by taking an average of recent demand values; assumes an average is a good estimator of future behavior

A

Moving Average Model

30
Q

A form of the moving average model that allows the actual weights applied to past observations to differ

A

Weighted Moving Average

31
Q

With moving average, the smaller the n, the more closely it

A

Mimics Demand

32
Q

Using averages to generate forecasts results in forecasts that are less susceptible to

A

Random fluctuations in demand (smoothing model=moving average model)

33
Q

Forecasts for the next period is calculated as the weighted average of the current periods actual value and forecast

A

Exponential smoothing model

34
Q

WMA follows demand

A

More closely

35
Q

An expanded version of the exponential smoothing models that includes a trend adjustment factor

A

Adjusted exponential smoothing model

36
Q

A statistical technique that expresses a forecast variable as a linear function. Can be used to develop time series and causal forecasting models

A

Linear regression

37
Q

In exponential smoothing weights decline

A

Exponentially

38
Q

In exponential smoothing most recent data is

A

Weighted the moist bc last period is seen as the best indicator

39
Q

Exponential smoothing requires

A

Smoothing constant which ranges from 0 to 1 and is subjectively chosen

40
Q

Exponential smoothing involves

A

Little record keeping of the past

41
Q

Premise of exponential smoothing

A

The most recent observations might have the highest predicted value; therefore we should give more weight to the more recent time periods when forecasting

42
Q

Smaller alpha in exponential smoothing results in

A

Smoother line

43
Q

Larger alpha in exponential smoothing

A

Mimics what Demand really is

44
Q

With forecast accuracy we pick

A

The model that gives us the lowest forecast error

45
Q

Two sources of error

A

Bias

Random

46
Q

Calculation error that we can fix

A

Bias

47
Q

We can’t predict it 100% and we expect to see it because it’s unpredictable refers to

A

Random

48
Q

What measures the bias of a forecast model

A

Mean Forecast Error (MFE)

49
Q

A negative MFE suggests that the model

A

Overforecasts (always bigger than demand)

50
Q

A positive MFE suggests that the model

A

Underforecasts (always lower than demand)

51
Q

The calculation that tracks the average size of the errors, regardless of direction

A

Mean Absolute Deviation (MAD)

52
Q

The ideal MAD is and what does it mean

A

Zero and it would mean there is no forecasting error

53
Q

The larger the MAD, the

A

Less accurate the forecasting model

54
Q

We choose the forecasting model that has the

A

Smaller MAD (more accurate)

55
Q

Provides a perspective of the true magnitude of the forecast error; for each period how off was your forecast percentage

A

Mean Absolute Percentage Error (MAPE)

56
Q

A class of quantitative forecasting models in which the forecast is modeled as a function of something other than time

A

Causal forecasting model

57
Q

A generalized form of linear regression that allows for more than one independent variable

A

Multiple regression

58
Q

Measure that indicates whether the forecast average is keeping pace with any genuine upward of downward changes in demand; used to flag a forecasting model that is getting out of control

A

Tracking Signal (TS)

59
Q

If the TS falls outside the pre-set control limits, there is a

A

Bias problem with the forecasting method and an evaluation of the way the forecasts are generated is warranted

60
Q

Limits of the TS

A

+/- 4

61
Q

A set of business processes backed up by information technology, in which supply chain partners agree to mutual business objectives and measures, develop joint sales and operational plans, and collaborate to generate and update sales forecasts and replenishment plans

A

Collaborative planning, forecasting and replenishment (CPFR)

SCM Exam 2 (5 decks)

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