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POM > ch 4. > Flashcards

ch 4. Flashcards

1
Q

What are the four different types of forecasting models?

A
  1. qualitative
  2. quantitative
  3. simulation
  4. miscellaneous
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2
Q

types of qualitative models?

A
  1. market survey (market research)
  2. expert opinion (jury of executive opinion)
  3. Sales force consensus estimate
  4. delphi method
  5. historical analogy
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3
Q

types of quantitative models?

A
  1. time series (last period, arithmetic average, moving average, weighted moving average, exponential smoothing)
  2. causal models (associative models)(simple regression and multiple regression)
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4
Q

Principles of forecasting? (4)

A
  1. forecasts are (almost) always wrong
  2. forecasts should include an estimate of error
  3. near-term forecast are more accurate than long-tern forecasts
  4. forecasts are more accurate for a product group than a single product
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5
Q

Keys factors in forecasting? (7)

A
  1. past data
  2. subjective or objective
  3. continuous or snapshot
  4. cost
  5. accuracy
  6. external factors
  7. model
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6
Q

forecasting process?

A
  1. Determine items to be forecasted
  2. determine time interval and forecast horizon
  3. collect data
  4. discover patterns/components
  5. consider internal/external factors
  6. select model
  7. forecast and implement
  8. monitor performance
  9. revise/replace model (if necessary)
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7
Q

Time series analysis

A
  1. process of discovering demand pattern with respect to time
  2. process of projecting the past behavior in the future
  3. demand is related to time
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8
Q

components of time series analysis?

A
  1. trend
  2. seasonality
  3. cyclical variations
  4. random
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9
Q

components of time series analysis: trend

A

gradual up or down movement

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10
Q

components of time series analysis: seasonality

A

any specific pattern that occurs periodically and is repeditive

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11
Q

components of time series analysis: cyclical variations

A

an up and down movement that repeats itself over a long period of time

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12
Q

components of time series analysis: random

A

fluctuations with no specific/discernible pattern

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13
Q

characteristics of last period demand?

A
  1. only one previous data point is used
  2. cannot dampen (filter) unexplained variations (actually overreacts to unexplained variations)
  3. responds well to demands with trend
  4. not appropriate for seasonal demand
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14
Q

characteristic of arithmetic average

A
  1. all past data are used
  2. appropriate for level demand (dampens unexplained variations well)
  3. responds to trends with a lag
  4. not appropriate for seasonal demand
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15
Q

characteristics of moving average?

A
  1. More flexible than both Last Period and Arithmetic Average models
  2. If n is small: behaves more like Last Period
  3. If n is large: behaves more like Arithmetic Average
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16
Q

characteristics of weighted moving average?

A

Same as Moving Average but more flexible because both n and weights can be controlled

17
Q

what are two opposing properties of forecasting demand?

A
  1. responsiveness: ability to quikcly respond to change in demand (desirable in stuations with a trend)
  2. stability: ability to stabilize demand (not to overreact to random variations) (desirable in situations with steady demand and no trend)
18
Q

exponentially weighted moving average: level correction

A

applies to level demand, with no trend or seasonal pattern

19
Q

exponentially weighted moving average: level and trend corrections

A

applies to demand with an upward or downward trend but no seasonal pattern

20
Q

exponentially weighted moving average: lever, trend, and seasonal correction

A

applies to situations that include both trend and seasonality

21
Q

level correction dampens (filters) unexplained variations? true? false?

A

true

22
Q

Exponentially weighted moving average: a?

A

value of a controls the responsiveness and stability of the model

a=0; less responsive, most stable
a=1; more responsive, least stable

23
Q

seasonality

A
  1. a unique characteristic
  2. each period in a cycle is a season
  3. within each cycle, the seasonal effect is different from one period to another
  4. however, for any period, the seasonal effect is identical from one cycle to another
24
Q

general procedure of forecastingdemand with seasonality

A
  1. Estimate It (seasonal index) for each t
  2. Deseasonalize the actual demand
    (by dividing actual demand by It )
  3. Use a model to forecast the demand (using the deseasonalized data from step 2)
  4. Reseasonalize the forecasts
    (by multiplying deseasonalized forecasts by It)

POM (8 decks)

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