Goods And Services Exam 2

Front 1

What are the two general types of forecast techniques?

Back 1

Qualitative and Quantitative

Front 2

What are the two sub-categories for each broad method?

Back 2

Qualitative: Judgmental and Market Research
Quantitative: Time Series and Causal

Front 3

Qualitative Forecasting Methods

Back 3

- involve greater subjectivity
- used when historical data are lacking
-Judgmental and Market research methods

Front 4

Judgmental methods

Back 4

- Qualitative
- based on experience and informed opinion
-jury of executive opinion(ppl who constantly have access to external and internal info)
- sales force estimation (have bias, req to come to consensus)
- Delphi (anonymous experts)

Front 5

Delphi-

Back 5

anonymous experts; independently give feedback on future. Summarize opinions of random sample

Front 6

Market research methods

Back 6

- Qualitative
- based on systematic data collection and statistical analysis
- focus groups (selected carefully, representative of demographics)
- consumer surveys (rely on large #s of people)

Front 7

Quantitative Forecasting Methods

Back 7

- based on mathematical techniques
- requires historical data
- more objective approach
Time Series methods and Causal modeling method

Front 8

Time Series Forecasting Method

Back 8

- Quantitative
- forecasts are based solely on past (historical) demand
- assume future demand is reflected in past demand patterns
- short term

Front 9

Causal Modeling Forecasting Method

Back 9

- Quantitative
- demand is influenced by one or more independent variables
- best for underlying cause
- long term

Front 10

Advantages of Causal Modeling compared to Time Series Modeling:

Back 10

- typically better for long-range forecasting
- typically better for predicting shifts (or turning points) in data patterns
- can generate better understanding of the mechanisms influencing sales/ demand

Front 11

Disadvantages of Causal modeling compared to time series modeling:

Back 11

- Causal requires more data
- Causal requires special expertise that many managers do not have

Front 12

5 Step Causal Modeling Procedure using Regression analysis:

Back 12

1. identify variables that you "think" might explain what you are trying to forecast - initial (or theoretical) regression model
2. collect the data (observations)
3. initial screening of the data
4. build regression model and test
- check that data meet necessary conditions
5. use model to forecast

Front 13

in the initial screening of the data, step 3, what can result in removal of certain problematic data points?

Back 13

- errors in recording the value
- observation is not part of the population of interest

Case 1: error in recording or reporting data value
Case 2: data representing extreme or rare conditions such as
-major disruption in supply chain
-rapid change in market conditions
problematic data points- should be removed prior to model development

Front 14

what is defined as "extreme observations" ?

Back 14

an extreme observation, or OUTLIER, is identified as being well separated from the remainder of the data
-an observation may be extreme, or outlying, with respect to its dependent variable, it independent variable, or both
- outliers warrant additional scrutiny

Front 15

how do you go about detecting observations?
aka Outlier Analysis:

Back 15

-use standardized residuals (errors of the regression equation)
- values greater than +-4 standard deviations from the mean indicate a potential outlier

Front 16

what are the four assumptions of regression?
(necessary conditions for regression analysis)

Back 16

a) linear relationship exists between the independent variables and dependent variables
b) independent variables are NOT highly correlated; correlation table IrI > 0.9
c) residuals (or errors) exhibit a constant variance; aka homoscedasticity (we want)
d) independent residuals (good)
if violated: (bad)
1) autocorrelation
2) serial correlation

Front 17

what two graphs check a linear relationship?

Back 17

scatter plots and residual plots

Front 18

scatter plots

Back 18

-plots with dep var on the y-axis and each ind var on the x-axis
- a plot should suggest a straight line relationship (with or without a trend)

Front 19

residual plots

Back 19

- plots with residuals on the y-axis against each ind variable and the predicted values on the x-axis
- a plot SHOULD exhibit a RANDOM pattern

Front 20

independent residuals

Back 20

- the residuals must be independent of each other
- plot should NOT exhibit a trend or any special pattern
- residuals should be distributed randomly between pos and negative pattern

Front 21

normally distributed residuals

Back 21

- residuals must be normally distributed
- histogram should exhibit a bell shaped curve about zero, using 8 to 10 bin categories

Front 22

Naive Method

Back 22

the naive forecast for the next period equals the demand for the current period.
a.k.a. what happens today will happen tomorrow

Front 23

Simple Moving Average (SMA)

Back 23

(Horizontal; no trend)
- SMA places the same weight (or emphasis) on each time period
- works well when demand is fairly STABLE over time
- does NOT do a good job of forecasting when a TREND is present
- the forecast LAGS THE ACTUAL DEMAND because of averaging effect
- DECREASING the number of periods in a forecast, creates a MORE RESPONSIVE forecast 

Front 24

Weighted Moving Average (WMA)

Back 24

(horizontal; no trend)
- weighted moving average allows DIFFERENT emphasis to be placed on different time periods
- works well when the demand is fairly stable over time
- does NOT do a good job of forecasting when trend is present
- the forecast lags the actual demand bc of averaging effect
- weights tend to be based on the forecaster's experience
- DECREASING the number of periods in forecasting and/or INCREASING the size of the weights for more recent demand creates a MORE responsive forecast

Front 25

what is forecasting error?

Back 25

Et= (At-Ft) Forecast Error is the difference between actual demand and the forecast
- used to evaluate the accuracy of a forecasting model
- for all measures of forecast accuracy, the CLOSER the measure is to ZERO the BETTER the forecast

Front 26

ultimate goal of any forecasting endeavor is to have an

Back 26

accurate and unbiased forecast

Front 27

Costs associated with prediction error inclue:

Back 27

cost of lost sales, safety stock, unsatisfied customers, and loss of good will

Front 28

Absolute measures of forecast include:

Back 28

RSFE, MFE, MAD, MSE

Front 29

Relative measures of forecast include:

Back 29

MAPE, tracking signal

Front 30

RSFE and MFE
Running Sum of Forecast Errors and Mean Forecast Error

Back 30

POSITIVE= underestimated demand; stockouts
NEGATIVE= overestimated demand; higher inventory costs
- a zero RSFE and MFE indicates that the forecast is UNBIASED, but NOT necessarily accurate

Front 31

bias

Back 31

represents the tendency of a forecast to be consistently higher or lower than the actual demand

Front 32

MAD

Back 32

Mean Absolute Deviation
- measures average magnitude or size of the forecast errors without regard to the direction of error
- simple way to compare forecasts
- greater than zero, overestimates or underestimates
- a ZERO MAD= PERFECT forecast of demand, overlap

Front 33

MSE

Back 33

mean squared error
- sensitive to large errors

Front 34

tracking signal

Back 34

- used to monitor the performance of a forecast overtime
-tracks forecast bias relative to the average magnitude of the forecast error