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Operations Management > Midterm- Forecasting > Flashcards

Midterm- Forecasting Flashcards

1
Q

statement about the future value of a variable of interest

A

forecast

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2
Q

forecast of day to day operations

A

short range forecasts

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3
Q

(S/L)types of products and services to offer

A

L

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4
Q

(S/L) Facilitites and equipments to have

A

L

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5
Q

(S/L) Location

A

L

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6
Q

(S/L) Scheduling

A

S

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7
Q

planning inventory and work force levels(S/L)

A

S

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8
Q

(S/L) purchasing and budgeting

A

S

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9
Q

involves ling range plans about the types of products and services to offer

A

Plan the system

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10
Q

short range and intermediate-range planning

A

Plan the use of the system

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11
Q

2 Uses of forecast

A

plan the system

plan the use

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12
Q

new product/process cost estimates (Bus. Org)

A

Accounting

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13
Q

(Bus. Org) equipment replacement needs

A

finance

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14
Q

hiring activities (Bus. Org)

A

HR

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15
Q

pricing and promotion (Bus. Org)

A

Marketing

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16
Q

new/revised info system (Bus. Org)

A

MIS

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17
Q

work assignments and work loads (Bus. Org)

A

Ops

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18
Q

revision of current features (Bus. Org)

A

product and service design

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19
Q

process cost estimates (Bus. Org)

A

acounting

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20
Q

outsourcing (Bus. Org)

A

operations

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21
Q

internet services (Bus. Org)

A

mis

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22
Q

e-business strategies (Bus. Org)

A

marketing

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23
Q

layoff planning (Bus. Org)

A

HR

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24
Q

cash management (Bus. Org)

A

accounting

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25
Q

funding/borrowing (Bus. Org)

A

finance

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26
Q

profit projections (Bus. Org)

A

accounting

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27
Q

outplacement (Bus. Org)

A

HR

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28
Q

global comp strategies (Bus. Org)

A

marketing

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29
Q

porject management (Bus. Org)

A

operations

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30
Q

Features common to all forecasts (4)

A

causal system that existed in the past will continue to exist in the future
rarely perfect
group items more accurate
accuracy decreases as time horizon increases

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31
Q

allowances should be made for____

A

errors

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32
Q

forecasting errors among items in a group have a ______

A

cancelling effect

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33
Q

grouping may arise if ____ are used to make _____products

A

raw materials

multiple

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34
Q

time period covered by forecast

A

time horizon

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35
Q

Elements of a good forecast (7)

A 
timely
accurate
reliable
meaningful units
in writing
simple to understand and use
cost-effective
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36
Q

enabling users to plan against possible errors&provide basis for comparing alternative forecast

A

accurate

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37
Q

forecast should work consistently

A

reliable

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38
Q

it will increase likelihood tgat all concerened are using the same information

A

in writing

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39
Q

benefits should outweigh the cost

A

cost effective

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40
Q

______ will permit an objective basis for evaluating the forecast once actual results are in

A

written forecast

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41
Q

6 Basic steps in forecasting process

A
  1. determine the purpose of the forecast
  2. establish time horizon
  3. select a forecasting technique
  4. obtain, clean and analyze appropriate data
  5. make the forecast
  6. monitor the forecest
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42
Q

2 general approaches to forecasting

A

qualitative

quantitative

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43
Q

subjective inputs&soft information

A

qualitative

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44
Q

objective inputs&hard data

A

quantitative

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45
Q

defies numerical description

A

qualitative

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46
Q

defies personal biases

A

quantitative

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47
Q

forecast that use subjective inputs

A

judgmental

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48
Q

project patterns identified in recent time-series observations

A

time series forecast

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49
Q

uses explanatory variables to predict future demand

A

associative model

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50
Q

Judgmental forecast (4)

A

executive
sales force
consumer
delphi approach

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51
Q

upper level managers (finance, matketing, manufacturing managers) join to prepare forecast

A

executive opinions

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52
Q

joint estimates of sales people and customer service people

A

sales force opinion

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53
Q

managers and staff complete a series of questionnaires

A

delphi approach

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54
Q

good sources of info because of their ditect contact with consumers

A

sales staff and customer service staff

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55
Q

determines demand

A

consumers

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56
Q

time-ordered sequence of observations taken at regular intervals

A

time-series

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57
Q

analysis of time-series data requires

A

identi of underlying behavior of the series

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58
Q

identification kf underlying behavior of series is done by

A

plotting the data

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59
Q

long-term upward or downward movement in dat

A

trend

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60
Q

short term regular variations to the calendar or time of the day

A

seasonality

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61
Q

wavelike variations lasting more than one year

A

cycles

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62
Q

caused by unusual circumstances and are not reflective if typical behavior

A

irregular variation

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63
Q

residual variations that remain after all other behaviors have been accounted for

A

random variation

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64
Q

populations shifts, changing incomes, cultural changes

A

trend

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65
Q

economic, political and agricultural conditions

A

cycles

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66
Q

may be caused by severe weather conditons, strikes, major change in product or service

A

irregular variation

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67
Q

forecast for any period that equals the previous period actual value

A

naive forecast

68
Q

basis of forecast

A

single previous value of a time series

69
Q

last data point=forecast for the next period

A

naive used with stable series

70
Q

forecast this season=value of the series of last season

A

naive used with seasonal variations

71
Q

last value of the series +|- the difference between the last two values of the series

A

naive used with trend

72
Q

Advantage if naive (3)

A

no cost
quick and easy to prep
data analysis is non existent thats why easily understandable

73
Q

Disadvantage of naive

A

inability to provide highly accurate forecast

74
Q

smooth variation in data

A

averaging techniques

75
Q

small variations

A

random

76
Q

large variations

A

real variations

77
Q

reflect recent value of time series

A

averaging tech

78
Q

average value ocer the last several periods

A

averaging tech

79
Q

3 techniques for averaging

A

moving
weighted moving
exponential smoothing

80
Q

averages a number of recent actual values, updated as new values become available

A

moving average

81
Q

more recent values in a series are given more weight in computing a forecast

A

weighted moving

82
Q

weighted moving average based on previous forecast plus a percentage of the forecast error

A

exponential smoothing

83
Q

previous forecast plus the difference with such forecast and the actual value of the series at that point

A

exponential smoothing

84
Q

next value in a series will ewual the previous value in comparable period

A

naive

85
Q

forecast is based in an average of recent values

A

moving average

86
Q

sophisticated form of weighted moving

A

exponential smoothing

87
Q

2 important techniques to develop forecast when trend is present

A

1 trend equation

2 trend-adjusted exponential smoothing

88
Q

used to develop forecast when trend is present

A

linear trend equation

89
Q

variation of exponential smoothing used when a time series exhibits linear trend

A

trend adjusted exponential smoothing

90
Q

2 elements of trend adjusted forecast

A

smoothed error

trend factor

91
Q

a forecast model for trend

A

adjusted expo smoothing

92
Q

regularly repeating movements in series values that can be tied up to recurring events

A

seasonal variations

93
Q

may refer to regular annual variations

A

seasonality

94
Q

percentage of average ir trend

A

seasonal relatives

95
Q

2 models of seasonality

A

additive

multiplicative

96
Q

seasonalityis expressed as a quantity

A

additive model

97
Q

seasonality is expressed as a percentage of a trend

A

multiplicative model

98
Q

two uses of seasonality

A

deseasonalize data

incorporate seasonality in a forecast

99
Q

removing the seasonal components from data in order to get a picture of non seasonal components

A

deseasonalize data

100
Q

useful when demand has both trend and seasonal component

A

incorporate seasonality in a forecast

101
Q

dividing each data point by its corresponding seasonal relative

A

deseasonalize

102
Q

obtaining trend estimates using trend equation.

add seasonality to the trend estimates by multiplying these trend estimates by corresponding seasonal relatives

A

Incorporte seasonality in forecast

103
Q

up and down movements similar to seasonal variations but of longer duration (2-6 yrs)

A

cycles

104
Q

search doe another variable that relates to and leads the variable of interest

A

cycles

105
Q

Time series forecasts (6)

A 
Naive
averaging
trend
trend adjusted expo smoothing
seasonality
cycles
106
Q

Associative forecasting techniques (3)

A

simple linear regression
comments on the use of linear regression analysis
curvilinear and multiple regression analysis

107
Q

rely on identification of related variables that can be used to predict bvalues of the variable of interest

A

associative forecasting tech

108
Q

associative tech has an equation that summarizes the effects of____

A

predictor variables

109
Q

the primary method used of analysis

A

regression

110
Q

it is a technique for fitting a line to a set of points

A

regression

111
Q

simplest and widely used form of regression

A

simple linear regression

112
Q

involves a linear relationship bet. two variables

A

simple linear regression

113
Q

minimizes the sum of the squared vertical deviations around the line

A

least square line

114
Q

uncontrollable bariables that tend to lead or precede changes on a variable of interest

A

indicators

115
Q

3 conditions for an indicator to be valid

A
  1. indi and varia should have logical explanations
  2. indicator must precede dependent variables; forecase isnt outdated
  3. small corellation may imply that other variables are important
116
Q

weaknesses of regression (3)

A

Applies obly to linear relationships with one independent variable

needs considerable amount of data

all observations are weighted equally

117
Q

measure the strength and direction of relationship bet. 2 variables

A

correlation

118
Q

Comments on the use of linear regression anaylsis (3)

A

variations around the line are random

deviations around the line be normally distributed

predictions within the range

119
Q

_____ the data to verify that a linear relationship is appropriate

A

always plot

120
Q

_____may be time dependent

A

data

121
Q

_____may imply that other variables are important

A

small correlation

122
Q

when non linear relationship are present

A

curvilinear regression

123
Q

modles that innvolve more than one predictor

A

multiple regression analysis

124
Q

______substantially increases data requirments

A

multiple data analysis

125
Q

basis of orgs schedules

A

forecasts

126
Q

difference between the actual value and the value that was predicted for a given period

A

forecast error

127
Q

actual-forecast

A

error

128
Q

significant factor to decide among forecasting alternatives

A

forecast accuracy

129
Q

average absolute forecast error

A

mean absolute deviation

130
Q

average of swuared forecast errors

A

mean squared error

131
Q

the average absolute percent error

A

mean absolute percent error

132
Q

will provide insight in WON forecasts are performing satisfactorily

A

tracking and analysis of forecast errors

133
Q

forecast is deemed to perform adequately if errors show only____

A

random variations

134
Q

inherent variation, remains in data, even after all causes for variation has been accounted for

A

random variations

135
Q

cisual toll for monitoring forecast error

A

control chart

136
Q

center line means

A

zero error

137
Q

How to construct control chart (3)

A

compute MSE
compute for the upper control limit
lower limit

138
Q

the ratio of cumulative forecast error to corresponding value of MAD, used to forecast

A

tracking signal

139
Q

its purpose is to detect ant bias in errors

A

tracking signal

140
Q

tendency for sequence of errors to be postive or negative

A

bias

141
Q

values outside of limits means

A

there is bias in forecast

142
Q

two most important factors in choosing forecasting tech

A

cost

accuracy

143
Q

SHORT prep time

A

movigg average
simple expo
trend adjusted
trend models

144
Q

Short-moderate prep time

A

seasonal

145
Q

long develpment prep time

A

causal regression models

146
Q

Stationary data pattern (2)

A

moving ave

simple expo

147
Q

Trend data pattern

A

adjusted

trend models

148
Q

complex patterns

A

causal regression models

149
Q

Short forecast horizon

A

moving ave

simple expo

150
Q

short to medium forcast horizon

A

trend

seasonal

151
Q

short medium long forecast horizon

A

causal regre

152
Q

2-3 observations

A

moving average

153
Q

5-10 observations

A

simple expo

154
Q

10-15 observations

A

trend adjused

155
Q

10-20, 5 pee season if seasonal

A

trend models

156
Q

2 peaks and troughs

A

seasonal

157
Q

10 obs per independent variable

A

causal reg

158
Q

2 approaches to forecat

A

reactive

proactive

159
Q

views forecast as probable future demand

A

reactive

160
Q

(approach)

adjust production rates

A

reactive

161
Q

inventories (approach)

A

reactive

162
Q

workforce (approach)

A

reactive

163
Q

seeks to influence the demand

A

proactive

164
Q

advertising (approach)

A

proactive

165
Q

pricing, product changes (approach)

A

proactive

Operations Management (6 decks)

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