Midterm- Forecasting Flashcards
(165 cards)
1
Q
statement about the future value of a variable of interest
A
forecast
2
Q
forecast of day to day operations
A
short range forecasts
3
Q
(S/L)types of products and services to offer
A
L
4
Q
(S/L) Facilitites and equipments to have
A
L
5
Q
(S/L) Location
A
L
6
Q
(S/L) Scheduling
A
S
7
Q
planning inventory and work force levels(S/L)
A
S
8
Q
(S/L) purchasing and budgeting
A
S
9
Q
involves ling range plans about the types of products and services to offer
A
Plan the system
10
Q
short range and intermediate-range planning
A
Plan the use of the system
11
Q
2 Uses of forecast
A
plan the system
plan the use
12
Q
new product/process cost estimates (Bus. Org)
A
Accounting
13
Q
(Bus. Org) equipment replacement needs
A
finance
14
Q
hiring activities (Bus. Org)
A
HR
15
Q
pricing and promotion (Bus. Org)
A
Marketing
16
Q
new/revised info system (Bus. Org)
A
MIS
17
Q
work assignments and work loads (Bus. Org)
A
Ops
18
Q
revision of current features (Bus. Org)
A
product and service design
19
Q
process cost estimates (Bus. Org)
A
acounting
20
Q
outsourcing (Bus. Org)
A
operations
21
Q
internet services (Bus. Org)
A
mis
22
Q
e-business strategies (Bus. Org)
A
marketing
23
Q
layoff planning (Bus. Org)
A
HR
24
Q
cash management (Bus. Org)
A
accounting
25
funding/borrowing (Bus. Org)
finance
26
profit projections (Bus. Org)
accounting
27
outplacement (Bus. Org)
HR
28
global comp strategies (Bus. Org)
marketing
29
porject management (Bus. Org)
operations
30
Features common to all forecasts (4)
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
31
allowances should be made for____
errors
32
forecasting errors among items in a group have a ______
cancelling effect
33
grouping may arise if ____ are used to make _____products
raw materials
| multiple
34
time period covered by forecast
time horizon
35
Elements of a good forecast (7)
```
timely
accurate
reliable
meaningful units
in writing
simple to understand and use
cost-effective
```
36
enabling users to plan against possible errors&provide basis for comparing alternative forecast
accurate
37
forecast should work consistently
reliable
38
it will increase likelihood tgat all concerened are using the same information
in writing
39
benefits should outweigh the cost
cost effective
40
______ will permit an objective basis for evaluating the forecast once actual results are in
written forecast
41
6 Basic steps in forecasting process
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
42
2 general approaches to forecasting
qualitative
| quantitative
43
subjective inputs&soft information
qualitative
44
objective inputs&hard data
quantitative
45
defies numerical description
qualitative
46
defies personal biases
quantitative
47
forecast that use subjective inputs
judgmental
48
project patterns identified in recent time-series observations
time series forecast
49
uses explanatory variables to predict future demand
associative model
50
Judgmental forecast (4)
executive
sales force
consumer
delphi approach
51
upper level managers (finance, matketing, manufacturing managers) join to prepare forecast
executive opinions
52
joint estimates of sales people and customer service people
sales force opinion
53
managers and staff complete a series of questionnaires
delphi approach
54
good sources of info because of their ditect contact with consumers
sales staff and customer service staff
55
determines demand
consumers
56
time-ordered sequence of observations taken at regular intervals
time-series
57
analysis of time-series data requires
identi of underlying behavior of the series
58
identification kf underlying behavior of series is done by
plotting the data
59
long-term upward or downward movement in dat
trend
60
short term regular variations to the calendar or time of the day
seasonality
61
wavelike variations lasting more than one year
cycles
62
caused by unusual circumstances and are not reflective if typical behavior
irregular variation
63
residual variations that remain after all other behaviors have been accounted for
random variation
64
populations shifts, changing incomes, cultural changes
trend
65
economic, political and agricultural conditions
cycles
66
may be caused by severe weather conditons, strikes, major change in product or service
irregular variation
67
forecast for any period that equals the previous period actual value
naive forecast
68
basis of forecast
single previous value of a time series
69
last data point=forecast for the next period
naive used with stable series
70
forecast this season=value of the series of last season
naive used with seasonal variations
71
last value of the series +|- the difference between the last two values of the series
naive used with trend
72
Advantage if naive (3)
no cost
quick and easy to prep
data analysis is non existent thats why easily understandable
73
Disadvantage of naive
inability to provide highly accurate forecast
74
smooth variation in data
averaging techniques
75
small variations
random
76
large variations
real variations
77
reflect recent value of time series
averaging tech
78
average value ocer the last several periods
averaging tech
79
3 techniques for averaging
moving
weighted moving
exponential smoothing
80
averages a number of recent actual values, updated as new values become available
moving average
81
more recent values in a series are given more weight in computing a forecast
weighted moving
82
weighted moving average based on previous forecast plus a percentage of the forecast error
exponential smoothing
83
previous forecast plus the difference with such forecast and the actual value of the series at that point
exponential smoothing
84
next value in a series will ewual the previous value in comparable period
naive
85
forecast is based in an average of recent values
moving average
86
sophisticated form of weighted moving
exponential smoothing
87
2 important techniques to develop forecast when trend is present
1 trend equation
| 2 trend-adjusted exponential smoothing
88
used to develop forecast when trend is present
linear trend equation
89
variation of exponential smoothing used when a time series exhibits linear trend
trend adjusted exponential smoothing
90
2 elements of trend adjusted forecast
smoothed error
| trend factor
91
a forecast model for trend
adjusted expo smoothing
92
regularly repeating movements in series values that can be tied up to recurring events
seasonal variations
93
may refer to regular annual variations
seasonality
94
percentage of average ir trend
seasonal relatives
95
2 models of seasonality
additive
| multiplicative
96
seasonalityis expressed as a quantity
additive model
97
seasonality is expressed as a percentage of a trend
multiplicative model
98
two uses of seasonality
deseasonalize data
| incorporate seasonality in a forecast
99
removing the seasonal components from data in order to get a picture of non seasonal components
deseasonalize data
100
useful when demand has both trend and seasonal component
incorporate seasonality in a forecast
101
dividing each data point by its corresponding seasonal relative
deseasonalize
102
obtaining trend estimates using trend equation.
| add seasonality to the trend estimates by multiplying these trend estimates by corresponding seasonal relatives
Incorporte seasonality in forecast
103
up and down movements similar to seasonal variations but of longer duration (2-6 yrs)
cycles
104
search doe another variable that relates to and leads the variable of interest
cycles
105
Time series forecasts (6)
```
Naive
averaging
trend
trend adjusted expo smoothing
seasonality
cycles
```
106
Associative forecasting techniques (3)
simple linear regression
comments on the use of linear regression analysis
curvilinear and multiple regression analysis
107
rely on identification of related variables that can be used to predict bvalues of the variable of interest
associative forecasting tech
108
associative tech has an equation that summarizes the effects of____
predictor variables
109
the primary method used of analysis
regression
110
it is a technique for fitting a line to a set of points
regression
111
simplest and widely used form of regression
simple linear regression
112
involves a linear relationship bet. two variables
simple linear regression
113
minimizes the sum of the squared vertical deviations around the line
least square line
114
uncontrollable bariables that tend to lead or precede changes on a variable of interest
indicators
115
3 conditions for an indicator to be valid
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
weaknesses of regression (3)
Applies obly to linear relationships with one independent variable
needs considerable amount of data
all observations are weighted equally
117
measure the strength and direction of relationship bet. 2 variables
correlation
118
Comments on the use of linear regression anaylsis (3)
variations around the line are random
deviations around the line be normally distributed
predictions within the range
119
_____ the data to verify that a linear relationship is appropriate
always plot
120
_____may be time dependent
data
121
_____may imply that other variables are important
small correlation
122
when non linear relationship are present
curvilinear regression
123
modles that innvolve more than one predictor
multiple regression analysis
124
______substantially increases data requirments
multiple data analysis
125
basis of orgs schedules
forecasts
126
difference between the actual value and the value that was predicted for a given period
forecast error
127
actual-forecast
error
128
significant factor to decide among forecasting alternatives
forecast accuracy
129
average absolute forecast error
mean absolute deviation
130
average of swuared forecast errors
mean squared error
131
the average absolute percent error
mean absolute percent error
132
will provide insight in WON forecasts are performing satisfactorily
tracking and analysis of forecast errors
133
forecast is deemed to perform adequately if errors show only____
random variations
134
inherent variation, remains in data, even after all causes for variation has been accounted for
random variations
135
cisual toll for monitoring forecast error
control chart
136
center line means
zero error
137
How to construct control chart (3)
compute MSE
compute for the upper control limit
lower limit
138
the ratio of cumulative forecast error to corresponding value of MAD, used to forecast
tracking signal
139
its purpose is to detect ant bias in errors
tracking signal
140
tendency for sequence of errors to be postive or negative
bias
141
values outside of limits means
there is bias in forecast
142
two most important factors in choosing forecasting tech
cost
| accuracy
143
SHORT prep time
movigg average
simple expo
trend adjusted
trend models
144
Short-moderate prep time
seasonal
145
long develpment prep time
causal regression models
146
Stationary data pattern (2)
moving ave
| simple expo
147
Trend data pattern
adjusted
| trend models
148
complex patterns
causal regression models
149
Short forecast horizon
moving ave
| simple expo
150
short to medium forcast horizon
trend
| seasonal
151
short medium long forecast horizon
causal regre
152
2-3 observations
moving average
153
5-10 observations
simple expo
154
10-15 observations
trend adjused
155
10-20, 5 pee season if seasonal
trend models
156
2 peaks and troughs
seasonal
157
10 obs per independent variable
causal reg
158
2 approaches to forecat
reactive
| proactive
159
views forecast as probable future demand
reactive
160
(approach)
| adjust production rates
reactive
161
inventories (approach)
reactive
162
workforce (approach)
reactive
163
seeks to influence the demand
proactive
164
advertising (approach)
proactive
165
pricing, product changes (approach)
proactive
