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MGST 391 > 1) CH3- Forecasting > Flashcards

1) CH3- Forecasting Flashcards

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1
Q

What is forecasting

A

process of predicting a future event

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2
Q

what is forecasting used for

A

production, inventory.personnel, facilities

WE USE THE PAST TO PREDICT THE FUTURE ALWAYS

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3
Q

WHY IS forecasting important

A

because businesses must make decisions, use resources, spend money before they know what the future holds

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4
Q

EX: call centre staffing, needs to forecast what? what is the pllaning horizon?

A

of calls per hour

1 day- 1 month

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5
Q

3 types of forecasting time horizions

A

1) short range : <3mos, up to 1 year
purchasing, job scheduling, workforce levels, job assignments, production levels

2) medium range: 3mos-3 years
sales and production planning, budget

3) long range: 3+ years
new product planning, facility location, R&D

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6
Q

Types of forecasts

A

economic
technological
demand

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7
Q

economic forecasts

A

address busienss cycle,

ifnlation rate, money cupply, housig starts

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8
Q

technologicla forecasts

A

predict rate of tech processes

impact development of new products

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9
Q

demand forecasts (focus)

A

predict sales of exisitng products and services

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10
Q

why does HR care about forecasts

A

hiring, training, lay offf workers

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11
Q

seven steps of forecasting

A

1) determine the use of the forecast
2) select the items to be forecasted
3) determine the time horizon of the forecast
4) select the forecasting models
5) gather data
6) make the forecast
7) validate and implement results

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12
Q

qualitiative methods of forecasting

A

subjective, used when no data available

new products and new tech! involves experience and intuition

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13
Q

quantiative methods of forecasting

A

used when situation is stable and historical data exists!

involves math techniques

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14
Q

5 examples of qualitiative methods of forecasting

A

1) jury of exec opinion (experts)
2) delphi and nominal group techniques (group consensus that arrives through an iterative process)
3) sales force ocmposite (Aggregatted from individual sales persons)
4) consumer market survey (Ask customers such as disney)
5) product life cycle curve (forecast life cycle stage A) INTRODUCTION B)GROWTH C) MATURITY D)DECLINE))

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15
Q

4 stages of product life cycle

A

introdcution
growth
maturity
decline

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16
Q

quantiative methods of forecasting example

A

1) time series forecasting (intrinsic) WE FOCUS ON THIS

2) casual methods (extrinsic)

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17
Q

time series forecasting (intrinsic)

A

relies on past data to predict the future

focus of this course

THIS IS WHAT WE USE

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18
Q

casual methods (Extrinsic)

A

regression, leading indicators, econometric models

includes use of external factors:
-forecasts for GDP based on housing starts
- exchange rates
- interest rates

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19
Q

what is selection criteria

A

important factors when determining which forecasting method to use

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20
Q

what are the selection criteria

why do we have these?

A

forecast horizon (daily-annual)

required accuracy (how much who shortage/excess cost???)

data availability (new porduct vs established one)

resources avaialble to make forecasts (people (knowledge), time, money)

b) because there are many techniques and we need to pick one

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21
Q

characterisitcs fo a good forecast

QUALITY AND COST- analysis

A

quality :
A) ACCURACY: Size (Spread) of forecast errors
B) BIAS: were predicitons consistently high or low? skew?

cost:
A) COST: cost of gaining additional data, more complicated models, and the value of an incrementally better forecast
B) CONSEQUENCES: what is the cost of not forecasting well? (Stocking too little or too much product/ under or over staffing)

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22
Q

5 characteristics of a good forecast

A

quality
cost
responsiveness
timeliness
simplicity

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23
Q

responsiveness and timeliness analysis

A

responsiveness: the forecast should reflect changes in market conditions quickly; outliers should be addressed

timelinesS: forecasts should be available for when they are needed

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24
Q

simplicty characteristic analysis

A

output should be easy to understad and interpet

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25
what is time series forecasting
set of observations on a quantitatiev variable collected over time ex: daily weekly monthly quarterly figures on sales/costs/profits
26
what does time series forecasting assue
factors influencing past and present will continue in the future
27
5 trends for analysing forecasting patterns graphically
1) no trend (demand is stable) 2) linear trend (postive or negative) 3) cyclical/seasonal (yearly) 4)trend and cyclical 5) little or no historical data
28
how do you forecast if no historical data (3 methods)
intutition/qualiitative methods lok at similar products do a market survey
29
statiionary time series forecasting
no discernible pattern in data inspect the data plotted over time and try to find a pattern
30
forecasting using time series analysis- if data is stable/showing no patterns what can you use
smoothing methods 1) simple moving average 2) weighted moving average 3) exmplonential smoothing
31
forecastin gusin gtime serizes analysis- for data with patterns
trends (upward/downard) cyclical seasonal cyclaical/seasonal with trend (combo)
32
cyclical vs seasonal
they are similar (Seasonal is actually a cyclical pattern) seasonal is related to season, cyclcilat related to something else
33
Naiive Approach - is this a good forecasting technique
assume demand in next period is the same as demand in most recent period if jan sales were 68 then feb is 68 NO!! NOT REALLY FORECASTING BRUH
34
why is naiive approach good? bad?
cost effective and efficienct can be a good starting point
35
what is the "last period" tecnique?
naiive approach
36
forecasting notation, Ft At et
Ft= forecast of unkown value for some period t in future (prediction) At= actual observen known value for some period t in future et= error between actual and forecasted value et= At-Ft
37
3 forecasting smoothing methods
1) simple moving average (Average of several periods of data is the forecast [add up n prior period and divide by n for average]) 2) weighted moving average (Allocate weights to prior periods) 3) exponential smoothing (choose alpha value for automativ weightings of a declining weighted moving average)
38
WEIGHTED MOVING AVG LOGIC hwo to calcualte weighted moving average
Most recent data is most accurate (weighted more) 1) assign weights to the different months 2) multiply weight by data 3) add up all products
39
Simple exponential smoothing
basically you assign more weights to the recent data, less weight to the less recent data our predicaiton for this month+alpha*error for this month REARRANGE TO: alpha*ActualT - (1-alpha)*PredictedT
40
notes about exponential smoothing method
-you are not going to use any 'n' value - to make prediction for march, you have to use the actual value from february and the prediction value from february as well
41
simple exponential smootghin formula
prediction for future month= alpha*actual value + 1-alpha*predicted value all the values on the RHS come from the current month (come from march if you are predicting for april)
42
when do we use low values of alpha? WHAT DOES IT MEAN IF ALPHA IS=0
when underlying average is fairly stablewh alpha*actual + (1-alpha)*predicted -> if alpha 0= we care about only the predicted, and ACTUAL demand is ignored
43
when do we use high alpha value WHAT DEOS IT MEAN WHEN ALPHA=1
when underlying average is likely change alpha*actual + (1-alpha)*predicted -> if alpha 1= we care about only the last actual demand of the last month- > "LAST PERIOD TECHNIQUE"
44
how do you see if your forecasting method is good or not?
use the model on the past data to predict known data to see if it is useful ! how much error is there
45
what should a good forecast look like
scattered around zero!!! (This means the error is 0)
46
how do you select the model?
the one with least error
47
how to choose the forecasting method
use all of the models on past data see if iti s accurate-> use them in the future graph the error values-> close to zero then good!
48
Ex of annual, seasonal, monthly, weekly, daily product
christmas product, pumpking plant ertilizer, posicles banking, moving companies (end of month) restaurants (depends on weekly promotions; wings on wednesday), bars, movies traffice, fast food restaurants
49
Forecast formula
base*cyclical index
50
how do you forecast a cylical pattern? (3 steps)
1) use historical data, determine avg demand (set cyclical index=1.00) CALCULATING THE BASE FOR THE FORECAST 2) use histroical data, caluclate cyclical endex for each period 3)Apply each periods cyclical index to forecast the demands
51
How do you forecast a cyclical trend [Simple steps]
1) how much data points do you have (dont count average or any summative value) BE VERY CAREFUL!!! 2) calculate the base (Average for every period) >average annual demand/12 =average monthly demand 3)calculate the cyclical index (Average for period / Average of EVERY period) > If below 1.0 it is below average > if greater than 1 it is above average 4) Add all the indexes up and when you add them, you should get this to be equal to the number of periods!!!!! (THIS IS HOW U KNOW U DID IT WELL)
52
WHY IS forecasting so critical to a business
because businesses have toallocate resources before they know what demand is
53
how to calculate simple moviing average
1) Add up previous FORECASTED n period values 2) divide by n
54
what is a stationary time series
data that has no discernible pattern or trend
55
Exponential smoothing carries along (implicitly) all historic demand data but weights recent demand more heavily similar to an “exponential” WMA.
Exponential smoothing carries along (implicitly) all historic demand data but weights recent demand more heavily similar to an “exponential” WMA.
56
We call it simple eexponential smoothing because?
the alpha declines at an exponential rate like a L shape
57
plotting error meaning
et=At-Ft if it is close to zero then it is a good forecast if positive= actual demand higher than predicted if negative= actual demand less than presicted if 0= no error
58
How do we measure how good a forecast is (3 ways)
1) Bias 2) MAD 3) Rise
59
Performance Measures: Bias
average error= sum of all errors/n
60
Performance Measures: MAD
mean absolute deviation= sum of abs errors/n
61
Performance Measures: RMSE
RMSE= sqrt((sum of error values squared)/n)
62
another way to calculate RMSE?
Root of MSE
63
What does MAPE mean
mean absolute percent error (for comparng datasets)
64
how to compare and select forecasting models
1) identiy the forecasting methods and parameters 2) apply historical data and determine the errorcs (BIAS, MAD, RMSE) 3) select the best forecasting method (the one w lowest errors)
65
for BIAS MAD RMSE what are the values we want (no error?)
lowest values (2/3 is good hard to have lowest error in all 3)
66
what are non stationary models
models that have patterns 1) trend 2) seasonal 3) trends and seasonal combined
67
Forecasting w Patterns: Trend
upward or downard movement in a time series use a base+trend model
68
Forecasting w Patterns: Seasonal
cyclical or repeating pattern in the data use a "base* cyclical index " tool - avg demand=1 -above avg demand= >1 -below avg demand= <1
69
Forecasting w Patterns: Trens and seasonal
combine the "base +trend" and the "base*cyclical index"
70
what does trend mean? how do you forecast trend?
it is persistent (positive or negative) holts approach: Base+K(trend)
71
How to calculate cyclical pattern
**FIRST: COUNT HOW MANY PERIODS YOU HAVE!! 1) calc average demand for a year avg demand monthly= [average annual demand/12] (set this as your 1.00 since 1.00 is the avg in cyclical index) 2) use hisotircal data, calculate cyclical index for each period cyclical index= [average of period/average demand monthly] (if <1 lower than avg, if >1 higher than avg) 3) applly each periods cyclical index to future demand -> find next years average monthly demand -> use the previous years corresponding month to caclulate average monthly demand (cyclical index*average monthly demand)
72
COMBO TIME! trend and cyclical index combo
Forecast= [Base + k(Trend)]*Cylical Index BASE= avg quarterly attendance Trend= given to you Cyclicla INdex= calculated earlier
73
How to deal with outliers in data
keep if relevant to your period
74

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