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COMM 225: Operations management > chapter 3 powerpoint: forecasting > Flashcards

chapter 3 powerpoint: forecasting Flashcards

1
Q

A demand forecast

A

is an estimate of demand expected over a future time period

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2
Q

3 Uses for Forecasts

A

Design the System

Use of the System

Schedule the System

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3
Q

Features of Forecasts

A

Assumes causal system(past ==> future)

Forecasts rarely perfect because of randomness

Forecasts more accurate forgroups vs. individuals

Forecast accuracy decreases as time horizon increases

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4
Q

Elements of a Good Forecast

A

reliable

meaningful

compatible

useful time horizon

easy to understand and use

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5
Q

Steps in the Forecasting Process

A
  1. Determine purpose of forecast
  2. Establish a time horizon
  3. Select a forecasting technique
  4. Obtain, clean and analyze data
  5. Make the forecast
  6. Monitor the forecast
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6
Q

Approaches to Forecasting

A

Judgmental forecasting

Quantitative forecasting

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7
Q

Judgmental forecasting

A

non-quantitative analysis of subjective inputs

considers “soft” information such as human factors, experience, gut instinct

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8
Q

what do we use for quantitative forecasting

A

Time series models

–> extends historical patterns of numerical data

Associative models

–> create equations with explanatory variables to predict the future

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9
Q

Judgmental forecasting methods

A

Executive opinions

Expert opinions

Sales force opinions

Consumer surveys

Historical analogies

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10
Q

Executive opinions

A

pool opinions of high-level executives

long term strategic or new product development

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11
Q

Expert opinions

A

Delphi method

technological forecasting

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12
Q

Delphi method

A

iterative questionnaires circulated until consensus is reached

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13
Q

Sales force opinions

A

based on direct customer contact

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14
Q

Consumer surveys

A

questionnaires or focus groups

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15
Q

Historical analogies

A

use demand for a similar product

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16
Q

What is a Time Series?

A

a time ordered sequence of observations

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17
Q

the 6 patterns of time series

A

Level

Trend

Seasonality

Cycles

Irregular variations

Random variations

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18
Q

Level

A

(average) horizontal pattern

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19
Q

Trend

A

steady upward or downward movement

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20
Q

Seasonality

A

regular variations related to time of year or day

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21
Q

Cycles

A

wavelike variations lasting more than one year

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22
Q

Irregular variations

A

caused by unusual circumstances, not reflective of typical behaviour

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23
Q

Time series models

A

Naive methods

Averaging methods

Trend models

Techniques for seasonality

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24
Q

Averaging methods

A

Moving average

Weighted moving average

Exponential smoothing

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25
Trend models
Linear and non-linear trend Trend adjusted exponential smoothing
26
Techniques for seasonality
Techniques for cycles
27
Naive Methods
Next period = last period if there is a trend, follow the trend Simple to use and understand Very low cost Low accuracy
28
Moving average
Forecast = (EActual) / n average of last few actual data values, updated each period fewer data points = more sensitive to changes more data points = smoother, less responsive
29
Weighted moving average
ex: 0.5 · 36 + 0.3 · 32 + 0.2 · 38 usually, the most recent actual demand is the one with heaviest weight
30
Exponential smoothing
Ft = Ft-1 + a(At-1 - Ft-1) sophisticated weighted moving average weights decline exponentially most recent data weighted most subjectively choose smoothing constant a which ranges from 0 to 1
31
when do we use a smaller smoothing constant (a) in the exponential smoothing?
When demand is fairly stable smoothes out random fluctuations
32
when do we use a higher smoothing constant (a) in the exponential smoothing?
When demand increasing or decreasing more responsive to real changes
33
True or False? A moving average forecast tends to be more responsive to changes in the data series when more data points are included in the average
False
34
True or False? As compared to a simple moving average, the weighted moving average is more reflective of the recent changes
True
35
True or False? A smoothing constant of .1 will cause an exponential smoothing forecast to react more quickly to a sudden change than a value of .3 will
False
36
Techniques for Trend
Develop an equation that describes the trend Look at historical data
37
Linear Trend Equation
yt = a + bt b = n(Eyt -Et · Ey) / n(Et^2 - (Et)^2) a = (Ey - bEt) / n
38
Trend-Adjusted Exponential Smoothing
a = smoothing constant for average B = smoothing constant for trend estimate starting smoothed average and smoothed trend by using most recent data
39
Trend-Adjusted Exponential Smoothing formula
TAFt+1 = St + Tt St = TAFt + a(At - TAFt) Tt = Tt-1 + B(st - St-1 - Tt-1)
40
Techniques for Seasonality
Additive or Multiplicative Model
41
Additive Model
Demand = Trend + Seasonality
42
Multiplicative Model
Demand = Trend x Seasonality
43
Seasonal Relative (or index)
= proportion of average or trend for a season in the multiplicative model ex: seasonal relative of 1.2 = 20% above average
44
Deseasonalizing
removing seasonal component to more clearly see other components dividing by seasonal relative
45
Reseasonalizing
adjusting the forecast for seasonal component multiplying by seasonal relative
46
Times Series Decomposition
1. Compute the seasonal relatives. 2. Deseasonalize the demand data. 3. Fit a model to deseasonalized demand data, - -> e.g., moving average or trend. 4. Forecast using this model and the deseasonalized demand data. 5. Reseasonalize the deseasonalized forecasts.
47
firecast error
Actual value - Forecast value positive is due to a forecast that was too low compared to actual negative is due to a forecast that was too high compared to actual
48
Three measures of forecasts errors are used
Mean absolute deviation (MAD) Mean squared error (MSE) Mean absolute percent error (MAPE)
49
Control charts
plot errors to see if within pre-set control limits A visual tool for monitoring forecast errors Used to detect non-randomness in errors Set limits that are multiples of the √MSE
50
Tracking signal
Ratio of cumulative error and MAD
51
Mean absolute deviation (MAD)
(E|Actual - Forecast|) / n Easy to compute Weights errors linearly
52
Mean squared error (MSE)
((Actual - Forecast)^2) / n Squares error More weight to large errors
53
Mean absolute percent error (MAPE)
(E[|Actual - Forecast| / Actual] / n Puts errors in perspective above 70% satisfactory
54
bias
the sum of the forecast errors positive bias = frequent underestimation negative bias = frequent overestimation
55
possible sources of error include:
Model may be inadequate (things have changed) Incorrect use of forecasting technique Irregular variations
56
when are forecasting errors “in control”?
when only random errors are present no errors from identifiable causes All errors are within control limits No patterns (e.g. trends or cycles) are present errors outside limit = need corrective action
57
Control Limits
Standard deviation of error = s = √MSE control limits = 2s 68% of all errors should be within 1s 95% of all errors should be within 2s 99.7% of all errors should be within 3s
58
Tracking signal
ratio of cumulative error to MAD can be plotted on a control chart investigate if Tracking Signal > 4 Tracking Signal = E(Actual - forecast) / MAD
59
True or False? When error values fall outside the limits of a control chart, this signals a need for corrective action
True
60
True or False? When all errors plotted on a control chart are either all positive, or all negative, this shows that the forecasting technique is performing adequately
False
61
True or False? A random pattern of errors within the limits of a control chart signals a need for corrective action.
False
62
Two most important factors to choosing a forecasting technique?
Cost Accuracy

COMM 225: Operations management (17 decks)

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