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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
Q

Trend models

A

Linear and non-linear trend

Trend adjusted exponential smoothing

26
Q

Techniques for seasonality

A

Techniques for cycles

27
Q

Naive Methods

A

Next period = last period

if there is a trend, follow the trend

Simple to use and understand

Very low cost

Low accuracy

28
Q

Moving average

A

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
Q

Weighted moving average

A

ex: 0.5 · 36 + 0.3 · 32 + 0.2 · 38

usually, the most recent actual demand is the one with heaviest weight

30
Q

Exponential smoothing

A

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
Q

when do we use a smaller smoothing constant (a) in the exponential smoothing?

A

When demand is fairly stable

smoothes out random fluctuations

32
Q

when do we use a higher smoothing constant (a) in the exponential smoothing?

A

When demand increasing or decreasing

more responsive to real changes

33
Q

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

A

False

34
Q

True or False?

As compared to a simple moving average, the weighted moving average is more reflective of the recent changes

A

True

35
Q

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

A

False

36
Q

Techniques for Trend

A

Develop an equation that describes the trend

Look at historical data

37
Q

Linear Trend Equation

A

yt = a + bt

b = n(Eyt -Et · Ey) / n(Et^2 - (Et)^2)

a = (Ey - bEt) / n

38
Q

Trend-Adjusted Exponential Smoothing

A

a = smoothing constant for average

B = smoothing constant for trend

estimate starting smoothed average and smoothed trend by using most recent data

39
Q

Trend-Adjusted Exponential Smoothing formula

A

TAFt+1 = St + Tt

St = TAFt + a(At - TAFt)

Tt = Tt-1 + B(st - St-1 - Tt-1)

40
Q

Techniques for Seasonality

A

Additive or Multiplicative Model

41
Q

Additive Model

A

Demand = Trend + Seasonality

42
Q

Multiplicative Model

A

Demand = Trend x Seasonality

43
Q

Seasonal Relative (or index)

A

= proportion of average or trend for a season in the multiplicative model

ex: seasonal relative of 1.2 = 20% above average

44
Q

Deseasonalizing

A

removing seasonal component to more clearly see other components

dividing by seasonal relative

45
Q

Reseasonalizing

A

adjusting the forecast for seasonal component

multiplying by seasonal relative

46
Q

Times Series Decomposition

A
  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
Q

firecast error

A

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
Q

Three measures of forecasts errors are used

A

Mean absolute deviation (MAD)

Mean squared error (MSE)

Mean absolute percent error (MAPE)

49
Q

Control charts

A

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
Q

Tracking signal

A

Ratio of cumulative error and MAD

51
Q

Mean absolute deviation (MAD)

A

(E|Actual - Forecast|) / n

Easy to compute

Weights errors linearly

52
Q

Mean squared error (MSE)

A

((Actual - Forecast)^2) / n

Squares error

More weight to large errors

53
Q

Mean absolute percent error (MAPE)

A

(E[|Actual - Forecast| / Actual] / n

Puts errors in perspective

above 70% satisfactory

54
Q

bias

A

the sum of the forecast errors

positive bias = frequent underestimation

negative bias = frequent overestimation

55
Q

possible sources of error include:

A

Model may be inadequate (things have changed)

Incorrect use of forecasting technique

Irregular variations

56
Q

when are forecasting errors “in control”?

A

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
Q

Control Limits

A

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
Q

Tracking signal

A

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
Q

True or False?

When error values fall outside the limits of a control chart, this signals a need for corrective action

A

True

60
Q

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

A

False

61
Q

True or False?

A random pattern of errors within the limits of a control chart signals a need for corrective action.

A

False

62
Q

Two most important factors to choosing a forecasting technique?

A

Cost

Accuracy

COMM 225: Operations management (17 decks)

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