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COMM 225: Operations management > chapter 3 book: demand forecasting > Flashcards

chapter 3 book: demand forecasting Flashcards

1
Q

A demand forecast

A

the estimate of expected demand during a specified future period

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2
Q

Anticipated demand is derived from which two possible sources

A

actual customer orders

forecasts

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3
Q

which are the three types of uses for demand forecasts in operations?

A

(1) to help managers design the system
(2) to help them plan the medium-term use of the system
(3) to schedule the short- term use of the system

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4
Q

Two most important aspects of forecasts

A

(1) the expected level of demand

(2) the degree of accuracy that can be assigned to a forecast

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5
Q

collαborative planning, forecαsting, and replenishment (CPFR)

A

supply chain partners collaborating on the forecasting process

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6
Q

features common to all techniques and forecasts

A
  1. Forecasting techniques generally assume that the same underlying causal system that existed in the past will continue to exist in the future
  2. Forecasts are rarely perfect; actual results usually differ from predicted values

–> Allowances should be made for inaccuracies

  1. Forecasts for groups of items tend to be more accurate than forecasts for individual items, because forecasting errors among items in a group usually have a cancelling effect
  2. Forecast accuracy decreases the farther the forecasted time period is into the future
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7
Q

The forecasting horizon

A

the range of time periods we are forecasting for

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8
Q

that flexible business organization require which type of forecasting horizon?

A

require a shorter forecasting horizon

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9
Q

Elements of a Good Forecast

A
  1. The forecast should be timely

–> The forecasting horizon must cover the time necessary to implement possible changes so that its results can be used

  1. The forecast should be accurate， and the degree of accuracy of the forecast should be stated
  2. The forecasting method/software chosen should be reliable
  3. The forecast should be expressed in meaning of units
  4. The forecast should be in writing
  5. The forecasting technique should be simple to understand and use
  6. The forecast should be cost effective
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10
Q

Steps in the Forecasting Process

which are used on a continuing basis?

A
  1. Determine the purpose of the forecast
  2. Establish a forecasting horizon
  3. Gather and analyze relevant historical data
  4. Select a forecasting technique.
  5. Prepare the forecast
  6. Monitor the forecast

(only Steps 4 to 6 are used on a continuing basis)

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11
Q

the two general approaches to forecasting

A

judgmental

quantitative

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12
Q

judgmental approach to forecasting

A

consist mainly of subjective inputs, which may defy precise numerical description

rely on non quantitative analysis of historical data and/or analysis of subjective inputs obtained from various sources

ex: consumers (surveys), similar products (historical analogies), the sales staff, managers and executives, and panels of experts

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13
Q

quantitative approach to forecasting

A

involve either the use of a time series model to extend the historical pattern of data into the future

or

development of associative models that attempt to utilize causal variables to make a forecast

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14
Q

which forecasting approach permits the inclusion of soft information?

A

judgmental approach

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15
Q

long term forecasting uses which forecasting approach?

A

judgmental approach

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16
Q

Time series models

A

identify specific patterns in the data and project or extrapolate those patterns into the future

does not try to identify causes of the patterns

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17
Q

Associative models

A

use equations that consist of one or more explanatory variables that can be used to predict future demand for the variable of interest

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18
Q

Forecasting long-term demand typically involves what type of data?

A

annual data?

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19
Q

Forecasting medium-term demand typically involves what type of data?

A

monthly data

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20
Q

Forecasting short-term demand typically involves what type of data?

A

daily or weekly data

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21
Q

When introducing new products, redesigning existing products, and using sales promotions, which? forecasting is needed

A

judgmental forecasting

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22
Q

judgmental forecasts are based on what?

A

Executive Opinions

Sales Force Opinions

Consumer Surveys

Historical Analogies

Expert Opinions

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23
Q

Executive Opinions for forecasts

A

A small group of upper level managers may meet and collectively develop a forecast

often used as part of long-term strategic planing and new product development

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24
Q

advantage of executive opinions for forecasts

A

bringing together the considerable knowledge and talents of various managers

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25
Q

Sales Force Opinions for forecasts

A

direct contact with customers

often aware of any plans that the customers may be considering for the future, including the current level of customer inventory

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26
Q

Consumer Surveys for forecasts

A

enable corporations to sample consumer opinions

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27
Q

Historical Analogies for forecasts

A

Sometimes the demand for a similar product in the past, after some adjustment, can be used to forecast a new product’s demand

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28
Q

Expert Opinions for forecasts

A

we use the Delphi method

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29
Q

the Delphi method

A

circulating a series of questionnaires among experts

Responses are kept anonymous, which tends to encourage honest responses and reduces the risk that one person’s opinion will prevail

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30
Q

technological forecasting

A

an application of the Delphi method

assessing changes in technology and their impact on an organization

Often the goal is to predict when a certain event will occur

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31
Q

A time series

A

a time-ordered sequence of observations

taken at regular intervals over a period of time

(e.g., hourly, daily, weekly, monthly, quarterly, annually)

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32
Q

forecasting techniques based on time series data are made on which assumption?

A

on the assumption that future values of the series can be estimated from their own past values

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33
Q

the different time series patterns

A

Level (average)

Trend

Seasonality

Cycles

Irregular variations

Random variations

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34
Q

Level (average) pattern

A

a horizontal pattern of time series

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35
Q

Trend pattern

A

a persistent upward or downward movement in the data

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36
Q

Seasonality pattern

A

regular repeating wavelike variations generally related to factors such as the calendar, weather, or recurring events

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37
Q

cycles pattern

A

wavelike variations lasting more than one year

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38
Q

Irregular variations pattern

A

due to unusual one-time explainable circumstances not reflective of typical behaviour

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39
Q

Random variations pattern

A

residual variations that remain after all other behaviours have been accounted for (also called noise)

This randomness arises from the combined influence of many -perhaps a great many -relatively unimportant factors

cannot be reliably predicted

smoothed by time series techniques

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40
Q

the naïve method

A

can be used with:

a stable series (level or average with random variations)

seasonal variations

trend

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41
Q

the naïve forecast for stable series

A

the next forecast equals the previous period’s actual value

ex: if the demand for a product last week was 20 cases, the forecast for this week is 20 cases

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42
Q

the naïve forecast for data with trend

A

equal to the last value of the series plus or minus the difference between the last two values of the series

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43
Q

advantages of the naive method

A

virtually no cost

it is quick and easy to prepare

it is easily understandable

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44
Q

weakness of the naive method

A

the forecast just traces the actual data, with a lag of one period

it does not smooth the random variations out at all

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45
Q

Averaging Methods description

A

smooth variations in the data

smooth fluctuations in a time series because the individual highs and lows in the data offset each other when they are combined into an average

exhibits less variability than the original data

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46
Q

the three averaging techniques

A
  1. Moving average
  2. Weighted moving average

3 . Exponential smoothing

47
Q

The moving average technique

A

averages a number of recent actual data values

uses the average as the forecast for the current period

updated as a new value becomes available.

48
Q

The moving average technique formula

A

Ft = MAn = (EAi) / n

i = An index that corresponds to age of the period (i = t - 1: last period; i = t - 2: two periods back, . . )

basically just the average bro

n = Number of periods (data points) in the moving average

Ai = Actual value in period i

MAn = n period moving average

Ft =Forecast for this period (i.e., period t)

49
Q

in the moving average technique, when is it more sensitive to new data changes?

A

the shorter the period (the fewer the data points)

50
Q

in the moving average technique, when will the curve be smoother? what is the disadvantage of this?

A

averages based on more data points will be smoother

will be less responsive to “real” changes

51
Q

The advantages of a moving average forecast

A

easy to calculate and easy to understand

52
Q

A possible disadvantage of a moving average forecast

A

all values in the moving average forecast are weighted equally

53
Q

weighted moving average technique

A

similar to a moving average

the difference is that it assigns larger weight to the most recent values in a time series in calculating a forecast

the weights sum to 1.00

the heaviest weights are assigned to the most recent values

involves trial and error

54
Q

the advantage of a weighted moving average over a simple moving average

A

the weighted moving average is more reflective of the most recent observations

55
Q

the Exponential Smoothing technique

A

a sophisticated weighted averaging method

a new forecast is based on the previous forecast plus a percentage of the difference between that forecast and the previous actual value

56
Q

the Exponential Smoothing technique formula

A

Forecast = Previous forecast + α · (Previous actual - Previous forecast)

(Previous actual - Previous forecast) = forecast error

α: a proportion less than 1

57
Q

the Exponential Smoothing technique, what is the importance of α

A

a smoothing constant

it determines the quickness of adjustment by forecast error

The closer its value is to zero, the slower the forecast will adjust by forecast error (i.e., the greater the smoothing)

the closer the value of αis to 1.0, the greater the responsiveness and
the less the smoothing

58
Q

Selecting a smoothing constant in the Exponential Smoothing technique is a matter of what?

A

a matter of judgment or trial and error

59
Q

adaptive (or variable response) exponential smoothing technique

A

Aversion of exponential smoothing where the smoothing constant is automatically modified in order to prevent large forecast errors from occurring

a feature that permits automatic modification of the smoothing constant so that the forecast errors do not become unacceptably large

one of the most widely used techniques in forecasting

60
Q

Analysis of trend involves what?

A

developing an equation that will suitably describe the trend (assuming that trend is present in the data)

61
Q

the linear trend equation

A

y = ax + b

a =

62
Q

trend-adjusted exponential smoothing

A

Variation of exponential smoothing used when a time series exhibits trend

If a series exhibits trend, and exponential smoothing is used on it, the forecasts will all lag behind the trend: if the data are increasing, each forecast will be too low; if the data are decreasing, each forecast will be too high

63
Q

The trend-adjusted forecast (TAF) for period t + 1 is composed of which two forecast?

A

TAFt+1 = St+ Tt

St = Smoothed series at the end of period t

St = TAFt + α · (At- TAFt)

At = actual value in period t

Tt = Smoothed trend at the end of period t

Tt =Tt-1 + ß(St - St-1 - Tt-1)

α and ß are smoothing constants

64
Q

Seasonal variations

A

regularly repeating wavelike movements in series values that can be tied to recurring events, weather, or a calendar.

Most seasonal variations repeat annually, but can be shorter too

65
Q

the two different models of seasonality

A

additive seasonality

multiplicative seasonality

66
Q

additive seasonality model

A

expressed as a quantity

is added to or subtracted from the series average (or trend)

67
Q

multiplicative seasonality model

A

seasonality is expressed as a proportion of the average (or trend) amount

it is then multiplied by the average (or trend) of the series

68
Q

seasonal relatives

A

The seasonal proportions in the multiplicative model

69
Q

why use seasonal relatives?

A

used first to deseasonalize the data

then later to incorporate seasonality in the forecast of deseasonalized data

70
Q

deseasonalizing data

A

to remove the seasonal component from the data in order to get a clearer picture of the nonseasonal components

accomplished by dividing each data point by its seasonal relative

71
Q

screw this one

A

vag

72
Q

The complete steps of forecasting seasonal demand (time series decomposition)

A
  1. Compute the seasonal relatives.
  2. Deseasonalize the demand data.
  3. Fit a model to the deseasonalized demand data (e.g., moving average or trend).
  4. Forecast using this model (to obtain the deseasonalized forecasts) .
  5. Reseasonalize the deseasonalized forecasts.
73
Q

how to compute seasonal relatives

A

We need to first compute the average (or trend) of all periods during the length of a repeating pattern

we use the centered moving average (CMA)

74
Q

the centered moving average (CMA)

A

A moving average positioned at the centre of the data that were used to compute it

The implication is that the CMA is most representative of that point in the series

75
Q

The number of periods needed in a centred moving average is equal to what?

A

the number of “seasons” involved

76
Q

annual average method

A

A simpler method for finding seasonal relatives

involves finding the ratio of actual demand relative to average seasonal demand in that year and averaging the ratios across years

77
Q

annual average method steps

A
  1. For each year, compute its total annual demand and average seasonal demand
  2. For each year, compute the ratio of the actual demand relative to average seasonal demand for each season
  3. For each season, average the ratios across years to get seasonal relatives
  4. Fit a linear trend to the total annual demand series
  5. Extend the model into the future to get next year’s total annual demand
  6. Divide the forecast of next year’s total annual demand by 4 to get the forecast of next year’s
    average seasonal demand
  7. For each season, multiply the forecast of next year’s average seasonal demand by the season’s seasonal relative
78
Q

Cycles

A

wavelike movements, similar to seasonal variations, but of longer duration

two to six years between peaks

79
Q

The most commonly used approach for cycles

A

Associative Models

80
Q

Associative Models

A

Search for another variable that relates to, and leads, the variable of interest

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

we want an equation that summarizes the effects of predictor variables on the variable of interest

81
Q

the primary method of analysis for associative models

A

regression

82
Q

regression

A

Technique for fitting a line to a set of points

83
Q

predictor variables

A

Variables that can be used to predict values of the variable of interest

84
Q

The simplest and most widely used form of regression

A

involves a linear relationship between two variables

linear regression

85
Q

The objective of linear regression

A

to obtain an equation of a straight line that minimizes the sum of squared vertical deviations of data points (x, y) from the line

we use the least squares line

86
Q

the least squares line

A

Minimizes the sum of the squared deviations around the line

y = a + bx

y=Predicted (dependent) variable

x =Predictor (independent) variable

b = Slope of the line

a = Value of y when x = 0 (i.e., the height of the line at the y intercept)

a = (Ey - bEx) / n or y - bx

b = (nExy) - (Ex)(Ey))
/
(n(Ex^2) - (Ex)^2)

n = Number of paired observations

87
Q

correlation coefficient (r)

A

measures the strength of a relationship between two variables

can range between -1.00 to +1.00

+1.00 means both variables increase together 10%

-1.00 means that whenever a variable increases, the other will 100% decrease

88
Q

correlation coefficient formula

A

r = (n · Exy - Ex · Ey)
/
((n · (Ex^2) - (Ex)^2)^(1/2) · (n · (Ey^2) - (Ey)^2)^(1/2))

89
Q

the square of the correlation coefficient (r^2)

A

provides a measure of the proportion of variability in the values of y

the proportion is explained by the independent variable

can only range from 0 to 1.00

90
Q

Models that involve more than one predictor require the use of which type of regression?

A

Multiple Regression

91
Q

Forecast error

A

the difference between the value that actually occurs and the value that was forecasted for a given time period

Forecast error = Actual value- Forecast value

et = At - Ft

92
Q

Positive errors result when?

A

when the forecast is too low relative to the actual value

93
Q

Negative errors result when?

A

when the forecast is too high relative to the actual value

94
Q

Accuracy of the forecasting process is measured using which three alternative forecast error summaries?

A

mean absolute deviation (MAD)

mean squared error (MSE)

mean absolute percent error (MAPE)

95
Q

mean absolute deviation (MAD)

A

the average of absolute values of forecast error

(E|Actual - Forecast|) / n

96
Q

mean squared error (MSE)

A

the average of squared forecast errors

(E(Actual - Forecast)^2) / n

97
Q

mean absolute percent error (MAPE)

A

the average absolute percent forecast error

can be used to measure forecasting accuracy irrespective of the specific time series data used

(E [ |Actual - Forecast| / Actual] · 100) / n

98
Q

why is it not meaningful to compare the MAD or MSE of two different time series variables?

A

Because MAD and MSE depend on the scale of data

99
Q

for MAD, MSE, and MAPE, which accuracy percent is considered satisfactory?

A

above 70 %

100
Q

bias

A

the sum of forecast errors

101
Q

what does a persistently positive bias imply?

A

that forecasts frequently underestimate the actual values

102
Q

what does a persistently negative bias imply?

A

implies that forecasts frequently overestimate the actual values

103
Q

why do bias occur?

A

because either demand pattern has changed or the way forecasts are determined is flawed

104
Q

Monitoring forecast errors is usually accomplished with what?

A

a control chart or a tracking signal

105
Q

A control chart for forecast errors

A

a time series plot of forecast errors

the errors are compared to two predetermined values, or control limits

Forecast errors that fall outside either control limit signal that corrective action is needed

106
Q

when using a control chart for forecast errors, when is corrective action necessary?

A

errors fall outside either control limit signal

Forecast eηors are randomly distributed around a mean of zero (i.e., there is no bias)

The distribution of forecast errors is normal

107
Q

how do we choose the control limits for control charts?

A

usually chosen as a multiple of the standard deviation of forecast errors

The square root of MSE is used in practice as an estimate of the standard deviation of forecast errors

s = (MSE)^(1/2)

then we do 0 + or - 2s

108
Q

Tracking Signal

A

An alternative measure to control the forecasting performance

sum of forecast errors divided by mean absolute
forecast error

TS = Ee / MAD

109
Q

when using the tracking signal, when should the analyst study the model and make changes?

A

When TS values exceed 4 in absolute value

110
Q

The two most important factors to consider when choosing a forecasting technique

A

cost and accuracy

111
Q

the higher the accuracy, the higher or lower the cost?

A

the higher the cost

112
Q

A reactive approach to forecast

A

views forecasts as probable descriptions of future demand

managers react to meet that demand,

113
Q

a proactive approach to forecast

A

seeks to actively influence demand

requires either a causal model or a subjective assessment of the influence on demand

COMM 225: Operations management (17 decks)

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