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Operations Management > Chapter 8: Forecasting > Flashcards

Chapter 8: Forecasting Flashcards

1
Q

Basic time series patterns

A

Horizontal
Trend
Seasonal
Cyclical
Random

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2
Q

Horizontal time series

A

Fluctuation of data around a constant mean

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3
Q

time series

A

The repeated observations of demand for a service or product in their order of occurrence

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4
Q

Trend time series

A

Systematic increase or decrease in the mean of the series over time

(moving in one direction)

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5
Q

Seasonal time series

A

A repeatable pattern of increases or decreases in demand, depending on the day, week, month or season

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6
Q

Cyclical time series

A

Less predictable gradual increases or decreases in demand over longer periods of time (years or decades)

Arises from two influences:
- business cycle
- service or product life cycle

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7
Q

Random time series

A

Unforecastable variation in demand

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8
Q

Service (product) life cycle

A

Stages of demand from development through decline

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9
Q

Demand management options

A

Processes to influence demand
Complementary products
Promotional pricing
Prescheduled appointments
Reservations
Revenue management
Backlogs
Backorders and stockouts
Forecasting

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10
Q

Complementary products

A

Services or products that have similar resource requirements but different demand cycles (use existing resources)

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11
Q

Prescheduled appointments

A

Ensures that demand does not exceed supply capacity

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12
Q

Revenue management

A

Aka yield management

Varying price at the right time for different customer segments to maximize revenue yielded by existing supply capacity

Mix of promotional pricing and reservations, works best when customer population can be segmented and prices varied by segment

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13
Q

Backlog

A

An accumulation of customer orders that a manufacturer has promised for delivery at a future date (grows during periods of high demand and shrinks during period of inactivity)

Use if planned lead times

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14
Q

Stockouts

A

An order that cannot be satisfied resulting in loss of the sale

With backorders: last resort demand management. Setting of lower standards

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15
Q

Aggregation

A

The act of clustering several similar services or products so that forecasts can be made for whole families (forecast for groups first and then break down to individual SKUs)

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16
Q

Types of forecasting techniques

A
  • Judgement methods (opinions and estimates -> quantitative estimates)
  • causal methods (use of historical data on independent variables)
  • time series analysis (statistical analysis of historical demand to estimate future demand)
  • trend projection with regression (combines causal and time series)
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17
Q

Forecasting error

A

The difference found by subtracting the forecast from the actual demand for a given period

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18
Q

Five basic measures of forecast error

A

Cumulative sum of errors
Mean squared error
Standard deviation of errors
Mean absolute deviation
Mean absolute percent error

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19
Q

Cumulative sum of forecast errora

A

CFE = sum total of all errors for a given set of periods

Also called bias error: assesses bias in the forecast (consistently below (CFE gets larger each period) or consistently above demand)

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20
Q

Mean bias

A

Aka average forecast error

= CFE / number of periods

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21
Q

Mean squared error

A

MSE

= (Sum of all (Error squared))/number of periods

Measurement of dispersion of forecast errors

Because error is squared the MSE gives more weight to large errors

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22
Q

Standard deviation of errors

A

σ

= (sum of all ((total forecast error - average forecast error) squared))/ (n-1)

Measurement of the dispersion of forecast errors

Because error is squared standard deviation gives more weight to large errors

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23
Q

Mean absolute deviation

A

MAD

= (Sum of all absolute values of errors) / number of periods

Measurement of the dispersion of forecast errors

Most widely used, does not differentiate between underestimate and overestimate

could also be calculated using an exponential smoothing method
= alpha * absolute value of error + (1-alpha) * MAD of previous period

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24
Q

Interpreting measures of dispersion errors

A

If small then the forecast is typically close to actual demand. Large = potential for large forecast errors

essentially shows how uncertain demand is

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25
Q

Mean absolute percent error

A

MAPE

Relates forecast error to level of demand. Useful for perspectives on forecasting performance

= (Sum of all (absolute value of error for the period / actual demand for the period)) * 100/ number of periods

The percentage error based on total demand

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26
Q

History file

A

Collection of past data used to determine if forecasting method has merit

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27
Q

Salesforce estimates

A

Forecasts that are compiled from estimates of future demands made periodically by members of a company’s salesforce

May need to be adjusted to account for individual bias

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28
Q

Executive opinion

A

Forecasting method in which the opinions, experience, and technical knowledge of one or more managers are summarized to arrive at a single forecast

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29
Q

Technological forecasting

A

An application of executive opinion to keep abreast of the latest advanced in technology

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30
Q

Market research

A

A systematic approach to determine external consumer interest in a service or product by creating and testing hypotheses through data-gathering surveys

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31
Q

Delphi methid

A

A process of gaining consensus from a group of experts while maintaining their anonymity

Used when no historical data is available/ managers lack applicable experience

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32
Q

Causal methods of quantitative forecasting

A

Used when:

  • historical data is available
  • relationships between the factor being forecasted and other internal or external factors can be identified

Linear regression

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33
Q

Linear regression

A

A casual method in which one variable (dependent) is related to one or more independent variables by a linear equation

Dependant variable is the one that is being forecast

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34
Q

Linear regression equation

A

Y= a + bX

Where:
a = Y intercept (X= 0)
b = slope of the line

Goal of linear regression to find values of a and b such that the sum of squared deviations from the actual data to the line is minimized

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35
Q

Linear regression measures of forecast accuracy

A

Sample correlation coefficient
Sample coefficient of determination
Standard error of the estimate

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36
Q

Sample correlation coefficient

A

r

Measures the direction and strength of the relationship between the dependent and independent variables

Range from -1 (x and y move in diametrically opposite directions) to 1 (x and y move in the same direction)

0 = no linear relationship

The Stronger the relationship the closet the value to +/- 1

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37
Q

Sample coefficient of determination

A

r squared

Measures the amount of variation in the dependent variable about it’s mean tha this explained by the regression line

Range from 0 to 1 with 1 = 100% of variation is explained by the regression formula

38
Q

Standard error of the estimate

A

s (sub xy)

Measures how closely the dependent variable data cluster around the regression line

Measures error from the dependent variable to the regression line (rather than to the mean)

Difference between actual demand for a given x and the estimated demand)

39
Q

Multiple regression analysis

A

Used when there are several independent variables

40
Q

Time series methods of forecasting

A

Uses historical information regarding only the dependent variable - assumed that variable’s past patterns of demand will continue in the future and looks to identify those patterns and build a model to replicate said pattern

41
Q

Naïve forecast

A

A time series methods where the forecast for the next period = the demand for the current period

May be adapted to account for a demand trend

Works best when the horizontal, trend, or seasonal patterns are stable and the random variation is small

42
Q

Horizontal pattern forecast

A

When there is no apparent trend, seasonal, or cyclical patterns

Based on the mean of demand

If a trend exists horizontal pattern forecasts will lag behind demand

43
Q

Horizontal forecasting techniques with adaptive qualities

A

Simple moving average
Weighted moving average
Exponential smoothing

44
Q

Simple moving average method

A

A time series method used to estimate the average of a demand time series by averaging the demand for the n most recent time period

(Recalculated at the end of each period dropping the older period from the calculation)

45
Q

Choosing an n value for moving average

A

Larger n for demand series that is stable

Smaller n for demands susceptible to changes

n=1 = naïve method

46
Q

Weighted moving average

A

A time series method in which each historical demand in the average can have its own weight

Sum of weights must = 1.0

Average = sum of all (weight * demand)

Allows you to emphasize more pertinent (recent, or same season past) demand numbers. More responsive than simple moving average

47
Q

Exponential smoothing method

A

A weighted moving average method that calculates the average of a time series by implicitly giving recent demands more weight than other demands. Considers all periods in history file

F[t] = last period’s forecast
D[t] = last period’s demand
α = smoothing parameter with value between 0 and 1.0

F[t+1] = α(D[t]) + ((1-α)*F[t])

Generally various values of α are tested and the one producing the best forecasts chosen

Needs initial forecast value. Most programs use 1st period actual demand

48
Q

Affect of changing α value in exponential smoothing

A

Larger α values: emphasize recent levels of demand. Forecast is more responsive to change

Smaller α values: more stable forecast (past demand treated more uniformly)

49
Q

Disadvantages of exponential smoothing

A

Simple. Will lag behind changes in underlying average demand

Generally if α values are larger than .5 a different method might be better suited

50
Q

Trend pattern regression analysis

A

Dependent variable = period demand
Independent variable = time period

Can change which periods are included to make more adaptive

51
Q

Multiplicative seasonal method

A

A method whereby seasonal factors are multiplied by an estimate of average demand to arrive at a seasonal forecast

52
Q

Steps of multiplicative seasonal methid

A
  1. For each year (period) calculate the average demand per season by dividing the annual demand by number of seasons per year (forecast yearly aggregate demand)

2) to get the seasonal factor divide ACTUAL demand for a season by average demand per season (indicates level of demand relative to average demand)

3) calculate the seasonal factor for each season (ideally looking at multiple periods so add the seasonal factors for given season & divide by number of periods of data)

4) forecast next year’s annual demand then divide by number of seasons per year to get average. Multiply average by seasonal factor for seasonal forecast

53
Q

Seasonal factor

A

Number that indicates a season’s level of demand relative to average demand

54
Q

Additive seasonal method

A

Whereby seasonal forecasts are generated by adding a seasonal constant to (or subtracting a seasonal constant from) the estimate of average demand per season

Based on assumption that the seasonal pattern is consistent regardless of level of demand

55
Q

Criteria used in making forecast method and parameter choices

A
  • minimizing bias (CFE)
  • minimizing MAPE, MAD, or MSE
  • maximizing r-squared if using regression
  • using a holdout sample analysis
  • using a tracking signal
  • meeting managerial expectations of changes in the components of demand
  • minimizing forecast errors in recent periods
56
Q

Choosing best time series models

A

To emphasize more stable demand patterns: use lower values of α or larger values of n (emphasize historical experience)

If demand patterns are dynamic: higher values of α or smaller values of n emphasis recent history

57
Q

Holdout sample

A

When building models “set aside” (don’t use) some more recent time series data, and instead use them to test the accuracy of the model built in/from the earlier periods

Attempt to avoid overfitting to past data

58
Q

Tracking signal

A

A measure that indicates whether a method of forecasting is accurately predicting actual changes in demand

= CFE/ MAD (or CFE/ MAD for period t) = number of mean absolute deviations represented by cumulative forecast error

Updated each period and compared to predetermined limits

59
Q

Weighted average alternate method of calculating MAD

A

= (α* (absolute value of error for period t )) + ((1-α)*(MAD for period t-1))

60
Q

Relationship between σ and MAD

A

IF forecast errors are normally distributed with a mean of 0

σ (standard deviation) = approx 1.25 MAD
MAD = approx 0.8σ

Allows use of normal probability tables to specify control limits for t tracking signal. If signal falls outside limits forecasting system is not working adequately.

because of this relationship if you calculate MAD you do not really need standard deviation

61
Q

Control chart

A

Chart for tracking a particular forecast error statistic with limits set so that if stat exceeds limits know forecasts are not good

62
Q

Three Vs of big data

A

Volume (of data)
Variety (of sources and types of data)
Velocity (speed at which data is created, collected, and analyzed)

63
Q

Considerations for using big data

A

Processing power required (solution: cloud providers for big data projects, AWS)

Skills required

Culture to accept big data findings

64
Q

Typical forecasting process

A

1- adjust history file on past demand
2- prepare initial forecasts (usually for multiple periods, aggregated sku forecasts rolled up into summaries)
3- consensus meetings and collaboration with stakeholders to get consensus forecasts
4- revise forecasts
5- review by operating committee to arrive at final forecasts
6- finalize and communicate

65
Q

Data recorded in history file

A

Past demand
Final forecasts (for tracking forecast errors)
Notes on unusual demand behavior, promotions or sales
Other estimates, market research, competitor behavior abdbmoreb

66
Q

Combination forecasts

A

Forecasts produced by averaging independent forecasts based on different methods, different sources, or different data. Different forecasts may be given equal or differing weights in the averaging

Research suggests this produces more accurate forecasts

67
Q

Focus forecasting

A

A method of forecasting that selects the best forecast (based on past error measures) from a group of forecasts generated by individual techniques

Forecast for current period compared to actual demand and one with least error used to make forecast for next period

Method may change period to period

68
Q

Collaborative planning, forecasting, and replenishment

A

CPFR

A process for supply chain integration that allows a supplier and it’s customers to collaborate on making the forecast by using then internet

69
Q

Four activities of CPFR

A
  • strategy and planning (of the collaborative relationship)
  • demand and supply management (forecasts, order procedures, inventory positions)
  • execution (generation of order though production and delivery)
  • analysis (watch for out of bounds conditions and evaluate achievement of goals)
70
Q

Contingency planning

A

knowing that forecast will not be perfect and build in capacity cushions

knowing average error (variation from forecast) can help both contingency planning and improving the forecast

71
Q

forecast horizon

A

how far out in time a company is trying to focus

companies are likely to do forecasts at multiple different horizons with different items considered for each

horizon may be determined by lead times needed to implement decisions

72
Q

strategic forecast

A

(per FedEx example in class)

aka long range

revised and updated yearly

73
Q

Tactical forecast

A

revised monthly, forecasts daily trends

74
Q

Operational forecast

A

Revised weekly, forecasts at smaller intervals

75
Q

Causal models

A

Looking for the external factors that influence demand to use in developing forecasts

76
Q

Short term forecasts

A

Generally 0-3 months
few major environmental changes - demand is expected to continue similar between periods

good for individual products with applications in inventory management and final assembly

often uses quantitative time series methods

77
Q

medium term forecasts

A

generally 3 - 18 months

good for total sales forecasts/ forecasting for a group of products

applications in distributions and workforce planning

often uses causal, quantitative methods

78
Q

Long term forecasting

A

generally longer than 24 months

forecasting large quantities like total sales / total production (forecasts for individual items at this range less meaningful)

applications in expansion/ capacity planning

methods likely to be quantitative, looking at causal models or delphi method

79
Q

considerations for choosing a forecasting method

A
  • horizon (short term requires something less time intensive)
  • data availability
  • level of accuracy desired
  • level of detail needed
  • available resources/ present constraints (time, funds, data, competencies)
80
Q

type of forecasting

A
  • Qualitative (subjective, relies on judgement)
  • time series estimates (based on historical demand, best used when demand is stable. shorter horizons )
  • Causal (based on a relationship between demand and some other factor. regression analysis.)
  • Simulation (attempts to imitate consumer choices that lead to demand)
81
Q

Judgement methods of forecasting

A
  • naive extrapolation
  • sales force composite
  • jury of executive opinion
  • dephi technique
  • historical analogy
  • senario methods
82
Q

Counting methods of forecasting

A
  • Market testing
  • Consumer market survey
  • Industrial market survey
83
Q

Time series methods of forecasting

A

o Moving averages
o Exponential smoothing
o Adaptive filtering
o Time series extrapolation
o Time series decomposition
o Box-jenkins

84
Q

Association/ Causal methods of forecasting

A

o Correlation methods
o Regression models
o Leading indicators
o Input-output models
o Economic models

85
Q

Exponential smoothing alpha value considerations

A

Larger alpha value gives more weight to most recent demand so the forecast is more responsive to change. If you believe demand has changed permanently may want to increase alpha value until forecast has caught up with demand

86
Q

Choosing control limits for tracking signal control chart

A

choice of limits based on a tradeoff between the cost of poor forecasts and the cost of checking for problems where none exist

since tracking signal is the number of mean absolute deviations represented by the forecast error - IF the deviations are normally distributed could use normal probability to determine what was an allowable probability of the forecast error (at what point is probability of a random error returning that result so unlikely that checking is worth it)

87
Q

Tighter control limits

A

For more risk averse situations - creates less room for variation

less risk of a problem going unnoticed, but more risk of investigating unnecessary problems

88
Q

Wider control limits

A

processes that aren’t very sensitive or aren’t very risk averse.

higher risk of a problem going unnoticed (not a big deal if problems aren’t a big deal), lower risk of investigating a random error

89
Q

aggregate forecasts

A

usually more accurate than disaggregate forecasts (forecasts for individual items)

90
Q

mitigating the uncertainty of forecasting

A
  • postpone forecasting decision (so not trying to forecast demand too far in advance)
  • capacity planning (planning to leave larger additional capacity available when it is likely to be needed)
  • consider impact of new product introductions (higher frequency introductions = product lifetime reduced = little time to recover from inaccurate forecasts)

Operations Management (11 decks)

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