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SCM 2160 > Forecasting > Flashcards

Forecasting Flashcards

1
Q

Forecast and examples of it being used for

A

Prediction of future events used for planning purposes etc product, labour, demand

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2
Q

Planning

A

Making management decisions on how to deploy resources to respond to demand forecast

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3
Q

Forecasts are based on (4)

A

multiple types of data, mathematical models, expert opinion, historical data

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4
Q

Forecast is used for (2)

A

process etc bottlenecks and supply chain management

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5
Q

Time/Demand Series

A

Repeated observations of demand for a product/service in their order of appearance

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6
Q

Horizontal

A

Fluctuation of data around a constant mean

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7
Q

Trend

A

Systematic increase or decrease in the mean over time

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8
Q

Seasonal

A

Repeatable pattern of increase or decrease in demand, depending on time, day, week, month, season

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9
Q

Cyclical and what is it caused by

A

Less predictable gradual increase/decrease in demand over longer periods of time (years, decades)

  • life cycle of product or economic recession/inflation
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10
Q

Random

A

Un-forecastable variation in demand (lots of variability)

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11
Q

Outliers

A

fluctuations in data that do not reflect or resemble overall pattern

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12
Q

Manage Demand (5)

A

Complementary Service, Promotional Pricing, Prescheduled Appointments, Revenue Managing, Backlogs/Backorders/Stockouts

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13
Q

Complementary Service

A

same resources, different demand cycles (Assiniboine Park)

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14
Q

Promotional Pricing

A

increase demand, shift to new period (clear excess stock and attract buyers)

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15
Q

Prescheduled Appointments

A

level demand based on capacity (balance how much you can accept)

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16
Q

Revenue Management

A

adjust prices in real life time based on demand

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17
Q

Backlogs

A

accumulate orders for future delivery, decrease service level and risk of losing customers

18
Q

backorders

A

orders that cannot be filled when demanded but filled later

19
Q

Stockouts

A

customer goes else where as order cannot be fulfilled

20
Q

Key forecasting decisions (3)

A

What are inputs
What are you predicting
What technique should you use

21
Q

Forecast Inputs (6)

A

History of Past Demand, Notes Explaining Past Demand, Past Forecasts, consumer research, planned promotions, Inputs from Partners

22
Q

CPFR

A

Collaborative Planning, Forecasting & Replenishment

23
Q

CPFR what does it require and do?

A

collaboration with suppliers, independent forecasts generated & compared, adjusted until consensus (everyone has same prediction)

24
Q

What are you trying to predict (2)
what is aggregation?
What is best way to predict revenue?

A

1.individual/family products 2.Units of measurements
2. cluster of similar products/services so company can make better forecasts
3. find units forecast then multiply by price

25
Q

What techniques used (3)

A

Judgment, Causal, Time-Series Analysis

26
Q

Judgement (opinions and subjectives -> quantitative) (4)

A

sales force estimates
executive opinion
market research
Delphi -> consensus of group but group remains anonymous

27
Q

Qualitative Benefits

A

subjective, variety of information, does not require numerical data

28
Q

Qualitative Downside

A

results biased or conflicting

29
Q

Quantitative Benefits

A

objective, volume of information, do not rely on individuals

30
Q

Quantitative Downside

A

data not available, models too simplistic

31
Q

Time Series Method

A

predictions based on historical data, dependent variable. Past can predict future

32
Q

Naive Method
appropriate for and what pattern
sensitive to?

A

Forecast for next period equals demand for most recent observed
short-term forecasts and horizontal trend
sensitive to random variation

33
Q

Simple Moving Average and etc
smooths out

A

Forecast for next period equals average demand for n most recent periods etc 2-period moving means average of 2 previous weeks
random variation

34
Q

Weighted Moving Average

weights given but most recent has

A

forecast for next period equals average demand for n most recent periods and each observation of demand has its own weight.

most weights

35
Q

Exponential Smoothing

3 data points required? and more weight to?

A

weighted moving average assigning differing levels of weight to recent demand compared to older historical data

last period forecast, demand and smoothing parameter
Exponential smoothing factor to previous demand, (1-a) applied to forecast

36
Q

Forecast Error

A

Observed demand - forecast

37
Q

Cumulative Sum if Forecast Errors and evaluates

A

Assesses total errors in forecasts over time, presence and detection of bias

38
Q

If forecast is consistently lower than demand then
If forecast is consistently higher than demand then

A

CFE is highly positive
CFE is highly negative

39
Q

Mean Squared Error

A

on average how close forecast is to demand, magnify large errors

40
Q

Mean Absolute Deviation

A

magnitude of error, does not reveal directional bias

41
Q

Mean Absolute Percentage Error

A

study magnitude of error relative to demand

SCM 2160 (11 decks)

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