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MGMT 355: Operations Management > Week 2 / Chapter 2: Forecasting > Flashcards

Week 2 / Chapter 2: Forecasting Flashcards

1
Q

Forecast

A

A statement about the future

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2
Q

Business forecasting

A

Demand, profits, costs, prices, interest rates, stock prices, general economic or political indicators, etc.,

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3
Q

In Operations management

A

Demand forecast is a major input to many decisions

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4
Q

Many management decisions are “________” decisions for the future

A

planning

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5
Q

Forecasts anticipate _______

Forecasts reduce ______

A 
future 
uncertainty (less uncertainty = better decision)
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6
Q

Therefore, forecasts are the basis of corporate planning:

A

Long-term: products/services, processes, capacity, facilities layout, equipment, location, etc.

Short/intermediate term: operations planning, purchasing, inventory levels, workforce levels, scheduling, production, etc

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7
Q

Planning decisions are made _______

Forecasts are needed __________

A

continuously

continuously

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8
Q

Main features of forecasting

A

Assumes causal system that existed in the past and will do so in the future

Forecasts are rarely “perfect” because of randomness

Forecasts are more accurate for groups vs. individual items

Forecast accuracy decreases as time horizon increases

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9
Q

Good forecasting has 3 components, these are?

A

1) Accurate
2) Affordable
3) Timely

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10
Q

6 Steps to creating a forecast

A

1) Determine the purpose of the forecast
2) Establish a time horizon
3) Gather and analyze data
4) Select a forecasting technique
5) Prepare the forecast
6) Monitor the forecast

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11
Q

Subjective Forecasting Techniques:

A

Rely solely on judgments and opinions of experts

Required when data is not available

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12
Q

Objective Forecasting Techniques:

A

Time Series Models:

Associative/Causal models:

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13
Q

Time Series Models:

A

use historical data to predict the future

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14
Q

Associative/Causal models:

A

use explanatory variables to predict the future

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15
Q

Subjective Forecasts are applicable when?

A

Applicable when:

  • There is no time to gather data
  • Data is obsolete (e.g. due to economic changes)
  • No data available (e.g. new products)
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16
Q

Judgmental Methods:

A 
Executive opinions
Sales force composite
Consumer surveys
Outside opinion
Opinions of managers and staff: Panel Consensus, Delphi Technique
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17
Q

Time Series Analysis (definition)

A

A chronological series of observations taken over time

18
Q

Details of Time Series Analysis

A

Assumes that future values can be calculated only from past data

In demand forecasting, past data on demand is used to predict future demand

Let At = Actual demand in period t
Ft = Forecast for period t

Basic Question: Given A1, A2, A3,… At-1, What is Ft?

19
Q

Components of a time series:

Identify the behaviour of past data, look for the patterns!

A

1) Level
2) Trend
3) Seasonality
4) Cyclical
5) Random Variations

20
Q

1) Level

A

short-term movement around a fixed average

21
Q

2) Trend

A

long-term movement in data (up/down)

22
Q

3) Seasonality

A

short-term regular variations in data (less than a year)

23
Q

4) Cyclical

A

long-term regular variations in data (more than a year)

24
Q

5) Random Variations

A

caused by chance / unusual circumstances

25
Q

Techniques for Averaging

A

Works best if the series is fluctuating around some average (stationary)

Moving Averages
Exponential Smoothing

26
Q

Moving Average Equation

This is the classic way to calculate an average. Add up all the numbers and divide by the amount of numbers

A

Ft = Demand in pervious n periods / n

10 + 5 + 6 + 11 + 9 / 5 = 6.2 average

Most times it asks for the three-period moving average in which you would take the 3 most recent data points (the higher periods) and average those

27
Q

Weighted Moving Average Equation (way to calculate GPA)

A

Ft = (weight of period n) x (demand of period n) / Weights

AKA Grade x Credits / Weight

Still legs behind, but not as bad as moving average - it is sharper than moving average

28
Q

Exponential Smoothing Equation

A

Ft= Ft-1 + alpha(At-1 - Ft-1)

F = Forecast
A = Actual
Alpha = Smoothing Constant
29
Q

Moving Average theory

A

Average of last few actual data values, updated each period:

  • Easy to calculate and understand
  • Lags behind changes
30
Q

Moving average

Choose number of periods to include:

A
  • Fewer data points = more sensitive to changes

- More data points = smoother, less responsive

31
Q

Moving average Exmaple

A

Literally calculates like a normal avergae

F3 = (43+40+41) / 3 = 41.33

32
Q

Weighted Moving Average - Example

A

Compute a 4-period weighted moving average forecast for period 6 using a weight of 0.4 for the most recent period, 0.3 for the next, 0.2 for the next, and 0.1 for the next.

Period 1: 42
Period 2: 40
Period 3: 43
Period 4: 40
Period 5: 41

F6 = 0.40(41) + 0.30(40) + 0.20(43) + 0.10(40) = 41.0

33
Q

When using the Weighted Moving Average the weights must add up to ____%

A

100%

If it is above 100% then an increase in terms of forecasting of observation of actual data

If it is less than 100% then the odds are decreasing

34
Q

Exponential Smoothing

A
  • Sophisticated weighted moving average
  • New forecast is based on the previous forecast plus a percentage of the difference between that forecast and the previous actual value
  • Subjectively choose smoothing constant alpha
  • Alpha ranges from 0 to 1 (commonly .05 to .5)
  • A forecast of zero means that it would be the same as forecast from the last period
  • Widely used
  • Easy to use
  • Easy to alter weighting
35
Q

Exponential Smoothing Forumla

A

Ft = Ft-1 + Alpha(At-1 - Ft-1)

Where F = Forecast
A = Actual
Alpha = % of the error applied to the previous
forecast to generate a new forecast. Between
0-1

36
Q

How to calculate the Error Term in exponential smoothing

A

Actual - Forecast

37
Q

The larger the smoothing constant (alpha) the more ____________________

A

responsive the forecast!

38
Q

Choosing Alpha

A

When demand is fairly stable, use a lower value for Alpha
-Smooths out random fluctuations

When demand increasing or decreasing, use a higher value for Alpha
-More responsive to real changes

Try to find balance:

  • Trial and error
  • Can change over time
39
Q

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

40
Q

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

A

True

41
Q

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

42
Q

Average techniques

A

Easy to understand and interpret

Smooth the variations in actual data points

Lags behind trends

MGMT 355: Operations Management (7 decks)

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