Week 2 / Chapter 2: Forecasting

Question 1

Forecast

Answer 1

A statement about the future

Question 2

Business forecasting

Answer 2

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

Question 3

In Operations management

Answer 3

Demand forecast is a major input to many decisions

Question 4

Many management decisions are “________” decisions for the future

Answer 4

planning

Question 5

Forecasts anticipate _______

Forecasts reduce ______

Answer 5

future 
uncertainty (less uncertainty = better decision)

Question 6

Therefore, forecasts are the basis of corporate planning:

Answer 6

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

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

Question 7

Planning decisions are made _______

Forecasts are needed __________

Answer 7

continuously

continuously

Question 8

Main features of forecasting

Answer 8

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

Question 9

Good forecasting has 3 components, these are?

Answer 9

1) Accurate
2) Affordable
3) Timely

Question 10

6 Steps to creating a forecast

Answer 10

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

Question 11

Subjective Forecasting Techniques:

Answer 11

Rely solely on judgments and opinions of experts

Required when data is not available

Question 12

Objective Forecasting Techniques:

Answer 12

Time Series Models:

Associative/Causal models:

Question 13

Time Series Models:

Answer 13

use historical data to predict the future

Question 14

Associative/Causal models:

Answer 14

use explanatory variables to predict the future

Question 15

Subjective Forecasts are applicable when?

Answer 15

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• Data is obsolete (e.g. due to economic changes)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)

Question 16

Judgmental Methods:

Answer 16

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

Question 17

Time Series Analysis (definition)

Answer 17

A chronological series of observations taken over time

Question 18

Details of Time Series Analysis

Answer 18

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?

Question 19

Components of a time series:

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

Answer 19

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

Question 20

1) Level

Answer 20

short-term movement around a fixed average

Question 21

2) Trend

Answer 21

long-term movement in data (up/down)

Question 22

3) Seasonality

Answer 22

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

Question 23

4) Cyclical

Answer 23

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

Question 24

5) Random Variations

Answer 24

caused by chance / unusual circumstances

Question 25

Techniques for Averaging

Answer 25

Works best if the series is fluctuating around some average (stationary) Moving Averages Exponential Smoothing

Question 26

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

Answer 26

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

Question 27

Weighted Moving Average Equation (way to calculate GPA)

Answer 27

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

Question 28

Exponential Smoothing Equation

Answer 28

Ft= Ft-1 + alpha(At-1 - Ft-1) ``` F = Forecast A = Actual Alpha = Smoothing Constant ```

Question 29

Moving Average theory

Answer 29

Average of last few actual data values, updated each period: - Easy to calculate and understand - Lags behind changes

Question 30

Moving average Choose number of periods to include:

Answer 30

- Fewer data points = more sensitive to changes | - More data points = smoother, less responsive

Question 31

Moving average Exmaple

Answer 31

Literally calculates like a normal avergae F3 = (43+40+41) / 3 = 41.33

Question 32

Weighted Moving Average - Example

Answer 32

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

Question 33

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

Answer 33

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

Question 34

Exponential Smoothing

Answer 34

- 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

Question 35

Exponential Smoothing Forumla

Answer 35

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

Question 36

How to calculate the Error Term in exponential smoothing

Answer 36

Actual - Forecast

Question 37

The larger the smoothing constant (alpha) the more ____________________

Answer 37

responsive the forecast!

Question 38

Choosing Alpha

Answer 38

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

Question 39

A moving average forecast tends to be more responsive to changes in the data series when more data points are included in the average.

Answer 39

False

Question 40

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

Answer 40

True

Question 41

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

Answer 41

False

Question 42

Average techniques

Answer 42

Easy to understand and interpret Smooth the variations in actual data points Lags behind trends