Heizer om10 ch04

10/16/2010

4 Forecasting Outline
Global Company Profile: Disney
World
What Is Forecasting?
PowerPoint presentation to accompany
Heizer and Render
Forecasting Time Horizons
F ti Ti H i
Operations Management, 10e
Principles of Operations Management, 8e
The Influence of Product Life Cycle
PowerPoint slides by Jeff Heyl Types Of Forecasts

© 2011 Pearson Education, Inc. publishing as Prentice Hall 4-1 © 2011 Pearson Education, Inc. publishing as Prentice Hall 4-2

Outline – Continued Outline – Continued
The Strategic Importance of Forecasting Approaches
Forecasting Overview of Qualitative Methods
Human Resources Overview of Quantitative Methods
Capacity Time-Series Forecasting
Supply Chain Management
Decomposition of a Time Series
Seven Steps in the Forecasting Naive Approach
System


Outline – Continued Outline – Continued
Time-Series Forecasting (cont.) Associative Forecasting Methods:
Regression and Correlation
Moving Averages
Analysis
Exponential Smoothing
Using Regression Analysis for
g g y
Exponential Smoothing with Trend Forecasting
Adjustment
Standard Error of the Estimate
Trend Projections
Correlation Coefficients for
Seasonal Variations in Data Regression Lines
Cyclical Variations in Data Multiple-Regression Analysis


1

10/16/2010

Outline – Continued Learning Objectives
Monitoring and Controlling When you complete this chapter you
Forecasts should be able to :
Adaptive Smoothing 1. Understand the three time horizons
Focus Forecasting
F F ti and which models apply for each use
Forecasting in the Service Sector 2. Explain when to use each of the four
qualitative models
3. Apply the naive, moving average,
exponential smoothing, and trend
methods


Learning Objectives Forecasting at Disney World
When you complete this chapter you Global portfolio includes parks in Hong
should be able to : Kong, Paris, Tokyo, Orlando, and
Anaheim
4. Compute three measures of forecast
accuracy Revenues are derived from people – how
many visitors and how they spend their
5. Develop seasonal indexes money
6. Conduct a regression and correlation Daily management report contains only
analysis the forecast and actual attendance at
7. Use a tracking signal each park

© 2011 Pearson Education, Inc. publishing as Prentice Hall 4-9 © 2011 Pearson Education, Inc. publishing as Prentice Hall 4 - 10

Forecasting at Disney World Forecasting at Disney World
Disney generates daily, weekly, monthly, 20% of customers come from outside the
annual, and 5-year forecasts USA
Forecast used by labor management, Economic model includes gross
maintenance, operations, finance,
maintenance operations finance and domestic product, cross-exchange rates,
product rates
park scheduling arrivals into the USA
Forecast used to adjust opening times, A staff of 35 analysts and 70 field people
rides, shows, staffing levels, and guests survey 1 million park guests, employees,
admitted and travel professionals each year

© 2011 Pearson Education, Inc. publishing as Prentice Hall 4 - 11 © 2011 Pearson Education, Inc. publishing as Prentice Hall 4 - 12

2

10/16/2010

Forecasting at Disney World What is Forecasting?
Inputs to the forecasting model include Process of predicting
airline specials, Federal Reserve a future event
policies, Wall Street trends,
vacation/holiday schedules for 3,000
Underlying basis
of all business
??
school districts around the world decisions
Average forecast error for the 5-year Production
forecast is 5% Inventory
Average forecast error for annual Personnel
forecasts is between 0% and 3% Facilities


Forecasting Time Horizons Distinguishing Differences
Short-range forecast
Up to 1 year, generally less than 3 months Medium/long range forecasts deal with
Purchasing, job scheduling, workforce more comprehensive issues and support
levels, job assignments, production levels management decisions regarding
planning and products, plants and
Medium-range forecast
g
processes
3 months to 3 years
Short-
Short-term forecasting usually employs
Sales and production planning, budgeting different methodologies than longer-term
Long-range forecast forecasting
3+ years Short-
Short-term forecasts tend to be more
New product planning, facility location, accurate than longer-term forecasts
research and development

Influence of Product Life Product Life Cycle
Cycle Introduction Growth Maturity Decline
Best period to Practical to change Poor time to Cost control
Introduction – Growth – Maturity – Decline increase market price or quality change image, critical
egy/Issues

share image price, or quality

R&D engineering is Strengthen niche Competitive costs
Introduction and growth require longer critical become critical
Defend market
forecasts than maturity and decline
Company Strate

position Drive-through
Internet search engines restaurants
As product passes through life cycle, iPods
CD-ROMs
LCD &
forecasts are useful in projecting Xbox 360 plasma TVs
Sales
Staffing levels Avatars

Inventory levels Boeing 787 Analog
TVs
Factory capacity Twitter
Figure 2.5

3

10/16/2010

Product Life Cycle Types of Forecasts
Introduction Growth Maturity Decline
Product design
and
Forecasting
critical
Standardization Little product
differentiation
Economic forecasts
Fewer product
development Product and changes, more Cost Address business cycle – inflation rate,
y/Issues

critical process minor changes minimization
Frequent reliability Optimum Overcapacity
money supply, housing starts, etc.
product and Competitive capacity in the
process design product
d t industry
i d t Technological forecasts
OM Strategy

changes Increasing
improvements stability of Prune line to
Short production and options process eliminate Predict rate of technological progress
runs Increase capacity items not
Long production
High production Shift toward runs returning Impacts development of new products
costs product focus good margin
Limited models Enhance
Product
improvement Reduce Demand forecasts
Attention to distribution and cost cutting capacity
quality Predict sales of existing products and
services
Figure 2.5

Strategic Importance of Seven Steps in Forecasting
Forecasting 1. Determine the use of the forecast
2. Select the items to be forecasted
Human Resources – Hiring, training,
laying off workers 3. Determine the time horizon of the
forecast
o ecast
Capacity – C
C it Capacity shortages can
it h t
result in undependable delivery, loss 4. Select the forecasting model(s)
of customers, loss of market share 5. Gather the data
Supply Chain Management – Good
supplier relations and price
6. Make the forecast
advantages 7. Validate and implement results


The Realities! Forecasting Approaches
Qualitative Methods
Forecasts are seldom perfect
Used when situation is vague
Most techniques assume an and little data exist
underlying stability in the system
New products
Product family and aggregated
forecasts are more accurate than New technology
individual product forecasts Involves intuition, experience
e.g., forecasting sales on
Internet

4

10/16/2010

Forecasting Approaches Overview of Qualitative
Methods
Quantitative Methods
1. Jury of executive opinion
Used when situation is ‘stable’ and
historical data exist Pool opinions of high-level experts,
sometimes augment by statistical
Existing products models
Current technology 2. Delphi method
Involves mathematical techniques Panel of experts, queried iteratively
e.g., forecasting sales of color
televisions

Overview of Qualitative Jury of Executive Opinion
Methods
Involves small group of high-level
experts and managers
3. Sales force composite
Group estimates demand by working
Estimates from individual together
salespersons are reviewed for
l i df
reasonableness, then aggregated Combines managerial experience with
statistical models
4. Consumer Market Survey Relatively quick
Ask the customer ‘Group-think’
disadvantage


Sales Force Composite Delphi Method
Iterative group
Decision Makers
Each salesperson projects his or process, (Evaluate
her sales continues until responses and
consensus is make decisions)
Combined at district and national reached
levels Staff
3 types of (Administering
Sales reps know customers’ wants participants survey)

Tends to be overly optimistic Decision makers
Staff Respondents
(People who can
Respondents make valuable
judgments)

5

10/16/2010

Consumer Market Survey Overview of Quantitative
Approaches
Ask customers about purchasing
plans 1. Naive approach
What consumers say, and what 2. Moving averages
time-series
time series
they actually do are often different 3. Exponential models
Sometimes difficult to answer smoothing
4. Trend projection
5. Linear regression associative
model


Time Series Forecasting Time Series Components

Set of evenly spaced numerical data
Obtained by observing response Trend Cyclical
variable at regular time periods
Forecast based only on past values,
no other variables important
Assumes that factors influencing
past and present will continue Seasonal Random
influence in future


Components of Demand Trend Component
Trend
component Persistent, overall upward or
downward pattern
Demand for product or service

Seasonal peaks

Changes due to population,
o

Actual demand technology, age, culture, etc.
t h l lt t
line
Typically several years
Average demand
over 4 years duration
Random variation
| | | |
1 2 3 4
Time (years)
Figure 4.1

6

10/16/2010

Seasonal Component Cyclical Component
Regular pattern of up and
down fluctuations Repeating up and down movements

Due to weather, customs, etc. Affected by business cycle,
political, and economic factors
Occurs within a single year
Multiple years duration
Number of
Period Length Seasons Often causal or
Week Day 7 associative
Month Week 4-4.5
Month Day 28-31 relationships
Year Quarter 4
Year Month 12
Year Week 52
0 5 10 15 20

Random Component Naive Approach
Erratic, unsystematic, ‘residual’ Assumes demand in next
fluctuations period is the same as
demand in most recent period
Due to random variation or unforeseen
events e g , Ja ua y sales e e
e.g., If January sa es were 68, t e
then
February sales will be 68
Short duration
Sometimes cost effective and
and nonrepeating
efficient
Can be good starting point

M T W T F

Moving Average Method Moving Average Example

MA is a series of arithmetic means Actual 3-Month
Month Shed Sales Moving Average
Used if little or no trend January 10
Used often for smoothing y
February 12
March 13
Provides overall impression of data April 16 (10 + 12 + 13 = 11 2/3
10 13)/3
over time May 19 (12 + 13 + 16)/3 = 13 2/3
June 23 (13 + 16 + 19)/3 = 16
July 26 (16 + 19 + 23)/3 = 19 1/3
∑ demand in previous n periods
Moving average = n


7

10/16/2010

Graph of Moving Average Weighted Moving Average
Moving Used when some trend might be
Average
30
28
–
–
Forecast present
Actual
26 –
Sales Older data usually less important
24 –
Shed Sales

22
20
–
–
Weights b
W i ht based on experience and
d i d
18 – intuition
16 –
14 – ∑ (weight for period n)
12 – Weighted x (demand in period n)
10 –
| | | | | | | | | | | |
moving average = ∑ weights
J F M A M J J A S O N D


Weights Applied Period
Weighted Moving Last month
3 Average Potential Problems With
2
1
Two months ago
Three months ago
Moving Average
6 Sum of weights
Increasing n smooths the forecast
Actual 3-Month Weighted but makes it less sensitive to
Month Shed Sales Moving Average
changes
January 10
February 12 Do not forecast trends well
March 13
April 16 [(3 x 13 + (2 x 12 + (10
13) 12) 10)]/6 = 121/6 Require extensive historical data
May 19 [(3 x 16) + (2 x 13) + (12)]/6 = 141/3
June 23 [(3 x 19) + (2 x 16) + (13)]/6 = 17
July 26 [(3 x 23) + (2 x 19) + (16)]/6 = 201/2


Moving Average And Exponential Smoothing
Weighted Moving Average
Weighted Form of weighted moving average
30 – moving
average Weights decline exponentially
25 –
Most recent data weighted most
and
Sales dema

20 – Actual
sales Requires smoothing constant (α)
15 –
Moving Ranges from 0 to 1
10 – average
Subjectively chosen
5 –
Involves little record keeping of past
| | | | | | | | | | | |
J F M A M J J A S O N D
data
Figure 4.2

8

10/16/2010

Exponential Smoothing Exponential Smoothing
Example
New forecast = Last period’s forecast
+ α (Last period’s actual demand Predicted demand = 142 Ford Mustangs
– Last period’s forecast) Actual demand = 153
Smoothing constant α = .20
Ft = Ft – 1 + α(At – 1 - Ft – 1)

where Ft = new forecast
Ft – 1 = previous forecast
α = smoothing (or weighting)
constant (0 ≤ α ≤ 1)


Exponential Smoothing Exponential Smoothing
Example Example
Predicted demand = 142 Ford Mustangs Predicted demand = 142 Ford Mustangs
Actual demand = 153 Actual demand = 153
Smoothing constant α = .20 Smoothing constant α = .20

New forecast = 142 + .2(153 – 142) New forecast = 142 + .2(153 – 142)
= 142 + 2.2
= 144.2 ≈ 144 cars


Effect of Impact of Different α
Smoothing Constants
225 –

Weight Assigned to Actual α = .5
200 – demand
Most 2nd Most 3rd Most 4th Most 5th Most
nd
Deman

Recent
R t Recent
R t Recent
R t Recent
R t Recent
R t
Smoothing Period Period Period Period Period
Constant (α) α(1 - α) α(1 - α)2 α(1 - α)3 α(1 - α)4
175 –
α = .1 .1 .09 .081 .073 .066

α = .5 .5 .25 .125 .063 .031 α = .1
150 – | | | | | | | | |
1 2 3 4 5 6 7 8 9
Quarter


9

10/16/2010

Impact of Different α Choosing α
225 –
The objective is to obtain the most
Actual α = .5 accurate forecast no matter the
Chose high values of α
200 – demand technique
when underlying average
nd
Deman

is likely to change We generally do this by selecting the
175 –
Choose low values of α model that gives us the lowest forecast
when underlying average error
α = .1
is stable|
150 – | | | | | | | |
Forecast error = Actual demand - Forecast value
1 2 3 4 5 6 7 8 9
Quarter = At - Ft


Common Measures of Error Common Measures of Error

Mean Absolute Deviation (MAD)
Mean Absolute Percent Error (MAPE)
∑ |Actual - Forecast|
MAD =
n n
∑100|Actuali - Forecasti|/Actuali
Mean Squared Error (MSE) MAPE = i=1
n
∑ (Forecast Errors)2
MSE =
n


Comparison of Forecast Comparison of Forecast
Error Error
Rounded Absolute Rounded Absolute
∑ |deviations|
Actual Forecast Deviation Forecast Deviation MAD =
Actual Forecast Deviation Forecast Deviation
Tonnage with for with for Tonnage n
with for with for
Quarter Unloaded α = .10 α = .10 α = .50 α = .50 Quarter Unloaded α = .10 α = .10 α = .50 α = .50
1 180 175 5.00 175 5.00 1
For α180.10 175
= 5.00 175 5.00
2 168 175.5 7.50 177.50 9.50 2 168 = 82.45/8 = 10.31
175.5 7.50 177.50 9.50
3 159 174.75 15.75 172.75 13.75 3 159 174.75 15.75 172.75 13.75
4 175 173.18 1.82 165.88 9.12 4 For α175.50 173.18
= 1.82 165.88 9.12
5 190 173.36 16.64 170.44 19.56 5 190 173.36 16.64 170.44 19.56
6 205 175.02 29.98 180.22 24.78 6 205 = 98.62/8 = 12.33
175.02 29.98 180.22 24.78
7 180 178.02 1.98 192.61 12.61 7 180 178.02 1.98 192.61 12.61
8 182 178.22 3.78 186.30 4.30 8 182 178.22 3.78 186.30 4.30
82.45 98.62 82.45 98.62


10

10/16/2010

Comparison of Forecast Comparison of Forecast
Error2 n Error
∑100|deviation |/actual
∑ (forecast errors) i i
Rounded Absolute Rounded Absolute Rounded Absolute Rounded Absolute
MSE = Actual Forecast Deviation Forecast Deviation MAPE = i = 1
Tonnage
n
with for with for Tonnage with n for with for
Quarter Unloaded α = .10 α = .10 α = .50 α = .50 Quarter Unloaded α = .10 α = .10 α = .50 α = .50
1
For α180.10 175
= 5.00 175 5.00 1
α=
For 180 .10 175 5.00 175 5.00
2 168 1,526.54/8 = 190.82
= , 175.5 7.50 177.50 9.50 2 168 = 44.75/8 = 7.50 %
175.5 5.59% 177.50 9.50
3 159 174.75 15.75 172.75 13.75 3 159 174.75 15.75 172.75 13.75
4 For α175.50 173.18
= 1.82 165.88 9.12 4 α=
For 175 .50 173.18 1.82 165.88 9.12
5 190 173.36 16.64 170.44 19.56 5 190 173.36 16.64 170.44 19.56
6 = 1,561.91/8 = 195.24
205 175.02 29.98 180.22 24.78 6 205 = 54.05/8 = 6.76%
175.02 29.98 180.22 24.78
7 180 178.02 1.98 192.61 12.61 7 180 178.02 1.98 192.61 12.61
8 182 178.22 3.78 186.30 4.30 8 182 178.22 3.78 186.30 4.30
82.45 98.62 82.45 98.62
MAD 10.31 12.33 MAD 10.31 12.33
MSE 190.82 195.24

Comparison of Forecast Exponential Smoothing with
Error Trend Adjustment
Tonnage with for with for
Quarter Unloaded α = .10 α = .10 α = .50 α = .50 When a trend is present, exponential
1 180 175 5.00 175 5.00
smoothing must be modified
2 168 175.5 7.50 177.50 9.50
3 159 174.75 15.75 172.75 13.75
4 175 173.18 1.82 165.88 9.12 Forecast Exponentially Exponentially
5 190 173.36 16.64 170.44 19.56 including (FITt) = smoothed (Ft) + smoothed (Tt)
6 205 175.02 29.98 180.22 24.78 trend forecast trend
7 180 178.02 1.98 192.61 12.61
8 182 178.22 3.78 186.30 4.30
82.45 98.62
MAD 10.31 12.33
MSE 190.82 195.24
MAPE 5.59% 6.76%

Exponential Smoothing with Exponential Smoothing with
Trend Adjustment Trend Adjustment Example
Forecast
Actual Smoothed Smoothed Including
Month(t) Demand (At) Forecast, Ft Trend, Tt Trend, FITt
Ft = α(At - 1) + (1 - α)(Ft - 1 + Tt - 1) 1 12 11 2 13.00
2 17
Tt = β(Ft - Ft - 1) + (1 - β)Tt - 1 3
4
20
19
5 24
6 21
Step 1: Compute Ft 7 31
Step 2: Compute Tt 8 28
9 36
Step 3: Calculate the forecast FITt = Ft + Tt 10

Table 4.1

11

10/16/2010

Trend Adjustment Example Trend Adjustment Example
Forecast Forecast
Actual Smoothed Smoothed Including Actual Smoothed Smoothed Including
Month(t) Demand (At) Forecast, Ft Trend, Tt Trend, FITt Month(t) Demand (At) Forecast, Ft Trend, Tt Trend, FITt
1 12 11 2 13.00 1 12 11 2 13.00
2 17 2 17 12.80
3 20 3 20
4 19 4 19
5 24 Step 1: Forecast for Month 2 5 24 Step 2: Trend for Month 2
6 21 6 21
7 31 F2 = αA1 + (1 - α)(F1 + T1) 7 31 T2 = β(F2 - F1) + (1 - β)T1
8 28 F2 = (.2)(12) + (1 - .2)(11 + 2) 8 28 T2 = (.4)(12.8 - 11) + (1 - .4)(2)
9 36 9 36
10 = 2.4 + 10.4 = 12.8 units 10 = .72 + 1.2 = 1.92 units
Table 4.1 Table 4.1

Trend Adjustment Example Trend Adjustment Example
Forecast Forecast
Actual Smoothed Smoothed Including Actual Smoothed Smoothed Including
Month(t) Demand (At) Forecast, Ft Trend, Tt Trend, FITt Month(t) Demand (At) Forecast, Ft Trend, Tt Trend, FITt
1 12 11 2 13.00 1 12 11 2 13.00
2 17 12.80 1.92 2 17 12.80 1.92 14.72
3 20 3 20 15.18 2.10 17.28
4 19 4 19 17.82 2.32 20.14
5 24 Step 3: Calculate FIT for Month 2 5 24 19.91 2.23 22.14
6 21 6 21 22.51 2.38 24.89
7 31 FIT2 = F2 + T2 7 31 24.11 2.07 26.18
8 28 FIT2 = 12.8 + 1.92 8 28 27.14 2.45 29.59
9 36 9 36 29.28 2.32 31.60
10 = 14.72 units 10 32.48 2.68 35.16

Table 4.1 Table 4.1

Exponential Smoothing with Trend Projections
Trend Adjustment Example
Fitting a trend line to historical data points
35 – to project into the medium to long-range
30 – Actual demand (At)
Linear trends can be found using the least
mand

25 – squares technique
Product dem

20 –
^
y = a + bx
15 –
^
where y = computed value of the variable to
10 – Forecast including trend (FITt)
with α = .2 and β = .4 be predicted (dependent variable)
5 – a = y-axis intercept
0 – | | | | | | | | |
b = slope of the regression line
1 2 3 4 5 6 7 8 9
x = the independent variable
Figure 4.3
Time (month)

12

10/16/2010

Least Squares Method Least Squares Method
Values of Dependent Variable

Values of Dependent Variable
Actual observation Deviation7 Actual observation Deviation7
(y-value) (y-value)

Deviation5 Deviation6 Deviation5 Deviation6

Deviation3 Deviation3 Least squares method
minimizes the sum of the
Deviation4
squared errors (deviations)
Deviation 4

Deviation1 Deviation1
(error) Deviation2 (error) Deviation2
^
Trend line, y = a + bx ^
Trend line, y = a + bx

Time period Figure 4.4
Time period Figure 4.4

Least Squares Method Least Squares Example
Time Electrical Power
Year Period (x) Demand x2 xy
Equations to calculate the regression variables
2003 1 74 1 74
2004 2 79 4 158
2005 3 80 9 240
^
y = a + bx 2006 4 90 16 360
2007 5 105 25 525
2008 6 142 36 852
Σxy - nxy 2009 7 122 49 854
b= ∑x = 28 ∑y = 692 ∑x2 = 140 ∑xy = 3,063
Σx2 - nx2 x=4 y = 98.86

∑xy - nxy 3,063 - (7)(4)(98.86)
a = y - bx b= = = 10.54
∑x2 - nx2 140 - (7)(42)

a = y - bx = 98.86 - 10.54(4) = 56.70

Least Squares Example Least Squares Example
Time Electrical Power
Year Period (x) Demand x2 xy Trend line,
160 – ^
2003 1 74 1 74 150 – y = 56.70 + 10.54x
2004 2 79 4 158 140 –
The trend line is 80
2005 3 9 240
emand

130 –
2006 4 90 16 360 120 –
2007 ^ 5 56 70 + 10 54x
y = 56.70 10.54x
105 25 525
Power de

110 –
2008 6 142 36 852 100 –
2009 7 122 49 854 90 –
∑x = 28 ∑y = 692 ∑x2 = 140 ∑xy = 3,063 80 –
x=4 y = 98.86 70 –
60 –
∑xy - nxy 3,063 - (7)(4)(98.86)
b= = = 10.54 50 –
∑x2 - nx2 140 - (7)(42) | | | | | | | | |
2003 2004 2005 2006 2007 2008 2009 2010 2011
a = y - bx = 98.86 - 10.54(4) = 56.70 Year

13

Heizer om10 ch04

Recomendados

Recomendados

Más contenido relacionado

La actualidad más candente

La actualidad más candente (20)

Similar a Heizer om10 ch04

Similar a Heizer om10 ch04 (18)

Heizer om10 ch04