hr om11 ch04 Forecasting 4th

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06-Nov-18
Forecasting Demand
© 2014
© 2014
Pearson
Pearson
Education,
Education,
Inc.Inc.
4
4-1
Outline
▶ Global Company Profile:
Walt Disney Parks & Resorts
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▶
▶
▶
▶
▶
What Is Forecasting?
The Strategic Importance of Forecasting
Seven Steps in the Forecasting System
Forecasting Approaches
Time--Series Forecasting
Time
Associative Forecasting Methods: Regression
and Correlation Analysis
▶ Monitoring and Controlling Forecasts
▶ Forecasting in the Service Sector
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Learning Objectives
When you complete this chapter you should
be able to :
1. Understand the three time horizons and which models
apply for each use
2. Explain when to use each of the four qualitative models
3. Apply the naive, moving average, exponential
smoothing, and trend methods
4. Compute three measures of forecast accuracy
5. Develop seasonal indices
6. Conduct a regression and correlation analysis
7. Use a tracking signal
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Forecasting Provides a Competitive
Advantage for Disney
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Global portfolio includes parks in Hong Kong, Paris, Tokyo,
Orlando, and Anaheim
Revenues are derived from people – how many visitors and
how they spend their money
Daily management report contains only the forecast and
actual attendance at each park.
Disney generates daily, weekly, monthly, annual, and 55-year
forecasts
Forecast used by labor management, maintenance,
operations, finance, and park scheduling
Forecast used to adjust opening times, rides, shows, staffing
levels, and guests admitted
© 2014
© 2014
Pearson
Pearson
Education,
Education,
Inc.Inc.
4-4
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Forecasting Provides a Competitive
Advantage for Disney
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20% of customers come from outside the USA.
Economic model includes gross domestic product, crosscrossexchange rates, arrivals into the USA.
A staff of 35 analysts and 70 field people survey 1 million
park guests, employees, and travel professionals each year.
Inputs to the forecasting model include airline specials,
Federal Reserve policies, Wall Street trends,
vacation/holiday schedules for 3,000 school districts around
the world.
Average forecast error for the 55-year forecast is 5%.
Average forecast error for annual forecasts is between 0%
and
3%.
© 2014
© 2014
Pearson
Pearson
Education,
Education,
Inc.Inc.
4-5
What is Forecasting?
►
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Process of predicting a
future event
Underlying basis
of all business decisions
►
Production
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Inventory
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Personnel
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Facilities
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Forecasting Time Horizons
1. Short
Short--range forecast
►
►
Up to 1 year, generally less than 3 months
Purchasing, job scheduling, workforce levels, job
assignments, production levels
2. Medium
Medium--range forecast
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3 months to 3 years
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Sales and production planning, budgeting
3. Long
Long--range forecast
►
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Over than 3 years
New product planning, facility location, research and
development
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Distinguishing Differences
1. Medium/long range forecasts deal with more
comprehensive issues and support
management decisions regarding planning
and products, plants and processes
2. Short-term forecasting usually employs
different methodologies than longer-term
forecasting
3. Short-term forecasts tend to be more
accurate than longer-term forecasts
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Influence of Product Life Cycle
Another factor to consider when developing sales forecasts,
especially longer one is product life cycle. Due to products and
services, do not sell at a constant level throughout their lives
Stages of LC: Introduction – Growth – Maturity – Decline
As product passes through life cycle, forecasts are useful in
projecting
►
Staffing levels
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Inventory levels
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Factory capacity
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Product Life Cycle
Strategy Issues
►
Introduction and growth require longer forecasts than
maturity and decline
Introduction
Growth
Best period to
increase market
share
Practical to change
price or quality
image
R&D engineering is
critical
Strengthen niche
Maturity
Decline
Poor time to
change image,
price, or quality
Cost control
critical
Competitive costs
become critical
Defend market
position
Drive-through
Internet search engines
restaurants
DVDs
Xbox 360
iPods
Boeing 787
Company
►
Sales
3D printers
3-D game
players
Electric vehicles
Analog
TVs
Figure 2.5
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Product Life Cycle
Introduction
OM Strategy/Issues
Product design
and development
critical
Frequent product
and process
design changes
Short production
runs
High production
costs
Growth
Forecasting
critical
Product and
process reliability
Competitive
product
improvements and
options
Increase capacity
Limited models
Shift toward
product focus
Attention to
quality
Enhance
distribution
Maturity
Standardization
Fewer product
changes, more
minor changes
Optimum capacity
Increasing
stability of
process
Long production
runs
Product
improvement and
cost cutting
Decline
Little product
differentiation
Cost
minimization
Overcapacity
in the industry
Prune line to
eliminate
items not
returning good
margin
Reduce
capacity
Figure 2.5
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Types of Forecasts
1. Economic forecasts
►
Address business cycle – inflation rate, money
supply, housing starts, etc.
2. Technological forecasts
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Predict rate of technological progress
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Impacts development of new products
3. Demand forecasts
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Predict sales of existing products and services
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Strategic Importance of Forecasting
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Supply-Chain Management – Good supplier
Supplyrelations, advantages in product innovation,
cost and speed to market
Human Resources – Hiring, training, laying off
workers
Capacity – Capacity shortages can result in
undependable delivery, loss of customers, loss
of market share
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Seven Steps in Forecasting
1. Determine the use of the forecast
2. Select the items to be forecasted
3. Determine the time horizon of the forecast
4. Select the forecasting model(s)
5. Gather the data needed to make the forecast
6. Make the forecast
7. Validate and implement results
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The Realities!
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Forecasts are seldom perfect, unpredictable
outside factors may impact the forecast
Most techniques assume an underlying
stability in the system
Product family and aggregated forecasts are
more accurate than individual product
forecasts
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Forecasting Approaches
Qualitative Methods
►
Used when situation is vague and little data exist
E.g., New products; New technology
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Involves intuition, experience
E.g., forecasting sales on Internet
Quantitative Methods
►
Used when situation is ‘stable’ and historical data
exist
E.g., Existing products; Current technology
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Involves mathematical techniques
E.g., forecasting sales of color televisions
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Overview of Qualitative Methods
1. Jury of executive opinion
Pool opinions of high-level experts, sometimes
augmented by statistical models
2. Delphi method
Panel of experts, queried iteratively
3. Sales force composite
Estimates from individual salespersons are
reviewed for reasonableness, then aggregated
4. Market Survey
Ask the customer
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Jury of Executive Opinion
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Involves small group of high-level experts
and managers
Group estimates demand by working
together
Combines managerial experience with
statistical models
Relatively quick
‘Group-think’
disadvantage
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Delphi Method
►
►
Iterative group process,
continues until
consensus is reached
Decision Makers
(Evaluate responses
and make decisions)
3 types of participants
Decision makers
Staff
(Administering
survey)
Staff
Respondents
Respondents
(People who can make
valuable judgments)
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Sales Force Composite
Each salesperson projects his or her sales
Combined at district and national levels
Sales reps know customers’ wants
May be overly optimistic
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Market Survey
Ask customers about purchasing plans
Useful for demand and product design and
planning
What consumers say, and what they actually
do may be different
May be overly optimistic
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Overview of Quantitative
Approaches
1.
2.
3.
4.
Naive approach
Moving averages
Exponential smoothing
Trend projection
5. Linear regression
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Time-series
models
Associative
model
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Time-Series Forecasting
►
►
Set of evenly spaced numerical data: Obtained by
observing response variable at regular time periods
Forecast based on past values, no other variables
important: Assuming that factors influencing past
and present will continue influence in future
Time-Series Components
Trend
Cyclical
Seasonal
Random
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Components of Demand
Demand for product or service
Trend
component
Seasonal peaks
Actual demand
line
Average demand
over 4 years
Random variation
|
1
|
2
|
3
Time (years)
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4
Figure 4.1
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Trend Component
Persistent, overall
upward or downward
pattern
► Changes due to
population, technology,
age, culture, etc.
► Typically several years
duration
►
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Seasonal Component
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Regular pattern of up and down fluctuations
Due to weather, customs, etc.
Occurs within a single year
E.g., restaurants and barber shops experience
weekly seasons at every weekend
PERIOD LENGTH
“SEASON” LENGTH
NUMBER OF “SEASONS” IN PATTERN
Week
Day
Month
Week
4 – 4.5
Month
Day
28 – 31
Year
Quarter
4
Year
Month
12
Year
Week
52
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Cyclical Component
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Repeating up and down movements
Affected by business cycle, political,
and economic factors
Multiple years duration
Often causal or
associative
relationships
0
5
10
15
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Random Component
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Due to an unusual situation or unexpected
events
Short duration and nonrepeating
Can not be predicted
M
F
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T
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Naive Approach
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Assumes that the demand in next
period is the same as demand in
most recent period
Sometimes cost effective and efficient
Can be good starting point
Example, If Nokia’s January sales were $68
million, then we predict that February sales will
be $68 million as well, according to the naïve
approach
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Moving Average Method
►
Moving Average is a series of arithmetic
means
►
Used if little or no trend
►
Used often for smoothing
►
Provides overall impression of data over time
Moving average =
∑ demand in previous n periods
n
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Moving Average Example
Donna's garden supply wants to forecast shed sales
using a 33-month moving average forecasting method.
MONTH
ACTUAL SHED SALES
January
10
3-MONTH MOVING AVERAGE
February
12
March
13
April
16
(10 + 12 + 13)/3 = 11 2/3
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
August
30
(19 + 23 + 26)/3 = 22 2/3
September
28
(23 + 26 + 30)/3 = 26 1/3
October
18
(29 + 30 + 28)/3 = 28
November
16
(30 + 28 + 18)/3 = 25 1/3
December
14
(28 + 18 + 16)/3 = 20 2/3
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Weighted Moving Average
►
Used when some trend might be present
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►
Older data usually less important
Weights based on experience and intuition
((
)(
Weighted ∑ Weight for period n Demand in period n
moving =
average
∑ Weights
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))
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Weighted Moving Average
MONTH
ACTUAL SHED SALES
January
10
February
12
March
13
April
16
May
July
August
September
October
November
[(3 x 13) + (2 x 12) + (10)]/6 = 12 1/6
19
WEIGHTS
APPLIED
23
3
26
June
3-MONTH WEIGHTED MOVING AVERAGE
PERIOD
Last month
Two months ago
30
2
28
1
Three months ago
6
Sum of the weights
18
Forecast for
16this month =
3 x 14
Sales last mo. + 2 x Sales 2 mos. ago + 1 x Sales 3 mos. ago
December
Sum of the weights
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Weighted Moving Average
MONTH
ACTUAL SHED SALES
January
10
3-MONTH WEIGHTED MOVING AVERAGE
February
12
March
13
April
16
[(3 x 13) + (2 x 12) + (10)]/6 = 12 1/6
May
19
[(3 x 16) + (2 x 13) + (12)]/6 = 14 1/3
June
23
[(3 x 19) + (2 x 16) + (13)]/6 = 17
July
26
[(3 x 23) + (2 x 19) + (16)]/6 = 20 1/2
August
30
[(3 x 26) + (2 x 23) + (19)]/6 = 23 5/6
September
28
[(3 x 30) + (2 x 26) + (23)]/6 = 27 1/2
October
18
[(3 x 28) + (2 x 30) + (26)]/6 = 28 1/3
November
16
[(3 x 18) + (2 x 28) + (30)]/6 = 23 1/3
December
14
[(3 x 16) + (2 x 18) + (28)]/6 = 18 2/3
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Potential Problems With
Moving Average
Increasing n smooths the forecast but
makes it less sensitive to changes
► Does not forecast trends well
► Requires extensive historical data
►
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Graph of Moving Averages
Weighted moving average
30 –
Sales demand
25 –
20 –
15 – Actual sales
Moving average
10 –
5–
|
|
|
|
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J
F
M
A
M
Figure 4.2
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J
J
Month
|
|
|
|
|
A
S
O
N
D
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Exponential Smoothing
►
►
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Form of weighted moving average
►
Weights decline exponentially
►
Most recent data weighted most
Requires smoothing constant (α)
►
Ranges from 0 to 1
►
Subjectively chosen
Involves little record keeping of past data
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Exponential Smoothing
New forecast = Last period’s forecast
+ α (Last period’s actual demand
– Last period’s forecast)
Ft = Ft – 1 + α (At – 1 - Ft – 1)
Where,,,
Ft = new forecast
Ft – 1 = previous period’s forecast
α = smoothing (or weighting) constant (0 ≤ α ≤ 1)
At – 1 = previous period’s actual demand
At – 1 - Ft – 1 = Forecasting error
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Exponential Smoothing: Example 1
In January, a Car Dealer Predicted February
Demand for 142 Ford Mustangs. Actual February
Demand was 153.
Using a Smoothing Constant of α = 0.20 (Chosen by
Management), the Dealer Wants to Forecast the
Demand for March Using Exponential Smoothing.
If α is Changed to 0.40, What Will be the New
Forecast?
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The Solution
If α equals to 0.20
Predicted demand = 142 Ford Mustangs
Actual demand = 153
Smoothing constant α = .20
New forecast
= 142 + .2(153 – 142) = 142 + 2.2
= 144.2 ≈ 144 cars
If α is Changed to 0.40
New forecast
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= 142 + .4(153 – 142) = 142 + 4.4
= 146.4 ≈ 146 cars
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Exponential Smoothing: Example 2
A checkcheck-processing center uses exponential smoothing to
forecast the number of incoming checks each month. The
number of checks received in June was 40 million, while the
forecast was 42 million. A smoothing constant of .2 is used.
A) What is the forecast for July?
B) If the center received 45 million checks in July, what would
be the forecast for August?
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Exponential Smoothing: Example 3
Using a smoothing constant equals to 0.5 what
would be the expected demand at the seventh week
given that the following data are the actual sales of
Kitchens at Kabnoury company.
Week
1
2
3
4
5
6
Sold Units
10
14
9
13
15
10
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The Solution
Data given are Actual Sales
To employ the exponential smoothing method we
need to calculate a predicted sales (demand) for a
given week
In such case, we have to use the average method
to calculate the predicted demand for that month
How many weeks should I use to get average?
Ν = (2/ α) − 1
Ν = (2/ 0.5) − 1 = 3
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Predicted demand for the week 4 =
(10+14+9) / 3 = 11 units
Then you can employ the exponential smoothing method
Predicted demand for the week 5 =
11 + 0.5 (13 - 11) = 12 units
Predicted demand for the week 6 =
12 + 0.5 (15 - 12) = 13.5 units
Predicted demand for the week 7 =
13.5 + 0.5 (10 – 13.5) = 11.75 units
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