Master Planning of Resources
Session 2
Forecasting Demand
What is a Forecast?
Forecast – An estimate of future demand. A forecast
can be determined by mathematical means using
historical data, it can be created subjectively by using
estimates from informal sources, or it can represent a
combination of both techniques.
Forecast Error – The difference between actual
demand and forecast demand, stated as an absolute
value or as a percentage.
Forecast Management – The process of making,
checking, correcting, and using forecasts. It also
includes determination of the forecast horizon.
Why Forecast?
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To plan for the future by reducing uncertainty
To facilitate a company in taking control of operations.
Without forecast, it would be a chaos.
To anticipate and manage change
To increase communication and integration of planning
teams
To anticipate inventory and capacity demands and
manage lead times
To project costs of operations into budgeting processes
To improve competitiveness and productivity through
decreased costs and improved delivery and
responsiveness to customer needs
Areas Impacted by the Forecast
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Investment decisions
Capital equipment decisions
Inventory planning
Capacity planning
Operations budgets
Lead-time management
Forecast System Design Issues
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Determine information that needs to be forecasted
Assign responsibility for the forecast
Set up forecast system parameters
Select forecasting models and techniques
Collect data
Test models
Record actual demand
Report accuracy
Determine root cause of variance
Review forecasting system for improved performance
General Forecasting Techniques
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Qualitative Techniques—based on intuitive
or judgmental evaluation
Quantitative Techniques—based on
computational projection of a numeric
relationship
Qualitative Techniques
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Expert opinion
Market research
Focus groups
Historical analogy
Delphi method
Panel consensus
2-7
Quantitative Techniques
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Moving average
Exponential smoothing
Regression analysis
Adaptive smoothing
Graphical methods
Econometric modeling
Life-cycle modeling
General Forecasting Data Methods
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Intrinsic forecasting methods are based on
historical patterns of the data itself from
company data
Extrinsic forecasting methods are based on
external patterns from information outside
the company such as published data and
data available from the Internet
Qualitative and quantitative forecasts may be generated based on intrinsic
or extrinsic information.
Internal (Intrinsic) Factors
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Product life-cycle
management
Planned price
changes
Changes in the sales
force
Resource constraints
Marketing and sales
promotion
Advertising
2-10
External (Extrinsic) Factors
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Competition
New customers
Plans of major
customers
Government policies
Regulatory concerns
Economic conditions
Environmental issues
Weather conditions
Global trends
2-11
Leading Indicators
Indicator
(Causal Factor)
Housing starts
Birth rate
Health trends
Desire for
Healthier lifestyle
Influences volume of
Building materials
Home furnishings
Baby products
Medical supplies
Nutritional products
Fitness products
2-12
Demand
A need for a particular product or component
Independent demand is demand for an item that is unrelated to the demand for
other items. Independent demand items are saleable products or services that are
added to the master schedule.
Dependent demand can be calculated directly from the demand for other products.
It is related to the bill of material structure.
2-13
Sources of Demand
Demand can come from many sources:
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Consumers
Customers
Referrers
Dealers
Distributors
Interplant
Service parts
Demand Characteristics
Internal Factors
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Product promotion
Product substitution
External Factors
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Random fluctuation
Seasonality
Trend
Economic cycle
Changing customer
preferences and demands
Seasonality
Sales in cases by month
800
700
600
500
400
300
200
100
0
Year 1
Year 2
J F M A M J
J A S O N D
Seasonality Calculation
Measures seasonal variation of demand
Relates the average demand in a particular
period to the average demand for all periods
period average demand
The Seasonal Index 
average demand for all periods
Calculation of Seasonal Index
Sales of Ice Cream
Month
Year 1
Year 2
January
10
12
22
22/409
0.05
February
10
12
22
22/409
0.05
March
10
12
22
22/409
0.05
April
50
55
105
105/409
0.26
May
150
160
310
310/409
0.76
June
400
420
820
820/409
2.00
July
600
620
1220
1220/409
2.98
August
700
730
1430
1430/409
3.49
September
350
360
710
710/409
1.74
October
100
105
205
205/409
0.50
November
10
12
22
22/409
0.05
December
10
12
22
22/409
0.05
2400
2510
Total
Average
Total
Calculation
4910
409.17 Round to 409
Index
Seasonality Exercise
Economic Cycle
Sales by Quarter
35
30
25
20
15
10
5
0
1
3
5
7
9
11 13 15 17 19
Quarter
Pyramid Forecasting
Total
business
volume
(dollars)
Product family volume
(units/dollars)
Product/item volume
(units)
Pyramid Forecasting
Pyramid Forecasting
Technique—Pyramid Forecasting Example
ROLL-UP
 Product-level forecast
X1 units—8,200
price—$20.61
 Family-level forecast
Family-adjusted forecast
FORCE-DOWN
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X1
X2 units—4,845
price—$10.00
—units—13,045
Family avg price—$16.67
—units—15,000
15,000
× 8,200 = 9,429 units
13,045
X2 15,000 × 4,845 = 5,571 units
13,045
Pyramid Forecasting Using Revenue
B
A
1
price
8,200 $20.61
units
F
E
Totals
X2
X1
units
D
C
price
$
Qty
4,845 $10.00 13,045
$217,452
1.15
2
3
4
9,429 $20.61
5,571 $10.00 15,000
$250,042
$250,070
2-25
Pyramid Forecasting Exercise
Historical Demand
Product A
Region 1
150
Region 2
300
Selling Price $4.50
Product B
Region 1
300
Region 2
450
Selling Price $8.50
Management has determined that next year’s demand will
be $10,000 total.
CALCULATE the projected demand in units for products A
and B in each region.
2-26
Pyramid Forecasting Exercise—Solution
Based upon historical demand
A = 150 + 300 = 450 × $4.50 = $2,025
B = 300 + 450 = 750 × $8.50 = $6,375
Total
= $8,400
$10,000
$8,400
= 1.19 (19% increase)
A: Region 1 = 1.19 × 150 = 178.5
Region 2 = 1.19 × 300 = 357.0
B: Region 1 = 1.19 × 300 = 357.0
Region 2 = 1.19 × 450 = 535.5
178.5 + 357.0 = 535.5 × $4.50 = $2,409.75
357.0 + 535.5 = 892.5 × $8.50 = $7,586.25
$9,996.00
2-27
Moving Average Forecasting
Advantages
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A simple technique that is easy to calculate
It can be used to filter out random variation
Longer periods provide more smoothing
Limitations
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If a trend exists, it is hard to detect
Moving averages lag trends
2-28
Moving Average Exercise
Actual sales
Jan
100
Feb
500
Mar
1000
Apr
1500
May
2800
June
5100
Jul
6200
Aug
5700
Sep
3200
Oct
1200
Nov
500
Dec
100
Next month’s
forecast
variation
Exponential Smoothing
New Forecast = ∝x Actual Demand + (1 - ∝) x Old Forecast
New Forecast = Old Forecast + ∝ x (Actual Demand – Old Forecast)
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Provides a routine method of updating item
forecasts
Alpha is a weighting factor applied to the
demand element
Works well for items with fairly constant
demand
Is satisfactory for short-range forecasts
Lags trends
2-31
Smoothing Factor
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Referred to as Alpha (a)
Determines the weight of historical
data on projection
Sets responsiveness to changes in
demand
Range 0  a  1
2
a=
n+1
2-32
Smoothing Factor (cont.)
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Determines how many periods of
actual demand will influence forecast
1.00 = 1 period
0.50 = 3 periods
0.29 = 6 periods
0.15 = 12 periods
0.10 = 19 periods
2-33
Comparison of Exponential Smoothing Alpha Factors
0.1 Low weighting -most smoothing
0.9 High weighting - close to actual
Actual sales
2-34
Exponential Smoothing Examples
New forecast = Old forecast + smoothing factor (a)
 (actual demand - old forecast)
Example: old forecast = 160, actual = 200, a = 0.1
new forecast = 160 + (0.1  (200 - 160))
= 160 + (0.1  40) = 164
Example: old forecast = 160, actual = 200, a = 0.8
new forecast = 160 + (0.8  (200 - 160))
= 160 + (0.8  40) = 192
Adapted from: Manufacturing for Survival, B.R. Williams, Addison Wesley, 1996
2-35
New Product Introduction
Every new product/service is a calculated risk.
Every new product/service has the potential to
be the next
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Blockbuster
Lifesaver
Money loser
Disaster
Liability nightmare.
2-36
Product Life Cycle
Volume
Introduction
Growth
Maturity
Decline
Product Life Cycle Stages
Time
2-37
Focus Forecasting—Assumptions/Methods
Assumptions
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The most recent past is the best indicator of the
future
One forecasting model is better than the others
Methods
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All forecasting models for all items forecasted will
be compared against recent sales history
The model that achieves the closest fit will be
used to forecast this item this time
Next time, a different model may be selected
2-38
Data Issues for Forecasting
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Availability of data
Consistency of data
Amount of history required
Forecast frequency
Frequency of model reevaluation
Cost and time issues
Recording true demand
Order date vs. ship date
Product units vs. financial units
Level of aggregation
Customer partnering
2-39
Planning Horizon and Time Periods
Forecast Length
Short
Mid
Long
Planning
Horizon
Weeks
Months
Quarters
1 2 3 4 5 6 7 8 9 1011 12 13 17 21 25 29 33 37 41 45 49 53 65
78
Time Periods (week numbers)
2-40
91 104
Data Preparation and Collection
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Record sales data in same
periods as forecast data
(daily, weekly, or monthly)
Monitor demand, not sales
and/or shipments
Record the circumstances of
exceptional demand
Record demand separately
for unique customer
groupings and market sectors
2-41
Dealing with Outliers
55
50
25
20
15
10
5
0
J F M A M J J A S O N D J F M A M J J A S O N D
2-42
Decomposition of Data
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Purify the data
Adjust the data
Take out the baseline and components
Identify demand components
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Trend
Seasonality
Nonannual cycle
Random error
Measure the random error
Project the series
Recompose
2-43
Session 2 Review
You should now be able to
 Explain why forecasting is important
 Identify and describe general methods of
forecasting
 Identify factors influencing demand
 Describe considerations in using data for
forecasts
 Outline the process of data decomposition
2-44