10/21/2013
The Intricacies of
Forecasting—Simplified
Concepts and
Techniques for Effective
Forecast Management
Introductions – Session Leader
David F. Ross PhD, CFPIM, CSCP
Senior Manager, Professional
Development, APICS
35 years of industry, consulting, ERP,
education, and professional development
experience
Meet your
session Teaching positions at NU Kellogg School
of Management and Elmhurst College
leaders
APICS Member since 1985
Published six books in supply chain
management
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Introductions – Session Leader
Bob Collins CFPIM, CIRM, CSCP
Director, Professional Development,
APICS (Staff position)
30 years of industry, consulting, ERP,
education, and professional development
experience
Meet your
session Former APICS Instructor and volunteer –
Chapter, District and APICS Board of
leaders
Directors, APICS President (2003)
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Agenda
• 23 major principles of forecasting
• Forecasting in the supply chain environment
• Defining demand management and role of the demand
planner
• Defining forecasting and the forecasting process
• Review of qualitative forecasting techniques
• Review of quantitative forecasting techniques
• Performing forecast decomposition: trends and seasonal
items
• Understanding associative (correlation) models
• Reviewing the tools to chart forecast error
• Detailing why forecasts fail
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Forecasting Themes
“All things pass away;
nothing remains”
- Heraclitus
“Those who have
knowledge don’t
predict. Those who
predict, don’t have
knowledge”
- Lao Tzu
“Prediction is very
difficult, especially if
it’s about the future”
- Niels Bohr
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Principles of Forecasting Management
1. Supply chain management (SCM) refers to getting the
right amount of the right product to where it is needed
while managing productive resources levels to achieve
maximum return on assets
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Forecasting is Everywhere in the Supply Chain
1. Analyzing customer
demand: What should we
make and when?
3. Production: Are we
producing the right
amount of the right
product?
2. Materials: Who do we
buy from and how
much?
5. Wholesale/retail: What is
the proper assortment and
allocation of merchandise in
stores?
4. Distribution: Where do
we distribute product?
Store
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Principles of Forecasting Management
1. Supply chain management (SCM) refers to getting the
right amount of the right product to where it is needed
while managing productive resources levels to achieve
maximum return on assets
2. Demand management is the process of managing all
independent demands for a company's product lines and
effectively communicating these demands to the master
scheduling and top management production functions
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Defining Demand Management
The process of planning, executing,
controlling, and monitoring the design,
pricing, promotion, and
distribution of products and services to
bring about transactions that meet
organizational and individual needs.
APICS Dictionary, 14th ed.
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Defining Demand Planning
The process of combining statistical forecasting
techniques and judgment to construct demand
estimates for products or services (both high and
low volume; lumpy and continuous) across the
supply chain from the suppliers' raw materials to
the consumer's needs. Items can be aggregated by
product family, geographical location, product life
cycle, and so forth, to determine an estimate of
consumer demand for finished products, service
parts, and services.
APICS Dictionary, 14th ed.
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Principles of Forecasting Management
1. Supply chain management (SCM) refers to getting the
right amount of the right product to where it is needed
while managing productive resources levels to achieve
maximum return on assets
2. Demand management is the process of managing all
independent demands for a company's product lines and
effectively communicating these demands to the master
scheduling and top management production functions
3. Demand forecasting is the process of predicting future
customer demand for a firm's goods and services
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Demand Management Process Model
Reviewing
Demand
Performance
Planning
Demand
Demand
Management
Communicating
Demand
Prioritizing
Demand
Influencing
Demand
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Principles of Forecasting Management
4. Sales and operational forecasting involves the input from
marketing, sales, production, and financial plans to
determine the disaggregated forecasts of product or
service demand
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Roles of Demand Management Functions
Executive
Sales
Marketing
Product/Brand Mgmt
Role
Role
Role
Role
• Ensure demand
strategies, tactics,
and execution are in
place
• Make visible sales
plans and volume of
demand
• Detail marketplace
changes
• Detail marketing
strategy and tactics
• Detail product plans,
launches, and phaseouts
Responsibilities
Responsibilities
Responsibilities
Responsibilities
• Detail demand status
to meet strategic and
financial objectives
• Participate in monthly
demand consensus
review
• Provide leadership
and oversight
• Ensure demand plan
synchronized with
company plans
• Performance
accountability
• Detail monthly
customer sales
volume and timing
• Detail monthly
demand assumptions
• Communicate at least
monthly market
problems and
opportunities
• Communicate any
significant changes in
demand
• Detail monthly
anticipated changes
to marketing strategy
and impact on
demand
• Detail monthly the
assumptions upon
which marketing
strategies are based
• Track and report
monthly the impact of
the marketplace on
anticipated demand
• Detail monthly product
plans, product
launches, promotions,
and product phase-outs
• Communicate delays in
product launches or
changes to product
plans impacting
demand
• Communicate and
update life cycle plans
and plan assumptions
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Role of the Demand Planner
Marketing
Data
Products/
Brands
Customer
Data
Analyze
and
Assimilate
Statistical
Analysis
Sales
Data
Business
Plan
Economy
Updated Demand Plan
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S&OP and the Demand Plan
S&OP
Meeting
Strategies
Financial
Review
Product
Review
Resources
Performance
Measurements
Supply
Review
Demand
Review
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Principles of Forecasting Management
4. Sales and operational forecasting involves the input from
marketing, sales, production, and financial plans to
determine the disaggregated forecasts of product or
service demand
5. Demand forecasting is performed at different levels of
detail incorporating dimensions of period, product, and
customer/location
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Forecasting Levels
Planning Horizon
ANNUAL –
1-10 years
MONTHLY –
3-12 Months
Focus
STRATEGIC
PLANNING
TACTICAL
PLANNING
WEEKLY –
1-52 Weeks
OPERATIONS
PLANNING
DAILY –
1-365
Days
SHORT-TERM
PLANNING
Financial Goals
and Objectives
Product Families
Finished
Goods
Manufacturing/
Purchased
items
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Examples of Forecasting by Levels
Forecast
Required by
Expected corporate growth for the
next 5 years (long range)
Executive team: investment, profit,
and asset/capital planning
Product life cycles (long range)
Marketing: product planning
Total production required for next
five years (long range)
Manufacturing: plant expansion
program
Current year’s sales of individual
products in family groupings
(medium range)
Sales: quotas
Finance: expense budgets
Manufacturing: labor/machine
capacities
Inventory: purchasing and storage
Sales for next week (short term)
Manufacturing: assembly schedules
and dispatching priorities
Materials: purchase order release
and follow-up
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Principles of Forecasting Management
4. Sales and operational forecasting involves the input from
marketing, sales, production, and financial plans to
determine the disaggregated forecasts of product or
service demand
5. Demand forecasting is performed at different levels of
detail incorporating dimensions of period, product, and
customer/location
6. Forecasting is a process that has as its objective the
prediction of future events or conditions
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Defining Forecasting
An objective estimate of future demand
attained by projecting the pattern found
in the events of the past into the future.
It is primarily a calculative rather than an
intuitive management process
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Principles of Forecasting Management
4. Sales and operational forecasting involves the input from
marketing, sales, production, and financial plans to
determine the disaggregated forecasts of product or
service demand
5. Demand forecasting is performed at different levels of
detail incorporating dimensions of period, product, and
customer/location
6. Forecasting is a process that has as its objective the
prediction of future events or conditions
7. Effective forecasting starts with an comprehensive
forecast design system
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Forecast System Design Issues
• Time horizon
• Level of aggregate detail
• Size of the historical database
• Forecast control
• Constancy
• Selection of forecasting models
• Designing the forecasting process
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The Forecasting Process
1. Data
gathering and
preparation
2. Forecast
generation
3. Volume and
mix
reconciliation #1
7. Volume and
mix
reconciliation #3
6. Decision
making and
authorization
8. Documenting
assumptions
4. Apply
judgment
5. Volume and
mix
reconciliation #2
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Principles of Forecasting Management
8. A forecasting technique is a systematic procedure for
producing and analyzing forecasts
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General Forecasting Techniques
Qualitative Techniques
Based on intuitive or judgmental evaluation
Quantitative Techniques
Based on computational projection of a numeric
relationship
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Forecasting Data Sources
Internal (Intrinsic)
Forecasting data sources based on historical
demand patterns from the company data
External (Extrinsic)
Forecasting data sources based on external
patterns from information outside the company
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Forecasting Categories
Qualitative
Techniques
Judgmental
•
•
•
•
•
•
•
•
Expert opinion
Sales force estimate
Pyramid forecasting
Panel consensus
Market research
Delphi technique
Visionary forecast
Product life cycle
analysis
Quantitative
Techniques
Time Series
(Intrinsic)
• Simple average
• Moving average
• Exponential
smoothing
• Time series
decomposition
Associative
(Extrinsic)
•
•
•
•
•
•
Correlation
Regression
Multiple regression
Historical analogy
Leading indicator
Econometric
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Principles of Forecasting Management
8. A forecasting technique is a systematic procedure for
producing and analyzing forecasts
9. Qualitative methods are most commonly used in
forecasting something about which the amount, type,
and quality of historical data are limited
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Qualitative Forecasting Techniques
Independent
Judgment
Executive/
Management
Judgment
Market
Research
Sales Force
Estimates
Historical
Analogy
• Expert opinion
• Visionary forecast
• Panel consensus
• Delphi technique
• Pyramid
• Focus group
• Survey
• Sales force composite
• Product life cycle analysis
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Principles of Forecasting Management
8. A forecasting technique is a systematic procedure for
producing and analyzing forecasts
9. Qualitative methods are most commonly used in
forecasting something about which the amount, type,
and quality of historical data are limited
10. Quantitative methods are characterized by a rigorous
data acquisition procedure along with an application of
mathematical techniques. A method based on historical
data will be no better than the quality of its data source
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Quantitative Techniques
Simple average
Year-to-date average
Moving average
Weighted moving average
Exponential smoothing
Time series decomposition
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Principles of Forecasting Management
8. A forecasting technique is a systematic procedure for
producing and analyzing forecasts
9. Qualitative methods are most commonly used in
forecasting something about which the amount, type,
and quality of historical data are limited
10. Quantitative methods are characterized by a rigorous
data acquisition procedure along with an application of
mathematical techniques. A method based on historical
data will be no better than the quality of its data source
11. Forecasts are usually wrong
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Principle of Entropy
Ludwig Boltzmann
Fighting the second law of thermodynamics. “Entropy law" is
a law of disorder or that dynamically ordered states are
"infinitely improbable"
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Principles of Forecasting Management
8. A forecasting technique is a systematic procedure for
producing and analyzing forecasts
9. Qualitative methods are most commonly used in
forecasting something about which the amount, type,
and quality of historical data are limited
10. Quantitative methods are characterized by a rigorous
data acquisition procedure along with an application of
mathematical techniques. A method based on historical
data will be no better than the quality of its data source
11. Forecasts are usually wrong
12. Forecasts are more accurate for aggregate groups
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Detail and Aggregate Forecasts
Detail ViewView
Aggregate
of Forecasts
of Forecasts
Period
1
2
3
4
5
6
7
8
9
10
11
Average
Demand
110
78
80
122
85
131
120
79
75
120
100
Average
94.00
79.00
101.00
103.50
108.00
125.50
99.50
77.00
97.50
98.44
3 Period
Year-to-Date 3 Period
Exponential
Weighted
Average
Average
Smoothing
Average
110
110.00
110.00
94.00
94.00
89.33
89.33
86.00
87.00
97.50
93.33
98.22
104.50
95.00
95.67
96.22
94.75
101.00
112.67
113.67
112.88
103.71
112.00
115.89
116.44
100.63
110.00
104.22
97.72
97.78
91.33
86.33
86.36
100.00
91.33
95.89
103.18
98.77
100.62
100.08
100.40
Alpha (α )
0.50
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Principles of Forecasting Management
13. Time series analysis assists forecasters to isolate
demand patterns occurring in the raw data
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Time Series Patterns
Sales (M)
5
Random
Variation
Trend
Horizontal
Seasonality
4
3
2
1
0
0
1
2
3
4
5
6
7
8
9
10 11 12
Months
Quarter 1
Quarter 2
Quarter 3
Quarter 4
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Principles of Forecasting Management
13. Time series analysis assists forecasters to isolate
demand patterns occurring in the raw data
14. The utility of averages becomes problematic when time
series data is affected by trend, seasonal, or cyclical
patterns. Forecasters must then “decompose” the
patterns into subpatterns to reveal how they impact the
behavior of the series
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Principles of Forecasting Management
13. Time series analysis assists forecasters to isolate
demand patterns occurring in the raw data
14. The utility of averages becomes problematic when time
series data is affected by trend, seasonal, or cyclical
patterns. Forecasters must then “decompose” the
patterns into subpatterns to reveal how they impact the
behavior of the series
15. A trend is the basic tendency of a measured variable to
grow or decline over a long period. The forecast
extrapolation can be calculated as additive or a trend
factor (percent)
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Trend Quantity Forecast
Three Step Process:
1. Base forecast calculation
Use of statistical technique to determine the base forecast from
the time series data
2. Trend quantity calculation
Tt = β (FBt - FBt -1) + (1 – β ) Tt - 1
3. Forecast calculation
The trend quantity is added to the base forecast to determine
the trended forecast. The forecast is extrapolated into the future
by adding the trend quantity to each future period’s trended
forecast
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Trend Quantity Forecast – Example
Additive trend quantity forecast using 3 period average
Beta Factor
Period
January Year 1
February
March
April
May
June
July
August
September
October
November
December
January Year 2
February
Demand Base Forecast
100
109
119
131
140
148
160
175
109.33
119.67
130.00
139.67
149.33
161.00
0.3
Trend Quantity
32.80
26.06
21.34
17.84
15.39
14.27
Forecast
142.13
145.73
151.34
157.51
164.72
175.27
189.54
203.81
218.09
232.36
246.63
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Principles of Forecasting Management
16. Seasonality is a regularly recurring variation (timing and
intensity) in a time series. Seasonal patterns are
fluctuations that can recur over months, weeks, days, or
even hours
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Seasonal Forecast – Calculation
Five Step Process
1. Determine the size of the historical time series to be used
in the calculation
Past Demand
Year
Demand
1
2
3
1-1 Qtr 1-2 Qtr 1-3 Qtr 1-4 Qtr 2-1Qtr 2-2 Qtr 2-3Qtr 2-4 Qtr 3-1Qtr 3-2Qtr 3-3 Qtr 3-4 qtr
160
225
350
425
165
190
335
390
175
245
360
430
2. Summarize the historical data by quarter
Summary
Total
Yrs 1,2,3 Ist Qtr
Yrs 1,2,3 2nd Qtr
Yrs 1,2,3 3rd Qtr
Yrs 1,2,3 4th Qtr
Totals
500
660
1,045
1,245
3,450
Avg
167
220
348
415
288
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Seasonal Forecast – Calculation (cont.)
3. Calculate the seasonal index
Summary
Total
Yrs 1,2,3 Ist Qtr
Yrs 1,2,3 2nd Qtr
Yrs 1,2,3 3rd Qtr
Yrs 1,2,3 4th Qtr
Totals
Avg
500
660
1,045
1,245
3,450
Season Index
167
220
348
415
288
0.5797
0.7652
1.2116
1.4435
4.000
4. Calculate a base deseasonalized forecast
Forecast (Yr)
1000
Avg Forecast per Quarter
250
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Seasonal Forecast – Calculation (cont.)
5. Calculate the new seasonal forecast
New Forecast
Year
Demand
4
1 Qtr
145
2 Qtr
3 Qtr 4 Qtr
191
303
361
Forecast average x seasonal index = 250 x 0.5795 = 145
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Principles of Forecasting Management
16. Seasonality is a regularly recurring variation (timing and
intensity) in a time series. Seasonal patterns are
fluctuations that can recur over months, weeks, days, or
even hours
17. Through associative (correlation) analysis, we measure
the effects of mutual dependence in values of an item
series
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Principles of Forecasting Management
16. Seasonality is a regularly recurring variation (timing and
intensity) in a time series. Seasonal patterns are
fluctuations that can recur over months, weeks, days, or
even hours
17. Through associative (correlation) analysis, we measure
the effects of mutual dependence in values of an item
series
18. An associative model with a single explanatory variable
is called a simple regression model. Multiple regression
refers to a model with one dependent and two or more
explanatory variables
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Multiple Variable Associative Forecast
Four Step Process
1. Establish the dependent (y) and independent (x) variables
Quarter
Interest Rates (x1 )
1
2
3
4
5
6
7
8
4.50
3.60
4.00
3.40
2.90
2.00
2.60
2.80
25.8
Totals
Number of
Sales (US$000,000)
Housing Starts
(y)
(0,000 units) (x2 )
1
3
2
3
4
6
5
4
28
2.0
3.0
2.4
3.1
3.7
4.5
4.0
3.5
26.2
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Multiple Variable Associative Forecast (cont.)
2. Use Excel to calculate the sales, interest rate, and
number of housing starts coefficients
SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
0.995315585
0.990653114
0.986914359
0.094280904
8
ANOVA
df
2
5
7
SS
4.710555556
0.044444444
4.755
Coefficients
3.144444444
-0.333333333
0.344444444
Standard Error
1.714808415
0.344265186
0.173561104
Regression
Residual
Total
Sales
Interest rates
Housing starts
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Multiple Variable Associative Forecast (cont.)
3. Determine forecast options
Forecast Options
Interest Rates
Opt1
2.3
Housing Starts
Opt1
5.0
Opt 2
2.6
Opt 3
3.0
Opt 4
3.5
Opt 2
4.8
Opt 3
4.2
Opt 4
3.5
4. Select associative options and determine forecast
Sales
Interest rates
Housing starts
Coefficients
3.144444444
-0.333333333
0.344444444
Forecast
Option
Opt 1
Opt 2
Opt 3
Opt 4
Sales Forecast
4.10
3.93
3.59
3.18
3.144 + (-0.333 x 2.3) + (0.344 x 5.0) = 4.10
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Principles of Forecasting Management
19. Forecasts are most useful when accompanied by a
method for determining forecast error
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Tools for Forecast Error Detection
•
•
•
•
•
•
•
Forecast error
Absolute percent of error (APE)
Mean absolute deviation (MAD)
Standard deviation (SD)
Bias
Mean Absolute Percent Error (MAPE)
Tracking signal
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Tools for Forecast Error – Analysis
Period Demand Forecast
1
2
3
4
5
6
7
8
9
10
Avg.
1,000
1,100
1,200
1,050
900
1,200
900
800
1,250
1,100
1,029
1,100
1,117
1,050
1,050
1,000
967
983
1,038
Forecast Absolute
Error (1)
Error
-50
-217
150
-150
-200
283
117
50.00
216.67
150.00
150.00
200.00
283.33
116.67
Avg Bias
Total Bias
1. FE = D – F
2. Bias = ∑(D – F) / n
3. MAD = ∑|D – F| / n
Bias (2)
-50.00
-133.33
33.33
-50.00
-50.00
38.89
9.52
-28.80
-201.59
MAD (3)
50.00
133.33
138.89
141.67
153.33
175.00
166.67
APE (4)
4.76%
24.07%
12.50%
16.67%
25.00%
22.67%
10.61%
Avg MAPE
MAPE (5)
4.76%
14.42%
13.78%
14.50%
16.60%
17.61%
16.61%
14.04%
TS (6)
-1.00
-2.00
-0.84
-1.88
-3.04
-1.05
-0.40
4. APE = |D – F| / D
5. MAPE = ∑|D – F/ D| / n
6. TS = ∑(D – F) / MAD
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Principles of Forecasting Management
19. Forecasts are most useful when accompanied by a
method for determining forecast error
20. Forecast error is a measure of forecast accuracy. Fitting
error is a measure of model adequacy. It is important to
distinguish between forecast errors and fitting errors
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Analyzing Forecast Fit
Period
Demand
1
2
3
4
5
6
7
8
9
10
11
110
78
80
122
85
131
120
79
75
120
Average
94.00
79.00
101.00
103.50
108.00
125.50
99.50
77.00
97.50
3 Period
Year-to-Date 3 Period
Exponential
Weighted
Smoothing
Average
Average
Average
110
110.00
110.00
94.00
94.00
89.33
89.33
86.00
87.00
97.50
93.33
98.22
104.50
95.00
95.67
96.22
94.75
101.00
112.67
113.67
112.88
103.71
112.00
115.89
116.44
100.63
110.00
104.22
97.72
97.78
91.33
86.33
86.36
100.00
91.33
95.89
103.18
∑Avg / n
Absoulute error
Period
Average
MAD
Y-to-D avg
1
|D
|D – YtD| 32.00
2 – Avg|
3
14.00
14.00
14.00
4
43.00
28.50
32.67
5
16.00
24.33
12.50
6
25.13
27.50
36.00
7
12.00
22.50
19.00
8
46.50
26.50
24.71
9
24.50
26.21
25.63
10
43.00
28.31
22.22
86.36
Avgerage
106.25
25.93
Per 4:10
MAD
32.00
23.00
26.22
22.79
25.43
24.36
24.41
24.56
24.30
24.58
3 Per avg
32.67
8.33
35.33
7.33
33.00
35.00
28.67
90.17
MAD
32.67
20.50
25.44
20.92
23.33
25.28
25.76
24.84
Alpha (α )
0.50
3 Per w/avg
36.00
13.22
34.78
6.33
36.89
29.22
33.67
95.06
MAD
36.00
24.61
28.00
22.58
25.44
26.07
27.16
27.12
Expon
32.00
14.00
35.00
19.50
36.25
7.13
37.44
22.72
33.64
95.84
MAD
32.00
23.00
27.00
25.13
27.35
23.98
25.90
25.50
26.41
25.90
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Principles of Forecasting Management
19. Forecasts are most useful when accompanied by a
method for determining forecast error
20. Forecast error is a measure of forecast accuracy. Fitting
error is a measure of model adequacy. It is important to
distinguish between forecast errors and fitting errors
21. The use of multiple methods to arrive at the final
forecast is highly recommended
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Visual - 57
© APICS CONFIDENTIAL AND PROPRIETARY
Principles of Forecasting Management
19. Forecasts are most useful when accompanied by a
method for determining forecast error
20. Forecast error is a measure of forecast accuracy. Fitting
error is a measure of model adequacy. It is important to
distinguish between forecast errors and fitting errors
21. The use of multiple methods to arrive at the final
forecast is highly recommended
22. Create an integrated forecasting process that
encourages communication, coordination, and
collaboration among marketing sales, product
management, production, distribution, finance, and
forecasting organizations
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Why Forecasts Fail
Management
Involvement
Integrated forecasting is needed at the top
management, operations management, and
operations execution levels of the business
OverSophistication
and Cost
Forecasting systems that are too difficult to
understand or cost too much to operate are
doomed to failure
Compatibility
There is a lack of compatibility between the
forecasting system and the ability of the using
organization to understand it
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Why Forecasts Fail (cont.)
Data Accuracy
The data used for the forecast must be
accurate, timely, complete, and easy to access
Unnecessary
Items
Often forecasts are developed for items that
should not be forecasted, for example
dependent demand item usage
Lack of
Management
Control
Forecasters must be diligent in monitoring the
forecast to ascertain the degree of error, when
the forecast should be altered, and what
parameters should be used to guide forecast
adjustment
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Principles of Forecasting Management
23. The philosophy of forecast places primary emphasis on
the forecasting process rather than on the numbers. If
the forecaster has meticulously followed a proper
forecasting process, the end result will be as good a
forecast as can be delivered
“As far as the laws of
mathematics refer to reality,
they are not certain, and as far
as they are certain, they do not
refer to reality”
- Einstein
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Thank you for
attending and
good forecasting!!
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Survey
http://tinyurl.com/lr3pjct
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