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BUSINESS FORECASTING & PLANNING. IBF Online Seminar – Fundamentals of Demand Planning & Forecasting Mark Lawless IBF Senior Consultant [email protected]. Biography. - PowerPoint PPT Presentation
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BUSINESSFORECASTING & PLANNING
IBF Online Seminar – Fundamentals of Demand Planning & Forecasting
Mark Lawless
IBF Senior Consultant
www.ibf.org
Biography
Mark Lawless is a Senior Consultant for the Institute of Business Forecasting and the founder and Managing Principal in Marlaw Business Advisory Services. He has extensive experience in forecasting, planning, business process development, and business management. Mark has been associated with the Institute of Business Forecasting since its inception years in the 1980’s. He has held a number of C-level positions, including Chief Planning Officer, Chief Financial Officer, and Chief Operating Officer. During his company affiliations in wide range industries, he has been responsible for:
Development of planning and forecasting processesDevelopment of forecasting modelsSelection and implementation of supporting automated systemsPresentation of forecasts and plans to all levels of management and to major investors and analyst groupsRemediation and continuous improvement of forecasting and planning processes and related forecasting models
During his association with the Institute of Business Forecasting, he has published articles in the Journal of Business Forecasting and served as an editorial advisor to the publication. He has made a variety of presentations at IBF Conferences on topics of forecasting. He has served as IBF conference chairperson, conference keynote speaker, and moderator for IBF topical groups at IBF conferences. He participated in the development of the IBF Forecaster Certification Program, and has developed and run tutorials to prepare those taking the certification examination. He has been a key participant in the IBF In-House Training Program since its inception.
Mark holds an undergraduate degree in Economics, and graduate degrees in Economics, Finance, and Accounting. He is an alumnus of Southern Illinois University (Edwardsville), Washington University (St. Louis), Boston College, and Bentley University. He is a member of Financial Executives International (FEI).
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Demand Forecasting & Planning Basics
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Demand Forecasting and Demand Planning is a Journey!!
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The Journey
Determine the Destination
Evaluate the Alternative Routes
Plan the Trip – Length, Time, Needs, Equipment, etc.
Organize and Prepare for Risks and Contingencies
Be Prepared – Adapt to Changing Conditions
Reach and Explore the Destination
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ProcessMethods& Models
SystemsCommunication
& Reports
Goals &Objectives
People
Business Models
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DATA
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How accurate are your demand plans and demand forecasts? How accurate should they be?
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Goal: To answer questions like the following regarding demand forecast accuracy……
What level of accuracy is expected by company management? Is it reasonable?
How do you measure accuracy? How accurate do your forecasts need to be? What are the limits of forecast accuracy? What affects the accuracy (and the error) of DP forecasts? What are the effects of accuracy or of error? What steps can be taken to improve forecast accuracy? What steps can be taken to improve accuracy expectations of
users and management?
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Difference Between Forecasting and Planning?
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Plans are built upon forecasts………
Demand Plann. a desired outcome at a future time based upon targets and goals
Demand Forecast n. an unbiased prediction or estimate of an actual Demand value at a future time
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Why be so concerned about about demand forecast accuracy??
Downstream effects on business planning and business mandagement processes
Impact on important business decisions
Potential impact on business resources and business performance – operational and financial
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What is the environment in which the forecast is being created?
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ForecastProcess
Methods& Models
ForecastingSystems
Key BusinessAssumptions
CompanyGoals &
Objectives
Domain Experts
Business Structure
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DATA
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Industry Economy
Technology Government & Regulation
COMPANY
Customers & Consumers
Competitors
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The demand plan is only as good as the forecast upon which
it is based
The demand forecast is a foundation element of the demand plan! And other downstream plans……
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Related Planning Processes
DemandPlan
Sales & Operations Planning
(S&OP)
FinancialPlanning &Budgeting
Inventory & Customer Service
Planning
Demand Forecast
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FormulateProblem
ObtainRelevant
Info.
DataAnalysis &Cleaning
Create & Issue
Forecasts
Internal Information
Sources
External Information
Sources
Methods Evaluation
and Testing
MethodSelection
Isolate andEvaluate
Error
CorrectSources of
Error
Demand Forecast Development Structure
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Assumptions
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What is the underlying work flow and business model being assumed? Channels of distribution?
CUSTOMER WAREHOUSE
RETAIL STORESHOPPING HOUSEHOLD
CUSTOMERORDERS
CUSTOMER HQ/BUYER
aka
CONSUMPTION
SELL-THROUGH
MANUFACTURER SHIPMENTS
CONSUMER
TAKEAWAY
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How Good Are the Assumptions of the Demand Forecast?
Marketing Programs
Sales Programs
Pricing
Product Relationships
Competitor Actions
Economic and Industry Environment
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Operations &Supply Chain
Marketing & Sales
FinanceKey
Management
Seek Reliable, Unbiased, Domain Experts!!
Who is participating in the forecasting? Bias??
DemandForecasts &
Plans
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Assess the potential sources of bias……
Risks that people may perceive Natural tendencies and behaviors Expected use of the information Relationship with You and others Incentives and other drivers
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How do perspectives vary that may create bias?
Supply Chain
Marketing
Sales
Finance & Accounting
General Management
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Supply Chain View
Demand by item/SKU
Inventory
Requirements and Costs– Material– Labor
Production efficiency and capacity
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Marketing View
Category/market size
Types of consumers/end users
Target market characteristics
Price trends
New product development
Competitive factors
Seasonal factors
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Sales View
Customer service & satisfaction
Customer trends
Geographic differences
Price trends
Competitive factors
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Finance & Accounting View
Accounting
Finance
Treasury
Budgeting
Capital Investment
Monetization of Business Actions and Plans
Shareholder and Lender Relations
Business Capitalization
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General Management View
Business expansion
Business investments
Merger/acquisition transactions
Strategic actions
Competitive positioning
Economic conditions
Financial market conditions
Business capitalization
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Natural BIAS of each function depends upon their responsibilities and the expected use of the info….
DEMAND SUPPLY Assess risk of “missing” demand vs. “missing” supply
Function Because
Marketing May call high Want idea to go forward
Sales May call high Want to ensure product available for their customer
May call low Then can exceed quota if based on forecast
Operations May call high Do not want to be out of stock
May call low Do not want to have too much inventory, to “compensate” forMarketing/Sales optimism
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Some products are harder to forecast and plan than others…… Products with highly volatile demand
New products
Highly promoted products
Products with many substitute products available– Internal– External
Products with a short life cycle
Products with intermittent demand
And… Some products are not reliably forecastable!!
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An Approach: Identify degree of “forecastability” for products using Coefficient of Variation
Ensure that outliers and missing data are adjusted for Ensure that trend, cyclicality, and seasonality are isolated
from the data Choose a threshold value for COV, usually a value
between .7 and 1.0 Identify those products with an adjusted COV > Chosen
Threshold If COV > Chosen Threshold, separate for other forecasting
approaches or for hedging strategies
Coefficient of Variation (COV)
COV = Standard Deviation/Average Value
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Data Analysis & Data Cleaning
Data Plot
Central Tendency - Mean
Variation – Volatility of Demand
Systematic Variation– Trend– Seasonality– Cyclicality
Data Issues for Data Cleaning
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Data Cleaning Issues
Missing Values
Outliers
Data Shifts
Structural changes
Non-Linear Series
Promotional, Marketing, and Sales Program Synchronization
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Types of Models
Decision trees Sales force estimates Executive opinion Surveys & market research
Naïve Trend Moving Average Filter Smoothing Decomposition ARMA/ARIMA
Regression Econometrics Neural network
Model Families
Quantitative71%
Qualitative/Judgmental17%
Time Series53%
Cause & Effect17%
Other12%
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Source: IBF Survey 2010
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Univariate Time Series Model Elements
N = random noise
component
S = seasonal
component
T = Trend component
L = level component
Y = time series
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Steps in the Model Selection Process
Specification of the Model
Estimation
Verification – Ex Post Forecasting- Ex Post Error Evaluation
Forecasting with the Model
Consider models that support the underlying business
Match the method with the data pattern
Ex-ante forecasting Error measurement,
analysis and model improvement
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Error Measurements
Error Absolute Error Mean Error (ME) Mean Absolute Deviation (MAD) Mean Percent Error (MPE) Mean Absolute Percent Error (MAPE) Weighted Mean Absolute Percent
Error (WMAPE)
Squared Error (SE) Mean Squared Error (MSE) Root Mean Squared Error (RMSE)
Used primarily to evaluate models
Used to evaluate forecasts and models
Error Measures Squared Error Measures
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Forecast Error Formulas
nsObservatioofNumber
|Forecast)(Actual| MAD
nsObservatioofNumber
Forecast)(Actual 2 MSE
nsObservatioofNumberActual
ForecastActual
MPE100
)(
nsObservatioofNumberMAPE
]Actual
|Forecast) -(Actual|[ 100
WeightofTotalSum
(Weight)}(100))Actual
|Forecast) -(Actual|{(
WMAPE
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Error increases with product detail and time horizon
All IndustriesMean Absolute Percent Error
(MAPE)
Source: IBF 2010 Survey
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What Are the Common Sources of Error?
Process Problems
Biased Estimates
Data Problems
Lack of or Poor Data Cleaning
Quality and Reliability of Assumptions Made
Poor Method Selection
Poorly Specified Models
Inherent Demand Volatility
Excessive Promotional Activity
Unstable Business, Economic, Competitor, and Political Environment
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Things To Know About Forecasting Errors
Quicker you can adjust operations to react to error…
Larger error can be tolerated
Shorter supply chain length…
Shorter lead time…
Larger absolute level of forecast… Larger inventory needed
Larger error…Larger safety stock
needed Larger standard deviation of error…
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Cost of Error – Reduced Net Cash Flow
Inventory carrying costs– Product costs– Storage costs– Handling & shipping costs– Interest expense on borrowed funds
Excess & obsolete inventory exposure
Greater mark-downs & discounts
Operating efficiency reduction
Revenue loss/gross profit loss
Reduction in customer satisfaction/repeat purchases
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Data Management & Data Cleaning
Topic 2
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Data Issues
Missing data
Outliers
Shifts, structural changes
Changing factors– Trend– Seasonal– Cyclical
Topic 2
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Example: Missing Value
Month Units (MM)
Jan 80
Feb 86
Mar 95
Apr 103
May
Jun 121
Jul 125
Zurich Trading Co.Monthly Sales (MM units)
0
20
40
60
80
100
120
140
Jan Feb Mar Apr May Jun Jul
Topic 2
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Examples: Outliers
Zurich Trading Co.Monthly Sales (MM units)
0
20
40
60
80
100
120
140
Jan Feb Mar Apr May Jun Jul
Bombay Trading Co.Monthly Sales (MM units)
0
100
200
300
400
500
600
Jan Feb Mar Apr May Jun Jul
Topic 2
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Mohawk Dept. StoresMonthly Sales ($MM)
$0
$500
$1,000
$1,500
$2,000
$2,500
$3,000
$3,500
$4,000
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
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Example: Structural Shift
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Example: Change in Seasonality
25% 24% 23% 23%
22% 21% 21% 21%
30% 31% 32% 34%
24% 24% 23% 22%
0%
50%
100%
2002 2003 2004 2005
Q4
Q3
Q2
Q1
Company XPercent of Sales by Quarter 2002-2005
Topic 2
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Data Analysis Checklist
How much data do you have?
How reliable is the data?
Are there data definition changes?
Are the data aggregated? Disaggregated?
Do the time periods synchronize and line-up?
Are we missing any values?
Are there outliers? Do we know why?
Are there structural shifts in the data?
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What patterns does the data exhibit?
– Randomness & volatility
– Trend: linear, non-linear
– Seasonality (weekly, monthly, quarterly)
– Cyclicality
Are there events and company programs affecting the data?
What phase of the product life cycle is reflected in the data?
Are the data normally distributed? Are they distributed otherwise?
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Normally Distributed Data Series
Topic 2
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Time Series Models – Univariate Forecasting
Pattern Forecasting in Stable Environments
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Types of Models
Decision trees Sales force estimates Executive opinion Surveys & market research
Naïve Trend Moving Average Filter Smoothing Decomposition ARMA/ARIMA
Regression Econometrics Neural network
Model Families
Quantitative71%
Qualitative/Judgmental17%
Time Series53%
Cause & Effect17%
Other12%
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Source: IBF Survey 2010
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Steps in the Model Selection Process
Specification of the Model
Estimation
Verification – Ex Post Forecasting- Ex Post Error Evaluation
Forecasting with the Model
Consider models that support the underlying business
Match the method with the data pattern
Ex-ante forecasting Error measurement,
analysis and model improvement
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Where time series models are most appropriate
Past pattern is expected to continue– Stable environment– Longer product lifecycles
Relatively short forecast horizon– Inventory management– Demand planning– Sales & operations planning– Annual budgeting
Limited information– Data available only for variable to be forecasted
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Time Series Implicit Assumption: STABILITY
Outside conditions and activities
Internal conditions and programs
Competitors and competitor actions
Products and substitute products
Relative pricing
Relationship to outside factors and market conditions
Company policy for sales, operations, pricing, promotion, advertising, etc.
Competitor policy for sales, operations, pricing, promotion, advertising, etc.
Recurrence of prior conditions and patterns of demand
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Univariate Time Series Model Elements
N = random noise
component
S = seasonal
component
T = Trend component
L = level component
Y = time series
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Random Noise
A variety of short-term unpredictable forces at work
Variance after accounting for trend, seasonality, cyclicality and known events
“Everything else” or inherent error
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Naïve Model
Next forecast value = previously observed actual value– Stable Environment– Slow Rate of Change (if any)
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Naïve ModelExample: Monthly Gasoline Prices
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Why Use The Naïve Model?
It’s safe. It will never forecast a value that has not happened before.
It is useful for comparing the quality of other forecasting models. If forecast error of another method is higher than the naïve model, it’s not very good.
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Averaging Methods
Averaging methods improve on “Naïve”– Changes occur from one period to the next
Changes – steps between periods – will be averaged
Changes can be unit steps or percentage steps
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Average Level Change
Use when the change from one period to the next is consistent – but not uniform – in magnitude
– Derive the series of changes from one period to the next– Average the change series over the historical period– Add the change to your latest actual period to get the next forecast
Ft+1 = Yt + average
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Average Level ChangePeriod Sales of City Stores
(Mil. of $)
Level Change
(Mil. of $)
1
2
3
4
5
6
7
8
9
10
Total
356.0
371.6
373.7
380.4
364.8
373.1
367.4
373.4
374.1
380.1
--
15.6
2.1
6.7
-15.6
8.3
-5.7
5.9
0.8
6.0
24.1
%7.2or027.5.393
8.3825.393Error%
.mil5.393$Actual
.mil8.382$$2.7 + $380.1 = Y
.mil $2.7 =9
$24.1 = Change Average
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Average Percent Change
Use when the percentage change from one period to the next is consistent – but not uniform – in magnitude
– Derive the series of percent changes from one period to the next – Average the change series over the historical period– Apply the percentage to your latest actual period, to forecast the
next
Ft+1 = Yt + average %
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Average % ChangePeriod Sales of City
Stores
(Mil. of $)
Level Change
(Mil. of $)
% Change
1
2
3
4
5
6
7
8
9
10
Total
356.0
371.6
373.7
380.4
364.8
373.1
367.4
373.4
374.1
380.1
--
15.6
2.1
6.7
-15.6
8.3
-5.7
5.9
0.8
6.0
24.1
----
4.38
0.57
1.79
-4.10
2.28
-1.53
1.61
0.21
1.60
6.81
.mil0.383$0076.1.380$)1.380($Y
%76.09
81.6Change%Average
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Weighted Average Percent Change
Use when the percentage steps, throughout history, are not equally representative
Derive the series of % changes from one period to the next Apply weights that value your periods differently Add the % change series over the historical period to get a weighted
sum of history Add the weights to get a sum of the weights Divide the weighted % sum of history by the sum of the weights Apply the resulting percent change to your latest period to get the
next
F = (WY)/W
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Weighted Average Percent ChangePeriod
(1)Sales of K-
Mart Stores (Mil. of $)
(2)
Level Change
(Mil. of $) (3)
% Change(Y)
(4)
Weight(W)
(5)
Col. 4 Col.5(YW)
(6)
123456789
10Total
3,1013,8374,6335,5366,7988,3829,94111,69612,73114,204
----736796903
1,2621,5841,5591,7551,0351,473
----23.7320.7519.4922.8023.3018.6017.658.85
11.57
----123456789
45W
----23.7341.5058.4791.20116.50111.60123.5570.80104.13741.48YW
Weighted Average % Change = (741.48) / (45) = 16.48%
Ŷ11 = (14,204) + (14,204) × (.1648) = $16,545 mil. ActualActual = $16,527 mil.= $16,527 mil.% Error =(16,527 -16,545) / (16,527) -16,545) / (16,527) = 0.1%= 0.1%
6767
Y11
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Moving Average Model
Easy to calculate– Select number of periods– Apply to actual
Assimilates actual experience
Absorbs recent change
Smooths forecast in face of random variation
Safe – never forecasts outside historical values
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Single Moving Average
Use when unit steps are best represented by a limited number of recent changes
Derive series of changes from one period to the next Choose number (n) of periods that you consider relevant Make the series of n-period averages throughout history Add the latest n-period change to the latest period value to forecast the
next
Ft+1 = Yt + avg for latest n periods
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Moving Average ModelExample: 3-Month Moving Average Forecast of Gasoline Prices
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Single Moving Average Level Change
Period Sales of Jewel Company
(Mil. of $)
Level Change
(Mil. of $)
3-Period Moving Total of
Changes
(Mil. of $)
3-Period Moving Avg. of Changes
(Mil. of $)
1
2
3
4
5
6
7
8
9
10
2009.3
2219.6
2598.9
2817.8
2981.4
3277.7
3516.4
3764.3
4267.9
5107.6
---
210.3
379.3
218.9
163.6
296.3
238.7
247.9
503.6
839.7
---
---
---
808.5
761.8
678.8
698.6
782.9
990.2
1591.2
---
---
---
269.5
253.9
226.3
232.9
261.0
330.1
530.4
%2.or0021.5650
56385650Error%
.mil 50$56 Actual
5638$.mil $530.4 + $5107.6 = Y11
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Single Moving Average %
Use when percentage steps are best represented by a limited number of recent changes
Derive series of percent changes from one period to the next Choose number (n) of periods that you consider relevant Make the series of n-period averages throughout history Apply the latest n-period % change to the latest period value to forecast
the next
Ft+1 = Yt + avg % for latest n periods
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Single Moving Average % Change
Period Sales of Jewel
Company
(Mil. of $)
Level Change
(Mil. of $)
%
Change
3-Period Moving Total
of % Changes
3-Period Moving Avg.
of % Changes
1
2
3
4
5
6
7
8
9
10
2009.3
2219.6
2598.9
2817.8
2981.4
3277.7
3516.4
3764.3
4267.9
5107.6
---
210.3
379.3
218.9
163.6
296.3
238.7
247.9
503.6
839.7
---
10.47
17.09
8.42
5.81
9.94
7.28
7.05
13.38
19.67
---
---
---
35.98
31.32
24.17
23.03
24.27
27.71
40.10
---
---
---
11.99
10.44
8.06
7.68
8.09
9.24
13.37
%5.2or025.5650
5.57905650Error%
.mil5650$Actual
.mil $5790.5 = .1337)0 ($5107.6 + $5107.6 = Y11
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Double Moving Average %Background
This is a lot easier than it sounds: Take a % moving average just like before Rather than quit, “recycle” the resulting series as though it were the
original Then we’ll have a moving average of a moving average
Ft+1 = Yt + average n-period average n-period %
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Double Moving Average %The Method
Use when extra smoothing is needed for erratic changes or to firm up a cycle
Derive series of percent changes from one period to the next
Choose number (n) of periods that you consider relevant
Make the series of n-period averages throughout history
Now make another series of n-period averages…of the n-period averages!
Apply the latest n-period n-period % change to the latest period value to forecast the next
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Double Moving Average Level Change
Period(1)
Sales of Jewel Co.(Mil. of $)
(2)
Level Change (Mil. of $)
(3)
3-Period Mov. Total of
Changes(Mil. of $)
(4)
3-Period Mov. Avg. of
Changes(Mil. of $)
(5)
3- Period Double Mov.
Total of Changes(Mil. of $)
(6)
3- Period Double
Moving Avg. of Changes(Mil. of $)
(7)
123456789
10
2009.32219.62598.92817.82981.43277.73516.43764.34267.95107.6
---210.3379.3218.9163.6296.3238.7247.9503.6839.7
---------
808.5 761.8 678.8 698.6 782.9 990.21591.2
---------
269.5253.9226.3232.9261.0330.1530.4
---------------
749.7 713.1 720.2 824.01121.5
---------------
249.9237.7240.1274.7373.8
Ŷ11 = $5107.6 +$373.8 = $5,481.4 mil.
Actual = $5650 mil.
% Error = ($5650 - $5,481.4) / ($5650) = 3%
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Double Moving Average % Change
Period
(1)
Sales of Jewel Co.
$Mil.
(2)
Level Change
$Mil.
(3)
% Change
(4)
3- Period Mov. Total
of % Changes
(5)
3- Period Mov. Avg.
of % Changes
(6)
3- Period Double
Mov. Total of %
Changes
(7)
3- Period Double
Mov. Avg. of %
Changes
(8)
123456789
10
2009.32219.62598.92817.82981.43277.73516.43764.34267.95107.6
---210.3379.3218.9163.6296.3238.7247.9503.6839.7
---10.4717.09 8.42 5.81 9.94 7.28 7.0513.3819.67
---------
35.9831.3224.1723.0324.2727.7140.10
---------
11.9910.44 8.06 7.68 8.09 9.2413.37
---------------30.4926.1823.8325.0130.70
---------------
10.16 8.73 7.94 8.3410.23
Ŷ11 = $5107.6 +$5107.6 × .1023) = $5630.1 mil.
Actual = $5650
% Error = ($5650 - $5630.1) / ($5650) = 0.4%
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Simple Trend
Use when the absolute change from one period to the next is consistent -increasing or decreasing in approximately a straight line– You can calculate this or…– Let Excel do it for you!
• Graph the historic actuals• Right click, “Add Trendline”
- Several trend lines available- Check “Display Equation on chart” and “Display R-squared value
on chart”
Ft+1 = a + bYt
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Seasonal Index100 = average month
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Classical Decomposition Approach
Calculate seasonality of series
De-seasonalize raw data
Apply forecasting method
Re-seasonalize forecast
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Exponential Smoothing
Widely used
Easy to calculate
Limited data required
Assumes random variation around a stable level
Expandable to trend model and to seasonal model
Automatically adjusts for the error experienced in the current period
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3 Smoothing Parameters
Level (Randomness) – Simple Model– Assumes variation around a level– α alpha
Trend – Holt’s Model– Assumes linear trend– β beta
Seasonality – Winter’s Model– Assumes recurring pattern due to seasonal factors– γ gamma
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Single Exponential Smoothing
Ft+1=αAt+(1- α)Ft
Where:
• Ft+1 = forecasted value for next period
• α = the smoothing constant (0 ≤ α ≤1)
• At = actual value of time series now (in period t)
• Ft = forecasted value for time period t
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Automatic adjustment for error in exponential smoothing
Ft+1 = α Xt + (1- α) Ft …. (1)
This can be re-written as:= α Xt + Ft - α Ft …. (2)
or = Ft + α Xt - α Ft …. (3)
or = Ft + α (Xt - Ft) …. (4)
The difference between Xt – Ft (actual - forecast) is forecast error. If we label Xt - Ft as “et” (forecast error of the current period), then: Xt - Ft = et .… (5)
The equation (4) becomes: Ft+1= Ft + α et
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Exponential Smoothing: Different Alpha Values
Moving Averages give equal weight to past values, Smoothing gives more weight to recent observations.
= 0.1 = 0.1 = 0.9 = 0.9
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Single Exponential SmoothingRule of Thumb
The closer to 1 the value of alpha, the more strongly the forecast depends upon recent values
If the value of alpha equals 1, it is the same outcome as the naïve model!!
In actual practice, alpha values from 0.05 to 0.30 work very well in most Single smoothing models. If a value of greater than 0.30 gives the best fit, this usually indicates that another forecasting technique would
work even better.
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Single Exponential Smoothing ModelExample: Forecast of Monthly Gasoline Prices, alpha = 0.3
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Sales Ratio Methods (Average and Cum)
Use when each month accounts for a consistent amount of annual sales (related to seasonality).
Also use to allocate annual forecast to months.
• Determine each month’s contribution (monthly or cumulative) to annual sales from historical actuals
• Apply monthly factors to remaining months of the year to get annual estimate
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Month
2003Actual Sales
($MM)2003
Sales Ratio
2004
Actual Sales($MM)
2004
Sales RatioAvg Sales
Ratio
January
February
March
April
May
June
July
August
September
October
November
December
Total
194,529
180,053
193,489
178,690
175,083
245,968
203,194
233,556
252,654
243,747
295,889
240,746
2,637,598
0.074
0.068
0.073
0.068
0.066
0.093
0.077
0.089
0.096
0.092
0.112
0.091
1.000
204,011
197,708
186,805
173,225
183,138
273,495
186,384
225,785
259,797
259,425
265,051
244,524
2,659,348
0.077
0.074
0.070
0.065
0.069
0.103
0.070
0.085
0.098
0.098
0.100
0.092
1.000
0.075
0.071
0.072
0.066
0.068
0.098
0.074
0.087
0.097
0.095
0.106
0.092
1.000
Average Sales Ratio: Example
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Month
2005Actual Sales
($MM)Avg. Sales Ratio of 2003 & 2004
Projected Annual Sales of 2005 ($MM) % Error
JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecemberTotal
198,947185,557177,166179,221168,905226,617231,820241,445214,259240,701256,150244,156
2,564,944
0.0750.0710.0720.0660.0680.0980.0740.0870.0970.0950.1060.0921.000
2,652,6272,613,4792,460,6392,715,4702,483,8972,312,4183,132,7032,775,2302,208,8562,533,6952,416,5092,653,870
-3.42-1.894.07-5.873.169.85
-22.14-8.2013.881.225.79-3.47
Sales of 2005 based on January sales = (198,947/ 0.075) =$2,652,627 MM
Actual sales = $2,564,944 MM
% Error = (2,564,944 – 2,652,627) / (2,564,944) = -3.42%
Average Sales Ratio: ExampleProjected annual sales based on average sales ratio of 2 previous years
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Month
2003
Sales Ratio
2003
Cum Sales Ratio
2004
Sales Ratio
2004
Cum Sales Ratio
Avg. Cum. Sales Ratio Based on
2003 & 2004 Data
January
February
March
April
May
June
July
August
September
October
November
December
Total
0.074
0.068
0.073
0.068
0.066
0.093
0.077
0.089
0.096
0.092
0.112
0.091
1.000
0.074
0.142
0.215
0.283
0.350
0.443
0.520
0.608
0.704
0.797
0.909
1.000
0.077
0.074
0.070
0.065
0.069
0.103
0.070
0.085
0.098
0.098
0.100
0.092
1.000
0.077
0.151
0.221
0.286
0.355
0.458
0.528
0.613
0.711
0.808
0.908
1.000
0.075
0.147
0.218
0.285
0.352
0.450
0.524
0.611
0.707
0.802
0.908
1.000
Cumulative Sales Ratio: Example
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Month 2005
Sales
2005Cum. Sales
($MM)
Avg. Cum. Sales Ratio
based on 2003 & 2004
Projected Annual Sales
($MM) % Error
JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecemberTotal
198,947185,557177,166179,221168,905226,617231,820241,445214,259240,701256,150244,156
2,564,944
198,947 384,504 561,670 740,891 909,796
1,136,4131,368,2331,609,6781,823,9372,064,6382,320,7882,564,944
0.0750.1470.2180.2850.3520.4500.5240.6110.7070.8020.9081.000
2,652,6272,615,6732,576,4682,599,6182,584,6482,525,3622,611,1322,634,4982,579,8262,574,3622,555,934
-3.42-1.98-0.45-1.35-0.771.54-1.80-2.71-0.58-0.370.35
Annual Forecast of 2005 based on cum sales through Feb-05 = (384,504/0.147) = $2,615,673Actual = $2,564,944% Error = 2,564,944 -2,615,673) / (2,564,944)
= -1.98%
Cumulative Sales Ratio: ExampleProjected annual sales based on cumulative sales ratio of 2 previous years
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Product Code Description
Rolling 12 Month Actual Sales(MM units)
Ratio of Memberto Family
Forecast for March
(MM units)
52005200-15200-25200-35200-45200-5
BrandSKU 1SKU 2SKU 3SKU 4SKU 5
139 35 10 9 64 21
….0.2520.0720.0650.4600.151
25.0 6.3 1.8 1.611.5 3.8
If Brand forecast for month of March = 25 MM,
Then:
Forecast for SKU 1 = 25 x 0.252 = 6.3 MM
Family Member Forecasting
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Types of Models
Decision trees Sales force estimates Executive opinion Surveys & market research
Naïve Trend Moving Average Filter Smoothing Decomposition ARMA/ARIMA
Regression Econometrics Neural network
Model Families
Quantitative71%
Qualitative/Judgmental17%
Time Series53%
Cause & Effect17%
Other12%
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Source: IBF Survey 2010
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BUSINESSFORECASTING & PLANNING
IBF Online Seminar – Fundamentals of Demand Planning & Forecasting
Mark Lawless
IBF Senior Consultant
