copyright © 2007, sas institute inc. all rights reserved. demystifying forecasting: the future of...
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Copyright © 2007, SAS Institute Inc. All rights reserved.
Demystifying Forecasting: The Future of Demand-Driven Forecasting for S&OP Charlie Chase Business Enablement Manager Manufacturing & Supply Chain Global Practice
Friday, April 21, 2023
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Planets are now aligned to facilitate Demand-Driven Forecasting…
Data Access/Storage
Data Processing
Forecasting Methods
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Over the past decade… Data availability and quality have improved substantially
along with the ability store it
Demand for -- and understanding of -- Predictive Analytics is accelerating rapidly across all industry verticals
Companies are leveraging predictive analytics to: • Uncover patterns in consumer behavior, • Measure the effectiveness of marketing investment strategies,
and • Optimize financial performance
Everyone is trying to move toward a demand pull forecasting strategy
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Key changes due to Supply Chain initiatives
Sales Forecasting is the primary driver of the Integrated Supply Chain Management Process
Current Sales Forecasting Methods and Applications are changing • Causal techniques are becoming more widely used
− Predictive Modeling & Simulation
Everyone is under pressure to integrate demand-based forecasts with supply-based forecasts • To improve forecast accuracy
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Still Challenges Ahead
Organizations struggle to analyze and make practical use of the mass of data collected and stored
Others are trying to synchronize their data across their technology architectures
All are looking for solutions that: • Provide actionable insight
• To make better decisions
• Improve corporate performance
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Cultural Challenges
Convincing executive management that statistical forecasts • Over time tend to out perform judgmental forecasts
Forecasting is a collaborative effort • Between statisticians and domain knowledge experts
Excel spreadsheets can no longer support the forecasting process • Task is too large (scalability issues)
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Question… How can forecasters do a better job evaluating
information/data through the use of predictive analytics to improve forecast accuracy?• While dealing with the cultural challenges of change
management.
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There are four key forecasting challenges
Process
Methods
Systems
Performance Metrics
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Process Challenges
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Generally speaking the word forecast…
There is only one forecast, a Sales forecast.
Most companies have a hard time distinguishing the difference between the forecasting process and the planning process…
• Dr. John (Tom) Mentzer, University of Tennessee at KnoxvilleDr. John (Tom) Mentzer, University of Tennessee at Knoxville
Sales ForecastSales Forecast(Unconstrained)(Unconstrained)
Financial PlanFinancial Plan
Production PlanProduction Plan
Marketing PlanMarketing Plan
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Creation of an Unconstrained Demand Forecast
Should be based on: • Statistical analysis of the historical data
• Including cause and effect information/data
− Establishing a hypothesis using domain knowledge
– Not “gut” feeling judgment − Validating or not validating assumptions with data
and analytics
Creating not just a baseline for manual adjustments• But, a “What If” simulation capability
Initiating “Fact-Based” discussions that drive better supply chain decisions
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Simple Judgmental techniques Not science is used exclusively when developing demand
based forecasts
In fact, “Juries of Executive Opinion” − Still most widely used technique across all industries
− This technique is not forecasting, but goal (or target) setting
More proactive “Fact-Based” forecasting is needed• Separate the process of forecasting from that of goal setting
The process needs to be more systematic and objective • Rather than subjective removing personal bias
• Using Domain Knowledge/Directional Consistency
– Rather than, Judgment/Bias
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Forecasting Art or Science?
Forecasting is neither Art of Science!!! • It’s mathematics and domain
knowledge
There is always some element of Domain Knowledge, “not” Judgment in every forecast
Use domain knowledge to create hypothesis • Use analytics to validate or not validate the
hypothesis
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Law of Universal Forecasting… The more people who touch the forecast, the
more inaccurate the forecast, and
The more fact-based (information\data supported) and mathematically derived the forecast, the more accurate the forecast…
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Everyone wants to touch the forecast… But no one wants to be accountable for the results
“Cultural” Issue
Requires change management driven by a “Champion”
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Forecasting Methods Challenges
“Forecasters have fallen short in this area.”
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Forecast = Pattern (s) + Randomness
Theory of Forecasting Methods...
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Forecast = Pattern (s) + Randomness
This simple equation is really saying that when the average pattern of the underlying data has been identified some deviation will occur between the forecasting method applied and the actual occurrence.
That deviation is called “Error”, or unexplainable variance.
Theory of Forecasting Methods...
Copyright © 2007, SAS Institute Inc. All rights reserved.
This simple equation is really saying that when the average pattern of the underlying data has been identified some deviation will occur between the forecasting method applied and the actual occurrence.
That deviation is called “Error”, or unexplainable variance.
Theory of Forecasting Methods...
Forecast = Pattern (s) + RandomnessError/Unexplained
Copyright © 2007, SAS Institute Inc. All rights reserved.
This simple equation is really saying that when the average pattern of the underlying data has been identified some deviation will occur between the forecasting method applied and the actual occurrence.
That deviation is called “Error”, or unexplainable variance.
Maximize Minimize
Theory of Forecasting Methods...
Forecast = Pattern (s) + RandomnessError/Unexplained
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Forecast = Pattern (s) + Randomness
Two Broad mathematical categories 1. Time Series 2. Causal
Theory of Forecasting Methods...
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Trend Seasonality
Cyclical
Forecast = Pattern (s) + RandomnessError/Unexplained
Theory of Forecasting Methods...
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Forecast = Pattern (s) + Randomness
Trend Seasonality
Cyclical
Error/Unexplained
Theory of Forecasting Methods...
Time Series Methods
Exponential Smoothing
Brown’s
Holts
Winter’s
Census X11/12
ARIMA
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Forecast = Pattern + Randomness
Theory of Forecasting Methods...
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Event
Forecast = Pattern + Randomness
Theory of Forecasting Methods...
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Three Basic Types:
1. Point Intervention
2. Step Intervention
3. Ramp-up (or down) Intervention
Forecast = Pattern + Randomness
Copyright © 2007, SAS Institute Inc. All rights reserved.
Forecast = Pattern (s) + RandomnessError
Trend Seasonality
Cyclical
EventsSales Promotions
Marketing Events
Copyright © 2007, SAS Institute Inc. All rights reserved.
Forecast = Pattern (s) + RandomnessError
Trend Seasonality
Cyclical
EventsSales Promotions
Marketing Events Time Series Methods
ARIMA
W/Interventions
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Forecast = Pattern (s) + RandomnessError
Trend Seasonality
Cyclical
EventsSales Promotions
Marketing Events
CausalPrice
AdvertisingCompetitive Activities
Etc.
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Forecast = Pattern (s) + RandomnessError
Trend Seasonality
Cyclical
EventsSales Promotions
Marketing Events
CausalPrice
AdvertisingCompetitive Activities
Etc. Causal Methods
ARIMAX
Dynamic Regression
Unobserved Component Models (UCM)
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Forecast = Pattern (s) + RandomnessError/Unexplained
The Ultimate Algorithm(No Greek Symbols Required)
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Selecting the Appropriate Method Based On Portfolio Management
Winter’s Winter’s
Dynamic RegressionDynamic Regression
Weighted Combined Weighted Combined
Models Models JudgmentalJudgmental
ARIMA ARIMA
Tool Box Approach to Forecasting
ARIMAX ARIMAX
UCM UCM
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HarvestBrands
GrowthBrands
Product Portfolio
New Products
Niche Products
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HarvestBrands
GrowthBrands
Product Portfolio
New Products
Niche Products
Line Extensions
Some surrogate history available
Short Life Cycle Products
Only primary research available
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HarvestBrands
GrowthBrands
Product Portfolio
New Products
Niche Products
Line Extensions
Some surrogate history available
Short Life Cycle Products
Only primary research available
Low Priority Products
Highly Seasonal
Trend
Cyclical
Minor Sales Promotions
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HarvestBrands
GrowthBrands
Product Portfolio
New Products
Niche Products
Line Extensions
Some surrogate history available
Short Life Cycle Products
Only primary research available
Low Priority Products
Highly Seasonal
Trend
Cyclical
Minor Sales Promotions
High Priority Products
Seasonal Fluctuations
Sales Promotions
National Marketing Events
Advertising Driven
Highly Competitive
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HarvestBrands
GrowthBrands
Product Portfolio
New Products
Niche Products
Line Extensions
Some surrogate history available
Short Life Cycle Products
Only primary research available
Regional Specialty Products
Irregular Demand
Little Seasonality
Some Trend
Local Events
High Priority Products
Seasonal Fluctuations
Sales Promotions
National Marketing Events
Advertising Driven
Highly Competitive
Low Priority Products
Highly Seasonal
Trend
Cyclical
Minor Sales Promotions
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Product Portfolio
New Products
“Juries” of Executive Opinion
Sales Force Composites Delphi
CommitteesIndependent Judgment
JudgmentalJudgmental
Niche Products
HarvestBrands
GrowthBrands
Bass Diffusion Model
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Product Portfolio
New Products
“Juries” of Executive Opinion
Sales Force Composites Delphi
CommitteesIndependent Judgment
JudgmentalJudgmental
ARIMA Box-Jenkins
Census X-11 Winters
Decomposition
Simple MovingAverage
Holt’s DoubleExponentialSmoothing
Niche Products
HarvestBrands
GrowthBrands
Time SeriesTime Series
Bass Diffusion Model
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Product Portfolio
New Products
“Juries” of Executive Opinion
Sales Force Composites Delphi
CommitteesIndependent Judgment
Judgmental
ARIMA Box-Jenkins
Census X-11 Winters
Decomposition
Simple MovingAverage
Holt’s DoubleExponentialSmoothing
ARIMAX ARIMA with
Interventions & Regressors
Dynamic Regression
Simple Regression
UCM Procedure
Niche Products
HarvestBrands
GrowthBrands
Time Series Causal Modeling
Bass Diffusion Model
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Product Portfolio
New Products
“Juries” of Executive Opinion
Sales Force Composites Delphi
CommitteesIndependent Judgment
Judgmental
ARIMA Box-Jenkins
Census X-11 Winters
Decomposition
Simple MovingAverage
Holt’s DoubleExponentialSmoothing
ARIMAX ARIMA with
Interventions & Regressors
Dynamic Regression
Simple Regression
UCM Procedure
Combined Weighted:•Judgment•Time Series•Causal
Combined Average: •Judgment•Time Series•Causal
Croston’s Intermittent Demand
Multiple Methods
Niche Products
HarvestBrands
GrowthBrands
Time Series Causal Modeling
Bass Diffusion Model
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Product Portfolio
New Products
Niche Products
Judgmental Multiple Methods
Vanilla Coke
Diet Coke W/Lemon
Dasani Water
Poweraid
Cherry Coke
Barq’s Root Beer
Minutemaid Orange Juice Classic Coke
Diet Coke
Sprite
Mr. PIB
TAB
HarvestBrands
GrowthBrands
Time Series Causal Modeling
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Product Portfolio
New Products
Niche Products
Judgmental Multiple Methods
Vanilla Coke
Diet Coke W/Lemon
Poweraid
Cherry Coke
Barq’s Root Beer
Minutemaid Orange Juice Classic Coke
Diet Coke
Sprite
Mr. PIB
TAB
10%10%
50%50% 35%35%
5%5%
Dasani Water
HarvestBrands
GrowthBrands
Time Series Causal Modeling
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One methodology fits all philosophy… Most systems are driven by this philosophy
• Always, time series methods
− Exponential Smoothing
− Winter’s most widely used mathematical method
– Easy to systematize
We have 99 different models
Actually 99 versions of the same model • Exponential Smoothing and/or ARIMA (Box Jenkins)
• What about Dynamic Regression, ARIMAX, and UCM?
− Dynamic versus static?
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Bottom Line… There is no Best Method
• The best method depends on the data, the purpose, the organizational environment and the perspective of the forecaster.
• Your market, products, goals, and constraints should be considered when selecting the forecasting tools best for you...
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Enabling Solution Challenges
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Selection of Software Software should include:
• Advanced analytics with optimized model selection
• Scalability
• Reduced forecast cycle times
• Exception-based forecasting
• The ability to support collaborative/consensus forecasting & planning
“You can’t install an off-the-shelf solution without some
customization (tailoring)...”
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Assess the current forecasting process Conduct SVA
• Strategic Value Assessment
− Identify strengths and weaknesses
− Design a process flow model that improves upon the weaknesses
− Then, build the enabling solution around the process
Rather than bending and twisting an off-the-shelf solution to fit your process
You can’t install an off-the-shelf solution without some customization (tailoring)
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In reality, most companies are saying… “Automate what I do, but don’t change what I do”.
Result: • They go out of business faster and more efficiently.
Justification: • “We don’t feel comfortable with system generated
forecasts.”
• “But, we don’t mind using the system to make manual (judgmental) overrides.”
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Challenges Measuring Forecasting Accuracy
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Why Measure Forecast Accuracy? “What get measured gets fixed…”
Remember, tracking forecast error alone is not the solution. • Instead of asking the question, “ what is this
months forecast error?” We need to ask, “ why was this months error so high (or low), and has it improved since last month?”
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Forecasting Performance Metrics 90% of the companies we interview don’t monitor
and track their forecast accuracy • No performance metrics
Those who do use MAPE (most widely used metric), but don’t compare it to KPI’s • WAPE is becoming more popular
Balance scorecard is the best way to demonstrate the impact of an improved forecast • With drill down capabilities
• Will get the attention of a C-level manager
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Forecast Score Card August 2000
Forecasts Actuals MAPE
Products StatisticalMarketing Adjustment
Sr. Mgmt. Override Statistical
Marketing Adjustment
Sr. Mgmt. Override
(units) (units) (units) (units)
Product Family X 2,000 1650 1500 1980 1.0% 20.0% 32.0% Product A 1500 1200 1000 1450 3.3% 20.8% 45.0% Product B 300 250 100 290 3.3% 16.0% 190.0% Product C 200 150 50 185 7.5% 23.3% 270.0% Product D 0 0 0 0 0 0 0
WAPE 4.7% 20.1% 168.3%
Typical Forecast Accuracy Report, right?
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Typical Forecasting Balanced Scorecard(Must be drillable)
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Forecast Accuracy Customer Service Inventory
Forecast Accuracy 75.4 78.8 85.6 92.4
Customer Service 85.9 86.9 88.5 93.9
Inventory 31.6 38.6 34.6 30.6
Jan Feb March April
Need to focus on the improvement, not the accuracy!!!
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Forecasting Is Like Taking Pictures
High Speed Digital FilmHigh Speed Digital Film
Sophisticated Professional CameraSophisticated Professional CameraNikon, Minolta, Hasselblatt Nikon, Minolta, Hasselblatt
Multiple Lens & FiltersMultiple Lens & FiltersZoom, Wide AngleZoom, Wide Angle
Highly SkilledHighly SkilledProfessionalProfessionalPhotographerPhotographer
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Forecasting Is Like Taking Pictures
High Speed Digital FilmHigh Speed Digital Film
Easy AccessEasy AccessHigh Quality High Quality
DataData
Enterprise DataWarehouse
Sophisticated Professional CameraSophisticated Professional CameraNikon, Minolta, Hasselblatt Nikon, Minolta, Hasselblatt
Sophisticated ForecastingSophisticated Forecasting SolutionSolution
Network
ServerServer
RemoteRemoteAccessAccess
GSMGSM
Global Global ForecastingForecasting
Regions/Regions/AffiliatesAffiliates
OperationalOperationalPresidentsPresidents
Finance Finance
Multiple Lens & FiltersMultiple Lens & FiltersZoom, Wide AngleZoom, Wide Angle
Multiple ForecastingMultiple Forecasting MethodologiesMethodologies
Tool BoxTool Box
Time-SeriesTime-SeriesCausalCausal
CompositeCompositeForecastingForecasting
JudgmentalJudgmental
ARIMA ARIMA
Highly SkilledHighly SkilledProfessionalProfessionalPhotographerPhotographer
Highly SkilledHighly SkilledProfessionalProfessionalForecastersForecasters
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In Closing…
Be careful about being too accurate with your forecasts… • “The mustard forecast that was too accurate…”
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