Copyright © 2007, SAS Institute Inc. All rights reserved. Demystifying Forecasting: The Future of...

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


Planets are now aligned to facilitate Demand-Driven Forecasting…

Data Access/Storage

Data Processing

Forecasting Methods


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


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


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


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)


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.


There are four key forecasting challenges

Process

Methods

Systems

Performance Metrics


Process Challenges


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


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


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


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


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…


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”


Forecasting Methods Challenges

“Forecasters have fallen short in this area.”


Forecast = Pattern (s) + Randomness

Theory of Forecasting Methods...



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.






Forecast = Pattern (s) + RandomnessError/Unexplained




Maximize Minimize





Two Broad mathematical categories 1. Time Series 2. Causal



Trend Seasonality

Cyclical





Trend Seasonality

Cyclical

Error/Unexplained


Time Series Methods

Exponential Smoothing

Brown’s

Holts

Winter’s

Census X11/12

ARIMA


Forecast = Pattern + Randomness



Event




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Three Basic Types:

1. Point Intervention

2. Step Intervention

3. Ramp-up (or down) Intervention



Forecast = Pattern (s) + RandomnessError

Trend Seasonality

Cyclical

EventsSales Promotions

Marketing Events



Trend Seasonality

Cyclical


Marketing Events Time Series Methods

ARIMA

W/Interventions



Trend Seasonality

Cyclical


Marketing Events

CausalPrice

AdvertisingCompetitive Activities

Etc.



Trend Seasonality

Cyclical


Marketing Events

CausalPrice

AdvertisingCompetitive Activities

Etc. Causal Methods

ARIMAX

Dynamic Regression

Unobserved Component Models (UCM)



The Ultimate Algorithm(No Greek Symbols Required)


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


HarvestBrands

GrowthBrands

Product Portfolio

New Products

Niche Products


HarvestBrands

GrowthBrands

Product Portfolio

New Products

Niche Products

Line Extensions

Some surrogate history available

Short Life Cycle Products

Only primary research available


HarvestBrands

GrowthBrands

Product Portfolio

New Products

Niche Products

Line Extensions




Low Priority Products

Highly Seasonal

Trend

Cyclical

Minor Sales Promotions


HarvestBrands

GrowthBrands

Product Portfolio

New Products

Niche Products

Line Extensions





Highly Seasonal

Trend

Cyclical


High Priority Products

Seasonal Fluctuations

Sales Promotions

National Marketing Events

Advertising Driven

Highly Competitive


HarvestBrands

GrowthBrands

Product Portfolio

New Products

Niche Products

Line Extensions




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


Highly Seasonal

Trend

Cyclical



Product Portfolio

New Products

“Juries” of Executive Opinion

Sales Force Composites Delphi

CommitteesIndependent Judgment

JudgmentalJudgmental

Niche Products

HarvestBrands

GrowthBrands

Bass Diffusion Model


Product Portfolio

New Products





ARIMA Box-Jenkins

Census X-11 Winters

Decomposition

Simple MovingAverage

Holt’s DoubleExponentialSmoothing

Niche Products

HarvestBrands

GrowthBrands

Time SeriesTime Series



Product Portfolio

New Products




Judgmental

ARIMA Box-Jenkins

Census X-11 Winters

Decomposition



ARIMAX ARIMA with

Interventions & Regressors

Dynamic Regression

Simple Regression

UCM Procedure

Niche Products

HarvestBrands

GrowthBrands

Time Series Causal Modeling



Product Portfolio

New Products




Judgmental

ARIMA Box-Jenkins

Census X-11 Winters

Decomposition



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




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



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



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?


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...


Enabling Solution Challenges


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)...”


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)


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.”


Challenges Measuring Forecasting Accuracy


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?”


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


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?


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!!!


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


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


ARIMA ARIMA

Highly SkilledHighly SkilledProfessionalProfessionalPhotographerPhotographer

Highly SkilledHighly SkilledProfessionalProfessionalForecastersForecasters


In Closing…

Be careful about being too accurate with your forecasts… • “The mustard forecast that was too accurate…”

Copyright © 2007, SAS Institute Inc. All rights reserved. Demystifying Forecasting: The Future of...

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Transcript of Copyright © 2007, SAS Institute Inc. All rights reserved. Demystifying Forecasting: The Future of...