stastical forecasting v01

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APO Statistical Forecast

Training

May 3, 2001

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Key Objectives

• Understand how to identify patterns visually

• Gain some basic understanding of statisticalforecasting methods

• Have an introduction to the models available in APO

• Learn guidelines for how to assign the products tothe right Forecast Model Profiles

• Gain basic understanding of how the statisticalforecasts are generated and how the batch jobs

work

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UNDERSTANDINGDEMAND PATTERNS

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Demand Pattern Components

• Demand patterns are found when data points are plotted.

These patterns are a result of demand• Common demand pattern components:

o Level (Mean)/Mean Shifto Trendo Seasonalo

Randomness• Some things to consider while visually inspecting the data

o Outlierso Promotional Effectso Life cycle of product

• Once the demand pattern has been identified, a model togenerate forecasts can be suggested

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Level Pattern

• Demand may fluctuate from period to period

•  Average demand should remain relatively constant• Mean shifts (increase or decrease of the mean) can occur 

60050

700

800

900

1,000

Unit demand

60 70 80 90

Mean Shift

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Trend Patterns

• General increase or decrease in a time series that lasts for approximately seven or more periods

• Caused by:o Growth during product and technology introductions (ex: for Duracell -

growth in electronic gadgets)o Changes in economic conditionso Changes in preferenceso Gradual increase in distribution (e.g. proliferation of club stores)

400

JAN

500

600

700

800

 APR JUL NOV MAR

Trend

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Seasonal Patterns• Caused by events that are periodic and recurrent

• Common influences:o Climatic seasonso Human habitso Holidayso New product announcements

• Can occur by:o Weeklyo Monthlyo Quarterly

o Yearly

6000

700

800

900

1,000

 AUG „98   AUG„99 

Seasonal

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Seasonal Patterns

• Seasonal Patterns can be Additive or Multiplicative

 Additive Multiplicative

Seasonal effects are“mutliplied”as the time periodincreases

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Randomness• Result of influences that act independently to yield

nonrepeating patterns• For random series, simple forecasting models are often

most accurate

Random

400

JAN

500

600

700

800

 APR JUL NOV MAR

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Outliers

• Values that are not typical of the past or future

• Not a result of demand• Can include:

o Supply interruptions/Back orderso Environment (weather/war)

• Should always be adjusted and addressed and noted

• Should not be confused with promotions

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Model the Future• Long term vs. short term

o With new products, tendency is to look at short termhistory to model the future

o With older, established products, tendency is to look atlong term history to model the future

• Lifecycleso Important to look at the lifecycle of the product to

understand its history

GrowthMaturity

Decline

Intro

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Example 1

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Example 2

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Example 3

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Example 4

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Introduction to StatisticalForecast Methods

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Forecasting Methods• Forecast methods make estimates of the future based on

past patterns, past relationships or subjective predictions

about the future• Common forecast methods:

o Time Series (Moving Average, Exponential Smoothing) Purpose is to model the patterns of past demands in order to project them into the

future

Good in very short-term horizon Requires past, internal data to forecast the future Most cost effective and easily implemented

o Causal (Linear Regression) Makes projections of the future by modeling the relationship between demand and

other external variables (model causes of demand) More time intensive and less easily systematized

Forecaster should have training in statisticso Qualitative (Judgement)

Requires strong product knowledge Good for promotional products

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Time Series Methods

• Moving Average

• Exponential Smoothingo Single: Used mainly for constant demand patterns

Where n=Numberof Rolling Months

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Time Series Methods

• Exponential Smoothing continuedo Holt‟s Linear (Trend) and Holt Winters (Seasonality) 

Holt’s Linear Trend is derived

from the Holt Winter’s method.

The difference is the seasonalportion of the equation has been

removed.

Holt Winters model for APO isMultiplicative and not Additive

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Time Series Methods

• Crostons - To be used for products with sporadic

demand for replenishment etc.

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Time Series Methods

• 2nd Order Exponential Smoothing

• “Doubly smoothed” model 

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Time Series -Smoothing Techniques

• Parameters: Always between 0 and 1

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Time Series -Smoothing Techniques

• Model Initialization:

Ti S i S hi T h i

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Time Series -Smoothing Techniques

Where• t=Time at period t• L= Seasonal Period length

• Model Initialization:

E i

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Exercise• Calculate a 3 Month Moving Average• Calculate a Single Exponential Smoothing; Try 3 times

with alpha=.05, .5, .9; Try also plotting and comparing• Bonus: Calculate Trend Forecast and Seasonal Forecastusing Alpha=.10, Beta=.3 and Gamma =.5

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 APO Forecast Models

F t P fil i APO

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Forecast Profiles in APO

• There are a total of 10 Forecast Profiles set up for each Business Unit/Market

• Each Forecast Profile is a step in the batch process

F t P fil i APO

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Forecast Profiles in APO

•  Any addition of a new model, or a duplicate model witha new parameter, results in another job in the Statisticalforecast runo Run Statistical models (total 10x4=40 Stat jobs)o Run Life Cycleo Run Zero Forecast

o Run Copy to Final Forecast

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Standard Guideline

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Standard Guideline

Tracking Quality of forecasts

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Tracking Quality of forecasts

• If in Interactiveo

Take a look at the MPE and MAPE of the fitted forecastso Visually examine the forecasts generated

• For constant models, you will not get error measurements• Create queries in BW to look at forecasts generated for 

next quarter and compare differences from last quarter 

• Create queries in BW to look at large differences betweenmanual adjustments and statistical (be sure to ignorevalues where one or the other are missing)

• Review Forecast Accuracy reports in BW