2006 Michael Graham

Copyright © 2006, SAS Institute Inc. All rights reserved.

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Point & Click Statistical ForecastingMichael GrahamSAS New Zealand


Agenda

Introduction

Statistics

Why Forecast?

Forecasting methods

SAS Forecast Studio

Questions


About me

BCA (Economics) from Victoria University

Used SAS at BNZ and Paxus in 1980’s

Experience with various other BI tools• FCS, Oracle Express, Actuate, Cognos

Worked in UK 1986-1994

Joined SAS in May 2006


Do you remember?

Frank Di Iorio (BNZ in mid ’80’s)

SAS Silver Circle winner – Oct 2006

SAS FS-Calc


Agenda

Introduction

Statistics

Why Forecast?

Forecasting methods

SAS Forecast Studio

Questions


Lies, Damn Lies & Statistics


The T-test

Homework and American Students• According an American newspaper, the statisticians at

the Census Bureau reported that:− For Girls, the total was 5.6 hours per week,

compared with 5.4 for Boys• And concluded that:

− The overall difference between Male & Female students, while small (about 12 minutes) is statistically significant


The T-test

Several Pitfalls• Not significant in the ordinary sense (2 minutes

a day)• The observed significance level is a function of

sample size (60,000)• The measurement of hours of homework is

based on students’ self-report− Ages ranged from 3 to 34 yrs

Use common sense!


Agenda

Introduction

Statistics

Why Forecast?

Forecasting methods

SAS Forecast Studio

Questions


Intelligence

Busin

ess V

alue

Optimization

Predictive ModelingForecasting

Reporting / OLAP

Data Management

Data Access

Going beyond the presentGoing beyond the present

What is the optimaloptimal solution? Can we influenceinfluence the outcome? What is the bestbest possible outcome? What will happen nextnext?

How Much?

How Many? What Happened?

Four main objectives• Decrease the uncertainty of the future• Better utilise resources today by knowing what’s going

to happen tomorrow• Manage risk by explicitly modelling it• Help identify what we know as well as what we don’t

know


What does inaccurate forecasting cost? (Simplified)

• Case of Over-forecast

• Case of Under-forecast

Taken from: “How to measure the impact of a forecast error on an enterprise?” by Kenneth B. Kahn

Total Monthly Item Volume 10,000,000 units1% Error 100,000 unitsAverage Item Cost $1 per unitInventory Cost Per Month $100,000Inventory Cost Per Year $1,200,000

Total Monthly Item Volume 10,000,000 units1% Error 100,000 unitsAverage Sales Margin $2 per unitLost Profit Per Month $200,000Lost Profit Per Year $2,400,000


Kenneth B. Kahn, Ph.D Forecasting Performance Measurement Considerations

It costs us money to catch up with everyone else!

We are paying more as we get our orders in late!

We’re late to market; we get a smaller market share

We can’t position complementary products!

Our customers are constantly unhappy that we’re lagging the market!

And, they’re all in the wrong place!

By the time I can finally sell them, they’re out of date!

Because I’ve got so many, I have to discount them!

I’m having too many items in my inventory!

It’s costing me a fortune to hold those items!


AMR Research, Analysts, Boston, USA

Forecast accuracy is the most important and advantageous supply chain metric. • Lora Cecere (AMR Research) - 25.01. 2005 • http://www.sas.com/news/feature/10nov05/forecast.html

Companies with improved demand forecasting, on average, experience the following returns: • 15% less inventory • 17% better perfect order ratings • 35% shorter cash-to-cash cycle times

AMR Research: The Case for Supply Chain Excellence: Superior Financial and Market Performancehttp://www.bitpipe.com/detail/RES/1087384739_708.html

http://www.sas.com/news/feature/10nov05/forecast.html

http://www.bitpipe.com/detail/RES/1087384739_708.html


Agenda

Introduction

Statistics

Why Forecast?

Forecasting methods

SAS Forecast Studio

Questions


HistoryForecast

ConfidenceIntervals

Statistical Forecasting

Prediction


The practicalities of predicting the future

Two general approaches• Time Series Analysis: Based on applying historical trends into

the future• Econometrics: Based on identifying the influence causal

factors have on the item to be forecast• Both offer potential for accurate prediction – degree of

accuracy depends on the characteristics of the data, the breadth of data available, and the amount of historical data available

In practice:• Evaluate a wide number of models to determine which may be

a good predictor• Test the best models against data set aside to see how well it

actually predicts• Measure the accuracy of the model over time to assess

whether it is still a good predictor


What is a Time Series?

Anything measured over time…• Weekly sales• Daily interest rates• Annual income• Hourly call center volume

So what is time series analysis?• Using the information encoded

within the time series to forecast the future

),...,,( 21 ntttt SSSfnS


Time Series Analysis: Classical Decomposition

Original SeriesOriginal SeriesSeasonally Seasonally Adjusted SeriesAdjusted Series

Seasonal ComponentSeasonal Component

Trend-Cycle ComponentTrend-Cycle Component

Irregular ComponentIrregular Component


What does “Econometrics” mean?

Econometrics refers to specialized statistical methods for analyzing economic data, which usually involves time relationships. • Typically suggest structural relationships with causal

directions that persist over time• Economics + Statistics = Econometrics = Prediction

Supply = f(Demand, Interest Rates, Cost of Inputs, etc)

...321 tttt CIIRDS


Time Series analysis with Econometric Models: Unobserved Component Modeling

Original SeriesOriginal SeriesSeasonal ComponentSeasonal Component

Trend ComponentTrend Component

Exogenous FactorsExogenous Factors

Cycle ComponentCycle Component

Autoregressive ComponentAutoregressive Component1,p

2,p

3,p

4,p

5,p

ForecastsForecasts


Agenda

Introduction

Statistics

Why Forecast?

Forecasting methods

SAS Forecast Server/Studio

Questions


SAS Forecast Server

Enterprise forecasting environment

Automatic and interactive usage

Business/novice forecasters• Automated model building

Experienced forecasters• Interactive & automated model building

Consumer of Forecasts• Accessing forecasting results• Automated model building


SAS Forecast StudioSAS Forecast Studio • Automatic model diagnosis and selection

• Can be run batch or interactively

• Incorporates Event Calendars and discrete event modeling

• Deconstructs forecast into seasonal, cyclical, trend and “unobserved” components

MethodsMethods• ARIMA• Exponential Smoothing• UCM• Croston’s Method• Intermittent Demand Model• Curve Fitting• Moving Average (window)• Multiple Regression• Random Walk• SAS Code • Compare models


Demonstration

SAS Forecast Studio• Time Series• Hierarchical


Forecasting Hierarchies:Top-down and Bottom-up Forecasting

Store

Zone

Region

TotalCompany

Bottom-up

Store

Zone

Region

TotalCompany

Top-down


“Traditional Approaches”

Bottom Up Approach• The forecasts are generated on the lowest level only

and then aggregated

Top Down Approach• The forecasts are generated on highest level only and

then disaggregated

Problems:• No confidence intervals• Potential loss of accuracy due to aggregation and

disaggregation


SAS Forecast Server:Reconciliation Methods

Bottom up • Sum • Average

Top down • Proportions (total or

average)• Equal split of difference

(total or average)

Middle-out• Specify “dominant”

hierarchy level• Forecasts of this level are

the base for the reconciliation adjustments of all other levels


Agenda

Introduction

Statistics

Why Forecast?

Forecasting methods

SAS Forecast Studio

Questions


SAS Users New ZealandProudly sponsored by…

knowareknoware

2006 Michael Graham

Technology

Transcript of 2006 Michael Graham