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Transcript of 2006 Michael Graham
Copyright © 2006, SAS Institute Inc. All rights reserved.
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Copyright © 2006, SAS Institute Inc. All rights reserved.
Point & Click Statistical ForecastingMichael GrahamSAS New Zealand
Copyright © 2006, SAS Institute Inc. All rights reserved.
Agenda
Introduction
Statistics
Why Forecast?
Forecasting methods
SAS Forecast Studio
Questions
Copyright © 2006, SAS Institute Inc. All rights reserved.
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
Copyright © 2006, SAS Institute Inc. All rights reserved.
Do you remember?
Frank Di Iorio (BNZ in mid ’80’s)
SAS Silver Circle winner – Oct 2006
SAS FS-Calc
Copyright © 2006, SAS Institute Inc. All rights reserved.
Agenda
Introduction
Statistics
Why Forecast?
Forecasting methods
SAS Forecast Studio
Questions
Copyright © 2006, SAS Institute Inc. All rights reserved.
Lies, Damn Lies & Statistics
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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
Copyright © 2006, SAS Institute Inc. All rights reserved.
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!
Copyright © 2006, SAS Institute Inc. All rights reserved.
Agenda
Introduction
Statistics
Why Forecast?
Forecasting methods
SAS Forecast Studio
Questions
Copyright © 2006, SAS Institute Inc. All rights reserved.
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
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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
Copyright © 2006, SAS Institute Inc. All rights reserved.
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!
Copyright © 2006, SAS Institute Inc. All rights reserved.
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
Copyright © 2006, SAS Institute Inc. All rights reserved.
Agenda
Introduction
Statistics
Why Forecast?
Forecasting methods
SAS Forecast Studio
Questions
Copyright © 2006, SAS Institute Inc. All rights reserved.
HistoryForecast
ConfidenceIntervals
Statistical Forecasting
Prediction
Copyright © 2006, SAS Institute Inc. All rights reserved.
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
Copyright © 2006, SAS Institute Inc. All rights reserved.
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
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Time Series Analysis: Classical Decomposition
Original SeriesOriginal SeriesSeasonally Seasonally Adjusted SeriesAdjusted Series
Seasonal ComponentSeasonal Component
Trend-Cycle ComponentTrend-Cycle Component
Irregular ComponentIrregular Component
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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
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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
Copyright © 2006, SAS Institute Inc. All rights reserved.
Agenda
Introduction
Statistics
Why Forecast?
Forecasting methods
SAS Forecast Server/Studio
Questions
Copyright © 2006, SAS Institute Inc. All rights reserved.
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
Copyright © 2006, SAS Institute Inc. All rights reserved.
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
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Demonstration
SAS Forecast Studio• Time Series• Hierarchical
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Forecasting Hierarchies:Top-down and Bottom-up Forecasting
Store
Zone
Region
TotalCompany
Bottom-up
Store
Zone
Region
TotalCompany
Top-down
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“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
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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
Copyright © 2006, SAS Institute Inc. All rights reserved.
Agenda
Introduction
Statistics
Why Forecast?
Forecasting methods
SAS Forecast Studio
Questions
Copyright © 2006, SAS Institute Inc. All rights reserved.
SAS Users New ZealandProudly sponsored by…
knowareknoware