SAS Users New Zealand Proudly sponsored by…
Point & Click Statistical Forecasting Michael Graham SAS New Zealand
Agenda <ul><li>Introduction </li></ul><ul><li>Statistics </li></ul><ul><li>Why Forecast? </li></ul><ul><li>Forecasting met...
About me <ul><li>BCA (Economics) from Victoria University </li></ul><ul><li>Used SAS at BNZ and Paxus in 1980’s </li></ul>...
Do you remember? <ul><li>Frank Di Iorio (BNZ in mid ’80’s) </li></ul><ul><li>SAS Silver Circle winner – Oct 2006 </li></ul...
Agenda <ul><li>Introduction </li></ul><ul><li>Statistics </li></ul><ul><li>Why Forecast? </li></ul><ul><li>Forecasting met...
Lies, Damn Lies & Statistics
The T-test <ul><li>Homework and American Students </li></ul><ul><ul><li>According an American newspaper, the statisticians...
The T-test <ul><li>Several Pitfalls </li></ul><ul><ul><li>Not significant in the ordinary sense (2 minutes a day) </li></u...
Agenda <ul><li>Introduction </li></ul><ul><li>Statistics </li></ul><ul><li>Why Forecast? </li></ul><ul><li>Forecasting met...
Going beyond the present Optimization Predictive Modeling Forecasting Reporting / OLAP Data Management Data Access What is...
What does inaccurate forecasting cost? (Simplified) <ul><ul><li>Case of Over-forecast </li></ul></ul><ul><ul><li>Case of U...
Kenneth B. Kahn, Ph.D  Forecasting Performance Measurement Considerations It costs us money to catch up with everyone else...
AMR Research, Analysts, Boston, USA  <ul><li>Forecast accuracy is the  most important  and  advantageous  supply chain met...
Agenda <ul><li>Introduction </li></ul><ul><li>Statistics </li></ul><ul><li>Why Forecast? </li></ul><ul><li>Forecasting met...
Statistical Forecasting History Forecast Confidence Intervals Prediction
The practicalities of predicting the future <ul><li>Two general approaches </li></ul><ul><ul><li>Time Series Analysis:  Ba...
What is a Time Series? <ul><li>Anything measured over time… </li></ul><ul><ul><li>Weekly sales </li></ul></ul><ul><ul><li>...
Time Series Analysis: Classical Decomposition Original Series Seasonally Adjusted Series Seasonal Component Trend-Cycle Co...
What does “Econometrics” mean? <ul><li>Econometrics refers to specialized statistical methods for analyzing economic data,...
Time Series analysis with Econometric Models: Unobserved Component Modeling Original Series Seasonal Component Trend Compo...
Agenda <ul><li>Introduction </li></ul><ul><li>Statistics </li></ul><ul><li>Why Forecast? </li></ul><ul><li>Forecasting met...
SAS Forecast Server <ul><li>Enterprise forecasting environment </li></ul><ul><li>Automatic and interactive usage </li></ul...
SAS Forecast Studio <ul><li>Automatic model diagnosis and selection </li></ul><ul><li>Can be run batch or interactively </...
Demonstration <ul><li>SAS Forecast Studio </li></ul><ul><ul><li>Time Series </li></ul></ul><ul><ul><li>Hierarchical </li><...
Forecasting Hierarchies: Top-down and Bottom-up Forecasting Store Zone Region Total Company Bottom-up Store Zone Region To...
“ Traditional Approaches” <ul><li>Bottom Up Approach </li></ul><ul><ul><li>The forecasts are generated on the lowest level...
SAS Forecast Server: Reconciliation Methods <ul><li>Bottom up  </li></ul><ul><ul><li>Sum  </li></ul></ul><ul><ul><li>Avera...
Agenda <ul><li>Introduction </li></ul><ul><li>Statistics </li></ul><ul><li>Why Forecast? </li></ul><ul><li>Forecasting met...
SAS Users New Zealand Proudly sponsored by…
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2006 Michael Graham

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2006 Michael Graham

  1. 1. SAS Users New Zealand Proudly sponsored by…
  2. 2. Point & Click Statistical Forecasting Michael Graham SAS New Zealand
  3. 3. Agenda <ul><li>Introduction </li></ul><ul><li>Statistics </li></ul><ul><li>Why Forecast? </li></ul><ul><li>Forecasting methods </li></ul><ul><li>SAS Forecast Studio </li></ul><ul><li>Questions </li></ul>
  4. 4. About me <ul><li>BCA (Economics) from Victoria University </li></ul><ul><li>Used SAS at BNZ and Paxus in 1980’s </li></ul><ul><li>Experience with various other BI tools </li></ul><ul><ul><li>FCS, Oracle Express, Actuate, Cognos </li></ul></ul><ul><li>Worked in UK 1986-1994 </li></ul><ul><li>Joined SAS in May 2006 </li></ul>
  5. 5. Do you remember? <ul><li>Frank Di Iorio (BNZ in mid ’80’s) </li></ul><ul><li>SAS Silver Circle winner – Oct 2006 </li></ul>SAS FS-Calc
  6. 6. Agenda <ul><li>Introduction </li></ul><ul><li>Statistics </li></ul><ul><li>Why Forecast? </li></ul><ul><li>Forecasting methods </li></ul><ul><li>SAS Forecast Studio </li></ul><ul><li>Questions </li></ul>
  7. 7. Lies, Damn Lies & Statistics
  8. 8. The T-test <ul><li>Homework and American Students </li></ul><ul><ul><li>According an American newspaper, the statisticians at the Census Bureau reported that: </li></ul></ul><ul><ul><ul><li>For Girls, the total was 5.6 hours per week, compared with 5.4 for Boys </li></ul></ul></ul><ul><ul><li>And concluded that: </li></ul></ul><ul><ul><ul><li>The overall difference between Male & Female students, while small (about 12 minutes) is statistically significant </li></ul></ul></ul>
  9. 9. The T-test <ul><li>Several Pitfalls </li></ul><ul><ul><li>Not significant in the ordinary sense (2 minutes a day) </li></ul></ul><ul><ul><li>The observed significance level is a function of sample size (60,000) </li></ul></ul><ul><ul><li>The measurement of hours of homework is based on students’ self-report </li></ul></ul><ul><ul><ul><li>Ages ranged from 3 to 34 yrs </li></ul></ul></ul>Use common sense!
  10. 10. Agenda <ul><li>Introduction </li></ul><ul><li>Statistics </li></ul><ul><li>Why Forecast? </li></ul><ul><li>Forecasting methods </li></ul><ul><li>SAS Forecast Studio </li></ul><ul><li>Questions </li></ul>
  11. 11. Going beyond the present Optimization Predictive Modeling Forecasting Reporting / OLAP Data Management Data Access What is the optimal solution? Can we influence the outcome? What is the best possible outcome? What will happen next ? How Much? How Many? What Happened? <ul><li>Four main objectives </li></ul><ul><ul><li>Decrease the uncertainty of the future </li></ul></ul><ul><ul><li>Better utilise resources today by knowing what’s going to happen tomorrow </li></ul></ul><ul><ul><li>Manage risk by explicitly modelling it </li></ul></ul><ul><ul><li>Help identify what we know as well as what we don’t know </li></ul></ul>Intelligence Business Value
  12. 12. What does inaccurate forecasting cost? (Simplified) <ul><ul><li>Case of Over-forecast </li></ul></ul><ul><ul><li>Case of Under-forecast </li></ul></ul>Taken from: “How to measure the impact of a forecast error on an enterprise?” by Kenneth B. Kahn $1,200,000 Inventory Cost Per Year $100,000 Inventory Cost Per Month $1 per unit Average Item Cost 100,000 units 1% Error 10,000,000 units Total Monthly Item Volume $2,400,000 Lost Profit Per Year $200,000 Lost Profit Per Month $2 per unit Average Sales Margin 100,000 units 1% Error 10,000,000 units Total Monthly Item Volume
  13. 13. 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!
  14. 14. AMR Research, Analysts, Boston, USA <ul><li>Forecast accuracy is the most important and advantageous supply chain metric.  </li></ul><ul><ul><li>Lora Cecere (AMR Research) - 25.01. 2005 </li></ul></ul><ul><ul><li>http://www.sas.com/news/feature/10nov05/forecast.html </li></ul></ul><ul><li>Companies with improved demand forecasting, on average, experience the following returns: </li></ul><ul><ul><li>15% less inventory </li></ul></ul><ul><ul><li>17% better perfect order ratings </li></ul></ul><ul><ul><li>35% shorter cash-to-cash cycle times </li></ul></ul>AMR Research: The Case for Supply Chain Excellence: Superior Financial and Market Performance http://www.bitpipe.com/detail/RES/1087384739_708.html
  15. 15. Agenda <ul><li>Introduction </li></ul><ul><li>Statistics </li></ul><ul><li>Why Forecast? </li></ul><ul><li>Forecasting methods </li></ul><ul><li>SAS Forecast Studio </li></ul><ul><li>Questions </li></ul>
  16. 16. Statistical Forecasting History Forecast Confidence Intervals Prediction
  17. 17. The practicalities of predicting the future <ul><li>Two general approaches </li></ul><ul><ul><li>Time Series Analysis: Based on applying historical trends into the future </li></ul></ul><ul><ul><li>Econometrics: Based on identifying the influence causal factors have on the item to be forecast </li></ul></ul><ul><ul><li>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 </li></ul></ul><ul><li>In practice: </li></ul><ul><ul><li>Evaluate a wide number of models to determine which may be a good predictor </li></ul></ul><ul><ul><li>Test the best models against data set aside to see how well it actually predicts </li></ul></ul><ul><ul><li>Measure the accuracy of the model over time to assess whether it is still a good predictor </li></ul></ul>
  18. 18. What is a Time Series? <ul><li>Anything measured over time… </li></ul><ul><ul><li>Weekly sales </li></ul></ul><ul><ul><li>Daily interest rates </li></ul></ul><ul><ul><li>Annual income </li></ul></ul><ul><ul><li>Hourly call center volume </li></ul></ul><ul><li>So what is time series analysis? </li></ul><ul><ul><li>Using the information encoded within the time series to forecast the future </li></ul></ul>
  19. 19. Time Series Analysis: Classical Decomposition Original Series Seasonally Adjusted Series Seasonal Component Trend-Cycle Component Irregular Component
  20. 20. What does “Econometrics” mean? <ul><li>Econometrics refers to specialized statistical methods for analyzing economic data, which usually involves time relationships. </li></ul><ul><ul><li>Typically suggest structural relationships with causal directions that persist over time </li></ul></ul><ul><ul><li>Economics + Statistics = Econometrics = Prediction </li></ul></ul><ul><li>Supply = f(Demand, Interest Rates, Cost of Inputs, etc) </li></ul>
  21. 21. Time Series analysis with Econometric Models: Unobserved Component Modeling Original Series Seasonal Component Trend Component Exogenous Factors Cycle Component Autoregressive Component Forecasts
  22. 22. Agenda <ul><li>Introduction </li></ul><ul><li>Statistics </li></ul><ul><li>Why Forecast? </li></ul><ul><li>Forecasting methods </li></ul><ul><li>SAS Forecast Server/Studio </li></ul><ul><li>Questions </li></ul>
  23. 23. SAS Forecast Server <ul><li>Enterprise forecasting environment </li></ul><ul><li>Automatic and interactive usage </li></ul><ul><li>Business/novice forecasters </li></ul><ul><ul><li>Automated model building </li></ul></ul><ul><li>Experienced forecasters </li></ul><ul><ul><li>Interactive & automated model building </li></ul></ul><ul><li>Consumer of Forecasts </li></ul><ul><ul><li>Accessing forecasting results </li></ul></ul><ul><ul><li>Automated model building </li></ul></ul>
  24. 24. SAS Forecast Studio <ul><li>Automatic model diagnosis and selection </li></ul><ul><li>Can be run batch or interactively </li></ul><ul><li>Incorporates Event Calendars and discrete event modeling </li></ul><ul><li>Deconstructs forecast into seasonal, cyclical, trend and “unobserved” components </li></ul><ul><li>Methods </li></ul><ul><ul><li>ARIMA </li></ul></ul><ul><ul><li>Exponential Smoothing </li></ul></ul><ul><ul><li>UCM </li></ul></ul><ul><ul><li>Croston’s Method </li></ul></ul><ul><ul><li>Intermittent Demand Model </li></ul></ul><ul><ul><li>Curve Fitting </li></ul></ul><ul><ul><li>Moving Average (window) </li></ul></ul><ul><ul><li>Multiple Regression </li></ul></ul><ul><ul><li>Random Walk </li></ul></ul><ul><ul><li>SAS Code </li></ul></ul><ul><ul><li>Compare models </li></ul></ul>
  25. 25. Demonstration <ul><li>SAS Forecast Studio </li></ul><ul><ul><li>Time Series </li></ul></ul><ul><ul><li>Hierarchical </li></ul></ul>
  26. 26. Forecasting Hierarchies: Top-down and Bottom-up Forecasting Store Zone Region Total Company Bottom-up Store Zone Region Total Company Top-down
  27. 27. “ Traditional Approaches” <ul><li>Bottom Up Approach </li></ul><ul><ul><li>The forecasts are generated on the lowest level only and then aggregated </li></ul></ul><ul><li>Top Down Approach </li></ul><ul><ul><li>The forecasts are generated on highest level only and then disaggregated </li></ul></ul><ul><li>Problems: </li></ul><ul><ul><li>No confidence intervals </li></ul></ul><ul><ul><li>Potential loss of accuracy due to aggregation and disaggregation </li></ul></ul>
  28. 28. SAS Forecast Server: Reconciliation Methods <ul><li>Bottom up </li></ul><ul><ul><li>Sum </li></ul></ul><ul><ul><li>Average </li></ul></ul><ul><li>Top down </li></ul><ul><ul><li>Proportions (total or average) </li></ul></ul><ul><ul><li>Equal split of difference (total or average) </li></ul></ul><ul><li>Middle-out </li></ul><ul><ul><li>Specify “dominant” hierarchy level </li></ul></ul><ul><ul><li>Forecasts of this level are the base for the reconciliation adjustments of all other levels </li></ul></ul>
  29. 29. Agenda <ul><li>Introduction </li></ul><ul><li>Statistics </li></ul><ul><li>Why Forecast? </li></ul><ul><li>Forecasting methods </li></ul><ul><li>SAS Forecast Studio </li></ul><ul><li>Questions </li></ul>
  30. 30. SAS Users New Zealand Proudly sponsored by…
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