Overview of Forecasting ; Outliers;Regression;Time Series;Supply Chain;Financial;AUTOBOX;ARIMA;TRANSFER FUNCTION

1. Capabilities of Autobox

3. Our Company

7. Some Recent Customers © Automatic Forecasting Systems 2008

9. Our Products

12. Autobox Functionality

13. Autobox Functionality © Automatic Forecasting Systems 2008 Uses statistics from the data to identify an appropriate model and if available incorporate user-known causal variables. Automatically identifies and models the effects of unknown interventions and the impact of history (i.e. unknown causal variables). The software forms models that reflect the “usual behavior” and not the “unusual behavior”. All of this is done in both a univariate and multivariate batch or interactive world. Allows the user to override conditions in the heuristic and allow a “expert user” to use their expertise. Autobox customizes the model to the data.

27. SAP vs. Autobox

28. SKU 1 Sales History SAP Forecast vs. Autobox Forecast © Automatic Forecasting Systems 2008 SAP forecast Autobox identifies a pattern of 4 seasonal pulses and projects them out

29. SKU 2 Sales History SAP Forecast vs. Autobox Forecast © Automatic Forecasting Systems 2008 SAP projects a 2 down 1 up forecast though the history shows no such pattern Autobox identifies a seasonal pulse and projects it

30. SKU 3 Sales History SAP Forecast vs. Autobox Forecast © Automatic Forecasting Systems 2008 SAP has a projection that looks like a sophisticated pattern exists but there is none and interestingly enough no period to period pattern exists in the historical data that matches the pattern in the forecast Autobox finds no pattern other than a seasonal pulse and projects it

31. © Automatic Forecasting Systems 2008 SAP APO in the IBF Discussion Group

33. © Automatic Forecasting Systems 2008 SAP APO in the International Journal of Applied Forecasting Foresight Issue Fall 2006 – p 54 Summary SAP APO is focusing on the whole supply chain and also on planning and process consistency. The mathematical accuracy of its forecasts may be worse than that of a stand-alone forecasting package , but the benefits to our company in terms of worldwide network planning and control more than compensate for this. © Automatic Forecasting Systems 2008

34. Integrating Autobox into SAP Writing to Disk Sales History & Causal Variables Written to Disk SAP Autobox Batch EXE Forecasts Written to Disk SAP © Automatic Forecasting Systems 2008

35. Integrating Autobox into SAP Calling a DLL SAP Autobox DLL SAP Forecasts Passed back to SAP in memory . Forecasts Passed to Autobox in memory . Mine models to “Push” Information 52 out of 300 series had a bump at period 54. You had no causal for that event, why? and why was it high? © Automatic Forecasting Systems 2008

36. Integrating Autobox into SAP Calling a DLL and What-if Scenario SAP Autobox DLL SAP Forecasts Written or Passed to an application to do What-if Scenarios . Forecasts Passed to Autobox in memory . © Automatic Forecasting Systems 2008

37. Using Causal Variables

41. © Automatic Forecasting Systems 2008 Case Study – What-if Analysis Baseline Forecast © Automatic Forecasting Systems 2008

42. © Automatic Forecasting Systems 2008 Case Study – What-if Analysis Baseline Future Values of Causals © Automatic Forecasting Systems 2008

43. Case Study – What-if Analysis Scenario #1 Adjust Price and TV Spots Up © Automatic Forecasting Systems 2008 © Automatic Forecasting Systems 2008

44. © Automatic Forecasting Systems 2008 Case Study – What-if Analysis Graph of Baseline and Scenario #1 © Automatic Forecasting Systems 2008