1. Session 8: Conclusions Demand Forecasting and Planning in Crisis 30-31 July, Shanghai Joseph Ogrodowczyk, Ph.D.

2. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis 30-31 July, Shanghai 2 Conclusions Session agenda  Forecasting changes  Evaluating forecasting capabilities of software  A word on Microsoft Excel  Creating a forecaster training program  Resources

3. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 3 Conclusions Forecasting changes  Very difficult to forecast sharp or hard-to-reverse changes  No magic bullet for forecasting quantities  Need tools that will guide a forecasting process Linear models estimate levels (quantities) of the dependent variable Non-linear models can estimate the probability of the state of the dependent variable

4. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 4 Conclusions Forecasting changes  Two model types 1. Modeling probabilities for future events  Statistical models called probit and logit estimate the probability that an event will occur  Independent variables are correlated with an event  ISM purchasing manager’s index, price of oil, stock price index, etc.  Event is the state of the macroeconomy (Good, Bad)  As the independent variables move, the probability of the dependent variable being either Good or Bad adjusts

5. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 5 Conclusions Forecasting changes  Two model types 1. Modeling probabilities for future events  Choosing the “right” independent variables  Variables can differ in effect on various occurrences (simple models are structurally more stable than complex models)  Try to include recent history as a basis (past events are not always an indication of future events)  Historical macroeconomic data are subject to change (re- test models for explanatory robustness)  Key independent variables can change (test the relevance of the independent variables)

6. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 6 Conclusions Forecasting changes  Two model types 2. Decision-directed forecasting  Forecasting tool using after a major disruption  Manager experience and statistical models may not be able produce reliable forecasts  Goal is to identify probable scenarios as guidelines for making operational decisions  Steps to the process  Manager identifies decision options and scenarios  Management assigns probabilities to future outcomes  Forecasters calculate new probabilities as new data become available (Bayesian calculations)  When one scenario becomes more likely, manager selects the appropriate course of action

7. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 7 Conclusions Forecasting changes  Two model types 2. Decision-directed forecasting: Example  Casino actions in Las Vegas following 9/11  Following 9/11, casinos would need to incorporate a sharp change in macroeconomic environment  Decision actions included: Business as usual (revenue decreases are short term), Plan for short term disruption (mandatory vacations, hold on capital projects, etc.), Recovery might never fully materialize (short and long term cutbacks), New business strategy (new ventures in other industries)

8. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 8 Conclusions Forecasting changes  Two model types 2. Decision-directed forecasting: Example

9. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 9 Conclusions Forecasting changes  Two model types 2. Decision-directed forecasting: Example Probabilities are updated each month. By January 2002, Scenarios A and D are highly unlikely By May 2002, Scenario B is the most likely

10. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 10 Conclusions Forecasting changes  Two model types 2. Decision-directed forecasting As the probabilities are updated each month, a manager can adjust the business plan Approach can also be used for new product introductions, responses to competitors, or new pricing strategies Other methods for choosing among scenarios include: Payoff tables and Decision Trees Techniques are available for assisting in choosing the starting probabilities

11. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 11 Conclusions Evaluating forecasting abilities of software  Background Companies make IT choices based on broad goals such as supply chain management (SCM) and enterprise resource planning (ERP) needs Demand planning software is often included with SCM or ERP systems  Stand-alone forecasting software requires additional IT knowledge, time, and funding  Decision makers must balance the sophistication of the forecasting techniques against the ease of interconnectivity of the software What is the best way for evaluating the forecasting capabilities of the demand planning packages?

12. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 12 Conclusions Evaluating forecasting abilities of software  Steps in evaluation Designing a test drive  Need to test the software forecast accuracy on the organization’s data  Work with vendors on a pilot study  Sample data should include a broad range of products and include all levels of the hierarchies  Keep in mind any model requirements with respect to the length of the dataset  Include various demand patterns (intermittent, new product, stable demand, etc.)

13. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 13 Conclusions Evaluating forecasting abilities of software  Steps in evaluation Evaluating results  Choose an evaluation period (ex post)  Choose an accuracy calculation  Preference should be given to the current calculation to ensure that the software provides a reduction in error Comparing performance  Accuracy improvement may not necessarily be due to the forecasting algorithms  Other benefits from the software can include  Incorporation of point-of-sale data  Ability to support Collaborative Planning, Forecasting, and Replenishment (CPFR)

14. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 14 Forecasting software options Modules in Broad Scope Statistical Software: SAS (ETS) STATASPSS Insightful ( S+FinMetrics)R Business Forecasting Software: AutoboxStampForecast Pro SmartForecastsDecision Time Demand Planning: McConnell-Chase (Forecasting for Demand) Demand Works (Smoothie) Oracle (Peoplesoft Enterprise Demand Planning) John Galt (Atlas Planning Suite) Demand Management (Demand Solutions) Delphus (Peer Planner) Modules in SAP, I2, and JDA (formerly Manugistics) Econometrics Packages: E-Views RATS and CATS PC Give See http://www.oswego.edu/~economic/econsoftware.htm

15. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 15 Conclusions A word on Excel  Overview Previous sessions of workshop used Excel  Excel can and is used for forecasting  Good for quick estimates and general guideline  However, Excel is not a robust forecasting tool  Specialized forecasting software (or forecasting capabilities within a demand planning software) is recommended in support of a forecasting process

16. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 16 Conclusions A word on Excel  Tips for using of Excel Advantages of Excel  Allows data to be visible  Formulas are accessible and can be edited  Calculations can be saved  Scenarios can be planned using parametric analysis Caution of Excel  Excel is not a statistical software  Statistical procedures do not yield accurate or precise solutions

17. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 17 Conclusions A word on Excel  Tips for using of Excel Guidelines  Match the tool to the job  Excel is a good tool to estimate a range  Understand how to use the tool to accomplish the job  Excel makes it easy for users to think they are properly applying the wrong model to a data set  Users can program statistical and forecasting equations into Excel to obtain correct calculations  Increasing the capabilities of the tool can increase the quantity and quality of the jobs finished by the tool  Consider add-ons for Excel  Education for users

18. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 18 Conclusions A word on Excel  Tips for using of Excel Useful resources for analysis on Excel  http://www.daheiser.info/excel/frontpage.html  Website documents the error and some potential solutions to those errors  Several studies documenting inconsistencies of statistical software  (McCullough 1999) SAS, SPSS  (McCullough and Wilson 2005) Excel 2003  Studies also show a willingness to correct errors by SAS and SPSS but not Microsoft (McCullough and Wilson 2002)

19. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 19 Conclusions A word on Excel  Nonlinear curves example In calculating coefficients of nonlinear functions (such as exponential functions), Excel transforms the data into a line  Transformation leads to incorrect calculations  Optimal coefficients of a function are found by a model error metric (such as the root mean square error or RMSE) Exponential function defined as a: intercept (assumed to be 0) b: growth rate c: lower limit T: time (year for this example) e: mathematical constant of 2.718 growth rate as a percent per year = e b -1

20. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 20 Conclusions A word on Excel  Nonlinear curves example Sample data Graph

21. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 21 Conclusions A word on Excel  Nonlinear curves example Choosing an exponential trend line in Excel Excel will find the optimal coefficient of the following transformed data

22. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 22 Conclusions A word on Excel  Nonlinear curves example Excel will convert the result from the linear function back into the exponential function for the fit line of where growth rate as a percent per year = e.1561 -1 = 16.89% This is the annual growth rate of sales Notice that Excel assumes an intercept of zero

23. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 23 A word on Excel  Nonlinear curves example A statistical software will optimize the exponential function directly for a fit line of where growth rate as a percent per year = e.1608 -1 = 17.44% This is the annual growth rate of sales Notice that there is a non-zero intercept Conclusions

24. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 24 Conclusions A word on Excel  Nonlinear curves example Comparing the results

25. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 25 Conclusions Creating a forecaster training program 1. Program outline Program mission statement Clearly defined objectives based on ideal forecaster characteristics Resource requirements 2. Create a measurement baseline Classify objectives into core learning areas  Forecasting and supply chain concepts  Technical and software skills  Process management and product knowledge  Interpersonal skills Collect data on the current ability level of the forecasters

26. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 26 Conclusions Creating a forecaster training program 3. Create development plan through gap analysis Define critical skills corresponding to core learning areas Compare current forecaster knowledge with critical skills Prioritize the gaps based upon criteria such as importance of core area, size of gap, quantity of forecasters, and estimated resources needed to close the gap 4. Implementation of education program Define the content of core learning areas Outline methods used for education  Separate sessions, week long workshops, etc.  Consultants or internal resources  Quantity of IT support needed  Use of mentoring, coaching, on-the-job training

27. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 27 Conclusions Creating a forecaster training program 5. Evaluation and areas of improvement for program Maintenance of forecaster education  Continuing education internally, plans for using external resources or outside education Lessons learned from implementation  Changing the course schedule or material covered  Incorporating participant feedback Defining additional supporting infrastructure  Guiding principles for demand management education  Roles and responsibilities of the instructors  Future expectations of additions to core areas

28. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 28 Conclusions Resources  Forecasting portals www.appliedforecasting.com  Latest news on forecasting events, tool and papers www.forecastingeducation.com  Listing of and links to software reviews and special reports on forecasting software www.demandplanning.net  S&OP and demand management consulting and courses

29. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 29 Conclusions Resources  Forecasting organizations International Institute of Forecasting  www.forecastingprinciples.com Institute of Business Forecasting  www.ibf.org Both offer  Courses, seminars, and publications

30. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 30 Special Features Special Features Overcoming Challenges in Operational Forecasting Projects Overcoming Challenges in Operational Forecasting Projects The Organizational Politics of Forecasting:6 Steps to Overcome Bias The Organizational Politics of Forecasting:6 Steps to Overcome Bias Forecast Accuracy Metrics for Inventory Control Forecast Accuracy Metrics for Inventory Control The What, Why, and How of Futuring for Forecasters The What, Why, and How of Futuring for Forecasters Benchmarking of Forecast Accuracy Benchmarking of Forecast Accuracy plus Software Reviews, Book Reviews and briefs on Hot New Research plus Software Reviews, Book Reviews and briefs on Hot New Research www.forecasters.org/foresight Concise, objective and readable articles on issues essential to the practicing forecaster. FORESIGHT: Concise, objective and readable articles on issues essential to the practicing forecaster.

31. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 31 Conclusions References (Forecasting changes)  Batchelor, Roy. 2009. Forecasting sharp changes. Foresight. Spring: 7-12.  Custer, Stephen and Don Miller. 2007. Decision-directed forecasting for major disruptions: The impact of 9/11 on Las Vegas gaming revenues. Foresight. Summer: 29-35.  Jain, Chaman L. and Jack Malehorn. 2005. Practical Guide to Business Forecasting (2nd Ed.). Flushing, New York: Graceway Publishing Inc.  Sephton, Peter. 2009. Predicting recessions: A regression (probit) model approach. Foresight. Winter: 26-32.

32. Session 8 Joseph Ogrodowczyk, Ph.D. Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 32 Conclusions References (Forecasting software)  Fields, Paul. 2006. On the use and abuse of Microsoft Excel. Foresight. February: 46-47.  Hesse, Rick. 2006. Incorrect nonlinear trend curves in Excel. Foresight. February: 39-43.  Hoover, Jim. 2005. How to evaluate the forecasting ability of demand- planning software. Foresight. June: 47-49.  McCullough, Bruce D. 1999. Assessing the reliability of statistical software: Part 2. The American Statistician. 53(2): 149-159.  McCullough, Bruce D. and B. Wilson. 2005. On the accuracy of statistical procedures in Microsoft Excel 2003. Computational Statistics and Data Analysis. 49(4): 1244-1252.  McCullough, Bruce D. and B. Wilson. 2002. On the accuracy of statistical procedures in Microsoft Excel 2000 and Excel XP. Computational Statistics and Data Analysis. 40(4): 713-721.  McCullough, Bruce D. 2006. The unreliability of Excel’s statistical procedures. Foresight. February: 44-45.