TM 745 Forecasting for Business & Technology Paula Jensen

1. TM 745 Forecasting for Business & TechnologyPaula Jensen 1st Session 1/11/2012: Chapter 1 Introduction to Business Forecasting South Dakota School of Mines and Technology, Rapid City

2. Agenda • Class Overview/Syllabus highlights • Assignment • Chapter 1 by Guest Lecturer Dr. Stuart Kellogg • Business Forecasting 6th Edition J. Holton Wilson & Barry KeatingMcGraw-Hill

4. Course Materials • Powerpoints & Class Information • Website: pjensen.sdsmt.edu via the ENGM 745 • Engineering Notebook – 9-3/4" x 7-1/2", 5x5 quad-ruled, 80-100 pp. (approx.) • Engineering/Scientific calculator • Book: Business Forecasting 6th Edition J. Holton Wilson & Barry KeatingMcGraw-Hill • One case from Harvard Business Review

5. Prerequisites • Probability and Statistics • Understanding of Excel/Spreadsheet software. • It is expected that students will be able to access and download internet files.

6. Course Objective • to educate prospective managers about the philosophies and tools of sound forecasting principles • to provide technical managers with a theoretical basis for statistical forecasting • to provide technical managers with the fundamentals methods available for technological and qualitative forecasts

7. Evaluation Procedures

8. Exams • Students signed up for the on-campus section are required to take the test at the given time. • Make-up Exams available for University-Approved reasons. • All exams are open engineering notebook, and use of a scientific calculator is encouraged. • Distance Students need proctors- See Syllabus for further details

9. Project & Interaction Grades • Project Criteria to be discussed through Class • Interaction Assignments will include discussions, quizzes, and other assignments

10. Email Policy: • If you are writing about issues relating to the class, make sure the subject line reads ENGM 745: (subject info) so I can sort my e-mails and answer accordingly. • Please be professional in your e-mails. (no texting lingo!)

11. Academic Honesty • Cheating: use or attempted use of unauthorized materials, information or study aids • Tampering: altering or interfering with evaluation instruments and documents • Fabrication: falsification or invention of any information • Assisting: helping another commit an act of academic dishonesty • Plagiarism: representing the words or ideas of another as one's own

12. ADA Students with special needs or requiring special accommodations should contact the instructor and/or the campus ADA coordinator, Jolie McCoy, at 394-1924 at the earliest opportunity.

13. First Assignment • Send me a contact info e-mail. Include all important contact information phones, e-mail, and mail addresses. Preferred mode. • Send via e-mail a Current Resume • Problems 1,4, & 8 in chapter 1 – I don’t need these sent. I will post solutions.

14. Introduction to Business Forecasting

16. Quantitative Forecasting Has Become Widely Accepted • Intuition alone no longer acceptable. • Used in • Future Sales • Inventory needs • Personnel requirements • Judgment still is needed

17. Forecasting in Business Today • Two Professional Societies • Accountants: costs, revenues (tax plans) • Personnel: recruitment, changes in workforce • Finance: cash flows • Production: raw-material needs & finished goods inventory • Marketing: sales

18. Forecasting in Business Today • mid-80’s 94% large American firmsused sales forecasts • Krispy Kreme • New stores model with errors of < 1% • Bell Atlantic • Data warehouse (shared) of monthly history • Subjective, regression, time series, • Forecasts monitored & compared

19. Forecasting in Business Today • Columbia Gas (natural gas company) • Design Day Forecast (supply) • Gas supply, transportation capacity, storage capacity, & related • Daily Operational (demand) • Regression on temperatures, wind speed, day of the week, etc.

20. Forecasting in Business Today • Segix Italia (Pharmaceutical company) • Marketing forecasts for seven main drugs • Targets for sales representatives • Pharmaceuticals in Singapore • Glaxo-Wellcome, Bayer, Pfizer, Bristol-Myers Squibb • HR, Strategic planning, sales • Quantitative & judgments

21. Forecasting in Business Today • Fiat Auto (2 million vehicles annually) • All areas use centrally prepared forecasts • Use macro-economic data as inputs • From totals sales to SKU’s • Douglas Aircraft • Top down (miles flown in 32 areas) • Bottom up (160 Airlines studied)

22. Forecasting in Business Today • Trans World Airlines • Uses a top down (from total market) approach for sales • Regression & Trend models • Brake Parts Inc. • 250,000 SKU’s • Forecast system saves $6M/mo. • 19 time series methods

23. Forecasting in the Public and Not-for-Profit Sectors • Police calls for service by cruiser district • State government • Texas: Personal income, electricity sales, employment, tax revenues • California: national economic models, state submodel, tax revenues, cash flow models • Hospitals: staff, procedures,

24. Collaborative Forecasting • Manufacturer’s forecast > RetailersRetailer’s extra info > Manufacturers • Lower Inventory • Fewer unplanned shipments or runs • Reduced Stockouts • Increase customer satisfaction • Better sales promotions • Better new product intros • Respond to Market changes

25. Computer Use and Quantitative Forecasting • Computer use common by mid 80’s • Packages run from $100 to thousands • PC systems generally have replaced mainframes for state government work • PC’s dominant at conferences • Chase of Johnson & Johnson • Forecasting 80% math, 20% judgment

26. Subjective Forecasting Methods • Only way to forecast 40 years out • Sale-Force Composite • Inform sales staff of data • Bonus for beating the forecast ?? • Surveys of Customers/Population • Jury of Executive Opinion • The Delphi Method (Experts)

27. New-Product Forecasting • A special consideration • Surveys • Test marketing ( Indy, K-zoo, not KC) • Analog Forecasts: movie toys

28. New Product Short Life Cycle

29. New Product Short Life Cycle

30. New Product Short Life Cycle

31. Product Life Cycle

32. Bass Model

33. Two Simple Naive Models (4th)

34. Two Simple Naive Models (4th)

35. Evaluating Forecasts

36. Evaluating Forecasts

37. Evaluating Forecasts

38. Measurement Errors Standard Deviation

39. Measurement Errors Standard Deviation

40. Measurement Errors MAE

41. Measurement Errors MAE In general, 0.8(.193) = 0.154

42. Measurement Errors Mean Error

43. Measurement Errors

44. Using Multiple Forecasts • Use judgment • Reference:Combining Subjective andObjective Forecasts.

45. Sources of Data • Internal records • Timeliness & formatting problems • Government & syndicated services (good) • Web • Used by gov’t & syndicated • Sites changes

46. Domestic Car Sales (4th ed ex.)

47. Domestic Car Sales (4th ed ex)

48. Domestic Car Sales (4th ed ex)

49. Forecasting Fundamentals Consider the following sales data over a 12 month period.

50. Summary Statistics Mean