TM 745 Forecasting for Business & Technology Paula Jensen 1st Session 1/11/2012: Chapter 1 Introduction to Business Forecasting South Dakota School of Mines and Technology, Rapid City Agenda Class Overview/Syllabus highlights Assignment Chapter 1 by Guest Lecturer Dr. Stuart Kellogg Business Forecasting 6th Edition J. Holton Wilson & Barry Keating McGraw-Hill Instructor Information Instructor Paula Jensen Office Location IE/CM 320 Office Hours Office Phone CM 320 M,W 2:00-3:00 pm IER T,TH, F 11:00-11:50 AM E-mail for an appointment outside of office hours. 605-394-1770 E-mail Website paula.jensen@sdsmt.edu pjensen.sdsmt.edu 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 Keating McGraw-Hill One case from Harvard Business Review Prerequisites 1) 2) 3) Probability and Statistics Understanding of Excel/Spreadsheet software. It is expected that students will be able to access and download internet files. 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 Evaluation Procedures 60% - 2 Exams 20% - 1 Project 20% - Interaction A B C D F 90-100 80-89 70-79 60-69 < 60 Exams Students signed up for the on-campus section are required to take the test at the given time. Make-up Exams available for UniversityApproved reasons. All exams are open engineering notebook, and use of a scientific calculator is encouraged. Distance Students need proctors- See Syllabus for further details Project & Interaction Grades Project Criteria to be discussed through Class Interaction Assignments will include discussions, quizzes, and other assignments 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!) 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 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. First Assignment Send me a contact info e-mail. Include all important contact information phones, email, 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. Introduction to Business Forecasting Quantitative Forecasting Has Become Widely Accepted Intuition alone no longer acceptable. Used in Future Sales Inventory needs Personnel requirements Judgment still is needed 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 Forecasting in Business Today mid-80’s 94% large American firms used 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 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. 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 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) 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 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, Collaborative Forecasting 1. 2. 3. 4. 5. 6. 7. Manufacturer’s forecast > Retailers Retailer’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 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 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) New-Product Forecasting A special consideration Surveys Test marketing ( Indy, K-zoo, not KC) Analog Forecasts: movie toys New Product Short Life Cycle New Product Short Life Cycle New Product Short Life Cycle Product Life Cycle Bass Model Two Simple Naive Models (4th) Two Simple Naive Models (4th) Evaluating Forecasts Evaluating Forecasts Evaluating Forecasts Measurement Errors Soda Demand (1,000,000's) 2 Month t At At Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Sum = Avg = St. Dev = 1 2 3 4 5 6 7 8 9 10 11 12 78 6.5 2.47 2.31 2.24 2.27 2.15 2.34 2.23 2.48 2.46 2.58 2.74 2.72 28.99 2.416 0.193 6.1009 5.3361 5.0176 5.1529 4.6225 5.4756 4.9729 6.1504 6.0516 6.6564 7.5076 7.3984 70.44 (At-Abar) 2 0.002934 0.011201 0.030917 0.021267 0.070667 0.005751 0.034534 0.004117 0.001951 0.026951 0.105084 0.092517 0.408 Standard Deviation S (X t nX ) 2 n 1 0.408 11 0193 . Measurement Errors Soda Demand (1,000,000's) 2 Month t At At Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Sum = Avg = St. Dev = 1 2 3 4 5 6 7 8 9 10 11 12 78 6.5 2.47 2.31 2.24 2.27 2.15 2.34 2.23 2.48 2.46 2.58 2.74 2.72 28.99 2.416 0.193 6.1009 5.3361 5.0176 5.1529 4.6225 5.4756 4.9729 6.1504 6.0516 6.6564 7.5076 7.3984 70.44 (At-Abar) 2 0.002934 0.011201 0.030917 0.021267 0.070667 0.005751 0.034534 0.004117 0.001951 0.026951 0.105084 0.092517 0.41 Standard Deviation S X 2 t nX 2 n 1 70.44 12(2.416) 2 11 0193 . Measurement Errors Soda Demand (1,000,000's) Month t Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Sum = Avg = St. Dev = 1 2 3 4 5 6 7 8 9 10 11 12 78 6.5 At |At - Abar| 2.47 2.31 2.24 2.27 2.15 2.34 2.23 2.48 2.46 2.58 2.74 2.72 28.99 2.416 0.193 0.0542 0.1058 0.1758 0.1458 0.2658 0.0758 0.1858 0.0642 0.0442 0.1642 0.3242 0.3042 1.91 0.159 MAE |X MAE t X | n | 2.47 2.416 | | 2.31 2.416 | ... 12 0.159 Measurement Errors Soda Demand (1,000,000's) Month t Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Sum = Avg = St. Dev = 1 2 3 4 5 6 7 8 9 10 11 12 78 6.5 At |At - Abar| 2.47 2.31 2.24 2.27 2.15 2.34 2.23 2.48 2.46 2.58 2.74 2.72 28.99 2.416 0.193 0.0542 0.1058 0.1758 0.1458 0.2658 0.0758 0.1858 0.0642 0.0442 0.1642 0.3242 0.3042 1.91 0.159 MAE MAE | X t X | n | 2.47 2.416 | | 2.31 2.416 | ... 12 0.159 In general, MAE 0.8S 0.8(.193) = 0.154 Measurement Errors Soda Demand (1,000,000's) Month t Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Sum = Avg = St. Dev = 1 2 3 4 5 6 7 8 9 10 11 12 78 6.5 At (At - Ahat) 2.47 2.31 2.24 2.27 2.15 2.34 2.23 2.48 2.46 2.58 2.74 2.72 28.99 2.416 0.193 0.0542 -0.1058 -0.1758 -0.1458 -0.2658 -0.0758 -0.1858 0.0642 0.0442 0.1642 0.3242 0.3042 0.00 0.000 Mean Error e ME t n (2.47 2.416) (2.31 2.416) ... 12 0.0 Measurement Errors Soda Demand (1,000,000's) Month t At Ft et |et| e t2 Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Sum = Avg = St. Dev = 1 2 3 4 5 6 7 8 9 10 11 12 78 6.5 2.47 2.31 2.24 2.27 2.15 2.34 2.23 2.48 2.46 2.58 2.74 2.72 28.99 2.416 0.193 2.416 2.416 2.416 2.416 2.416 2.416 2.416 2.416 2.416 2.416 2.416 2.416 28.99 2.416 0.054 -0.106 -0.176 -0.146 -0.266 -0.076 -0.186 0.064 0.044 0.164 0.324 0.304 0.00 0.000 0.054 0.106 0.176 0.146 0.266 0.076 0.186 0.064 0.044 0.164 0.324 0.304 1.91 0.159 0.003 0.011 0.031 0.021 0.071 0.006 0.035 0.004 0.002 0.027 0.105 0.093 0.41 0.034 Using Multiple Forecasts Use judgment Reference: Combining Subjective and Objective Forecasts. Sources of Data Internal records Timeliness & formatting problems Government & syndicated services (good) Web Used by gov’t & syndicated Sites changes Domestic Car Sales (4th ed ex.) Domestic Car Sales (4th ed ex) Domestic Car Sales (4th ed ex) Forecasting Fundamentals Soda Demand (1,000,000's) Month t At Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun 1 2 3 4 5 6 7 8 9 10 11 12 2.47 2.31 2.24 2.27 2.15 2.34 2.23 2.48 2.46 2.58 2.74 2.72 Consider the following sales data over a 12 month period. Summary Statistics Mean Soda Demand (1,000,000's) Month t At Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun 1 2 3 4 5 6 7 8 9 10 11 12 2.47 2.31 2.24 2.27 2.15 2.34 2.23 2.48 2.46 2.58 2.74 2.72 X X t n 2.47 2.31 ... 2.72 12 2.42 Summary Statistics Sorted Demand t At 5 7 3 4 2 6 9 1 8 10 12 11 2.15 2.23 2.24 2.27 2.31 2.34 2.46 2.47 2.48 2.58 2.72 2.74 Median Xm 2.34 2.46 2 2.40 Summary Statistics Mode Soda Demand (1,000,000's) Month t At Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun 1 2 3 4 5 6 7 8 9 10 11 12 2.47 2.31 2.24 2.27 2.15 2.34 2.23 2.48 2.46 2.58 2.74 2.72 No number repeats no mode Summary Statistics Sorted Demand t At 5 7 3 4 2 6 9 1 8 10 12 11 2.15 2.23 2.24 2.27 2.31 2.34 2.46 2.47 2.48 2.58 2.72 2.74 Modal Range 2.31 - 2.47 Summary Statistics Soda Sales Frequency 40 30 20 10 0 0.5 1.0 1.5 2.0 2.5 Volume Modal Range 2.5 to 3.0 3.0 More Overview of the Text Ch Ch Ch Ch Ch Ch Ch Ch Ch 1 2 3 4 5 6 7 8 9 Intro Forecast Process (more Intro) MA & Exponential Smoothing Regression Multiple Regression Time-Series Decomposition ARIMA Box-Jenkins Combining Forecasts Forecast Implementation Upcoming Events No Class next week Figure out what your log-in/password is to D2l if you have not yet. It is the same as WebAdvisor - (Here is the website for D2L: https://d2l.sdbor.edu/) Watch U-tube videos posted on Website Discussions on D2L- Ready 1/20/2012 Read Chapter 2 for Class on 1/25/2012