TM 745 Forecasting for Business & Technology Dr. Frank Joseph Matejcik

1. TM 745 Forecasting for Business & TechnologyDr. Frank Joseph Matejcik 10th Session 4/28/08: Chapter 9 Forecast Implementation South Dakota School of Mines and Technology, Rapid City

2. Tentative Schedule Chapters Assigned 28-Jan 1 problems 1,4,8 e-mail, contact 4-Feb 2 problems 4, 8, 9 11-Feb 3 problems 1,5,8,11 18-Feb President’s Day 25-Feb 4 problems 6,10 3-Mar 5 problems 5,8 10-Mar Exam 1 Ch 1-4 Revised 17-Mar Break 24-Mar Easter 31-Mar 6 problems 4, 7 Chapters Assigned 7-Apr 7 3,4,5(series A) 7B 14-Apr Out of town No class 21-Apr 8 Problem 6 28-Apr 9 05-May Final

3. Web Resources • Class Web site on the HPCnet system • http://sdmines.sdsmt.edu/sdsmt/directory/courses/2008sp/tm745M021 • Streaming video http://its.sdsmt.edu/Distance/ • Answers will be online. Linked from ^ • The same class session that is on the DVD is on the stream in lower quality. http://www.flashget.com/ will allow you to capture the stream more readily and review the lecture, anywhere you can get your computer to run.

4. Agenda & New Assignment • Chapter 9 no problems • Final is next week • Study guide is posted • Chapter 9 Forecast Implementation

5. 9 Forecast Implementation • Keys (a list) • Forecast Process (steps) • Choosing the right forecast • New Product • Artificial Intelligence

6. Keys to Obtaining Better Forecasts • 1. Understand what forecasting is & is not • Focus on management processes & controls, not computers; Establish forecasting group • Implement management control systems before selecting forecasting software • Derive plans from forecasts • Distinguish between forecasts and goals • Forecasting is acknowledged as a critical • Accuracy emphasized; not game-playing

7. Keys to Obtaining Better Forecasts • 2. Forecast demand, plan supply • Don’t use shipments as actual demand • Identify sources of demand information • Build systems to capture key demand data • Get improved customer service & capital planning

8. Keys to Obtaining Better Forecasts • 3. Communicate, cooperate, & collaborate • Avoids duplication & Mistrust of "official“ forecast • Creates understanding of impact throughout • Establish a cross-functional approach to forecasting

9. Keys to Obtaining Better Forecasts • 3. Communicate, cooperate, & collaborate • Establish an independent forecast group that sponsors cross-functional collaboration • All relevant information used to generate forecasts • Forecasts trusted by users • More accurate & relevant forecasts

10. Keys to Obtaining Better Forecasts • 4. Eliminate islands of analysis • Mistrust & inadequate information leading different users to create their own forecasts • Build 1 "forecasting infrastructure" • More accurate, relevant, & credible forecasts • Provide training for both users & developers of forecasts • Optimized investments in information & communication systems

11. Keys to Obtaining Better Forecasts • 5. Use tools wisely • Relying solely on qualitative or quantitative • Integrate quantitative & qualitative methods • Identify sources of improved accuracy & increased error • Provide instruction • Process improvement in efficiency & effectiveness

12. Keys to Obtaining Better Forecasts • 6. Make it important • Have accountability for poor forecasts • So developers can understand forecast uses • Training developers to understand implications of poor forecasts • Include forecast performance in performance plans & reward systems • Striving for accuracy & credibility

13. Keys to Obtaining Better Forecasts • 7. Measure, measure, measure • Know if the firm is getting better • Measure accuracy at relevant levels of aggregation • Ability to isolate sources of forecast error • Establish multidimensional metrics • Incorporate multilevel measures • Measure accuracy whenever & wherever forecasts are adjusted

14. Keys to Obtaining Better Forecasts • 7. Measure, measure, measure • Forecast performance can be included in individual performance plans • Sources of errors can be isolated and targeted for improvement • Achieve greater confidence in forecast process

15. The Forecast Process • 1. Specify objectives • Articulate role of forecast in decisions • If forecasts don’t effect decisions, Why? • 2. Determine what to forecast • Sales: revenue or units? • weekly, annually, quarterly? • Communicate with user

16. The Forecast Process • 3. Identify time dimensions • Horizon • Frequency • Urgency • 4. Data considerations • Internal needs database management & disaggregation: time, unit, region • External gov’t, trade association

17. The Forecast Process • 5. Model selection (next section) • 6. Model evaluation • Less important for subjective methods • Use holdout method if quantitative • Go back to step five if a problem • 7. Forecast preparation • Try for multiple & multiple types

18. The Forecast Process • 8. Forecast presentation • Management must understand & be confident (corporate culture) • Oral & written • same time & same level • be generous with charts etc. • 9. Tracking results • process continues • reviews open, objective, & positive

19. Choosing the Right Forecasting Techniques • Few hard and fast rules (guidelines) • Focus on data, time, & personnel • Subjective Methods • Sales force composite • short to medium term • Preparation time is quick once set up • Customer surveys • medium to long term, take 2-3 months • survey research is a profession

20. Choosing the Right Forecasting Techniques • Subjective Methods • Jury of Executive Opinion • Requires Expertise • Is relatively quick to prepare • Delphi • long to medium term • useful for new products • can be slow; computers help • alternatives are better

21. Choosing the Right Forecasting Techniques • Objective Methods • Naive (little data, sometimes good) • Moving Averages (easy, little data) • Exponential Smoothing Simple • Need to establish weight • Easy to compute, quick • Adaptive response ES • short term, no seasonality • Users need little background

22. Choosing the Right Forecasting Techniques • Objective Methods • Holt's ES • short term, no seasonality, trend included • Users need little background • Winters’ ES • short term, seasonality, trend included • Need 4 or 5 observations per season • Need computer for updates • Users need little background (tell them about weighted dates)

23. Choosing the Right Forecasting Techniques • Objective Methods • Regression-Based • Trend (10 observations, quick to develop, easy for users, modest developer skills) • Trend with Seasonality (Need 4 or 5 observations per season, short to medium term, need a computer, usually little sophistication) • Causal (10 observations per independent variable, short, medium, or long term, developers need regression skills.)

24. Choosing the Right Forecasting Techniques • Objective Methods • Time-Series Decomposition (two peaks and two troughs per cycle, 4 to 5 seasons of data, can use turning points, short to medium range, modest sophistication, managers like it.) • ARIMA (managers don’t like it, it takes a skillful developer, Need a computer to do ACF and PACF plots)

25. New Product Forecasting • Product Life Cycle (PLC) curve

26. New Product Forecasting • Analog forecasts • Similar products • Think Christmas movie toys • Test marketing • Pick a “smaller” representative place • Ex. given is Indianapolis • Product clinics (panel lab study) • Type of Product Affects NPF

27. Artificial Intelligence and Forecasting • Expert systems • Neural Networks Summary • Difficult task; many considerations • New opportunities

28. Using “ProCastTM” in ForecastXTM to Make Forecasts • It is okay now that you know what you are doing. • You understand that a selection method is choosing the best of things that you already know.