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ENGM 745 Forecasting for Business & Technology Paula Jensen

ENGM 745 Forecasting for Business & Technology Paula Jensen. Chapter 10 Forecast Implementation. South Dakota School of Mines and Technology, Rapid City. Forecast Implementation. Keys (a list) Forecast Process (steps) Choosing the right forecast New Product Artificial Intelligence.

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ENGM 745 Forecasting for Business & Technology Paula Jensen

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  1. ENGM 745 Forecasting for Business & TechnologyPaula Jensen Chapter 10 Forecast Implementation South Dakota School of Mines and Technology, Rapid City

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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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)

  20. 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.)

  21. 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)

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

  23. 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

  24. Data Mining (6th) • Works with large databases (unrelated?) • Diapers and Beer • Sports Cars have fewer insurance claims

  25. Summary • Difficult task; many considerations • New opportunities

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