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HFT 3431

HFT 3431. Chapter 9 Forecasting Methods. Forecasting. How Important Is Forecasting? Is Forecasting Only Financial? How Is It Done? Does Forecasting Help Be Successful? What Are Forecasting Limitations? Does Forecasting Differ From Planning?. Forecasting.

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HFT 3431

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  1. HFT 3431 Chapter 9 Forecasting Methods

  2. Forecasting • How Important Is Forecasting? • Is Forecasting Only Financial? • How Is It Done? • Does Forecasting Help Be Successful? • What Are Forecasting Limitations? • Does Forecasting Differ From Planning?

  3. Forecasting • What Is the Difference Between Seasonal and Cyclical Patterns? • How Do Quantitative and Qualitative Forecasting Methods Differ?

  4. Forecasting • How Is a Moving Average Calculated? • When Are Causal Forecasting Approaches Useful?

  5. Implicit versus Explicit Forecasts • Implicit - (Intuitive) Unsystematic Imprecise Difficult to Evaluate

  6. Implicit versus Explicit Forecasts • Explicit - (Analytical) Systematic Reasonably Reliable and Accurate Rational Evaluation

  7. The Nature of Forecasting • Done by All Levels of Management • Looks at the Future • Involves Uncertainties • Based on Historical Data • Less Accurate Than Desired • Extensive Use of Naive Models

  8. The Nature of Forecasting • Large Properties Need to Use More Sophisticated Models • Trend - Straight Line Projection • Long run estimate-several years • Seasonal - Fluctuate Over Time • Cyclical - Movements Over a Trend • Movements around a trend line • Random Variations Create Uncertainty

  9. Data Pattern

  10. Formal Forecasting Methods • Qualitative • Emphasize human judgment • Quantitative • Causal & time period approaches

  11. Qualitative Methods • Market Research • Gather information from potential customers • Juries of Executive Opinion • Top executive jointly prepare forecasts • Sales Force Estimates • Bottom up approach from unit managers • Delphi Method • Formal process with a group of experts

  12. Quantitative Methods • Time Series • Naïve • Smoothing • Decomposition • Causal methods • Regression Analysis • Econometrics

  13. Time Series • Naïve • Simples rules • Smoothing • Uses moving average or recent pst values (exponential smoothing) • Decomposition • Time series broken down into cyclical, seasonality randomness

  14. Forecasting Methods • Naïve Method - Multiply current sales level (or sales price) by a percentage increase (or decrease); or add (or subtract) a fixed amount. • This method does not use any analytical or scientific method

  15. Forecasting Method • Naïve Example • Current year sales level is 1,000 units • Current year sales price is $15.00 • Next year levels increase 10% • Next year price decreases $0.50 • Current Year Total Sales Equals 1,000 * $15.00 = $15,000

  16. Forecasting Method • Naïve Example - Continued • Next year sales level equals 1,000 * 1.10 = 1,100 • Next year price equals $15.00 - $0.50 = $14.50 • Next year Total Sales equals 1,100 * $14.50 = $15,950

  17. Forecasting Methods • Moving Averages - Sum of Activity in Previous N Periods Divided by N, Where N Is the Number of Periods

  18. Moving Average • Page 408; Forecast week 13 using 3 week moving average Thus use data in weeks 10, 11, 12 and divide by 3 (1025 + 1000 + 1050) / 3 = 1025

  19. Forecasting Methods • Exponential Smoothing - Uses a Smoothing Constant and Recent Actual and Forecasted Activity to Estimate Future Activity

  20. Exponential Smoothing • Forecast for Period 3: • Using Data below • Period 1 Forecast 1,025 • Period 1 Actual 1,000 • Period 2 Forecast 1,020 • Period 2 Actual 1,050

  21. Exponential Smoothing • Forecast for Period 3: • Step 1 - Determine Smoothing Constant Period 2 Forecast - Period 1 Forecast Period 1 Actual - Period 1 Forecast (1,020 - 1025 ) / (1,000 – 1,025 ) = 0.20

  22. Exponential Smoothing • Step 2 • Forecast for Week 3 • Wk 3 F = Wk 2 F + SC(WK2 Act – Wk2 F) • Wk 3 F = 1,020 + 0.2(1,050 - 1,020) • Wk 3 F = 1,020 + 0.2(30) • Wk 3 F = 1,026

  23. Forecasting Methods • Causal - Regression Analysis Which Is Estimating an Activity (Dependent Variable) on the Basis of Other Activities (Independent Variables)… • How Closely Related Is Measured by Coefficient of Correlation and Coefficient of Determination

  24. Forecasting Methods • Coefficient of Correlation ( r )– is the measure of the relationship between the dependent and independent variables. Closer to 1 the stronger the relationship. • Coefficient of Determination ( r2 ) – reflects the extent to which the change in the independent variable explains the change in the dependent variable

  25. Regression Analysis • Formula Y = A + BX Y is the dependent variable A is a constant B is a multiplier X is the independent variable

  26. Regression Analysis • Page 412 Y = 370 + 1.254x If X = 3,000 rooms Y = 370 + 1.254(3000) Y = 6,013 meals

  27. Forecasting Limitations • Scarcity of Data • Assumes Continuation of Trends • Unforseeable Occurrences

  28. Qualitative Methods • Based on human judgment • Market Research • Jury of Executive Opinion • Sales force estimates • Delphi Method

  29. Consideration In Choosing a Forecasting Method • Effectiveness in Providing Information • Cost of Implementation • Frequency of Forecast Updates • Turnaround Time of Forecasting

  30. Consideration In Choosing a Forecasting Method • Size and Complexity of Operation • Forecasting Skills of Personnel • Purpose of Making Forecast

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