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Forecasting Methods

Forecasting Methods. Chapter 9. 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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Forecasting Methods

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  1. Forecasting Methods Chapter 9

  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 • Seasonal - Fluctuate Over Time • Cyclical - Movements Over a Trend • Random Variations Create Uncertainty

  9. Data Pattern

  10. 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

  11. 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

  12. 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

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

  14. 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

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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

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

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

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

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

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

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

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