Forecasting

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From Hospitality finance Class

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Forecasting

  1. 1. What is Forecasting? Process of predicting a future event Underlying basis of ?? all business decisions  Production  Inventory  Personnel  Facilities
  2. 2. The Nature of Forecasting• Involves the future• Involves uncertainty• Relies on history• Accuracy? (usually less than desired)• Revise as conditions change• Plan to cover deviations from forecast
  3. 3. Underlying Pattern of the Data• See Exhibit 2, page 405• Trend pattern – projection of the ‘long run’• Seasonal – data fluctuates over time according to a pattern (constant intervals)• Cyclical – movement about a trend line over a period of > 1 year (difficult to predict!)• Random variations – have NO pattern!
  4. 4. Components of Demand Trend componentDemand for product or service Seasonal peaks Actual demand Average demand over Random four years variation | | | | 1 2 3 4 Year Figure 4.1
  5. 5. Types of Forecasting Methods• See breakdown in Exhibit 3 – page 406• Informal – use of Intuition (‘gut feel’)• Formal – 3 types – Qualitative methods – Time series methods – Causal methods• Selection of methods – effectiveness & cost
  6. 6. Qualitative Methods• All 4 emphasize a ‘human judgment’• Do NOT assume that historical trends will continue into the future (quantitative does)• Market research – costly if external to firm• Jury of executive opinion – ask Sr. Mgmt.• Sales force estimates – ‘bottoms up’• Delphi method – panel of outside ‘experts’ (for long term estimates, such as travel trends)
  7. 7. Time Series Methods• Naïve – Just use last month’s #, or last month’s # plus or minus a percentage or fixed amount• Example: 2002 room sales were $150,000• Forecast for 2003 room sales is done by using 2002 data plus an anticipated 10% increase in sales• $150,000 (1.1) = $ 165,000
  8. 8. Time Series Methods• Moving Averages – better approach! – Takes into account the past n periods and removes randomness (unanticipated events) by averaging or “smoothing” Moving Avg. = Activity in previous n periods n• See p. 408-409 – examples of n-week moving averages• Consider the last 3 periods
  9. 9. Time Series Methods Moving Avg. = Activity in previous n periods n• Forecast demand for meals during week 13 (see data page 408)• 3week Moving Avg.= 1,025 + 1,000 + 1,050 3 = 1,025 meals (forecast for week 13)
  10. 10. Moving Average Method• Advantages: – Better than simple naïve approach – Using more weeks “dampens” out any ‘random variations’ that took place• Disadvantages: – Need to continually store/update historical data – Gives equal weight to each observation (ie, past monthly room sales, or # of covers)
  11. 11. Weighted Moving Average Used when trend is present  Older data usually less important Weights based on experience and intuition ∑ (weight for period n) Weighted x (demand in period n) moving average = ∑ weights
  12. 12. Weights Applied Period Weighted Moving Average 3 Last month 2 Two months ago 1 Three months ago 6 Sum of weights Actual 3-Month WeightedMonth Shed Sales Moving AverageJanuary 10February 12March 13April 16 [(3 x 13) + (2 x 12) + (10)]/6 = 121/6May 19 [(3 x 16) + (2 x 13) + (12)]/6 = 141/3June 23 [(3 x 19) + (2 x 16) + (13)]/6 = 17July 26 [(3 x 23) + (2 x 19) + (16)]/6 = 201/2
  13. 13. Exponential SmoothingAccounts for forecasting errors and requires less dataNew forecast = last period’s forecast + α (last period’s actual demand – last period’s forecast) Ft = Ft – 1 + α (At – 1 - Ft – 1) where Ft = new forecast Ft – 1 = previous forecast α = smoothing (or weighting) constant (0 ≤ α ≥ 1)
  14. 14. Exponential Smoothing• Avoids need to keep extensive historical data• Uses only recent actual and forecasted data• Uses only the last 2 periods• Calculates a smoothing constant (SC): SC = Period 2 forecast – Period 1 forecast Period 1 actual – Period 1 forecast• Insert SC into formula• New forecast=past forecast (period 2)+SC(period actual demand-period 2 forecast)
  15. 15. Exponential Smoothingexample: Period 1 actual demand = 220 meals;Period 1 forecast = 200 meals and Period 2 forecast = 210 meals. Forecast demand for period 3.• 1. Calculates a smoothing constant (SC): SC = Period 2 forecast – Period 1 forecast Period 1 actual – Period 1 forecastSC = 210-200 220 – 200SC = .5
  16. 16. Exponential Smoothing• Insert SC into formula• New forecast=past forecast +SC (actual demand-past forecast)• New forecast= 210+.5(220-210)• New forecast = 215 meals
  17. 17. Causal Methods• Assume the value of one variable (dependent) can be ‘predicted’ by some other variable (independent); for example: – Forecast repair & maintenance expense based on hotel room sales• Simple linear regression• Multiple linear regression• Econometric modeling (not in this class)
  18. 18. Regression Analysis• Mathematical approach to fit a straight line to data points ‘perfectly’• Better than scatter diagram• Uses formulas to make calculations without plotting points or drawing lines!• Estimates an activity based on factors that are assumed to cause that activity
  19. 19. Regression Concepts• Dependent variable (DV) = the activity to be forecasted – Dependent variable goes on the vertical axis• Independent variable (IV) = what the forecast is based on – Independent variable goes on the horizontal axis• Examples: F&B sales based on occupancy, or F&B sales based on advertising expenses
  20. 20. Regression Output• Output is the formula for a straight line: y = a + bxWhere: y = value of the DV x = value of the IV b = slope of the line (rise/run) a = value of the y-axis interceptExample: y = 370 + 1.254*x (Exhibit 5)
  21. 21. Regression Measures• Coefficient of correlation (r) Measures relation of DV and IV r is a + number between 0 and 1 The closer to 1, the more related they are• Coefficient of determination (r2) r2 is also a + number between 0 and 1 The closer to 1, the better the regression Reflects how much of the change in the DV is ‘explained’ by the IV

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