From Hospitality finance Class

1. What is Forecasting?
 Process of predicting
a future event
 Underlying basis of ??
all business
decisions
 Production
 Inventory
 Personnel
 Facilities

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. 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. Components of Demand
Trend
component
Demand for product or service
Seasonal peaks
Actual
demand
Average
demand over
Random four years
variation
| | | |
1 2 3 4
Year Figure 4.1

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. 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. 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. 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. 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. 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. 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. Weights Applied Period
Weighted Moving Average
3 Last month
2 Two months ago
1 Three months ago
6 Sum of weights
Actual 3-Month Weighted
Month Shed Sales Moving Average
January 10
February 12
March 13
April 16 [(3 x 13) + (2 x 12) + (10)]/6 = 121/6
May 19 [(3 x 16) + (2 x 13) + (12)]/6 = 141/3
June 23 [(3 x 19) + (2 x 16) + (13)]/6 = 17
July 26 [(3 x 23) + (2 x 19) + (16)]/6 = 201/2

13. Exponential Smoothing
Accounts for forecasting errors and requires
less data
New 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. 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. Exponential Smoothing
example: 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 forecast
SC = 210-200
220 – 200
SC = .5

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. 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. 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. 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. Regression Output
• Output is the formula for a straight line:
y = a + bx
Where: y = value of the DV
x = value of the IV
b = slope of the line (rise/run)
a = value of the y-axis intercept
Example: y = 370 + 1.254*x (Exhibit 5)

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