What is Forecasting?
Process of predicting a future event
Underlying basis of all business decisions Production Inventory Personnel Facilities
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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
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!
Components of DemandD
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r se
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| | | |1 2 3 4
Year
Average demand over four years
Seasonal peaks
Trend component
Actual demand
Random variation
Figure 4.1Figure 4.1
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
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)
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
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
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)
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)
Used when trend is present Older data usually less important
Weights based on experience and intuition
Weighted Moving Average
WeightedWeightedmoving averagemoving average ==
∑∑ ((weight for period nweight for period n)) x x ((demand in period ndemand in period n))
∑∑ weightsweights
JanuaryJanuary 1010FebruaryFebruary 1212MarchMarch 1313AprilApril 1616MayMay 1919JuneJune 2323JulyJuly 2626
ActualActual 3-Month Weighted3-Month WeightedMonthMonth Shed SalesShed Sales Moving AverageMoving Average
[(3 x 16) + (2 x 13) + (12)]/6 = 14[(3 x 16) + (2 x 13) + (12)]/6 = 1411//33
[(3 x 19) + (2 x 16) + (13)]/6 = 17[(3 x 19) + (2 x 16) + (13)]/6 = 17[(3 x 23) + (2 x 19) + (16)]/6 = 20[(3 x 23) + (2 x 19) + (16)]/6 = 2011//22
Weighted Moving Average
101012121313
[(3 x [(3 x 1313) + (2 x ) + (2 x 1212) + () + (1010)]/6 = 12)]/6 = 1211//66
Weights Applied Period
3 Last month2 Two months ago1 Three months ago6 Sum of weights
Exponential Smoothing
Accounts for forecasting errors and requires Accounts for forecasting errors and requires less dataless data
New forecast =New forecast = last period’s forecastlast period’s forecast+ + ((last period’s actual demand last period’s actual demand
– – last last period’s forecastperiod’s forecast))FFtt = F = Ft t – 1– 1 + + ((AAt t – 1– 1 - - F Ft t – 1– 1))
wherewhere FFtt == new forecastnew forecast
FFt t – 1– 1 == previous forecastprevious forecast
== smoothing (or weighting) smoothing (or weighting) constant constant (0 (0 1) 1)
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)
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 forecastSC = 210-200
220 – 200SC = .5
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
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)
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
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
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)
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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