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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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