mba.782.forecastingcaj9.11.1 demand management qualitative methods of forecasting quantitative...

MBA.782.Forecasting CAJ9.11.1

• Demand Management

• Qualitative Methods of Forecasting

• Quantitative Methods of Forecasting

• Causal Relationship Forecasting

• Focus Forecasting

• Development of a Forecasting System

Operations Management

Forecasting


• Forecasts are seldom __________ - find the best method

• Forecasting methods assume there is some underlying stability in the system

• _______________ product forecasts are more accurate than individual product forecasts

• Basis of long-run planning– budget planning and cost control

• Marketing - sale forecast

• Operations - capacity, scheduling, inventory

Forecasting

Forecasting in Business


Forecasting

Demand Management

• Independent demand– demand for item is independent of

demand for _____ other item

• Dependent demand– demand for item is dependent upon the

demand for ______ _______ item

MBA.782.Forecasting CAJ9.11.4 14

• The greater the ability to react, the less accurate the forecast has to be

• A __________ between the cost of doing the forecast and the opportunity cost of proceeding with misleading numbers

• Factors: 1. Time horizon to forecast

2. Data availability

3. Accuracy required

4. Size of forecasting budget

5. Availability of qualified personnel

Forecasting

Choice of Forecasting Model


• Qualitative (Judgmental)

• Quantitative– Time Series Analysis

> past data

– Causal Relationships> related to some other factors

– Simulation> test assumptions

Forecasting

Types of Forecasting


1. Choose the participants - never meeting as a ________

2. Through a questionnaire, obtain forecasts from all participants

3. Summarize the results and redistribute them to the participants along with appropriate new questions

4. Summarize again, refining forecasts and conditions, and develop new questions.

5. Repeat Step 4 if necessary. Distribute the final results to all participants.

Qualitative Methods

Delphi Method


• Components of Demand

– Trend, Seasonal, Cyclic, Random

• Time Series Analysis

• Causal Relationships

• Simulation

Forecasting

Quantitative Methods


• _______________, overall upward or downward pattern

• Due to population, technology etc.

• Linear; S-curve; asymptotic; exponential

Mo., Qtr., Yr.

Response

Components of Demand

Trend Component


• Regular pattern of ____ & ________ fluctuations

• Due to weather, customs etc.

• Occurs within __ _______

Mo., Qtr.

ResponseSummer


Seasonal Component


• Repeating up & down movements

• Due to interactions of factors influencing economy

• Non-annual; __________ ;

Mo., Qtr., Yr.Mo., Qtr., Yr.

ResponseResponse

Cycle


Cyclical Component


• Erratic, unsystematic, ‘residual’ fluctuations

• Unexplained portion


Random Component


• Set of ________ spaced numerical data– Obtained by observing response variable at regular time

periods

• Forecast based only on _______ values– Assumes that factors influencing past, present, & future will

continue

• ExampleYear: 1993 1994 1995 1996 1997

Sales: 78.7 63.5 89.7 93.2 92.1

Quantitative Methods

What is a Time Series?


• Used if demand is ____ growing nor declining rapidly

• Used often for smoothing– Remove ____________ fluctuations

• Equation

where:

Ft = forecast for period t,

At = actual demand realized in period t,

Time Series Analysis

Simple Moving Average

F = A + A + A +...+A

ntt-1 t-2 t-3 t-n


• Let’s develop 3-week moving average forecasts for demand.

• Assume you only have 3 weeks of actual demand data for the respective forecasts


Simple Moving Average

Actual ForecastWeek Demand 3-Week

1 6502 6783 72045


• Allows different ________ to be assigned to past observations

– Older data usually ______ important

• Weights based on experience, trial-and-error

• Equation......


Weighted Moving Average

F = w A + w A + w A +...+w At 1 t-1 2 t-2 3 t-3 n t-n

w = 1ii=1

n


Determine the 3-period weighted moving average forecast for period 4.

Weights: t-1 0.5 t-2 0.3 t-3 0.2


Weighted Moving Average

Actual ForecastWeek Demand 3-Week

1 6502 6783 72045


• Increasing n makes forecast

______ sensitive to changes

• Do not forecast _______ well

• Require ______ historical data


Disadvantages of M.A. Methods


To ramp changes of demand

Demand

High weight n = 265

55

45

35RampShift

-3 2 1 T +1 2 3 4 5 6 7 8

Low weight n = 6


Responsiveness of M.A. Methods

• Forecast _____ with increasing demand, and

_______ with decreasing demand


• Premise--The most ________ observations might have

the highest predictive value.

• Therefore, we should give _______ weight to the more

recent time periods when forecasting

• Requires smoothing constant ()

– Ranges from 0 to 1

– Subjectively chosen

• Involves _______ record keeping of past data


Exponential Smoothing


The equation used to compute the forecast is...

Ft = Ft-1 + ·(At-1 - Ft-1)

where....

Ft = forecast demand

At = actual demand realized

= smoothing constant

Exponential because each increment in the past is decreased by (1 - ):




• Determine exponential smoothing forecasts for periods 2-10 using=0.20(Let F1=D1)



Actual ForecastWeek Demand 0.2

1 650234


3000

2500

2000

1500

1000

1 2 3 4 5 6 7 8 9 10 11 12

Actual demandalpha = .1alpha = .5alpha = .9


Responsiveness to Different Values of


• Attempts to __________ (somewhat) the lag in the exponential smoothing method

• Trend equation with a smoothing constant, ___ (delta)

• formulae……

FITt = Forecast including trend

FITt = Ft + Tt

Ft = FITt-1 + (At-1 - FITt-1)

Tt = Tt-1 + (At-1 - FITt-1)


Exponential Smoothing with Trend


• Error = Actual - ForecastEt = At - Ft

• RSFE = running sum of the forecast errors

RSFE = Et

• Bias = Average Error

– occurs when a _______________ mistake is made

Bias = RSFE / n

• Random errors– cannot be explained by the forecast model being used.


Forecast Accuracy


MAD = A - F

n

t tt=1

n

• Mean Absolute Deviation is the sum of each error’s magnitude divided by the number of error--so we get the

___________ magnitude of the forecast error


Forecast Errors

• If the errors are normally distributed,

the standard deviation, _________________


=n

F-A =MAD

n

1=ttt


Forecast Errors

Month Sales Forecast Error Abs. Error1 2202 250 2553 210 2054 300 3205 325 315


• TS measures the ________ of MADs that the forecast is above or below the actual value of the variable

– Good tracking signal has _____ values

• In the usual statistical manner, if control limits were set at plus or minus 3 standard deviations (or + 3.75 MADs),

then _____ percent of the points would fall within these limits.

TS =RSFE

MAD=

Running sum of forecast errors

Mean absolute deviation

• Measures how _____ the forecast is predicting actual values


Tracking Signal


• Describe functional relationship between two or more correlated variables.

• Equation of the form: Y = a + bx

– used to predict Y for some _________ value of x

• Useful for long-run decisions and aggregate planning

• Assumes a straight-line (linear) relationship

• Use in _____ _______ and _______ forecasting


Linear Regression


• Seasonality…..

A seasonal factor (index) is the amount of the correction

necessary to _________ for the season of the year

• Decomposition…..

To ___________ the basic components of trend and seasonality

Forecasting

Integrative Example


• Given three years of quarterly data:

Determine the seasonal factors.

Forecasting

Integrative Example

Demand Data:Qtr Year 1 Year 2 Year 3 Total Index1 6 8 72 12 13 143 9 11 104 15 17 18


• Deseasonalize the actual demand data by _________ by the appropriate seasonal factor:

Forecasting

Integrative Example

Qtr Demand DeSeas.1 6 10.02 12 10.83 9 10.54 15 10.55 8 13.36 13 11.77 11 12.88 17 11.99 7 11.710 14 12.611 10 11.712 18 12.6


• The perform a linear regression, least squares approximation of the relationship between quarter

(x) and ___ - seasonalized sales (y):

y = a + b x

Forecasting

Integrative Example

a = y - bx

b =xy - n(y)(x)

x - n(x2 2

)


• Project the ________ using the predictive equation for each quarter of year 4:

Forecasting

Integrative Example

Quarter 13: F13 = 10.44 + 0.1882 ( ___ ) = _____

Quarter 14: F14 = 10.44 + 0.1882 ( ___ ) = _____

Quarter 15: F15 = 10.44 + 0.1882 ( ___ ) = _____

Quarter 16: F16 = 10.44 + 0.1882 ( ___ ) = _____


• Adjust for seasonality by multiplying by the seasonal factors for the appropriate quarters:

Forecasting

Integrative Example

Qtr Proj. ReSeas.13 12.9 7.7 14 13.1 14.6 15 13.3 11.4 16 13.5 19.2


Forecasting

Integrative Example

0

5

10

15

20

25

0 4 8 12 16 20

Quarter

Dem

and Demand

Proj.

ReSeas.


• One occurrence causes another

• If the causing element if _____ enough in advance, it can be used as a basis for forecasting

• The independent variable must be a

_________ indicator

• Challenge is to find those occurrences that are

________ the causes

Forecasting

Causal Relationship Forecasting


• Uses simulation

– ____________ to test various forecasting models

• Pick the model that produces the smallest error

• Illustrate….

Forecasting

Focus Forecasting

0

100

200

300

400

500

600

700

800

900

1000

1996 1997 1998 1999 2000 2001

Year

Sa

les Actual

Forecast


• Choice depends _________ on – the type of business, and

who is using the forecast

• No pattern or direction in forecast error– Seen in plots of errors over time

• ____________ forecast error– Mean absolute deviation (MAD)

• Focus Forecasting– has merit

– computer time is not an issue

– component of many business systems

Forecasting

Developing a Forecasting System


Forecasting


Forecasting

Chapter Wrap-Up

• Read Chapter 11

• Concepts and Terminology

• Review Lecture Notes

• Recommended Problems

mba.782.forecastingcaj9.11.1 demand management qualitative methods of forecasting quantitative...

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