03 MGT 3050 - Forecasting.pptx

8/14/2019 03 MGT 3050 - Forecasting.pptx

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Forecasting

MGT 3050

Management Science


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Introduction

An essential aspect of managing anyorganization is planning for the future

The long run success of an organizationdepends upon how well the managers anticipateor foresee the future & consequently developthe appropriate strategies


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Introduction

There is some tendency for managers to forecastusing their experience & intuition this type offorecast based upon human judgments areacceptable sometimes but not necessarily viableall the time


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Simple Weighted

Moving Average ExponentialSmoothing

Smooting Trend Projection Trend ProjectionSeasonal Adjustment

Time Series

Regression

Causal

Quantitative

Delphi

Qualitative

Forecasting Methods


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Introduction

We should forecast as accurate as possible need to use scientific forecasting method to helpus make good decisions

A number of forecasting methods have beendeveloped

Quantitative methods or statistical forecastingmethod by utilizing historical dataQualitative or judgmental forecasting methods

when historical data is not available or difficult toobtain


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Introduction

Quantitative forecast methodsPast information about the variable being forecast isavailableInformation can be quantified

Assumption that the pattern of the past will continueinto the future


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Introduction

Use historical data to forecast the futureHistorical data provide some pattern of the past (eg. sales),leading to a better prediction of the future (sales)

Historical data forms a time series A time series is a set of observations of a variable measuredat successive point in time or over successive periods of time

The objective of analyzing past data is to provide good

forecasts or predictions of future values of time seriesForecasting procedure by using historical data is calledtime series method


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Components of a Time Series

Trend componentCyclic component

Seasonal componentIrregular or Random


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Components of Time Series

Trend ComponentIn a time series, the data may gradually increase ordecrease the gradual shifting of upward ordownward movement of the data over time isreferred to as trend

Cyclical Component

If the time series data are recurring sequence ofpoints or repeated after a certain number of periods,

we call the time series hascyclical component


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Components of Time Series

Seasonal component The component of the time series that represents the variability in the data due to seasonal influences iscalled seasonal component

Irregular or random component A time series that consist of components whose

occurrence is totally unpredictable or having random variability is referred to asirregular component (ie. itdoes not follow any discernable pattern)


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Time Series Smoothing Methods

Applicable only when the time series does not have anyclear trend or seasonal influence the data are relativelystable over time

Moving averageSimple moving average

Weighted moving averageExponential moving average (also calledexponentialsmoothing )

The objective is to smooth out the randomfluctuations caused by the irregular components of thetime series ( also calledsmoothing method )


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Simple Moving Average

Simple Moving Average (s.m.a.) uses the averageof the most recent n data values in the timeseries as the forecast for the next period

Moving average = (most recent n data values)

n


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Simple Moving Average

Simple Moving Average (s.m.a.)

tn+1 =

d in

i=1n

d1, d2, ,d n are most recent data & n is number of periods


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Eg. Demand of computers at Amir shop

Month DemandJanuary 61February 66

March 60 April 75May 71June 70

July 77 August 80

September 66October 70


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Forecast for NovemberSay n = 3

80 + 66 + 70

3Forecast for November =

= 72


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Forecast error = Actual data Forecast data75 72 = 3

Assume actual demand for November is 75


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Month Demand S.M.A Forecast ForecastError

SquaredForecast

Error

January 61

February 66

March 60

April 75 (61+66+60)/3 = 62 13 169

May 71 (66+60+75)/3 = 67 4 16June 70 69 1 1

July 77 72 5 25

August 80 73 7 49

September 66 76 -10 100October 70 75 -4 16

November 75 72 3 9

December 67 70 -3 9

394


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Simple moving average

Forecast accuracyMeasured by computing the average of the sumsquared errors called mean square error (MSE)

MSE for s.m.a. = 3949

= 43.78


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Weighted Moving Average

In simple moving average, each observation inthe calculation receives the same weightage

Weighted moving average involves selectingdifferent weights for each data value & computethe weighted average of the most recent n data

values as the forecast


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Weighted Moving Average

Weighted Moving Average (w.m.a.)

w.m.a. =

w id

i

n

i=1n w ii=1


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Month Demand W. M.A Forecast ForecastError

SquaredForecast

Error

January 61

February 66

March 60

April 75 13 169

May 71 3 9

June 70 70 0 0

July 77 71 6 36

August 80 74 6 36

September 66 77 -11 121

October 70 72 -2 4

November 75 70 5 25

December 67 82 -5 25

62

123

611662603

68

123

661602753


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Weighted moving average

Forecast accuracyMeasured by computing mean square error (MSE)

MSE for w.m.a. = 4259

= 47.22


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MSE for s.m.a. 43.78= MSE for w.m.a. = 47.22

03 MGT 3050 - Forecasting.pptx

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