chapter two mm.ppt

7/28/2019 CHAPTER TWO MM.ppt

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Chapter TwoMaterials Demand

Forecasting


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INTRODUCTION

Forecasting is important because it helps reduceuncertainty.

Forecasting is the art and science of predicting

future events. It involves estimation of the occurrence, timing,

and/or magnitude of uncertain future events orlevels of activities.


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FEATURES COMMON TO ALL FORECASTS Assume that the same underlying causal system

that existed in the past will continue to exist inthe future

Rarely perfect Forecasts for groups of items tend to be more

accurate than individual items

Accuracy as the time period covered by theforecast


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1. Determine the purpose of the forecast

2. Determine the time horizon

3. Select an appropriate technique

4. Identify the necessary data and gather it

5. Make the forecast

6. Monitor forecast errors

THE FORECASTING PROCESS


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There are two general approaches to forecasting:

Qualitative methods consists mainly subjectiveinputs, based on judgment about the casual

factors.Quantitative methods involve either the

extension of historical data or the developmentof associative models

APPROACHES TO FORECASTING


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QUALITATIVE METHODS

Individual Opinion: consists of collecting opinions andjudgments of individuals

Executive Committee Consensus: a committee of executivesis constituted with the responsibility of developing aforecast.

The Delphi Method: seeks to eliminate the undesirable

consequences of group thinking Field Expectation Method: individual members of sales

force are required to submit sales forecasts of theirrespective regions.

Users Expectation method: estimates of future sales areobtained directly from customers.

Historical Analogy: estimate of future sales of product toknowledge of a similar products sales.

Market Surveys: questionnaires, telephone talks or field


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TIME SERIES FORECASTING

Time series is a collection of data of someeconomic variable or composite of variablesrecorded over a period time- weekly, monthly,quarterly, or yearly.

It is an arrangement of statistical data inchronological order.

Time-series models predict on the assumptionthat the future is a function of the past.


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DECOMPOSITION OF A TIME SERIES

The Secular Trend (T) is the smooth long-termdirection of a time series.

The Cyclical Variation (C) is the rise and fall of a

time series over periods longer than one year The Seasonal Variation (S): are patterns of change

in a time series with in a year

The Irregular Variation is a time series issubjected to occasional influences


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FIGURE 1 EXAMPLES OF PATTERNS IN TIME SERIES DATA


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NAIVE FORECAST

assume that the forecast in the next period will beequal to demand in the most recent period

may take in to account a demand trend


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MOVING AVERAGE Attempts to forecast values on the basis of the

average of the values of past few periods.

The formula for computing the simple moving

average is as follows:

n

Di

MA

n

i

n

1


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EXAMPLE

The Bright Company sells and delivers office supplies to variouscompanies, schools, and agencies. The office supply business is

extremely competitive, and the ability to deliver orders promptly

is an important factor in getting new customers and keeping old

ones. The manager of the company wants to be certain that

enough drivers and delivery vehicles are available so that orderscan be delivered promptly. Therefore, the manager wants to be

able to forecast the number of orders that will occur during the

next month. From records of delivery orders, the manager has

accumulated data for the past 10 months. These data are shownbelow:


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3- AND 5-MONTH AVERAGES


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FIGURE

0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11

Month

QuantityOrde

red

Order Per Month

3 Month Moving Average

5 Month Moving Average


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WEIGHTED MOVING AVERAGE Involves making a forecast values by giving

differential weights to the values entering into moving

average calculation.

n

i

iin DWWMA1

Example: If the Bright Company wants to compute a 3-month

weighted moving average with a weight of 50% for the Octoberdata, a weight of 30% for the September data, and a weight of

20% for August, it is computed as:

104)130)(20(.)110)(30(.)90)(50(.33

1

i

WiDiWMA


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EXPONENTIAL SMOOTHING the forecast for the next period is calculated as

weighted average of all the previous values.

We will consider two forms of exponential smoothing:

simple exponential smoothing and adjustedexponential smoothing

The simple exponential smoothing forecast is

computed by using the formula:

ttt FDF )1(1


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EXAMPLE

PM Computer Services assembles customized personal computers

from generic parts. The company was formed and is operated by

two part-time Hawassa University students, Mark and John, and

has had steady growth since it started. The company assembles

computers mostly at night, using other part-time students as labor.

Mark and John purchase generic computer parts in volume at a

discount from a variety of sources whenever they see a good deal. Itis therefore important that they develop a good forecast of demand

for their computers so that they will know how many computer

component parts to purchase and stock. The company has

accumulated the demand data for its computers for the past 12

months, from which it wants to compute exponential smoothing

forecasts, using smoothing constants equal to .30 and .50.


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Exponential Smoothing Forecasts, = .30 AND = .50


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EXERCISE

KK Textile Factory uses cotton as a raw material

in order to produce various textile products.

Assume that an initial starting forecasted value of

1,000 tons for year 2012 and of 0.2. If actualdemand turned to out to be 1,100 tons rather than

the forecast, what is the forecast for the year

2013?


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Adjusted Exponential Smoothing Consists of the exponential smoothing forecast with a

trend adjustment factor added to it. The formula for

the adjusted forecast is:

AFt+1 = Ft+1 + Tt+1

A forecast model for trend:

Tt+1 = (Ft+1 - Ft) + (1 - )Tt


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EXAMPLE

PM Computer Services now wants to develop anadjusted exponentially smoothed forecast, using the

same 12 months of demand. It will use the

exponentially smoothed forecast with = .50 computed

in with a smoothing constant for trend of .30. The adjusted forecast for March:

T3 = (F3 - F2) + (1 - )T2

= (.30)(38.5 37.0) + (.70)(0) = 0.45AF3 = F3 + T3 = 38.5 + 0.45 = 38.95


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Adjusted Exponentially Smoothed Forecast


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LINEAR TREND LINE A linear trend line relates a dependent variable,

which for our purposes is demand, to oneindependent variable, time, in the form of a linearequation, as follows:

Y = a + bx These parameters of the linear trend line can be

calculated by using the least squares formulas forlinear regression:

xbya

nxx

yxnxyb

22

n

yy

n

xx


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LEAST SQUARES CALCULATION

Therefore, the linear trend line isY = 35.2 + 1.72XTo calculate a forecast for period 13, X = 13 would

be substituted in the linear trend line:

Y = 35.2 + 1.72(13) = 57.56


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EXERCISE

A cosmetics manufacturer production departmenthas developed a linear trend equation that can beused to predict the annual material requirementused to produce its popular product.

Y = 80 + 15X Where:

Y = Annual materials requirement (000 units)X = 1993

1) Are annual materials requirement increasing ordecreasing? By how much?

2) Predict annual materials requirement for 2005using the equation


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SEASONAL ADJUSTMENTS A SEASONAL FACTOR is a numerical value that is

multiplied by the normal forecast to get a seasonallyadjusted forecast.

D

DS

i

i

Example: ELFORA Farms is a company that raisessheep, which it sells to a meat-processingcompany throughout the year. However, the peak

season obviously occurs during the fourth quarterof the year, October to December. ELFORA Farmshas experienced a demand for sheep for the past 3years as shown


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Y = 40.97 + 4.30X= 40.97 + 4.30(4) = 58.17Using this annual forecast of demand, the seasonallyadjusted forecasts for 2006 are as follows:

SF1 = (S1) (F5) = (.28) (58.17) = 16.28SF2 = (S2) (F5) = (.20) (58.17) = 11.63SF3 = (S3) (F5) = (.15) (58.17) = 8.73SF4 = (S4) (F5) = (.37) (58.17) = 21.53


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CAUSAL RELATIONSHIP FORECASTING

Simple Linear Regression is a method offorecasting similar to time series analysis. Timeseries analysis tries to see the relationshipbetween a variable and time, where as regression

analysis observes relationship existing betweentwo variables not necessarily time.

Example: Suppose you are a general manager of a

building material manufacturing plant, and youfeel that the demand for the brick that yourcompany selling is related to the constructionpermit issued by the municipality where you sell

the product.


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Calculate the demand for the bricks if youdiscover that the municipality has issued 30permits for the next year

You have collected the following data.


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CORRELATIONS

Correlation measures the strength and direction of

relationship between two variables. Correlationcan range from 1.00 to + 1.00.

A correlation close to zero indicates little linear

relationship between two variables. The correlations between two variables can be

computed using the equation below:


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EXERCISE

The owner of a small hardware store has noted asales pattern for window locks that seems toparallel the number of break-ins reported eachweek in the newspaper. The data are:

1. Develop a regression equation for the data2. Estimate sales when the number of break-ins

is five


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FORECAST ACCURACY The difference between the forecast and the

actual is referred to as the forecast error

The objective of forecasting is for the error to beas slight as possible.

There are a variety of different measures offorecast error:

Mean Absolute Deviation (MAD)

Mean Absolute Percent Deviation (MAPD)Cumulative Error (E)

Average Error or Bias

Mean Squared Error (MSE)


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Mean Absolute Deviation (MAD)MAD is an average of the difference

between the forecast and actual demand

n

FDMAD

tt

//

We cannot compare a MAD value of twodifferent data and say the former is good and

the latter is bad; they depend to a certainextent on the relative magnitude of the data.MAD.doc
http://localhost/var/www/apps/conversion/tmp/scratch_1/MAD.dochttp://localhost/var/www/apps/conversion/tmp/scratch_1/MAD.dochttp://localhost/var/www/apps/conversion/tmp/scratch_1/MAD.dochttp://localhost/var/www/apps/conversion/tmp/scratch_1/MAD.doc


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Mean Absolute Percent Deviation (MAPD)

MAPD measures the absolute error as apercentage of demand rather than perperiod.

t

tt

D

FDMPAD

//


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Cumulative Error (E)

Cumulative Error is computed simply bysumming the forecast errors

teE


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AVERAGE ERROR

Computed by averaging the cumulativeerror over the number of time periods

n

eE

t


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MEAN SQUARED ERROR

Each individual error value is squared,and then these values are summed andaveraged.


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McGraw

-


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EXERCISE A computer software firm has experienced the following

demand for its Personal Finance software package:

Develop an exponential smoothing forecast, using = .40, andan adjusted exponential smoothing forecast, using = .40 and = .20. Compare the accuracy of the two forecasts, usingMAD and cumulative error(assume the forecast of the firstperiod is the same with the actual demand and the trend iszero.


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End of Chapter Two

chapter two mm.ppt

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