Forecasting

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Meaning

Elements

Steps

Types of forecasting

Forecasting

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FORECAST:A statement about the future

Used to help managersPlan the systemPlan the use of the system

Common Features

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Assumes causal systempast ==> future

Forecasts rarely perfect because of randomnessForecasts more accurate for

groups vs. individuals Forecast accuracy decreases

as time horizon increases

I see that you willget an A this quarter

Elements of a Good Forecast

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Timely

AccurateReliable

Mea

ningfu

l

Written

Easy

to u

se

Steps in the Forecasting Process

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Step 1 Determine purpose of forecast

Step 2 Establish a time horizon

Step 3 Select a forecasting technique

Step 4 Gather and analyze data

Step 5 Prepare the forecast

Step 6 Monitor the forecast

“The forecast”

Types of forecast

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i. Qualitative

ii. Time series analysis

iii. Causal relationship

iv. Simulation

Qualitative

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subjective; Judgmental, based on estimates and opinions

Can be of many types such as:i. Grass rootsii. Market researchiii. Panel consensusiv. Historical analogyv. Delphi method

Grass roots

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Forecast by adding successively from the bottom

Derives a forecast by compiling input from those at the end of hierarchy who deal with what is being forecast.

As for example: An overall sales forecast may be derived by combining inputs from each sales person who is closest to his territory.

Market research

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Firms often hire outside companies that specialize in market research to conduct this type of forecast.

Typically used to forecast long range and new product sales.

Panel consensus

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Free open exchange at meeting.

Idea is that two heads are better than one

Group discussion will produce better forecast than any one individual

Participants may be executive, salespeople and customers

Historical analogy

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Where a forecast may be derived by using the history of a similar product

Where an existing product or generic product could be used as a model.

Example can be complementary or substitute product.

Demand for CD is caused by DVD players.

Delphi method

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Group of experts responds to questionnaires

A moderator compiles results and formulates a new questionnaire and submitted again to the respondents

As a results initiate learning process and no influence of group pressure.

Time series analysis

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Tries to predict the future based on past data

Such as collected six weeks sales data can be used to predict 7th week sales

Can be of i. Simple moving averageii. Weighted moving averageiii. Simple exponential smoothingiv. Exponential smoothing with trendv. Linear regression

Guide to select appropriate method

FM Amt of historical data

Data pattern

Forecast horizon

Simple moving average 6 to 12 months stationary Short-medium

Weighted moving average 5-10 observations do short

Simple exponential smoothing

do Stationary and trend

short

Exponential smoothing with trend

do do do

Linear regression 10-20 observations at least 5 observations/season

Stationary, seasonality, trend

Short to medium

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Which model you choose?

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Depends on

Time horizon to forecast

Data availability

Accuracy required

Size of forecasting budget

Availability of qualified personnel

Simple moving average

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A time period containing a number of data points is averaged by dividing the sum of the points values by the number of points

When demand fro product is neither growing nor declining and if it does not have seasonal characteristics, this model can be used.

Ft =At-1 +At-2+At-3……+At-n /nFt = forecast for the coming periodAt-1 = Actual occurrence for the past periodAt-2 =Actual occurrence two periods ago

n= no of periods to be averaged

Weighted moving average

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Moving average allows any weight to be placed on each element

The sum of all weights equal 1Ft =w1At-1 + w2 At-2+ w3 At-3……+ w n At-n

F5=.40*95+.3*105+.20*90+.1*100=97.5

M1 m2 m3 m4

100 90 105 95 ?

Exponential smoothing

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Only three pieces of data are used such as The most recent forecast The actual demand that occurred for that forecast period Smoothing constant α

F t =Ft-1 + α (At-1 –Ft-1)

F t = the exponential smooth forecast for period t Ft-1=Exponentially smoothed forecast made for the prior

period. At-1 = The actual demand in the prior period

α = the desired response rate or smoothing constant

Why exponential smoothing

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Because of four reasons

Are surprisingly accurateFormulating the model is relatively easyLittle computation is requiredThe user can understand how the model

works.

Linear regression analysis

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The past data and future projections are assumed to fall about a straight line

Linear regression line is of the form Y is the dependent variable, a is the y

intercept b is the slope t is the independent variable

Yt = a + bt

0 1 2 3 4 5 t

Y

Calculating a and b

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B(Slope) = n (ty) - t y

n t2 - ( t)2

A Intercept = y - b t

n

Linear Trend Equation Example

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t yW e e k t 2 S a l e s t y

1 1 1 5 0 1 5 02 4 1 5 7 3 1 43 9 1 6 2 4 8 64 1 6 1 6 6 6 6 45 2 5 1 7 7 8 8 5

t = 1 5 t 2 = 5 5 y = 8 1 2 t y = 2 4 9 9( t ) 2 = 2 2 5

Linear Trend Calculation

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y = 143.5 + 6.3t

a = 812 - 6.3(15)

5 =

b = 5 (2499) - 15(812)

5(55) - 225 =

12495-12180

275-225 = 6.3

143.5

Example

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Sunrise baking company markets cakes through a

chain of food stores. It has been experiencing over

and underproduction because of forecasting errors.

The following data are its demand in dozens of

cakes for the past four weeks. Cakes are made for

the following day; for example Sunday's cake

production is for Monday’s sale….the bakery is

closed Saturday, so Friday’s production must satisfy

demand for both Saturday and Sunday

Example

Day 4weeks ago

3weeks ago

2 weeks ago

Last week

Monday 2200 2400 2300 2400

Tuesday 2200 2100 2200 2200

Wednesday

2300 2400 2300 2500

Thursday 1800 1900 1800 2000

Friday 1900 1800 2100 2000

Saturday

Sunday 2800 2700 3000 2900MKA/13 29

Example

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Make a forecast for this week on the following basis

i. Daily using a simple four week moving average

ii. Daily using a weighted average of .4,.3,.2,.1 for the past

four weeks

iii. Sun rise is also planning its purchases of ingredients for

bread production. If bread demand had been forecast for

last week at 22000 loaves and only 21000 loaves were

actually demanded, what would sunrise’s forecast be for

this week using exponential smoothing with a=.10

iv. suppose with the forecast made in c this week’s demand

actually turns out to be 22500.what would be the new

forecast be for the next week

Causal relationship forecasting

One occurrence causes another

The rain causes the sale of rain gear

If housing starts are known then sale of carpet forecasting is possible

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Forecasting using a causal relationship

Year Housing Permits x

Sales sqr mtry

1998 18 13000

1999 15 12000

2000 12 11000

2001 10 14000

2002 28 16000

2003 35 19000

2004 30 17000MKA/13

Y=a+bxa is y interceptb is slope= y2-y1/x2-x1= 17000-1000/30-10 y = 7000+350x if house permit is 26 y= 7000+350*26 is the forecast of next

year.Book1.xlsx

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Simulation

Dynamic model usually computer basedAllow the forecaster to make

assumptions about the internal variables and external environment in the model

Depending on the variable in the model forecaster may ask such question as what would happen to my forecast if price increased by 10%

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