Forecasting 1

Linkages

• How much we are going to sell is obviously important to marketing

• Forecasts help us to plan investments - or to determine if an investment is a good idea

• Forecasts tell us if we will have to hire new people and or train our existing people in new skills

• Technological forecasts might indicate the need to change our MIS function

Forecasting 2

Forecasts as part of planning

• How much demand we are going to have leads to a number of other questions

– large demand for standard products: line flow

– demand for custom products: jumbled flow

– demand leads to capacity

– demand indicates when we schedule work

– etc.

• In other words a forecast is one of the first things we need when planning – for the long term and the short term.

Forecasting 3

Why do forecasts matter ?

• People: If we do a bad job forecasting demand we may not have the right number (or type) of people on hand.

• Capacity: If we under forecast we will not be able to make enough stuff (lost sales) over forecasting will result in expensive wasted capacity.

• Supply chain: Our suppliers are also dependent on our forecasts:– What if we have them build stuff based on an

erroneously high forecast?

Forecasting 4

Characteristics of forecasts

• Short term: Less than a year– quantitative– can be very accurate– dis-aggregated

• Long term: More than a year– often very qualitative– much harder to be accurate– generally aggregated

Forecasting 5

Types of forecasts

• Economic: What is happening in the world, country, state, and locality. Aggregated across companies and usually industries.– ISM index– The federal reserve

• Technological: changes in technology that may change products and / or processes– BW survey of research labs

• Demand: Sales of our company’s products - often driven (partially) by economic and technological.

Forecasting 6

Quantitative verses Qualitative

• When numbers do not exist and or are inaccurate we can use qualitative methods (long term forecasts especially)– delphi methods– market research– the “gut”

• Most people want to use numbers– why?– is this always best?

• See readings on methods people do choose- and what would be best.

Forecasting 7

Forecasting demand

• There are 5 components of demand:– Average demand – not in book– Trends– Cyclicality– Seasonality – Random factors

• What should we be able to forecast ?

Forecasting 8

Trend

Sales of Dallas Cowboys Paraphernalia

Volume

Year

1999 2000 2001 2002 2003 - projected

Forecasting 9

Seasonality

Beverage sales at the 6 pac shop

MON

TUS

WEN

TUR

FRI

SAT

SUN

MON

Forecasting 10

Seasonality 2

Umbrella sales

Summer

Summer

Fall

Fall

Winter

Winter

Spring

Spring

Forecasting 11

Cyclicality

The business cycle– Where are we in the business cycle?– to forecast the end of a period of growth what

signs would you look for?• What do you think Greenspan looks for?

Forecasting 12

Determining the quality of a forecast

Forecast error = demand - forecast

negative errors indicate ?

Mean Absolute Deviation (MAD)

Mean percentage deviation (MAPE)

n

errorsMAD

n

100%*demand

error

MAPE

n

1i i

i

Forecasting 13

Determining the quality of a forecast 2

• Why don’t we use the average deviation?

• What does the MAPE tell us that the MAD does not ?– can we compare the MADS for two different

products ? – can we use MAD to compare the same

forecasting method in a variety of situations ?

• We also want to examine BiasBias

Forecasting 14

MAD / MAPE example

Forecasting 15

A quick aside

• The forecasting tools we are going to use are generally basic and fairly simple.

– See the articles I placed on the web- this is what people use

– Regression is “to fancy” for many managers

• Our goal is to find the method that best fits our pattern of demand- no one right tool

Forecasting 16

Actual forecasting tools

• The simplest method: the naive forecast– this period’s demand = last period’s demand– when is this acceptable ?

• Time series methods: future demand is predicted from past (historical) demand.– moving averages

• simple and weighted

– exponential smoothing

Forecasting 17

Moving averages

• A simple tool to predict demand when it is safe to assume that over time demand is fairly stable (change is slow).

• A 3 period moving average:

• A five period moving average:

5

54321 tttttt

DDDDDF

Forecasting 18

Moving average example

Period Demand 3 period MA

5 period MA

1 15

2 12

3 13

4 17 13.33

5 19 14

6 18 16.33 15.2

7 20 18 16.2

Forecasting 19

Weighted moving averages

• Moving averages work fine when the world is fairly stable - but what if our world is changing ?

• Weighted moving averages (WMA) - place more weight on recent events (why) .

• WMA = (Σ (weight period n) (demand in period n)) / Σ weights

• Determining weights is an art - generally do not weight most recent period more than 50%.

Forecasting 20

AWMA example:Weights 5,3,2

Period Demand Forecast 1 15 2 18 3 26 4 35 ((15*2)+(18*3)+(26*5)) / 10 =21.4

5 32 28.9 6 36 31.7 7 38 34.6 8 40 36.2

Forecasting 21

Exponential smoothing

• Exponential smoothing is a very popular (and simple) form of the weighted moving average.

• Basic form:

• What happens as the smoothing constant increases ?

)( 11 ttt EFF

Forecasting 22

Exponential smoothing: examples• smoothing constant = .2

period demand forecast

1 25 21

2 24 21+.2(4) = 21.8

3 21.8+.2(2.2) = 22.24

• smoothing constant = .5period demand forecast

1 25 21

2 24 21+.5(4) = 23

3 23+.5(1) = 23.5

Forecasting 23

Seasonality

• Because seasonality is a pattern we can predict it using indices.

• For example:

yearly demand = 800 units

indices:spring = .85 summer = 1.46 fall = .76 winter = .93

– F spring = 200 * .85 = 170

– F summer = 200 * 1.46 = 292

– f fall = 200 * .76 = 152

– f winter = 200 * .93 = 186

Forecasting 24

Determining Indices

Quarter Sales 95

Sales 96

Sales 97

quarter average

average quarter

index

1 400 375 425 400 500 .80

2 300 280 320 300 500 .60

3 575 600 625 600 500 1.2

4 700 650 750 700 500 1.4

Forecasting 25

More indices stuff

• The sum of the indices should = the number of seasons.

• Formula for the index :

average demand specific season

average demand all seasons

Forecasting 26

Regression Models• The basic regression model

– F = constant + b1X1 + b2X2

– b1 is a constant

– X1 is an independent variable

– You can of course use only 1 independent variable in your model- or more than 2 (sometimes many more than 2)

Forecasting 27

Some obvious uses of regression• We can use regression to forecast when we have a trend in

the data.– If the trend is the major source of change in the data

might be able to use a simple regression model where time is our only independent variable

• Ft = constant + b (time period)

• We might also make the season an independent variable

• We can obviously include just about anything else in the model that makes sense

• demand for ice cream - might include temperature

• demand for MBA classes - unemployment rate

Forecasting 28

Other issues

• Double exponential smoothing – Fancy way to try and cope with trends – works

well when there is a one time change – not as well when the trend is always going up / down

• Focused forecasting– Economist

Forecasting 29

Book problems you should be able to do

• 4.2, 4.4, 4.6, 4.8, 4.10, 4.26, 4.28