forecasting sk

8/3/2019 Forecasting Sk

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Forecasting Introduction

An essential aspect of managing anyorganization is planning for the future.

Organizations employ forecasting techniquesto determine future inventory, costs,capacities, and interest rate changes.

There are two basic approaches toforecasting:

-Qualitative

-Quantitative


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Time Span of Forecasts

Long-range

time spans usually greater than one year

necessary to support strategic decisions about

planning products, processes, and facilities Short-range

time spans ranging from a few days to a fewweeks

cycles, seasonality, and trend may have littleeffect

random fluctuation is main data pattern


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Qualitative Approaches toForecasting

Delphi Approach A panel of experts, each of whom is physically separated from

the others and is anonymous, is asked to respond to a

sequential series of questionnaires. After each questionnaire, the responses are tabulated and the

information and opinions of the entire group are made knownto each of the other panel members so that they may revisetheir previous forecast response.

The process continues until some degree of consensus isachieved.


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Qualitative Approaches(continued)

Scenario Writing Scenario writing consists of developing a conceptual scenario of

the future based on a well defined set of assumptions.

After several different scenarios have been developed, the

decision maker determines which is most likely to occur in thefuture and makes decisions accordingly.

Subjective or Interactive Approaches These techniques are often used by committees or panels

seeking to develop new ideas or solve complex problems. They often involve "brainstorming sessions".

It is important in such sessions that any ideas or opinions bepermitted to be presented without regard to its relevancy andwithout fear of criticism.


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Qualitative Approaches(continued)

Subjective or Interactive Approaches These techniques are often used by committees or panels

seeking to develop new ideas or solve complex problems.

They often involve "brainstorming sessions". It is important in such sessions that any ideas or opinions be

permitted to be presented without regard to its relevancy andwithout fear of criticism.


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Quantitative Approaches toForecasting

Quantitative methods are based on an analysis of historical dataconcerning one or more time series.

A time series is a set of observations measured at successive

points in time or over successive periods of time. If the historical data used are restricted to past values of the series

that we are trying to forecast, the procedure is called a time seriesmethod.

If the historical data used involve other time series that are believedto be related to the time series that we are trying to forecast, theprocedure is called a causal method.


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Time Series Data

Time Series Data is usually plotted on a graphto determine the various characteristics or

components of the time series data. There are 4 Major Components: Trend,

Cyclical, Seasonal, and Irregular Components.


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Data Patterns

Trends accounts for the gradual shifting of thetime series over a long period of time.

Seasonality of the series accounts for regular

patterns of variability within certain time periods,such as over a year.

CycleAny regular pattern of sequences of valuesabove and below the trend line is attributable

Random fluctuation series is caused by short-term, unanticipated and non-recurring factors thataffect the values of the time series.


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Data Patterns

Horizontal

When there is no trend in the data pattern, wedeal with horizontal data pattern.

Mean

Time

ForecastVariable


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Data Patterns

Trend

Long-term growth movement of a time series

t t

tt

YtYt

Yt Yt

Trend Trend

Trend

Trend


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Data Patterns

Seasonal Pattern

A predictable and repetitive movement observedaround a trend line within a period of 1 year or

less.

Time

Forecas

tVariable


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Data Patterns

Cyclical

Occurs with business and economic expansionsand contractions.

Lasts longer than 1 year.

Correlated with business cycles.


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Smoothing Methods: MovingAverage

Moving Average MethodThe moving average method consists of

computing an average of the most recent n

data values for the series and using thisaverage for forecasting the value of the timeseries for the next period.

Error in Forecasting

Measures the average error that can be expected over time.ttt

YYe

n

e

n

t

t

1

2)(

MSE


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Moving Averages

Diamond Garden SuppliesForecasting

Period ActualValue Three-Month Moving Averages

January 10

February 12

March 16

April 13 10 + 12 + 16 / 3 = 12.67

May 17 12 + 16 + 13 / 3 = 13.67

June 19 16 + 13 + 17 / 3 = 15.33July 15 13 + 17 + 19 / 3 = 16.33

August 20 17 + 19 + 15 / 3 = 17.00

September 22 19 + 15 + 20 / 3 = 18.00

October 19 15 + 20 + 22 / 3 = 19.00

November 21 20 + 22 + 19 / 3 = 20.33

December 19 22 + 19 + 21 / 3 = 20.67

Storage Shed Sales


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Moving Averages Forecast

Diamond Garden SupplyForecasting 3 period moving average

Input Data Forecast Error Analysis

Period Actual Value Forecast Error

Absolute

error

Squared

errorMonth 1 10

Month 2 12

Month 3 16

Month 4 13 12.667 0.333 0.333 0.111

Month 5 17 13.667 3.333 3.333 11.111

Month 6 19 15.333 3.667 3.667 13.444

Month 7 15 16.333 -1.333 1.333 1.778

Month 8 20 17.000 3.000 3.000 9.000

Month 9 22 18.000 4.000 4.000 16.000

Month 10 19 19.000 0.000 0.000 0.000

Month 11 21 20.333 0.667 0.667 0.444

Month 12 19 20.667 -1.667 1.667 2.778

Average 1.333 2.000 6.074

Next period 19.667 BIAS MAD MSE

Actual Value - Forecast


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Weighted Moving Average

This is a variation on the simple moving average where insteadof the weights used to compute the average being equal, theyare not equal

This allows more recent demand data to have a greater effecton the moving average, therefore the forecast

The weights must add to 1.0 and generally decrease in valuewith the age of the data

The distribution of the weights determine impulse response ofthe forecast

1tF

= w1Y

t+ w

2Y

t-1+w

3Y

t-2+ + w

nY

t-n+1

Swi= 1


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Weighted Moving AverageDiamond Garden SupplyForecasting

Period

Actual

Value Weights Three-Month Weighted Moving Averages

January 10 0.222February 12 0.593

March 16 0.185

April 13 2.2 + 7.1 + 3 / 1 = 12.298

May 17 2.7 + 9.5 + 2.4 / 1 = 14.556

June 19 3.5 + 7.7 + 3.2 / 1 = 14.407

July 15 2.9 + 10 + 3.5 / 1 = 16.484

August 20 3.8 + 11 + 2.8 / 1 = 17.814

September 22 4.2 + 8.9 + 3.7 / 1 = 16.815

October 19 3.3 + 12 + 4.1 / 1 = 19.262

November 21 4.4 + 13 + 3.5 / 1 = 21.000

December 19 4.9 + 11 + 3.9 / 1 = 20.036

Next period 20.185

Sum of weights = 1.000

Storage Shed Sales


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Diamond Garden Supply Forecasting 3 period weighted moving average

Input Data Forecast Error Analysis

Period Actual value Weights Forecast Error

Absolute

error

Squared

errorMonth 1 10 0.222

Month 2 12 0.593

Month 3 16 0.185

Month 4 13 12.298 0.702 0.702 0.492

Month 5 17 14.556 2.444 2.444 5.971

Month 6 19 14.407 4.593 4.593 21.093

Month 7 15 16.484 -1.484 1.484 2.202

Month 8 20 17.814 2.186 2.186 4.776Month 9 22 16.815 5.185 5.185 26.889

Month 10 19 19.262 -0.262 0.262 0.069

Month 11 21 21.000 0.000 0.000 0.000

Month 12 19 20.036 -1.036 1.036 1.074

Average 1.988 6.952 6.952

Next period 20.185 BIAS MAD MSE

Sum of weights = 1.000


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Following data is available about actual sales for the past 13 years.

YR 1 2 3 4 5 6 7 8 9 10 11 12 13

Sales 2.3 2.2 2 2.25 2.6 3 4.1 3.8 4 4.3 4.2 4.8 5.2

Find the Forecast for the yr 14 using Two Years as well as threeyears moving averages. Which of the two forecasts is more reliable on

the basis of Mean Squared Error (MSE) criterion ?

Moving Average - Example


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Vacuum cleaner sales for 12 months is given below. Theowner of the supermarket decides to forecast sales byweighting the past 3 months as follows

Wt Applied Month

3 Last month

2 Two months ago

1 Three months ago

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

Actualsales(units)

10 12 13 16 19 23 26 30 28 18 16 14

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