Forecast and methods

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Forecasting & Methods

Group 6: Bajji Reddy Kruttika Vinodh Mukesh

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“Predict or estimate (a future event or trend)”.

Or

“Estimating the MAGNITUDE & TIMING of occurrence of future events.”

What is Forecasting ?

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Why Forecasting?

Forecasting lays a ground for reducing the risk in all decision making because many of the decisions need to be made under uncertainty.

In business applications, forecasting serves as a starting point of major decisions in finance, marketing, productions, and purchasing.

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Decisions Requiring Forecasting in Operations Management Predicting demands of new and existing products Predicting results of new product research and

development Projecting quality improvement Anticipating customer’s needs Predicting cost of materials

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Decisions Relevant to Demand Forecasts Predicting new facility location.

Anticipating capacity needs.

Identifying labor requirements.

Projecting material requirements.

Developing production schedules.

Creating maintenance schedules.

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Simple Moving Average

Weighted Moving Average

Exponentially Weighted Moving Average

(Exponential Smoothening)

Forecasting Methods for random demand

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MA is a series of arithmetic means

Used if little or no trend, seasonal, and cyclical patterns.

Used often for smoothing

Provides overall impression of data over time

Equation

Moving Average Method

MAn

n

Demand in Previous Periods

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Moving Average Solution

Time ResponseYi

MovingTotal(n=3)

MovingAverage

(n=3)1995 4 NA NA1996 6 NA NA1997 5 NA NA1998 3 4+6+5=15 15/3 = 51999 72000 NA

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Time ResponseYi

MovingTotal(n=3)

MovingAverage

(n=3)1995 4 NA NA1996 6 NA NA1997 5 NA NA1998 3 4+6+5=15 15/3 = 51999 7 6+5+3=14 14/3=4 2/32000 NA

Moving Average Solution

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Time ResponseYi

MovingTotal(n=3)

MovingAverage

(n=3)1995 4 NA NA1996 6 NA NA1997 5 NA NA1998 3 4+6+5=15 15/3=5.01999 7 6+5+3=14 14/3=4.72000 NA 5+3+7=15 15/3=5.0

Moving Average Solution

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Used when trend is present Older data usually less important

Weights based on intuition Often lay between 0 & 1, & sum to 1.0

Equation

WMA =Σ(Weight for period n) (Demand in period n)

Σ Weights

Weighted Moving Average Method

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ExampleWeek Actual Data Weight

7 85

8 100

9 110

Calculate the forecast for 10th week?Weights of 3 weeks are 0.50,0.30 & 0.20.

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Form of weighted moving averageWeights decline exponentiallyMost recent data weighted most

Requires smoothing constant (α)Ranges from 0 to 1Subjectively chosen

Involves little record keeping of past data

Exponential Smoothing Method

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Ft = Ft-1 + (At-1 - Ft-1)

= At-1 + (1 - ) Ft-1

Ft = Forecast value At = Actual value = Smoothing constant

Exponential Smoothing Equations

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You’re organizing a Kwanza meeting. You want to forecast attendance for year 2000 using exponential smoothing ( = 0.10). In1995 (made in 1994) forecast was 175.

Exponential Smoothing Example

Year Actual Data

1995 180

1996 168

1997 159

1998 175

1999 190

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Ft = Ft-1 + ·(At-1 - Ft-1)

Time ActualForecast, F t

( α = .10)

1995 180 175.00 (Given)

1996 168

1997 159

1998 175

1999 190

2000 NA

175.00 +

Exponential Smoothing Solution

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Time ActualForecast, F t

(α = .10)

1995 180 175.00 (Given)

1996 168 175.00 + .10(180 -

1997 159

1998 175

1999 190

2000 NA

Ft = Ft-1 + ·(At-1 - Ft-1)

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Time ActualForecast, F t

(α = .10)

1995 180 175.00 (Given)

1996 168 175.00 + .10(180 - 175.00)

1997 159

1998 175

1999 190

2000 NA

Ft = Ft-1 + ·(At-1 - Ft-1)

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Time ActualForecast, F t

(α = .10)

1995 180 175.00 (Given)

1996 168 175.00 + .10(180 - 175.00) = 175.50

1997 159

1998 175

1999 190

2000 NA

Ft = Ft-1 + ·(At-1 - Ft-1)

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Time ActualForecast, F t

( α = .10)

1995 180 175.00 (Given)

1996 168 175.00 + .10(180 - 175.00) = 175.50

1997 159 175.50 + .10(168 - 175.50) = 174.75

1998 175

1999 190

2000 NA

Ft = Ft-1 + ·(At-1 - Ft-1)

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Time ActualForecast, F t

( α = .10)

1995 180 175.00 (Given)

1996 168 175.00 + .10(180 - 175.00) = 175.50

1997 159 175.50 + .10(168 - 175.50) = 174.75

1998 175

1999 190

2000 NA

174.75 + .10(159 - 174.75)= 173.18

Ft = Ft-1 + ·(At-1 - Ft-1)

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Time ActualForecast, F t

( α = .10)

1995 180 175.00 (Given)

1996 168 175.00 + .10(180 - 175.00) = 175.50

1997 159 175.50 + .10(168 - 175.50) = 174.75

1998 175 174.75 + .10(159 - 174.75) = 173.18

1999 190 173.18 + .10(175 - 173.18) = 173.36

2000 NA

Ft = Ft-1 + ·(At-1 - Ft-1)

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Time ActualForecast, F t

( α = .10)

1995 180 175.00 (Given)

1996 168 175.00 + .10(180 - 175.00) = 175.50

1997 159 175.50 + .10(168 - 175.50) = 174.75

1998 175 174.75 + .10(159 - 174.75) = 173.18

1999 190 173.18 + .10(175 - 173.18) = 173.36

2000 NA 173.36 + .10(190 - 173.36) = 175.02

Ft = Ft-1 + ·(At-1 - Ft-1)

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Any Queries ?

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Thank you