1

EMBA-2, BUP

Forecasting

M. AsadEMBA-2

EO - 702EO - 702Demand Management

A

B(4) C(2)

D(2) E(1) D(3) F(2)

Independent Demand: What a firm can do to manage it?

• Can take an active role to influence demand• Can take a passive role -simply respond to demand

Independent DemandDependent Demand

M. AsadEMBA-2

EO - 702EO - 702Components of Demand

• Average demand for the period• Trend• Seasonal element• Cyclical elements• Irregular variation• Random variation

M. AsadEMBA-2

EO - 702EO - 702

Components of Demand Q

ua

nti

ty

Average demand for the period

Time

M. AsadEMBA-2

EO - 702EO - 702

Components of Demand Q

ua

nti

ty

Time

Trend: Data consistently increase or decrease, not necessarily in a linear fashion

M. AsadEMBA-2

EO - 702EO - 702

Components of Demand Q

ua

nti

ty

| | | | | | | | | | | |J F M A M J J A S O N D

Months

Seasonal: Data consistently show peaks and valleys with a one-year cycle

Year 1

Seasonal variations

908988

M. AsadEMBA-2

EO - 702EO - 702

Qu

an

tity

| | | | | |1 2 3 4 5 6

Years

Cyclical: Data reveal gradual increases and decreases over extended periods of more than one-year in length

Components of Demand

M. AsadEMBA-2

EO - 702EO - 702

Components of Demand Q

ua

nti

ty

Time

Irregular Variation: any event interrupting the regular phenomena

Irregularvariation

Random Variation: are caused by chance events

M. AsadEMBA-2

EO - 702EO - 702Types of Forecasts

• Qualitative

• Time Series Analysis

• Casual Relations• Simulations

M. AsadEMBA-2

EO - 702EO - 702

Qualitative Methods

Grass Roots

Market Research

Panel Consensus

Executive Judgment

Historical analogy

Delphi Method

Qualitative

Methods

M. AsadEMBA-2

EO - 702EO - 702Time Series Analysis

• Time series forecasting models try to predict the future based on past data

• The analysis can be done through various methods:

1. Simple moving average

2. Weighted moving average

3. Exponential smoothing

4. Regression analysis

5. Others

M. AsadEMBA-2

EO - 702EO - 702Simple Moving Average Formula

F = A + A + A +...+A

ntt-1 t-2 t-3 t-nF =

A + A + A +...+A

ntt-1 t-2 t-3 t-n

• The simple moving average model assumes an average is a good estimator of future behavior

• The formula for the simple moving average is:

Ft = Forecast for the coming period N = Number of periods to be averagedA t-1 = Actual occurrence in the past period for up to “n” periods

M. AsadEMBA-2

EO - 702EO - 702Simple Moving Average Problem

Week Demand1 6502 6783 7204 7855 8596 9207 8508 7589 892

10 92011 78912 844

F = A + A + A +...+A

ntt-1 t-2 t-3 t-nF =

A + A + A +...+A

ntt-1 t-2 t-3 t-n

Question: What are the 3-week and 6-week moving average forecasts for demand?

Assume you only have 3 weeks and 6 weeks of actual demand data for the respective forecasts

Question: What are the 3-week and 6-week moving average forecasts for demand?

Assume you only have 3 weeks and 6 weeks of actual demand data for the respective forecasts

M. AsadEMBA-2

EO - 702EO - 702

Week Demand 3-Week 6-Week1 6502 6783 7204 785 682.675 859 727.676 920 788.007 850 854.67 768.678 758 876.33 802.009 892 842.67 815.33

10 920 833.33 844.0011 789 856.67 866.5012 844 867.00 854.83

F4=(650+678+720)/3

=682.67F7=(650+678+720 +785+859+920)/6

=768.67

Calculating the moving averages gives us:

M. AsadEMBA-2

EO - 702EO - 702

Weighted Moving Average Formula

F = w A + w A + w A +...+w At 1 t-1 2 t-2 3 t-3 n t-nF = w A + w A + w A +...+w At 1 t-1 2 t-2 3 t-3 n t-n

w = 1ii=1

n

w = 1ii=1

n

While the simple moving average formula implies an equal weight being placed on each value that is being averaged, the weighted moving average permits an unequal weighting on prior time periods

While the simple moving average formula implies an equal weight being placed on each value that is being averaged, the weighted moving average permits an unequal weighting on prior time periods

wt = weight given to time period “t” occurrence (weights must add to one)

wt = weight given to time period “t” occurrence (weights must add to one)

The formula for the moving average is:The formula for the moving average is:

M. AsadEMBA-2

EO - 702EO - 702

Weighted Moving Average Problem Data

Weights: t-1 .5t-2 .3t-3 .2

Week Demand1 6502 6783 7204

Question: Given the weekly demand and weights, what is the forecast for the 4th period or Week 4?

Question: Given the weekly demand and weights, what is the forecast for the 4th period or Week 4?

Note that the weights place more emphasis on the most recent data, that is time period “t-1”

Note that the weights place more emphasis on the most recent data, that is time period “t-1”

M. AsadEMBA-2

EO - 702EO - 702

Weighted Moving Average Problem Solution

Week Demand Forecast1 6502 6783 7204 693.4

F4 = 0.5(720)+0.3(678)+0.2(650)=693.4

M. AsadEMBA-2

EO - 702EO - 702Exponential Smoothing Model

• Exponential Smoothing assigns exponentially decreasing weights as the observation get older.

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

constant smoothing Alpha

period epast t tim in the occurance ActualA

period past time 1in alueForecast vF

period t timecoming for the lueForcast vaF

:Where

1-t

1-t

t

M. AsadEMBA-2

EO - 702EO - 702

Exponential Smoothing Problem Data

Week Demand1 8202 7753 6804 6555 7506 8027 7988 6899 775

10

Question: Given the weekly demand data, what are the exponential smoothing forecasts for periods 2-10 using =0.10 and =0.60?

Assume F1=D1

Question: Given the weekly demand data, what are the exponential smoothing forecasts for periods 2-10 using =0.10 and =0.60?

Assume F1=D1

M. AsadEMBA-2

EO - 702EO - 702

Week Demand 0.1 0.61 820 820.00 820.002 775 820.00 820.003 680 815.50 820.004 655 801.95 817.305 750 787.26 808.096 802 783.53 795.597 798 785.38 788.358 689 786.64 786.579 775 776.88 786.61

10 776.69 780.77

Answer: The respective alphas columns denote the forecast values. Note that you can only forecast one time period into the future.

Answer: The respective alphas columns denote the forecast values. Note that you can only forecast one time period into the future.

M. AsadEMBA-2

EO - 702EO - 702Simple Linear Regression Model

Yt = a + bx0 1 2 3 4 5 x (Time)

YThe simple linear regression model seeks to fit a line through various data over time

The simple linear regression model seeks to fit a line through various data over time

Is the linear regression modelIs the linear regression model

a

Yt is the regressed forecast value or dependent variable in the model, a is the intercept value of the the regression line, and b is similar to the slope of the regression line.

M. AsadEMBA-2

EO - 702EO - 702

Simple Linear Regression Formulas for Calculating “a” and “b”

a = y - bx

b =xy - n(y)(x)

x - n(x2 2

)

a = y - bx

b =xy - n(y)(x)

x - n(x2 2

)

M. AsadEMBA-2

EO - 702EO - 702Simple Linear Regression Problem Data

Week Sales1 1502 1573 1624 1665 177

Question: Given the data below, what is the simple linear regression model that can be used to predict sales in future weeks?

Question: Given the data below, what is the simple linear regression model that can be used to predict sales in future weeks?

M. AsadEMBA-2

EO - 702EO - 702

Week Week*Week Sales Week*Sales1 1 150 1502 4 157 3143 9 162 4864 16 166 6645 25 177 8853 55 162.4 2499

Average Sum Average Sum

b =xy - n(y)(x)

x - n(x=

2499 - 5(162.4)(3)=

a = y - bx = 162.4 - (6.3)(3) =

2 2

) ( )55 5 9

63

106.3

143.5

b =xy - n(y)(x)

x - n(x=

2499 - 5(162.4)(3)=

a = y - bx = 162.4 - (6.3)(3) =

2 2

) ( )55 5 9

63

106.3

143.5

Answer: First, using the linear regression formulas, we can compute “a” and “b”

Answer: First, using the linear regression formulas, we can compute “a” and “b”