lec.4 demand forecasting

8/3/2019 Lec.4 Demand Forecasting

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DEMAND FORECASTING

TECHNIQUES


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Introduction

The major reasons for Wall Martsincreased revenue :

Efficiently managed Supply Chain and

Within supply chain its efficient collaborativePlanning , forecasting and replenishmentsystem (CPFR)


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Forecasting

Forecasting provides an estimate of future

Good forecasting techniques do:

Minimize the gap between forecasted and actual data

For good forecast: Identification of demand influencers are utmost important

Benefits of good forecasts are:

Lower Inventory

Reduced stocklessness

Smoother production

Reduced overall cost

Better Customer Satisfaction


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Why Forecasting????

Long Term Decision (Life time normally)

New product introduction

Plant expansion

Medium Term Decision(6months to 2 years)

Aggregate production planning

Manpower planning

Inventory Planning

Short Term Decision( From 1 month to a day)

Production Panning (deployment of resources)

Scheduling of Job Orders (FIFO, LIFO etc)


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Losses of Inaccurate Forecast

Bull whip effect

Lost sale

High cost of Inventory

Stoclessness

Raw Material shortage

Poor response to market dynamics

And of course..poor profitability


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Losses of Inaccurate Forecast

Examples:

Sony Website crashincident..

P&G lost sales 41%when went out of stock

U-fone lost sales by15% because of oversubscribing..

Omore..


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Forecasting Techniques Qualitative forecast

Jury of executive opinion

Delphi method

Sales force composite

Consumers surveys

Quantitative forecast Simple Moving Average Forecasting (SMA)

Weighted Moving Average Forecasting (WMA)

Exponential Smoothing Moving Average Forecasting (ESMA)

Econometrics Techniques


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Qualitative forecast

Jury of executive Opinion

Group of senior management executives are assembled

Opinion of members have prime importance

Used for

long range planning

High fashionery or faddy business

New business

When no historical data is available

Draw back is:

Dominance of senior members

Example:

Pak Qatar Takaful market entry in Pakistan..

A.Pardesi Icons.


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Qualitative forecast

Delphi method

Senior executives opinions are taken through surveys

Survey results are summarized

Results are send to same individuals for revision

Revised summary of opinion is prepared and resend..

Practice continues till consensus

Benefit

Avoid dominance of senior members

Disadvantage: Too much time taking

Example:

ABC Electro-medical equipment manufacturer..


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Qualitative forecast Sales force composite

Questioners are filled out by sales persons, stating forecasted sale(MBO)

Provides right market information

Can be biased when more than targeted sale involves bonuses etc

Examples:

Epoch Pharmaceutical.

Consumers Survey Questionnaires are filled out by consumers

Questionnaires contain information about consumer (Demanddeterminants)

Buying habits

New Product ideas

Opinion about existing product


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Quantitative forecast (Time Series Model

/Intrinsic Technique)

Simple Moving Average

Use of historical data

How to forecast

Actual Demand data of previous periods is required

Just take average of previous period data

Fore detail Excel Sheet

More responsive if fewer numbers are used

Adv: Simple to use and easy to understand

Disadv: Inability to respond quickly to trend change

Best for short term forecasting


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Question

Demand over the past three months hasbeen 120, 135, and 114 units. Using athree month data, calculate the forecast

for the fourth month.


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Quantitative forecast (Time Series

Model/Intrinsic Technique)

Weighted Moving Average Forecasting (WMA)

Actual data of four previous periods is taken

Most recent sale is given the higher ratio

Period Actual Demand % Forecasted

1 1600 0.1

2 2200 0.2

3 2000 0.3

4 1600 0.45 1840

Q i i f (Ti S i M d l


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Quantitative forecast (Time Series Model/Intrinsic Technique)

Exponential Smoothing Moving Average

Last months actual demand

Last months forecasted demand

Both are combined according to givenpercentage

Ft+1= At + 1-(Ft)

For detail Excel sheet


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Exponential Smoothing Moving Average

Period Actual Demand EWMA

1 1600 #N/A

2 2200 1600.0

3 2000 2020.0

4 1600 2006.0

5 2500 1721.8

6 3500 2266.5

7 3300 3130.0

8 3200 3249.0

9 3900 3214.7

10 4700 3694.411 4300 4398.3

12 4400 4329.5

13 4378.8

500

V

alue


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ThanxIn next session we will discuss

the use of RegressionTechniques for demandforecasting.

lec.4 demand forecasting

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