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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.