demand forecating,final

8/14/2019 Demand Forecating,Final

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DEMAND FORECASTING

Talwinder Singh

Roll No.80782011


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.

Before making investment in men, machinery, toolingetc.,organization has to answer certain questions. Fore.g. it must knows in advance:

3) What is the capacity of company like Power plant200MW?

4) What is the amount of capital to carry out forproduction?

5) What is the type of production system the companyshould follow?

6) What is the level of inventories that have to kept atdifferent intervals of time?


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Definitions

Demand forecasting means to look into future, whatproducts do the customer prefer, in what quantity andby what time or during which period.

Forecasting is the process of estimating future eventsby casting forward past data, the past data issystematically combined in a predetermined way toobtain the estimate of the future.


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Scope of forecasting

Planning activities

1) Capacity planning, layout decisions.

2) Process design3) Strategy for marketing

4) Budget


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.

Scheduling / Controlling activities

1) Aggregate planning2) Master production schedule

3) Production programme,Prodution rate, Lot size etc.

4) Man power requirement planning

5) Material requirement planning6) Inventory management and control


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Difference between forecasting andprediction

Method

Personnel bias Reproducibility

Error analysis


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Cost of forecasting

Costs of increased activity

Opportunity costs

Opportunity cost

Cost of increased activity

Total cost

Forecasting activity

Accuracy

Cos

tofforecasting

Judgment method

Time series

method

Causal method


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Types of forecasting techniques

Qualitative techniques(Judgment methods)

- Salesforce estimate- Experts opinion

- Market research

- Panel consensus- Delphi method


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Quantitative techniques

(a) Causal methods

- Correlation and regression analysis

(b) Time series analysis

- Simple moving averages

- Weighted moving averages

- Exponential smoothing forecasting


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Type of forecasting on the basis oftime horizon

Short range forecasting- Up to 3 months into the future

Medium range forecasting- 3 months 2 years into the future

Long range forecasting

- > 2 years


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Qualitative techniques(Judgment methods)

Past sales data is not available

Forecasting has to be done for a verydistinct period (say for 5 year)


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Salesforce estimates

Also called Grass root method

Employees from low level like salesrepresentative, marketing people.

Forecast errors occur due topersonnel bias, incentive schemes.


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Executive opinion method

Managerial level of people

Costly Forecast errors occurs if managers

changes the forecast figure


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Panel consensus

Very popular method

People from all level of management

Open meeting

Everybody comes turn by turn, with opinionand why they feel in such a way

At the end of meeting common figure

comes out with the consensus of allmembers

Forecast error occurs due to organizationculture


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Delphi method

Introduce to overcome the limitations of panelconsensus method

Group of experts Their identity is concealed

All work in written form and not a open meeting Coordinator / moderator Questionnaire Summary report

Again revised by experts Generally in 3 rounds, we will get the forecast figure


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Quantitative techniques

(a) Causal methods- These methods are based on cause and effect relationship

- Relationship between 2 or more factors

- Dependent and independent variables

- Dependent variables like demand, sales etc.- Independent variables like population, advertisement expenditures etc.

Regression line , y=a + bx

Depende

ntvariable

Independent variable


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Example


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.


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Time series methods

Driving a car by seeing rear view

Simple moving averages

Weighted moving averages Exponential smoothing


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Simple moving average


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Weighted moving average


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Example


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Solution


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Exponential smoothing method

One of the disadvantages of the movingaverage forecasting is the laboriousoperation of maintaining the data for allprevious years.

This method gives more weightage torecent demand and the weight assigned toolder periods decreases exponentially.

This method requires:

- most recent actual demand- most recent forecast- smoothing constant ()


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Exponential smoothing method

Ft+1 = Dt + (1- )Ft It is a modified form of weighted average method Ft+1 = Dt + (1- ){ Dt-1+ (1- )Ft-1}

= Dt + (1- ) Dt-1+ (1- )2 Dt-2 + ..}

The weights are : (1- )0

(1- )1

(1- )2 and so on. And this decay in weightages is in exponential

manner.


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Impact of n and

n low

- more fluctuating

- but there is a closer following of the trend

If we want to give more weightage to recent demand ascompared to old data, take large value of

If demand is stable , take small value of


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Multiplicative seasonal time seriesmethod

Find out average demand per year

Find out the parameter / quantity

called seasonal index (S.I.) S.I. = Actual demand during a season / Average demandduring a season

Calculate average seasonal index for

a season (A.S.I.)(A.S.I.)Q1 = {(S.I.Q1)y1 + (S.I.Q1)y2 + (S.I.Q1)y3 +

(S.I.Q1)y4} / 4


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Example Find out quarterly forecast for the number of products expected in

the next year. The quarterly demand for the past four years is shown

in table. The total forecasted demand for the next year be 2600units.

215285170100Q41160830590520Q3

725585370335Q2

1001007045Q1

Y4Y3Y2Y1

S l ti


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Solution

Step 1. Calculate average demand

per year.for example, year 1 = 45 + 335 + 520 +100) /4 = 250

year 2 = 300, year 3 = 450, year 4 =550

Step 2. Find S.I., like 45/250 =0.18 Step 3. Find A.S.I.

A.S.I.(Q1) = (0.18 + 0.23 + 0.22 +0.18 ) /4 = 0.2

A.S.I.(Q2) = 1.3A.S.I.(Q3) = 2.0A.S.I.(Q4) = 0.5So, forecast for the next yearQ1 = 0.2 x (2600/4) = 0.2 x 650 = 130

units

Q2 = 1.3 x (2600/4) = 1.3 x 650 = 845units

Q3 = 2.0 x (2600/4) = 2.0 x 650 = 1300units

Q4 = 0.5 x (2600/4) = 0.5 x 650 = 325units

0.390.630.560.4Q4

2.111.841.962.08Q3

1.321.31.231.34Q2

0.180.220.230.18Q1

Y4Y3Y2Y1


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Thanks!

demand forecating,final

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