Wk4 Forecasting

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Marketing.. Demand Forecasting, Marketshare, Trend in pricesOperations.. Material requirements,Material and Labour costs, Idle time,Inventory, Defective partsFinance.. Cash flows, Expenses,Revenues, Costs

Personnel.. Labour Turnover,Absenteeism..


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Sources of data Sales force estimates.Point of sales (POS) data systems.Trade /Association Journals.Economic Surveys and indicators.


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Coordinate and Control all the sources of demand to- Use Production system efficiently- Delivery on Time

Types of Demand- Dependent Demand : part of a product

- Independent Demand : either influenceDemand or respond to Demand


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Demand Management

A

B(4) C(2)

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

Independent Demand

Dependent Demand

Independent demand is uncertain. Dependent demand is certain.


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PATTERNS OF DEMAND

Average Demand - steady requirement TREND - LONG-RUN GENERAL MOVEMENTS INCREASING

OR DECREASING

SEASONAL - RECURRENT AND PERIODIC EVERY 12

MONTHS CYCLICAL - CAUSED BY ECONOMIC EXPANSIONS AND

CONTRACTIONS, TECHNOLOGICAL, DEMOGRAPHICAL, ETC.VERY HARD TO FORECAST

RANDOM - NO DISCERNIBLE PATTERN


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Demand Forecasting

Qualitative analysis Quantitative analysis

Customer survey

Sales forcecomposite

Executiveopinion

Delphimethod

Past analogy

Time seriesanalysis

Causalanalysis

Forecast by linear regression

Simplemovingaverage

Simpleexponentialsmoothing

Trend analysis

Holts doubleExponentialsmoothing

Winters tripleExponentialsmoothing


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Forecasting methods

Qualitative. . Where no data available, useful for new products. Quantitative

Time series .. Based on past data, use of computers for fasterprocessing Causal.. Based on factors influencing demand e.g.

Advertisement, quality, competition, economic factors, Govtpolicies..


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Forecasting...Qualitative Methods

Grass Roots - Talk to Sales Force, Talk toCustomersMarket Research - Surveys of Customers,Experimental Test Markets

Panel Consensus - Bring in ExpertsExecutive Judgment - Surveys or Formal Input fromExecutivesHistorical Analogy - For New Products/ Technology,Find a Similar ProductDelphi Method - Formal, Sequential Method of Polling and Pooling Expert Opinions


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Forecasting methods... Time series analysis

Simple Moving average : Neither growing nor declining, w/oseasonal characteristics, e.g.items in inventory

Weighted Moving Average : More weightage to recentpast.. .5/.3/.2, sales in a dept stores

Simple exponential smoothing :Accurate, easilyunderstandable, less computation, retail/wholesaletrade,services

Regression Analysis: useful for long-term forecasting of family of products, past and future fall in a straight line


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Casual :

The demand for product or service is dependent ondifferent factors or variables like price, quality,availability of substitute and/or complementary

products/ services, income levels of customers,

number of competitors, etc.A causal method evaluates the relationship betweendifferent variables and their influence on each other.Causal methods include linear regression and

multiple regression analysis.


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In-Class Exercise

Week Demand

1 11

1 11

1 11

1 11

1 11

1 11

1 11

Develop 3-week and 5-week moving average

forecasts for demand. Assume you only have 3

weeks and 5 weeks of

actual demand data for the respective forecasts


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In-Class Exercise (Solution)

Week Demand -Week1 -Week1

1 111

1 1111 111

1 111 .11111

1 111 .11111

1 111 .11111 .1111

1 111 .11111 .1111


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Weighted Moving Average

F = w A + w A + w A +. ..+w At 1 t- 1 1 t- 1 1 t- 1 n t-n

w =ii=1

n

Determine the 3-periodweighted moving averageforecast for period 4.

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

t-3 .2

Week Demand

1 11

1 666

1 111


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Solution

Week Demand Forecast

1 111

1 666

1 111

1 .666 6

F = . ( )+.1111 ( )+. (1111 1 )1111


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In-Class Exercise

Determine the 3-periodweighted moving averageforecast for period 5.

Weights:t-1 .7

t-2 .2t-3 .1

Week Demand1 11

1 11

1 11

1 11


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Solution

Week Demand Forecast

1 111

1 111

1 111

1 111

1 11


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Causal Models for

forecastingA set of independent variables are identified and associatedwith the dependant variable through a functionalrelationshipIn general the forecast for a dependant variable Y using nindependent variables X1, X2, X3, , Xn involvesdeveloping a functional relationship as follows:

Y = f(X1, X2, X3, , Xn)There are several computer packages available today suchas SPSS to help the forecast designer in this process

M f F i


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Measures of ForecastingAccuracy

A forecasting error is the differencebetween the forecasted demand for aparticular period and the actual demandin that period.

To determine how well the forecastsfrom a forecasting model fit with theactual demand pattern, the averageerror of the model is calculated.


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