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Forecasting

[ref. Chopra & Meindl pages 68 to 75]

Forecasting is a scientific method of determining

demand in future

Starting point for all strategic planning

Importance of strategy in spite of uncertainty in

future

Logistical areas of production scheduling,

inventory control, and aggregate planning need

demand forecast

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Some characteristics of forecasts

Forecasts are almost always wrong

Forecasts are more accurate for groups or

families of items

• motor cars and models

Aggregate forecasts are more accurate

• annual rainfall and daily rainfall

Forecasts are more accurate for short periods

(tomorrow, next year)

Forecast should include an estimate of error

Forecasts are no substitutes for facts

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Components of forecast

Past demand

Planned advertising or marketing efforts

Planned price discounts

State of economy

Competitors’ actions

forecaster’s knowledge and judgment

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Major categories of forecasts

(forecasting methods)

Qualitative & quantitative forecasts

Qualitative forecasting

• Forecast is based on personal judgment

• Subjective (opinion based)

• can be obtained in less time

• When facts are unavailable for other methods

• Made for specific items based on aggregate

forecast for markets)

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Some qualitative methods of forecasting

Market surveys – potential customers’ opinions

Delphi method

Panel consensus

Life cycle analogy

Informed judgment – sales force

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Quantitative forecasting

Fact based, scientific models

Causal-Correlating demand to specific causal

factors in environment. Estimate these causal

factors and forecast demand. Ambient temperature

and coffee consumption! Monsoon and rice

production!

Econometric models-statistical analysis of

various sectors of economy

Input-output models

• Examine flow of products and services for

markets and market segments

• Generally used for project needs

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Simulation – using computer simulation to

simulate sectors of economy

Time series

1.Regression analysis

• Statistical method

• Developing analytical relationship between two

variables

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• Using statistical tools on past data to identify

trend, under stable environmental situations and

demand

2.Moving average method

Simple moving average – estimator decides

the period over which average is taken. 3 months

or so

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MONTHS ACTUAL FORECAST

JANUARY 4200 -

FEBRUARY 4300 -

MARCH 4350 -

APRIL - 4283

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MONTHS WEIGHTS SALES WEIGHTED SALES

JANUARY 2 4200 8400

FEBRUARY 3 4300 12,900

MARCH 5 4350 21,750

TOTAL 10 43050

Weighted forecast for April = 4305

Weighted moving average

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Major factors that influence demand forecast:

Demand and promotions

-one product stealing demand of another

product (tooth powder and tooth paste, motor car

and motorbikes)

Lead times

-forecast methods need to be more accurate

(sophisticated) if lead times are longer, as

forecasts tend to become weak for a long span of

time. If supply sources are available with short

lead times, forecast methods need not be very

accurate (sophisticated)

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Influence of product variants on each other

is to be judged and if required

joint forecast may be made

Full shirts and half shirts, shirts and T-shirts,

different models of same product

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Appropriate technique for forecast

Take the dimensions of forecast into account to

determine forecasting method. These dimensions

are

• geographical area

• product groups

• customer groups

Take criteria into account

• accuracy

• time horizon

• data availability

• experience of the forecaster

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Establish performance and error measures to

use forecast accurately:

Lead time as a performance measure. Forecast

accuracy is required to be highest at the end of

this lead-time.

Difference between forecast and actual should

be measured for estimating error.

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Forecast Approaches

Top-Down Approach (decomposition approach)

• A national level forecast for SKU of company

• performance pattern of locations in the past

• forecast for various locations

• demand is assumed to be uniform across the

national market

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Bottom-Up Approach (decentralized approach)

• Forecast for individual locations

• Cumulative forecast for company at national

level