Forecasting

What is Forecasting?

Process of predicting a future event and it is a mere guess.

It is the estimating the future demand for products and services are commonly referred as a sales forecast

Underlying basis of all business decisions: Production Inventory Personnel Facilities

NEED OF DEMAND FORECASTING

New facility planningProduction planningWorkforce schedulingFinancial planning

Short-range forecastUp to 1 year (usually less than 3 months)Job scheduling, worker assignments, plan for

purchasingMedium-range forecast

3 months to 3 yearsSales & production planning, budgeting

Long-range forecast3 years, or moreNew product planning, facility location

Forecasts by Time Horizon

Types of Forecasts

Economic forecastsAddress the future business conditions

(e.g., inflation rate, money supply, etc.)Technological forecasts

Predict the rate of technological progressPredict acceptance of new products

Demand forecastsPredict sales of existing products

Features of demand forecasting

It generally assume the same underlying reasons

Forecasts are rarely perfectForecast for group items will be more

perfect than the individual itemsForecast accuracy decreases as the

time period covered by the forecast

Seven Steps in Forecasting

Determine the purpose of the forecastSelect the items to be forecastedDetermine the time horizon of the forecastSelect the forecasting model(s)Gather the dataMake the forecastValidate and implement results

Objectives of demand forecasting

Short range objectives• Formulation of production strategy and

policy• Formulation of pricing policies• Planning and control of sales• Financial planning

Objectives of demand forecasting

Medium or Long range objectives• Long range planning for production

capacity• Labour requirements• Restructuring the capital structure

Forecasting Approaches

Used when situation is stable & historical data existExisting productsCurrent technology

Involves mathematical techniquese.g., forecasting sales of

color televisions

Quantitative Methods Used when situation is

vague & little data existNew productsNew technology

Involves intuition, experiencee.g., forecasting sales on

Internet

Qualitative Methods

Qualitative MethodsJury of executive opinion

Pool opinions of high-level executives, sometimes augment by statistical models

Delphi method or judge mental method Panel of experts, queried iteratively

Sales force composite Estimates from individual salespersons are

reviewed for reasonableness, then aggregated Consumer (Market research) Survey

Ask the customer

Quantitative Approaches

Time series model(Trend, Seasonality, Cycles)

Naive approachMoving averageExponential smoothingCasual modelsTrend projectionLinear regression analysis

Set of evenly spaced numerical data Obtained by observing response variable at

regular time periods Forecast based only on past values

Assumes that factors influencing past and present will continue influence in future

ExampleYear: 19981999200020012002Sales: 78.763.589.7 93.292.1

Time Series Models

TrendTrend

SeasonalSeasonal

CycleCycle

RandomRandom

Time Series Components

Persistent, overall upward or downward pattern

Due to population, technology etc.Several years duration

Trend Component

Regular pattern of up & down fluctuations

Due to weather, customs, etc.Occurs within 1 year

Seasonal Component

Repeating up & down movementsDue to interactions of factors influencing

economyCan be anywhere between 2-30+ years

duration

Cyclical Component

Erratic, unsystematic, ‘residual’ fluctuationsDue to random variation or unforeseen events

Union strike Tornado

Short duration & non-repeating

Random Component

1.Naive Approach

Assumes demand in next period is equal to the actual demand in most recent period e.g., If May sales were 48, then June sales

will be 48Sometimes cost effective & efficient

Moving average uses a number of most recent historical actual data values to generate a forecast.

MA is a series of arithmetic means Used if little or no trend Used often for smoothing

Provides overall impression of data over time Equation:

MAMAnn

nn Demand inDemand in PreviousPrevious PeriodsPeriods

2.Moving Average Method

example

Forecast demand for 4 monthsd1+d2+d3 *4

3

3.Exponential Smoothing Method

• It requires only three items of data this periods forecast, the actual demand for this period and α which is referred to as a smoothing constant and having value between 0 and 1

• Next period’s forecast = This period forecast + α{this period’s actual dd – this periods forecast}

Ft = Ft-1 + (At-1 - Ft-1)

Ft = forecast for this period

Ft-1 = forecast for the previous period

At-1= Actual demand for the previous period

Smoothing constant (0 to 1)

Exponential Smoothing Equations

Used for forecasting linear trend lineAssumes relationship between

response variable, Y, and time, X, is a linear function

Estimated by least squares methodMinimizes sum of squared errors

iY a bX i

Linear Trend Projection

Answers: ‘how strong is the linear relationship between the variables?’

Coefficient of correlation Sample correlation coefficient denoted rRange: -1 < r < 1Measures degree of association

Used mainly for understanding

Correlation

Linear regression analysis

The demand or sales forecast is a dependent variable and other factors are independent variables

Factors to be considered in the selection of forecasting method

Cost and accuracyData availableTime spanNature of products and servicesImpulse response and noise dampening

You want to achieve:No pattern or direction in forecast error

Error = (Yi - Yi) = (Actual - Forecast)Seen in plots of errors over time

Smallest forecast errorMean Absolute Deviation (MAD), or Mean

Absolute Percentage Error (MAPE)Mean Squared Error (MSE)

Selecting a Forecasting Model

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Which Model Is “Best” So Far?

The Naïve model has both the lowest MAD (1.91) and MSE (4.45) of the first five models tested

Therefore, the Naïve model is the “best”However, it may be that one model has

the lowest MAD or MAPE and another model has the lowest MSE…

So Which Model Do You Choose?

If you only require the forecast with the smallest average deviation, choose the model with the smallest MAD or MAPE

However, if you have a low tolerance for large deviations choose the model with the smallest MSE