Demand forecastingForecasting of demand is the art of predicting demand for a product or a service at some future date on the basis of certain present and past behavior patterns of some related events.Forecasting is used in process design, capacity and facilities planning, aggregate planning scheduling inventory management etc

Types of forecastsThere are long-term forecasts as well as

short-term forecasts.Operations managers need long-range

forecasts to make strategic decisions about products, processes and facilities. Long tem forecasts are used to make location, layout,and capacity decisions.

They also need short-term forecasts to assist them in making decisions about production issues that span only the next few weeks.

Since forecasting forms an integral part of planning and decision-making , production managers must be clear about the horizon of forecasts-month or year, for example, additionally they must also be clear about the method of forecasting and unit of forecasting

Importance of Demand forecasting

Determination of sales territory.To decide to enter a new market or not.To determine how much production

capacity to be builds up.Helpful in deciding the number of

salesman required to achieve the sales objective.

To prepare standard against to which measure performance.

To assess the effect of a proposed marketing programme.

Helpful in the product mix decisions.To decide the promotional mix.To assess the effect of a proposed

marketing programme.In deciding the channels of distribution

and physical distribution decision.

Criteria of good forecasting MethodSimplicity and ease of comprehension.EconomyAvailabilityDurabilityAccuracy

Methods of demand forecastingI. Opinion polling.II. Statistical method.Opinion polling method:1. Consumer survey method. 2. Sales force opinion method. 3. Delphi method.

Consumer survey method:Complete enumeration.Sample survey test.End use method.

Statistical methodTime series analysis.Barometric method.Regression analysis.Simultaneous equation method.

Time series analysis: Chronologically arranged continuous past

data.Trend analysis method.Least square method.Moving average methodExponential smoothing.:α Dt-1+(1- α)Ft-1α = Exponential smoothing constant (0

to1).Ft= forecasting period.

Dt-1= Actual demand for periodt-1.Eg: F July= α DJune + (1- α)F June

Strategies for developing aggregate plans: The aggregate plan is developed after careful

consideration of the different Variables which influence the production plan.

Similarly the aggregate plan also influenced by no. of factors.

Trend projection method:These are generally based on analysis of past

sales pattern.Least squares method: certain statistical

formulae are here to find the trend line which best fits the available data.

The trend line is the basis to extrapolarate the line for future demand for the given product or service on graph.

MOVING AVERAGE METHOD:This method is based on the assumption

that the future is the average of past achievements.

Hence based on past achievement, future is predicted.

When the demand is stable this method can provide good forecasts.

The main issue in moving averages is determining the ideal number of periods to include the average.

Customer needs demand forecasts competition.

Financial conditions of the firm.Labour training capacity.New products product design changes

Machines.Suppliers capability storage capacity material

availabity.Machine capacity, workforce capabilities.

Barometric techniques:Under the barometric technique one set of

data is used to predict another set. In other words, to forecast demand for a

particular product or service, use some other relevant indicator of future demand.

Ex: the demand for cable TV may be linked to the number of new houses occupied in a given area.

Simultaneous equation methodIn this method, all variables are

simultaneously considered with the conviction that every variable influences the other variables in an economic environment.

It is a system of ‘n’ unknowns. It can be solved, the moment the model is specified because it covers all the unknown variable. It is also called complete systems approach to demand forecasting.

Correlation and regression methods:Correlation and regression methods are

statistical techniques.Correlation describes the degree of

association between two variables such as sales and advertisement expenditure. When two variables are tend to change together, then they are said be correlated.

The extent to which they are correlated is measured by correlation coeffient.

Of these two variables, one is a dependent variable and the other is independent variable.

regression analysisAn equation is estimated which best fits in the

sets of observations of dependent variables and independent variables .

The best estimate which best fits in the sets of observation of dependent variables and independent variables.

the best estimate of the underlying relationship between these variables is thus generated.

The dependent variables is then forecast based on this estimated equation for a given value of the independent variable.