Everything you need to know about the methods and techniques of need forecasting. Demand forecasting is done by companies and they utilize different methods ranging from qualitative to quantitative methods.

The option of method generally depends upon the size and complexity of the firm. Smaller firms commonly have more informal form of human resource planning and hence many times rely on more than qualitative methods.

Whereas, larger firms commonly having multiple departments, levels and higher mobility of workforce both within and outside the business firm, mostly employ more quantitative methods.

A: The following are the methods used in demand forecasting:-

one. Delphi Method 2. Production Life Cycle Model three. Fourth dimension Series Analysis 4. Regression Analysis v. Exponential Smoothing 6. Extrapolation 7. Independent Technological Comparisons 8. Barometric Forecasting 9. Econometric Models 10. Opinion Polling.

B: A great variety of methods are available for forecasting the demand of a consumer:-

They are – 1. Jury Method/Executive Stance Method ii. Survey of Expert Opinion Method 3. The Delphi Method 4. Need strength Composite Method five. User Expectation Method/End-Use Method/Survey of Buyers Intentions Method 6. Market place Share Method. 7. Belittling and Statistical Methods 8. Market Survey Method.

C: There are two main methods of demand forecasting:

They are – 1. Qualitative Methods 2. Quantitative Methods.

D: Following are the two methods adopted for demand forecasting:-

1. Survey Methods 2. Statistical Methods.

Although, there are several methods of forecasting the demand for a product or a service, yet no forecasting method is useful for all the products.

A company should use more than one forecasting method for correct conclusion making. The choice of the forecasting method likewise differs from product to product to the objective of the company to data availability besides others. Although forecasting may act as a light to guide the path, merely it may not be authentic all the fourth dimension in this dynamic business surround.

Learn about the Methods and Techniques of Demand Forecasting

Methods of Demand Forecasting – Acme 10 Methods: Delphi Method, Product Life Cycle Model, Time Series Analysis, Regression Analysis and Opinion Polling

Forecaster utilise by information in the following way:

(a) The forecaster analyses the past information in gild to place a pattern that can exist used to draw information technology.

(b) The pattern is extrapolated or extended into the future in guild to set up a forecast.

The following are the method used in demand forecasting:

Method # ane. Delphi Method :

This method involves using a panel of experts to produce predictions concerning a specific question, such as when a new evolution would occur. The utilize of the Delphi method assumes that the panel members are recognized experts, and it besides assumes that the combined knowledge of the panel members will produce predictions atleast as adept as those that would be produced by one member.

This relies on the judgment of a number of skilled judges such as experienced marketing managers, and marketing consultants. This is a method of scenario building, in which a grouping of experts are asked individually to provide their views almost the demand of the product.

The Delphi technique adopts the post-obit methodology:

Stride 1 – Judge of sales are obtained from each gauge independently.

Stride ii – These independent estimates are circulated in an aggregated class for information and reflection to all other judges.

Step iii – This leads to new estimates existence fabricated by all judges.

Step 4 – The opinions of each skillful are collated. 'Extreme' views are discarded, and a draft 'consensus' view is formulated.

Pace 5 – The draft 'consensus' is circulated to the experts for their further comments, and depending on how they respond, the 'consensus' might be amended.

Step 6 – The procedure will continue until a forecast for the future has been prepared which has the acceptance of all or almost of the console of experts.

Stride 7 – When in that location is some uncertainty amongst the experts, probability weightage might be given to possible future 'scenarios' or 'events'.

Pace viii – The entire process is repeated until consensus is reached on an acceptable sales forecast.

Delphi method is based on the assumption that the judgement and skill of experienced managers is the most valuable forecasting tool. However, the technique remains subjective and inexact. Information technology can also be fourth dimension consuming-especially if initial estimates differ significantly from 1 some other.

Method # two. Product Life Wheel Model :

Consider the instance of a company introducing a new product and wishing to forecast sales of the product for the side by side three years. When predicting sales of a new product, it might exist appropriate to consider the 'product life cycle'. The production life bike model splits the life of the product into iv stages- Introduction, growth, maturity and decline. When forecasting sales of the product during the growth stage, the company might use the experts opinion of its sales and marketing personnel to subjectively construct an 'S' curve.

The S curve then exist used to forecast sales during this phase. The company might use its experience with other products and cognition concerning the new product, in constructing the S curve. It will demand to determine how long information technology will take for the rapid increment in sales to begin, how long this rapid growth will proceed, and when sales of the product will begin to stabilize. Estimating such curve is an case of subjective curve fitting and it requires a great deal of expertise and judgment.

Method # 3. Time Series Assay :

Time series analysis means analyzing the historical patterns of sales that accept occurred in the by as a means of predicting futurity sales.

It helps to place and explain the following:

(a) Whatever regular or systematic variation in the serial of data which is due to seasonality-the 'seasonals'.

(b) Cyclical patterns

(c) Trends in the information

(d) Growth rates of these trends

This method tin be useful when no major environmental changes are expected and it does highlight seasonal variations in sales and consumer demand. Still, fourth dimension serial analysis is limited when organizations face volatile environments.

Time series analysis helps in understanding the past behaviour of a variable in determining the rate of growth and the extent and management of periodic fluctuations. The study of past behaviour of a variable enables us to predict futurity tendencies. The actual operation can exist compared with expected functioning and the cause of variation analyzed. Different time series are compared and important conclusions drawn from time series data.

Method # 4. Regression Assay :

Regression analysis attempts to constitute the nature of relationship between variables to study the functional relationship between the variables and thereby provide a mechanism for prediction or forecasting. Regression analysis is a statistical technique with the help of which we are in a position to predict or forecast unknown values of one variable from the known values of another variable.

For example by setting the corporeality for advertisement entrada we cm predict sales through regression analysis technique. Here, advertisement expenditure budgeted is chosen independent variable and the sales to exist forecasted is called dependent variable.

The independent variable is denoted by 5 and the dependent variable is denoted by 'y'. The assay is called 'simple linear regression analysis', which is concerned with bivariate distributions, that is distributions of two variables. If 2 variables are functionally related then a noesis of ane will brand possible an judge of the other. If we know that advertising and sales are correlated, then for a given advertizing expenditure, nosotros tin find out the likely increase in sales and vice versa.

Simple regression analysis provides estimates of values of the dependent variable from values of the contained variable. The device used to accomplish this estimation procedure in the regression line. The regression line describes the average relationship existing between 5 and 'y' variables i.eastward. it displays hateful values of Y for given values of 'y'. The equation of this line, known every bit the regression equation, provides estimates of the dependent variable when values of the independent variable are inserted into the equation.

The equation of this line is described by:

y = a + bx

Where,

y = Dependent variable i.eastward. sales

x = Independent variable i.due east. advertisement expenses per unit of sales

a = Value of 'y' when 'ten' is equal to nada

b = The amount of change that come up in 'y' for a unit alter in 'ten'

Method # 5. Exponential Smoothing:

Exponential smoothing is a forecasting technique that attempts to 'track' changes in time series by using newly observed time serial values to 'update' the estimates of the parameters describing the fourth dimension series.

The model used is:

Forecast for period T + 1 = Forecast for period T + α (Actual for menstruation T – Forecast for menstruation T)

The terms in the brackets are regarded as the error term, as it is the departure between the actual issue and its forecast. The forecast for the next menstruation is the previous forecast plus a proportion of the error term. The proportion of alpha lies between 0 and 1 and is known as the smoothing constant. Each new forecast depends upon the previous result and its forecast, only this former forecast depends upon the previous result and so on.

Method # 6. Extrapolation :

Extrapolation is the simplest yet often a useful method of forecasting. Extrapolation relies on the relative consistency in the pattern of past movements in some fourth dimension series. Extrapolation is used ofttimes for sales forecasts and for other estimates when '^^'forecasting methods may not be justified.

Under extrapo­lation, the supposition that adjacent year's volume of sales will exist equal to this year'southward figure or that side by side twelvemonth'southward growth of sales will exist equal to this yr'due south. A slightly more sophisticated version is to identify any trends over the recent past so to extrapolate those trends forward into the future.

Method # 7. Contained Technological Comparisons :

This method is often used to produce technological alter. The method involves predicting changes in one area by monitoring changes that have identify in another area. That is, the forecaster tries to determine a pattern of modify in one area, called a master trend, which he believes will result in new developments being fabricated in some other expanse.

A forecast of developments in the second area tin can then exist made by monitoring developments in the first area. This type of forecasting poses two basic problems. Firstly, the forecaster must identify a primary tendency that will reliably predict events in the area of interest. Secondly, the forecaster must apply his expertise to determine the precise relationship between the primary trend and the events to be forecast.

Method # eight. Barometric Forecasting :

Time series analysis apply information nearly the past in order to make forecast of the future. Barometric forecasting uses indicators of current activeness in order to provide forecasts of the future. Perhaps the most common barometric technique is the apply of leading indicators. A leading indicator is a variable which is known, or believed, to be correlated with the hereafter behaviour of the variable for which a forecast is required.

Given the importance to businessmen of existence able to predict full general movements in the level of economic activeness, at that place are a large number of leading indicators which are used in the effort to identify changes in total spending, income and employment. These include new orders for auto tools, which often rising in accelerate of an increment in economic activity, the length of the working week, and the performance of the Stock Substitution.

A number of such general leading indicators used are as follows:

(a) New orders for automobile tools

(b) Average hours worked in manufacturing

(c) Alphabetize of new business formation

(d) New orders for durable goods

(east) Orders for institute and equipment

(f) New building starts

(g) Changes in manufacturing inventories

(h) Industrial material prices

(i) Stock Exchange indices

(j) Profit figures

(k) Price to unit labour cost ratios

(fifty) Increase in consumer debt

Method # 9. Econometric Models:

A multifariousness of forecasting models might be used by an organization. One type of model which can be used for both brusk-term and medium-term forecasting is an econometric model. Econometrics is the study of economic variables and how they are interrelated, using a figurer model. Econometric techniques have recently gained popularity in forecasting.

The term 'econometrics' refers to the application of mathematical economic theory and statistical procedures to economic data in guild to verify economic theorems and to establish quantitative results in economic science. Econometric models have the course of a set of simultaneous equations.

The values of the constants in such equations are supplied by a study of statistical fourth dimension series and a big number of equations may be necessary to produce an acceptable model. The work of computations is profoundly facilitated by electronic information processing equipment like computer etc.

Econometric models can be used to obtain information about economic developments which might be important for an organization'due south future plans, such every bit the following:

(a) The likely rate of cost inflation

(b) The probable level of involvement rates

(c) The expected growth in the economy and consumer need

(d) Expected movements in foreign exchange rates

Method # ten. Opinion Polling:

Stance poll is the survey of opinion of experts i.e. knowledgeable persons in the field whose views carry lot of weight. For example, a survey of stance of sales representatives, wholesalers, retailers etc. shall be of great assistance in formulating demand projections.

Methods of Demand Forecasting – Top 8 Need Forecasting Methods Adopted by a Firm

A groovy diverseness of methods are bachelor for forecasting the need of a consumer.

The noteworthy ones among them are listed below:

Demand Forecasting Methods:

i. Jury Method/Executive Opinion Method

(a) Peak Jury Method

(b) Percolated Jury Method

two. Survey of Expert Opinion Method

3. The Delphi Method

four. Demand Force Blended Method

5. User Expectation Method/Terminate-Use Method/Survey of Buyers Intentions Method

6. Market Share Method.

7. Analytical and Statistical Methods

(a) Uncomplicated Projection Method

(b) Extrapolation Method

(c) Moving Averages Method

(d) Exponential Smoothing

(east) Time Series Assay

(f) Regression Analysis

(grand) Complex Econometric Models

viii. Market Survey Method

We shall hash out these methods sequentially:

ane. Jury Method/Executive Stance Method :

The jury method is a commonly applied method of demand forecasting. It is as well known as executive opinion method. Judgment is the crucial factor in this method. This is true of both the 'top jury method' and the 'percolated jury method'. The difference is that in the former the participants are limited to the pinnacle executives and in the latter, a large number of marketing/ demand executives participate.

In both, the participants exercise their judgment and requite out their opinions. By a rough averaging of these opinions, the terminal forecast is arrived at. Apparently, for the forecasts arrived at by this method to be reliable, the executives participating in the forecasting exercise must have versatile feel in and sound knowledge of the business.

They must as well exist well informed about the overall economic environment and the conditions prevailing in the industry. They should also know the strengths and weaknesses of their house.

Merits and Demerits of the Jury Method :

The jury method is a judgment method. The method gives due weight to the experience and judgment of people who know the market place and the firm. Information technology is an easy and elementary method; the demand forecast can be ironed out in a short fourth dimension. Farther, when a firm lacks the experience and expertise required for using sophisticated analytical methods of forecasting, the jury method would perhaps be the only method that would be handy.

Once again, when acceptable past statistics on need and market are not bachelor with the firm, the jury method can exist used. The jury method has some demerits too. Later all, the estimates or forecasts arrived at past this method are based on 'opinions' and not 'facts'.

And, the method disperses the responsibility for forecasting on a number of people. Another limitation is that the forecasts worked out by this method are not readily acquiescent for breaking downward into Product-wise, Calendar month-wise and territory-wise forecast, separate exercises are oftentimes required for this purpose.

2. Survey of Expert Stance Method :

This is yet another judgment based method of demand forecasting but is somewhat dissimilar from the jury method. In the jury method, opinions of executives give ascent to the forecast. In survey of expert opinion method, experts in the concerned field inside or outside the organization are approached for their estimates. This method may be more useful in developing total industry forecast than consumer level demand forecast.

3. The Delphi Method :

The Delphi method as well is a kind of survey of practiced opinion method; it is used more for wide-based futuristic estimates than for demand forecasts. In this method, experts in the field are interrogated by a sequence of questionnaires. Any information that is bachelor with anyone member of the console is passed on to others likewise, enabling all the members of the panel to have admission to all the available information.

This technique' eliminates the 'bandwagon' effect of bulk opinion. The panel members are asked to react to a checklist of questions which are meaning to the forecast that is attempted. Their opinions and reactions are analyzed and where there is a sharp deviation on an effect, interchanges are peffilitted and the final forecasts are presented issue by effect.

4. Need Force Composite Method :

As per the demand force composite method, the demand forecasting is done by the demand force. It is besides a judgment based method. Each demand man develops the forecast for his respective territory; the territory- wise forecasts are consolidated at branch/area/region level; and the aggregate of all these forecasts is taken every bit the corporate forecast.

It can be hands seen that the auction force blended method is similar to the jury method. The deviation is that the jury method depends on the judgment of a few executives and the demand strength composite method seeks to amass the judgments of the entire need force

Merits and Demerits of the Need Strength Composite Method :

The responsibleness for forecasting and the responsibility for achieving the demand as per the forecasts are entrusted with the same ready of people viz the demand men, Since the demand men themselves take made the forecast, they will accept the demand quotas based on this forecast as off-white allocation and effort their all-time to achieve the quotas.

Moreover, the forecasts developed by this method accept greater stability and reliability because of the largeness of the sample. Again, the forecasts derived past this method could be easily and meaningfully broken down territory-wise, product-wise, customer type wise and month- wise Since the forecast is developed on this ground, to outset with, keeping the micro level conditions as the base of operations. And coordination past field need direction becomes more meaningful when forecast are made by the demand force and integrated by the field demand direction.

The method has some demerits likewise. Demand men are certainly not experts in forecasting. They cannot use sophisticated techniques of forecasting. Nor, exercise they have all the information required for 'fact-based forecasting'. They are often heavily influenced by the weather prevailing in their territories and tend to be over-optimistic or over- pessimistic well-nigh hereafter need.

Since their need quotas are to be based on their demand forecasts, they may tend to under estimate demand and play it safe. Again, while the demand men may know their territories well, they may not know every bit well the broad changes taking place in the economy and the given manufacture. Such knowledge volition be necessary to predict the future, peculiarly when major changes are taking place in the macro environs.

five. User Expectation Method/Finish Utilise Method/Survey of Buyers':

Intentions Method :

Equally per the 'user expectation' or' end use' method, the various users of the production under forecasting are listed showtime; then their individual likely demand of the production is ascertained and from that data, the need forecast for the product u: consolidated.' This method is alternately known equally 'Survey of buyers' intentions.'

The user survey will give an thought of the total possible consumption of the product, the buying plans of the users and the possible market share for the consumer doing the survey. The user survey can be made either on a sampling footing or on a demography basis depending on the size of the user grouping to the covered. Demography survey would naturally provide a more reliable forecast.

User Expectation Method, More Suited for Industrial Products :

The user expectation method is particularly suited for forecasting the need of industrial appurtenances-industrial raw materials likewise as intermediates and industrial durables. And there is a variety of reasons for this. For case, unlike consumer appurtenances, the users of industrial goods are express in number thus making it possible to exhaustively survey them.

Again, the demand made to individual buyers are substantial in the example of industrial appurtenances. In the case of consumer goods, several thousands of buyers brand up the total, each with a very small quantity of purchase. The industrial customers are mostly clustered in the industrial belts unlike the general consumers who are scattered everywhere. Industrial selling usually takes place direct from the suppliers to the user industries without going through a long-winding distribution channel.

The buyer behaviour in this case is besides more rational and predictable as the buyers are professional people. And finally, applying the method of user survey to industrial need forecasting is like shooting fish in a barrel and inexpensive. In any user survey, the decision-making groups in the user companies as well equally persons with good knowledge of the product are contacted.

For case, the product people, the finance people, the materials and corporate planning executives of the user consumer are contacted to know how much their firm is probable to consume and how much of it they are likely to buy from the consumer doing the survey.

vi. Market Share Method :

Need forecast can be developed past still another method-the marketplace share method. The planned market share of the firm is the key gene in this method. The firms first work out the manufacture forecast, use the market share gene and deduce the consumer's demand forecast. The market place share factor is developed based on past trend, consumer'due south present competitive position, brand preference, etc.

Such conversion of industry forecast into consumer demand forecast requires considerable expertise. By a detailed marketing audit, the firm must correctly assess its market standing, brand image, marketplace share and strengths and weaknesses as compared with the competitors in the industry.

It must also correctly assess through reliable marketing intelligence, its competitor'southward plans policies and activities. Only then, the marketplace share factor and therefore tile need forecast arrived at by this method will have a good degree of reliability. Retail audit would also exist of considerable help in employing the market share method; it would help appraise the industry position as well as the individual firm's marketplace shares.

seven. Analytical and Statistical Methods :

Equally mentioned earlier, besides judgment methods, a wide diversity of belittling and statistical methods is available for forecasting the demand of a concern firm. The firm tin choose the most advisable i depending on its forecasting needs.

The methods include:

i. Uncomplicated Projection Method

ii. Extrapolation Method

three. Moving Averages Method

iv. Exponential Smoothing

v. Fourth dimension Series Analysis

vi. Regression Analysis

vii. Complex Econometric Models

In what follows, we shall describe the salient features of each of these methods.

i. Uncomplicated Projection Method :

Among the projection methods, the simplest is the one that uses the 'rule of the thumb' by which current year'south forecast is arrived at by simply adding an assumed growth rate to the last year's demand; some firms go past the industry growth charge per unit and project the demand; some others take the growth rate adopted by the industry leader.

Another formula equally shown below is also used by some firms:

Only if the year past yr demand sire stable and show an increasing trend, this formula will provide a reasonably reliable estimate.

Merits and Demerits of the Elementary Projection Method :

The simple projection method provides a crude and fix forecast. Sometimes, the forecast arrived at by this method tin be wide off the murk. The main limitation is that this method assumes past demand equally the only cistron influencing time to come demand. The method does not provide for the sharp changes that may happen in the current year, due to a variety of factors.

However, when the forecasting job of the firm is relatively uncomplicated and when the firm is in the mature stage of its business organisation without much of growth or reject and when the external changes are non violent either, the simple projection method will serve the purpose. Information technology has the added advantage of being inexpensive.

ii. Extrapolation Method :

Some firms rely on the extrapolation method. Extrapolation is as well a projection/ trend method. It involves the plotting of the demand figures for the past several years and stretching of the line, or the curve as the example may exist. The extrapolation will requite the figures for the coming years.

Extrapolation basically assumes that the variables will follow its previously established pattern. Accordingly" this method will exist constructive where the blueprint of past move has been relatively steady and sharp disruptions are unlikely in the future. In other words, the supposition is that the time to come will mirror the past.

iii. Moving Averages Method :

This method helps eliminate the effects of seasonality and other irregular trends in demand while forecasting the future figures. The method gives a time series of moving averages; each point of the time series is the arithmetical or weighted average of a number of preceding consecutive points of the series.

If seasonal furnishings are nowadays in the need pattern of the product, a minimum of ii years need history is needed for applying this method.

iv. Exponential Smoothing :

Exponential Smoothing is yet another projection method used for demand forecasting. It is like to moving averages and used fairly extensively) It besides represents the weighted sums of all past numbers in a fourth dimension series wit.~ the heaviest weight placed on the most contempo information. This method is specially useful when forecasts of a large number of items are made.

It is not necessary to continue a long history of past data. -The method can take a stable response to changes and responses tin be adjusted as required. This method is also adaptable for trend corrections and smoothing of forecast errors. Exponential smoothing is one of the most accurate statistical techniques bachelor for forecasting.

v. Time Series Assay :

Another statistical method that is extensively used in demand forecasting is the Time series analysis besides known as Trend cycle analysis. A Time series is a set of chronologically ordered points of raw data, for example, need of a given production, by month, for several continuous years.

Time series analysis helps place and explain:

(i) Systematic variation or 'seasonal' variation which arises due to seasonality in the serial of data,

(ii) Cyclical patterns that repeat every two years or every three years and so on.

(iii) Trends in the data,

(iv) Growth rates of these trends.

The main assumption in Time series analysis is that the factors influencing demand will not change very much over a flow of time and that the future will reflect the by. In this sense, this method is as well basically a projection method. Merely, in Time series analysis, a statistical procedure is used to analyze historical demand data. Projections of future demand are made by studying the interaction of the bones and meaning influences of demand. A thorough and systematic analysis of past data is carried out.

All bones factors underlying need fluctuations are analyzed.

The iv main types of demand variations:

(one) Long term growth trends (secular trends),

(2) Cyclical changes,

(3) Seasonal variations, and

(four) Irregular or random fluctuations-are isolated and measured using the statistical procedure.

The trend lines for each type of variation are studied and need estimates are fabricated. A mathematical model describing the past behavior of the series is selected, assumed values for each type of variation are inserted and the need forecast is cranked out.

Time serial analysis is specially suited for long range forecasts. This method volition give more reliable forecasts for a 10-fifteen year catamenia than for a twelvemonth to year prediction. When demand patterns and therefore, the demand variation patterns, are well defined and relatively stable and factors leading to variations are easily established, this method could exist successfully used for even the curt-term forecasts.

A problem with the Time series method is that whereas it is easy to explicate why a particular trend is going the fashion it does, it is non as like shooting fish in a barrel to predict when the turns volition really set in.

vi. Regression Assay:

Regression analysis is some other analytical technique used for demand forecasting. This technique tries to functionally chronicle need to those variables that influence demand. They may exist economic factors, competitive factors or price. The variable which is to be forecast is the dependant variable and the factors which cause modify.') In the dependant variable are explanatory or causal variables.

'The clan between the dependant variable (i.eastward., the demand forecast of the consumer) and the explanatory or causal variables is determined and measured. An equation is fitted to explain the fluctuations in demand in terms of casual variables.

A study of by demand tendency may bear witness different relationships between need and the factors influencing the demand this relationship can exist expressed as:

y = a + b1* x1 + b2* x2 +… + bn *xn

Called the 'regression equation' representing the relationship between demand and a host of causal factors, y represents need; XI, X2, , Xn correspond causal factors; and a, b1, b2 , bn are constants indicating the extent of contribution of each causal cistron to total demand.

Afterward establishing the relationship based on past data and with the estimated values for the factors for time to come years, we can get the demand estimates for the future years.

Where need are influenced past two or more causal variables acting together, multiple regression analysis is applied. Computers Eire used for regression assay involving complex calculations.

The regression method, in general, will give more accurate forecasts than the trend method since regression takes into account causal factors. At the same time, in regress ion assay involving a number of causal variables, the error of forecasting will multiply along with the mistake in determining and measuring the relationship or influence of each of these variables.

vii . Econometric Models :

Econometric models establish withal another belittling method of demand forecasting. Econometrics basically attempts to express economic theories in mathematical terms so that they can be verified by statistical methods and used to measure the impact of one economic variable upon another for predicting time to come events. The econometric forecasting models vividly portray the real world situations and the multiple variables involved in the demand state of affairs.

The econometric model is different from the regression model of forecasting.

The econometric model relies on the following principle/ theory:

i. Need of product depends on several variables

ii. While demand is the dependant variable, the causal factors are the contained variables.

three. There is constant interaction between demand and each of the causal/ contained variables.

iv. At that place is too constant interaction among the contained variables themselves.

5. The independent variables consist of 2 sets; there are exogenous variables constituted by non-economic forces such every bit nature, or politics, and there are endogenous variables constituted past economic forces such every bit income, employment, cost, etc.

six. The interrelationships between need and independent variables tin exist estimated by statistical analysis of past data.

The econometric model is constituted past a set of interdependent equations that' describe and simulate the full 3ales state of affairs. The forecast is derived through this set of equations. Stated in simple terms, in this method, consumption figures for the past few years are taken as tile bones data; the human relationship for analyzing the time series of consumption figures is provided past the model; the best trend is selected by adopting appropriate statistical tests for the goodness of fit; and based on the analysis of the time series of consumption figures, the forecast for the future specified menstruum is derived.

The econometric models are quite complex and expensive to develop. But they predict Ute turning points more accurately. The econometric models are used more in forecasting the demand of durable goods-indu5trial as well equally consumer durables, where 'replacement need' is a pregnant factor to be projected. Likewise, they are used more for forecasting industry- level demand than consumer-level demand.

eight. Market Survey Method :

The market survey method is yet another method bachelor for demand / demand forecasting. The term market survey is used by some every bit synonymous with market place research or market analysis. This is incorrect. Market survey is simply a technique or method of market research or market assay. Its purpose is to collect specific information apropos the market that cannot be had from the consumer's internal records or from external published sources of data.

When a market survey is used for generating relevant market data and such information forms the ground of the demand forecasts, the forecasting method is referred to as the market place survey method of need forecasting.

When Primary Information Become Essential for Forecasting, Market Survey Assumes Importance :

Ordinarily, when a consumer wants to innovate a new production or an improved product, information technology resorts to a marketplace survey to assess the likely demand for the production. Likewise, any new consumer entering the market for the first fourth dimension, resorts to the marketplace survey method for forecasting its demand/ demand. This is quite natural. The firm does not have any information of past demand or past demand patterns to fall back upon. It has to assemble the information from the market place and take decisions.

Usually, the business firm conducts a survey among a sample of consumers and gauges their attitudes, probable purchases and purchase habits. Sometimes, a survey is conducted among the channel members -wholesalers, and/ or retailers to elicit information on their attitudes, likely purchases, etc. The merit of the market survey method lies in the fact that the method facilitates gathering of original or primary data that is specific to the problem on hand.

The main demerit is that it is time consuming and expensive. Moreover, the reliability of the information generated is dependent on the statistical accuracy of the survey procedures. Market survey, as a technique of market research and every bit a technique of data drove from the field.

Selection of Advisable Forecasting Method :

The forecaster must carefully cull a method of forecasting from among the broad multifariousness of methods available. Basically, the method called must friction match the requirements of his product and the organization. Each organization and each product has certain peculiarities from the standpoint of demand forecasting.

The forecaster must analyse thoroughly the organizational and product peculiarities and choose those methods of forecasting that friction match the requirements of his products and system. The method should also lead to timely forecasts. It must also generate the forecasts in such a manner that they are readily discernible to the people implementing the demand forecasts demand plans.

Similarly, the chosen forecasting method should also friction match the surroundings in which the business firm is operating-the technological environs, the competitive surround, the governmental influences, etc. The menstruation range of the forecast is also a consideration in the option of the forecasting method. And finally, the cost consideration and the extent of availability of qualified personnel for the forecasting chore are also relevant factors in the selection of the forecasting methods.

In curt, information technology must exist understood that since all the methods have their associated merits and demerits, at that place is nothing like an ideal forecasting method that could be practical to advantage in the situations. The forecaster must appraise the suitability of the specific method to his specific situation earlier commissioning the forecasting exercise.

Forecast tin exist improved past Using a Combination of Methods :

The forecaster can ameliorate his forecast by choosing a combination of more than one method. Since no one method of demand forecasting is perfect or foolproof, it would be by and large advantageous to try out a combination of different methods. Using more than ane method would give a better insight into the situation.

Cross-checking betwixt one method and the other would minimize the risks involved in the forecast and amend the reliability of the forecast. Combinations would also assist the forecaster to probe deeply the reasons for wide variations between the forecasts arrived at by different methods.

Such a probing will somewhen enable the forecaster to arrive at a more than accurate and reliable forecast. Conversely, if the results arrived at past the different methods converge, the conviction in the forecasts is enhanced to that extent.

Depending totally on a single method or one category of methods has certain pitfalls. For example, if the firm depends total on the jury method, it exposes itself to one type of bias. For, the jury method relies heavily on the judgment of persons who are non experts in forecasting and who usually do not utilise elaborate database in working out the forecasts. It would exist advantageous to supplement this method with one of the statistical methods.

The different methods of forecasting are not mutually competitive, nor intractably exclusive. Quite frequently they supplement 1 another and can be easily used in conjunction. The forecaster cam choose one method from the statistical/ analytical group and ane method from the non-statistical group and compare the forecasts; he can also apply two different methods from the same group and compare the position.

For instance, in a consumer product, both the jury method and the demand strength composite method could be tried out and the variations between the ii forecasts could be studied. Such an approach may pb to a more reliable forecast. For, even though both the jury method, and the demand force blended method vest to the category of judgement methods, they have complementary merits and demerits.

Whereas the jury method is a 'break- downwardly' method, the need force composite method is a "build-up" method. The jury method involves working out the overall consumer forecast in the commencement instance and then breaking it into parts demand human-wise, territory-wise, channel-wise and month-wise; the demand force composite method involves the contrary process. When both the methods are employed, the accuracy of the forecast is improved through comparison and. through an analysis of why pregnant variances occur in the two methods. And when an analytical or quantitative method is as well used in addition to the higher up two methods, the comparison would go complete.

Methods of Need Forecasting – Qualitative and Quantitative Methods

Demand forecasting is done by companies and they utilise different methods ranging from qualitative to quantitative methods. The choice of method by and large depends upon the size and complexity of the firm. Smaller firms usually accept more breezy form of human resource planning and hence many times rely on more qualitative methods. Whereas, larger firms usually having multiple departments, levels and college mobility of workforce both within and outside the house, by and large use more quantitative methods.

Permit u.s. have expect at the various qualitative and quantitative methods that can be employed to forecast the demand of workforce:

i. Qualitative Methods :

a. Judgmental Methods:

Employee and managerial judgement are used to forecast the demand of labour. The arroyo could differ from choosing to employ simply managerial judgement (more than of a tiptop to down driven forecasting) to something like using a combination of employee and managerial judgement (more of a downward-up driven approach).

b. Delphi Technique:

The Delphi technique employs the sentence of the experts. More often than not a panel of experts is chosen. They are then polled for their forecasts. The average of such forecasts is then taken as the demand of workforce in that house.

two. Quantitative Methods :

Qualitative methods have their ain limitations and hence reliance is more on more hard-data driven forecasts.

2 commonly used quantitative techniques are:

a. Trend Analysis:

In trend analysis first a concern factor relevant to human being resources needs is chosen, for case sales, production, etc. Later this a historical trend of the concern factor in relation to number of employees is plotted. The ratio of employees to the business factor provides a labor productivity ratio (for example- sales per employee or units produced per employee etc.).

And so such ratios are compared for at least the past five years. Finally, human resource demand is calculated past dividing the business organisation factor past the productivity ratio and projection of man resources demand is washed to the target twelvemonth.

b. Workload Analysis:

In workload assay, subsequently considering the workload, the planned man-hours are calculated. Then the productive hours per worker is estimated. The full of planned-human-hours is divided by the productive hours per worker to forecast the demand of workers.

Methods of Demand Forecasting – Survey and Statistical Methods (With Example)

Although there are several methods of forecasting the demand for a product or a service, yet no forecasting method is useful for all the products. A company should utilize more than ane forecasting method for correct decision making. The option of the forecasting method besides differs from production to product to the objective of the visitor to data availability besides others. Although forecasting may act as a light to guide the path, but it may not be authentic all the time in this dynamic business organisation environment.

For example, the forecasting washed for two cars i.e. Maruti Suzuki Swift and Mahindra Logan, met with different fate in reality. Maruti Suzuki Swift was launched in 2005 with the sales target of 4000 units a month. But it became a runaway hit among the car buyers and sold more 40000 cars in the first half-dozen months, which is nearly 65% higher than the forecast.

The success story does not finish here. It went on to sell as many as 12000 units a month on an average. To continue the success story, Maruti also introduced the sedan version of swift in the proper name of Swift Dzire. Now Dzire is also selling 10000 units a calendar month on an average.

On the other manus, Mahindra and Ranault'due south partnership came to an cease with the debacle of their articulation venture production Logan. With a sales forecast of 50000 units a year, Logan could non achieve even its xv% of its target.

Following are the forecasting methods, which are more often than not used. All these methods are divided into 2 categories i.due east. Survey methods and Statistical Methods:

1. Survey Methods :

Survey methods assistance a marketer to forecast the need based on the buyer intentions, expert opinions and the feedback from the market place

i. Survey of Buyer Intentions:

In survey of buyer intentions, a sample size of cur­hire and / or potential customers is selected and is asked well-nigh the buy intentions of a particular production at a given cost within a specified period. Then the data is extrapolated to the whole population, to have the right forecast.

Simply the sample may not exist the representative of the population. This may event in inaccurate need forecast. It may also be possible that the intentions may not translate into the bodily purchases past the consumers.

two. Sales Force Blended:

Sales force composite is based on the collective information pro­vided by the sales people all beyond the region or state or territory or state. In terms of reliability, information technology scores meliorate than the other methods, as it is based on the real data nerveless by right people. Although there are still chances of being over estimation or under estimation.

iii. Reasoned Stance:

Reasoned opinion is likewise known as Delphi method, which is a variant of poll and survey method. This method was adult past Rand Corpora­tion, USA for technical changes in the late 1940s. Only like sales strength composite, this method uses the opinions of the experts and a grouping of knowledgeable indi­viduals anonymously to estimate the future demand. Simply, this method can also be subjective, equally information technology is based on somebody'south interpretation.

iv. Test Marketing:

Under test marketing, a company test markets its products in a limited geographical area, which may be representative of the whole popu­lation to measure the demand. Test marketing also facilitates feedback on the packaging, pricing, pattern, likability etc. besides gauging the demand. In Bharat, Pune and Hyderabad has been a major testing footing for FMCG companies.

Now Lucknow is also added to the listing, with Hindustan Unilever Ltd. (HUL) exam marketing its mobile tea/coffee vendors. Now the exam marketing has expanded to Hyderabad, Mumbai and Patna.

two. Statistical Methods :

Demand forecasting based on statistical methods is used a lot by companies, due to its nature of being logical and unbiased. As it depends upon the historical data and its extrapolation, it is reliable and useful for a company. But, it lacks the dynamism associated to the business, where by may non requite correct moving-picture show of the present. Secondly, information technology cannot be used for new products, as there volition be no historical data.

Demand forecasting tin either be washed by time series analysis or regression analysis.

i. Time Series Analysis:

Time serial is a statistical system of data in a chrono­logical lodge. Its' analysis helps a company to analyse the trend in the future. The arrangement of data tin be washed in the form a graphical representation or it can be in the form of a table. Fourth dimension series analysis helps to requite usa a trend, long term tendency of the information. It can besides show us the seasonal variations due to festivals, holidays, atmospheric condition, fashions etc.

Similarly, cyclical variations like nail, recession, depression and recovery can besides help usa in assay. Sometimes un­foreseen events like alluvion, state of war, epidemic, fire etc. leads us to residual variations, which can also help in demand forecasting.

Following methods tin can be used un­der time series analysis:

a. Graphical Method

b. Semi Averages Method

c. Moving Averages Method

d. To the lowest degree Squares Method

2. Regression Analysis:

Regression analysis is one of the most popular methods of demand forecasting. It analyses the average relation betwixt two or more than vari­ables mathematically. Under simple regression, assay is done for two variables simply. While under multiple regression, information technology is done for multiple factors simultane­ously.

This method is not only descriptive, but also prescriptive and objective in nature. Information technology analyses time series and cross section data for more accuracy. But, when explanatory variables are non called realistically, it may exist misleading. In the instance of auto­correlation, the assay results may be biased.

Demand Forecasting for New Products :

Demand forecasting for new products is more complex than the erstwhile and existing products. Information technology requires a good understanding of the consumer per se, besides the points of differentiation from the competition. Generally it has been constitute that demand forecasting for new products in the yr one has been off the target. It becomes more than difficult, if the product or service is totally new to the market like Tata Nano, Ginger Hotels, Mahindra Scorpio, Tata Ace or say Mahindra Quanto.

The demand for new products tin can exist estimated on the basis of former products and its data in the similar category or product segment. Another tried and tested method is the test marketing for the product to understand the interest and willingness to buy the product at a given toll point.

For FMCG products, another method, which works for calculating the year i information, is the retail simulation. In retail simulation, the consumer is asked to cull the top v or 10 brands in a product category. Then the new product is added to the listing and the feedback in the class of pick is counted. Thus survey of buyers' intention is understood in the to a higher place way, too the survey approach. When the product is just an improvement on existing production, and so the demand tin be estimated with an evolutionary approach and be projected as an outgrowth.